*AI Summary*
*# Part 1: Analyze and Adopt*
*Domain:* Immunology and Molecular Biology
*Persona:* Senior Principal Investigator and Chair of Immunology
---
### Part 2: Summarize
*Abstract:*
This session features an in-depth professional retrospective and technical discussion with Dr. Leslie Berg, a preeminent figure in T cell biology and former President of the American Association of Immunologists (AAI). The dialogue traces Dr. Berg's trajectory from her doctoral work on bovine papilloma virus to her foundational postdoctoral contributions in the laboratory of Mark Davis, where she developed early T cell receptor (TCR) transgenic mouse models. The technical core of the discussion focuses on the "rheostat" model of TCR signaling, specifically how the Tec kinase ITK modulates signal strength to determine T cell fate—discriminating between positive and negative selection in the thymus and effector versus memory differentiation in the periphery. Dr. Berg highlights recent findings showing that while NFAT and MAPK pathways exhibit digital (all-or-none) activation, the NF-κB pathway is analog and highly sensitive to ITK activity. The conversation concludes with an analysis of the limitations of current CAR-T therapies regarding signaling uniformity and the strategic importance of departmental resources, such as embedded bioinformatics and grant-writing support, in sustaining modern academic research.
*T Cell Signaling, Selection, and the Professional Trajectory of Leslie Berg*
* *0:00 - Introduction to the Session:* Cindy Lifer introduces Dr. Leslie Berg at the 2025 AAI Conference. Dr. Berg is recognized for her role in developing early TCR transgenic mice and her extensive leadership within the AAI and as a Department Chair at the University of Colorado.
* *2:24 - Transition from Viral Molecular Biology:* Dr. Berg describes her PhD work at UC Berkeley on the bovine papilloma virus genome. Her transition to immunology was driven by the desire to apply molecular tools to complex "black box" biological systems in whole-animal models.
* *6:08 - The Stanford Postdoc and TCR Transgenics:* Joining Mark Davis’s lab shortly after the cloning of the TCR, Dr. Berg was instrumental in creating early transgenic models. These models were designed to observe positive and negative selection in the thymus, providing a controlled environment where the majority of T cells shared a single receptor specificity.
* *8:42 - Kinase Specialization at Harvard:* Dr. Berg attributes her focus on signaling to her time at Harvard, influenced by colleagues specializing in kinases (e.g., the discovery of Src as a tyrosine kinase). This led her to investigate the role of kinases like LCK and the identification of new T cell-specific tyrosine kinases.
* *9:41 - The Mystery of Thymic Selection:* A central theme of Dr. Berg's research is the "signaling paradox": how the same TCR induces apoptosis (negative selection) upon strong signaling but promotes survival and maturation (positive selection) upon weak signaling.
* *11:19 - Professional Environment ("Seed and Soil"):* Dr. Berg emphasizes that a scientist's research direction is profoundly shaped by their immediate colleagues. She notes that the "soil" (institutional environment) dictates which questions become prominent through daily technical and intellectual exchange.
* *18:05 - Mentorship Philosophy:* Drawing from her PhD advisor, Mike Botchan, Dr. Berg advocates for a "rank-agnostic" approach to scientific data. Key takeaways include the necessity of being emotionally detached from hypotheses and the value of "failed" experiments as the primary drivers of new mechanistic insight.
* *26:51 - TCR Signal Strength and ITK:* The discussion pivots to current research on how signal strength regulates T cell fate. Dr. Berg’s lab identifies ITK as a signaling amplifier or rheostat. While some pathways (NFAT, MAPK) trigger digitally, NF-κB activation is graded and contingent on ITK-mediated diacylglycerol (DAG) production.
* *29:00 - Mechanistic Insights for CAR-T Therapy:* Current CAR-T constructs are criticized for being "unidimensional." Dr. Berg suggests that understanding the TCR's ability to produce heterogeneous fates (effector vs. memory) via varied signal strengths could lead to better CAR-T designs, potentially using multiple constructs to mimic natural T cell repertoire diversity.
* *39:26 - Leadership and Resource Allocation:* As a Department Chair, Dr. Berg highlights the success of providing centralized "discretionary" resources. Key implementations include a dedicated bioinformatician and a grant-writing consultant to improve the technical clarity and success rates of faculty submissions.
* *42:48 - Historical Context and Close:* A brief personal note on Dr. Berg’s background in Beverly Hills and her interactions with notable figures before concluding the session with a reminder of the AAI's role in supporting the immunology community.
---
### Part 3: Reviewer Recommendation
*Target Review Groups:*
1. *Molecular Immunologists:* To evaluate the mechanistic data regarding ITK and its differential effects on NF-κB versus NFAT translocation.
2. *Academic Clinical Oncologists (Cellular Therapy):* To review the implications of TCR signaling "wiring" on the development of more persistent memory-phenotype CAR-T cells.
3. *University Research Administrators/Deans:* To analyze the "Colorado Model" of centralized departmental support (bioinformatics and grant consulting) as a method for improving faculty productivity and retention.
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Fluidigm Polaris Part 2- illuminator and camera
mikeselectricstuff
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Fluidigm Polaris part 1 : • Fluidigm Polaris (Part 1) - Biotech g...
Ebay listings: https://www.ebay.co.uk/usr/mikeselect...
Merch https://mikeselectricstuff.creator-sp...
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mikeselectricstuff
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40 Comments
@robertwatsonbath
6 hours ago
Thanks Mike. Ooof! - with the level of bodgery going on around 15:48 I think shame would have made me do a board re spin, out of my own pocket if I had to.
1
Reply
@Muonium1
9 hours ago
The green LED looks different from the others and uses phosphor conversion because of the "green gap" problem where green InGaN emitters suffer efficiency droop at high currents. Phosphide based emitters don't start becoming efficient until around 600nm so also can't be used for high power green emitters. See the paper and plot by Matthias Auf der Maur in his 2015 paper on alloy fluctuations in InGaN as the cause of reduced external quantum efficiency at longer (green) wavelengths.
4
Reply
1 reply
@tafsirnahian669
10 hours ago (edited)
Can this be used as an astrophotography camera?
Reply
mikeselectricstuff
·
1 reply
@mikeselectricstuff
6 hours ago
Yes, but may need a shutter to avoid light during readout
Reply
@2010craggy
11 hours ago
Narrowband filters we use in Astronomy (Astrophotography) are sided- they work best passing light in one direction so I guess the arrows on the filter frames indicate which way round to install them in the filter wheel.
1
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@vitukz
12 hours ago
A mate with Channel @extractions&ire could use it
2
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@RobertGallop
19 hours ago
That LED module says it can go up to 28 amps!!! 21 amps for 100%. You should see what it does at 20 amps!
Reply
@Prophes0r
19 hours ago
I had an "Oh SHIT!" moment when I realized that the weird trapezoidal shape of that light guide was for keystone correction of the light source.
Very clever.
6
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@OneBiOzZ
20 hours ago
given the cost of the CCD you think they could have run another PCB for it
9
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@tekvax01
21 hours ago
$20 thousand dollars per minute of run time!
1
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@tekvax01
22 hours ago
"We spared no expense!" John Hammond Jurassic Park.
*(that's why this thing costs the same as a 50-seat Greyhound Bus coach!)
Reply
@florianf4257
22 hours ago
The smearing on the image could be due to the fact that you don't use a shutter, so you see brighter stripes under bright areas of the image as you still iluminate these pixels while the sensor data ist shifted out towards the top. I experienced this effect back at university with a LN-Cooled CCD for Spectroscopy. The stripes disapeared as soon as you used the shutter instead of disabling it in the open position (but fokussing at 100ms integration time and continuous readout with a focal plane shutter isn't much fun).
12
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mikeselectricstuff
·
1 reply
@mikeselectricstuff
12 hours ago
I didn't think of that, but makes sense
2
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@douro20
22 hours ago (edited)
The red LED reminds me of one from Roithner Lasertechnik. I have a Symbol 2D scanner which uses two very bright LEDs from that company, one red and one red-orange. The red-orange is behind a lens which focuses it into an extremely narrow beam.
1
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@RicoElectrico
23 hours ago
PFG is Pulse Flush Gate according to the datasheet.
Reply
@dcallan812
23 hours ago
Very interesting. 2x
Reply
@littleboot_
1 day ago
Cool interesting device
Reply
@dav1dbone
1 day ago
I've stripped large projectors, looks similar, wonder if some of those castings are a magnesium alloy?
Reply
@kevywevvy8833
1 day ago
ironic that some of those Phlatlight modules are used in some of the cheapest disco lights.
1
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1 reply
@bill6255
1 day ago
Great vid - gets right into subject in title, its packed with information, wraps up quickly. Should get a YT award! imho
3
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@JAKOB1977
1 day ago (edited)
The whole sensor module incl. a 5 grand 50mpix sensor for 49 £.. highest bid atm
Though also a limited CCD sensor, but for the right buyer its a steal at these relative low sums.
Architecture Full Frame CCD (Square Pixels)
Total Number of Pixels 8304 (H) × 6220 (V) = 51.6 Mp
Number of Effective Pixels 8208 (H) × 6164 (V) = 50.5 Mp
Number of Active Pixels 8176 (H) × 6132 (V) = 50.1 Mp
Pixel Size 6.0 m (H) × 6.0 m (V)
Active Image Size 49.1 mm (H) × 36.8 mm (V)
61.3 mm (Diagonal),
645 1.1x Optical Format
Aspect Ratio 4:3
Horizontal Outputs 4
Saturation Signal 40.3 ke−
Output Sensitivity 31 V/e−
Quantum Efficiency
KAF−50100−CAA
KAF−50100−AAA
KAF−50100−ABA (with Lens)
22%, 22%, 16% (Peak R, G, B)
25%
62%
Read Noise (f = 18 MHz) 12.5 e−
Dark Signal (T = 60°C) 42 pA/cm2
Dark Current Doubling Temperature 5.7°C
Dynamic Range (f = 18 MHz) 70.2 dB
Estimated Linear Dynamic Range
(f = 18 MHz)
69.3 dB
Charge Transfer Efficiency
Horizontal
Vertical
0.999995
0.999999
Blooming Protection
(4 ms Exposure Time)
800X Saturation Exposure
Maximum Date Rate 18 MHz
Package Ceramic PGA
Cover Glass MAR Coated, 2 Sides or
Clear Glass
Features
• TRUESENSE Transparent Gate Electrode
for High Sensitivity
• Ultra-High Resolution
• Board Dynamic Range
• Low Noise Architecture
• Large Active Imaging Area
Applications
• Digitization
• Mapping/Aerial
• Photography
• Scientific
Thx for the tear down Mike, always a joy
Reply
@martinalooksatthings
1 day ago
15:49 that is some great bodging on of caps, they really didn't want to respin that PCB huh
8
Reply
@RhythmGamer
1 day ago
Was depressed today and then a new mike video dropped and now I’m genuinely happy to get my tear down fix
1
Reply
@dine9093
1 day ago (edited)
Did you transfrom into Mr Blobby for a moment there?
2
Reply
@NickNorton
1 day ago
Thanks Mike. Your videos are always interesting.
5
Reply
@KeritechElectronics
1 day ago
Heavy optics indeed... Spare no expense, cost no object. Splendid build quality. The CCD is a thing of beauty!
1
Reply
@YSoreil
1 day ago
The pricing on that sensor is about right, I looked in to these many years ago when they were still in production since it's the only large sensor you could actually buy. Really cool to see one in the wild.
2
Reply
@snik2pl
1 day ago
That leds look like from led projector
Reply
@vincei4252
1 day ago
TDI = Time Domain Integration ?
1
Reply
@wolpumba4099
1 day ago (edited)
Maybe the camera should not be illuminated during readout.
From the datasheet of the sensor (Onsemi): saturation 40300 electrons, read noise 12.5 electrons per pixel @ 18MHz (quite bad). quantum efficiency 62% (if it has micro lenses), frame rate 1 Hz. lateral overflow drain to prevent blooming protects against 800x (factor increases linearly with exposure time) saturation exposure (32e6 electrons per pixel at 4ms exposure time), microlens has +/- 20 degree acceptance angle
i guess it would be good for astrophotography
4
Reply
@txm100
1 day ago (edited)
Babe wake up a new mikeselectricstuff has dropped!
9
Reply
@vincei4252
1 day ago
That looks like a finger-lakes filter wheel, however, for astronomy they'd never use such a large stepper.
1
Reply
@MRooodddvvv
1 day ago
yaaaaay ! more overcomplicated optical stuff !
4
Reply
1 reply
@NoPegs
1 day ago
He lives!
11
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1 reply
Transcript
0:00
so I've stripped all the bits of the
0:01
optical system so basically we've got
0:03
the uh the camera
0:05
itself which is mounted on this uh very
0:09
complex
0:10
adjustment thing which obviously to set
0:13
you the various tilt and uh alignment
0:15
stuff then there's two of these massive
0:18
lenses I've taken one of these apart I
0:20
think there's something like about eight
0:22
or nine Optical elements in here these
0:25
don't seem to do a great deal in terms
0:26
of electr magnification they're obiously
0:28
just about getting the image to where it
0:29
uh where it needs to be just so that
0:33
goes like that then this Optical block I
0:36
originally thought this was made of some
0:37
s crazy heavy material but it's just
0:39
really the sum of all these Optical bits
0:41
are just ridiculously heavy those lenses
0:43
are about 4 kilos each and then there's
0:45
this very heavy very solid um piece that
0:47
goes in the middle and this is so this
0:49
is the filter wheel assembly with a
0:51
hilariously oversized steper
0:53
motor driving this wheel with these very
0:57
large narrow band filters so we've got
1:00
various different shades of uh
1:03
filters there five Al together that
1:06
one's actually just showing up a silver
1:07
that's actually a a red but fairly low
1:10
transmission orangey red blue green
1:15
there's an excess cover on this side so
1:16
the filters can be accessed and changed
1:19
without taking anything else apart even
1:21
this is like ridiculous it's like solid
1:23
aluminium this is just basically a cover
1:25
the actual wavelengths of these are um
1:27
488 525 570 630 and 700 NM not sure what
1:32
the suffix on that perhaps that's the uh
1:34
the width of the spectral line say these
1:37
are very narrow band filters most of
1:39
them are you very little light through
1:41
so it's still very tight narrow band to
1:43
match the um fluoresence of the dies
1:45
they're using in the biochemical process
1:48
and obviously to reject the light that's
1:49
being fired at it from that Illuminator
1:51
box and then there's a there's a second
1:53
one of these lenses then the actual sort
1:55
of samples below that so uh very serious
1:58
amount of very uh chunky heavy Optics
2:01
okay let's take a look at this light
2:02
source made by company Lumen Dynamics
2:04
who are now part of
2:06
excelitas self-contained unit power
2:08
connector USB and this which one of the
2:11
Cable Bundle said was a TTL interface
2:14
USB wasn't used in uh the fluid
2:17
application output here and I think this
2:19
is an input for um light feedback I
2:21
don't if it's regulated or just a measur
2:23
measurement facility and the uh fiber
2:27
assembly
2:29
Square Inlet there and then there's two
2:32
outputs which have uh lens assemblies
2:35
and this small one which goes back into
2:37
that small Port just Loops out of here
2:40
straight back in So on this side we've
2:42
got the electronics which look pretty
2:44
straightforward we've got a bit of power
2:45
supply stuff over here and we've got
2:48
separate drivers for each wavelength now
2:50
interesting this is clearly been very
2:52
specifically made for this application
2:54
you I was half expecting like say some
2:56
generic drivers that could be used for a
2:58
number of different things but actually
3:00
literally specified the exact wavelength
3:02
on the PCB there is provision here for
3:04
385 NM which isn't populated but this is
3:07
clearly been designed very specifically
3:09
so these four drivers look the same but
3:10
then there's two higher power ones for
3:12
575 and
3:14
520 a slightly bigger heat sink on this
3:16
575 section there a p 24 which is
3:20
providing USB interface USB isolator the
3:23
USB interface just presents as a comport
3:26
I did have a quick look but I didn't
3:27
actually get anything sensible um I did
3:29
dump the Pi code out and there's a few
3:31
you a few sort of commands that you
3:32
could see in text but I didn't actually
3:34
manage to get it working properly I
3:36
found some software for related version
3:38
but it didn't seem to want to talk to it
3:39
but um I say that wasn't used for the
3:41
original application it might be quite
3:42
interesting to get try and get the Run
3:44
hours count out of it and the TTL
3:46
interface looks fairly straightforward
3:48
we've got positions for six opto
3:50
isolators but only five five are
3:52
installed so that corresponds with the
3:54
unused thing so I think this hopefully
3:56
should be as simple as just providing a
3:57
ttrl signal for each color to uh enable
4:00
it a big heat sink here which is there I
4:03
think there's like a big S of metal
4:04
plate through the middle of this that
4:05
all the leads are mounted on the other
4:07
side so this is heat sinking it with a
4:09
air flow from a uh just a fan in here
4:13
obviously don't have the air flow
4:14
anywhere near the Optics so conduction
4:17
cool through to this plate that's then
4:18
uh air cooled got some pots which are
4:21
presumably power
4:22
adjustments okay let's take a look at
4:24
the other side which is uh much more
4:27
interesting see we've got some uh very
4:31
uh neatly Twisted cable assemblies there
4:35
a bunch of leads so we've got one here
4:37
475 up here 430 NM 630 575 and 520
4:44
filters and dcro mirrors a quick way to
4:48
see what's white is if we just shine
4:49
some white light through
4:51
here not sure how it is is to see on the
4:54
camera but shining white light we do
4:55
actually get a bit of red a bit of blue
4:57
some yellow here so the obstacle path
5:00
575 it goes sort of here bounces off
5:03
this mirror and goes out the 520 goes
5:07
sort of down here across here and up
5:09
there 630 goes basically straight
5:13
through
5:15
430 goes across there down there along
5:17
there and the 475 goes down here and
5:20
left this is the light sensing thing
5:22
think here there's just a um I think
5:24
there a photo diode or other sensor
5:26
haven't actually taken that off and
5:28
everything's fixed down to this chunk of
5:31
aluminium which acts as the heat
5:32
spreader that then conducts the heat to
5:33
the back side for the heat
5:35
sink and the actual lead packages all
5:38
look fairly similar except for this one
5:41
on the 575 which looks quite a bit more
5:44
substantial big spay
5:46
Terminals and the interface for this
5:48
turned out to be extremely simple it's
5:50
literally a 5V TTL level to enable each
5:54
color doesn't seem to be any tensity
5:56
control but there are some additional
5:58
pins on that connector that weren't used
5:59
in the through time thing so maybe
6:01
there's some extra lines that control
6:02
that I couldn't find any data on this uh
6:05
unit and the um their current product
6:07
range is quite significantly different
6:09
so we've got the uh blue these
6:13
might may well be saturating the camera
6:16
so they might look a bit weird so that's
6:17
the 430
6:18
blue the 575
6:24
yellow uh
6:26
475 light blue
6:29
the uh 520
6:31
green and the uh 630 red now one
6:36
interesting thing I noticed for the
6:39
575 it's actually it's actually using a
6:42
white lead and then filtering it rather
6:44
than using all the other ones are using
6:46
leads which are the fundamental colors
6:47
but uh this is actually doing white and
6:50
it's a combination of this filter and
6:52
the dichroic mirrors that are turning to
6:55
Yellow if we take the filter out and a
6:57
lot of the a lot of the um blue content
7:00
is going this way the red is going
7:02
straight through these two mirrors so
7:05
this is clearly not reflecting much of
7:08
that so we end up with the yellow coming
7:10
out of uh out of there which is a fairly
7:14
light yellow color which you don't
7:16
really see from high intensity leads so
7:19
that's clearly why they've used the
7:20
white to uh do this power consumption of
7:23
the white is pretty high so going up to
7:25
about 2 and 1 half amps on that color
7:27
whereas most of the other colors are
7:28
only drawing half an amp or so at 24
7:30
volts the uh the green is up to about
7:32
1.2 but say this thing is uh much
7:35
brighter and if you actually run all the
7:38
colors at the same time you get a fairly
7:41
reasonable um looking white coming out
7:43
of it and one thing you might just be
7:45
out to notice is there is some sort
7:46
color banding around here that's not
7:49
getting uh everything s completely
7:51
concentric and I think that's where this
7:53
fiber optic thing comes
7:58
in I'll
8:00
get a couple of Fairly accurately shaped
8:04
very sort of uniform color and looking
8:06
at What's um inside here we've basically
8:09
just got this Square Rod so this is
8:12
clearly yeah the lights just bouncing
8:13
off all the all the various sides to um
8:16
get a nice uniform illumination uh this
8:19
back bit looks like it's all potted so
8:21
nothing I really do to get in there I
8:24
think this is fiber so I have come
8:26
across um cables like this which are
8:27
liquid fill but just looking through the
8:30
end of this it's probably a bit hard to
8:31
see it does look like there fiber ends
8:34
going going on there and so there's this
8:36
feedback thing which is just obviously
8:39
compensating for the any light losses
8:41
through here to get an accurate
8:43
representation of uh the light that's
8:45
been launched out of these two
8:47
fibers and you see uh
8:49
these have got this sort of trapezium
8:54
shape light guides again it's like a
8:56
sort of acrylic or glass light guide
9:00
guess projected just to make the right
9:03
rectangular
9:04
shape and look at this Center assembly
9:07
um the light output doesn't uh change
9:10
whether you feed this in or not so it's
9:11
clear not doing any internal Clos Loop
9:14
control obviously there may well be some
9:16
facility for it to do that but it's not
9:17
being used in this
9:19
application and so this output just
9:21
produces a voltage on the uh outle
9:24
connector proportional to the amount of
9:26
light that's present so there's a little
9:28
diffuser in the back there
9:30
and then there's just some kind of uh
9:33
Optical sensor looks like a
9:35
chip looking at the lead it's a very
9:37
small package on the PCB with this lens
9:40
assembly over the top and these look
9:43
like they're actually on a copper
9:44
Metalized PCB for maximum thermal
9:47
performance and yeah it's a very small
9:49
package looks like it's a ceramic
9:51
package and there's a thermister there
9:53
for temperature monitoring this is the
9:56
475 blue one this is the 520 need to
9:59
Green which is uh rather different OB
10:02
it's a much bigger D with lots of bond
10:04
wise but also this looks like it's using
10:05
a phosphor if I shine a blue light at it
10:08
lights up green so this is actually a
10:10
phosphor conversion green lead which
10:12
I've I've come across before they want
10:15
that specific wavelength so they may be
10:17
easier to tune a phosphor than tune the
10:20
um semiconductor material to get the uh
10:23
right right wavelength from the lead
10:24
directly uh red 630 similar size to the
10:28
blue one or does seem to have a uh a
10:31
lens on top of it there is a sort of red
10:33
coloring to
10:35
the die but that doesn't appear to be
10:38
fluorescent as far as I can
10:39
tell and the white one again a little
10:41
bit different sort of much higher
10:43
current
10:46
connectors a makeer name on that
10:48
connector flot light not sure if that's
10:52
the connector or the lead
10:54
itself and obviously with the phosphor
10:56
and I'd imagine that phosphor may well
10:58
be tuned to get the maximum to the uh 5
11:01
cenm and actually this white one looks
11:04
like a St fairly standard product I just
11:06
found it in Mouse made by luminous
11:09
devices in fact actually I think all
11:11
these are based on various luminous
11:13
devices modules and they're you take
11:17
looks like they taking the nearest
11:18
wavelength and then just using these
11:19
filters to clean it up to get a precise
11:22
uh spectral line out of it so quite a
11:25
nice neat and um extreme
11:30
bright light source uh sure I've got any
11:33
particular use for it so I think this
11:35
might end up on
11:36
eBay but uh very pretty to look out and
11:40
without the uh risk of burning your eyes
11:43
out like you do with lasers so I thought
11:45
it would be interesting to try and
11:46
figure out the runtime of this things
11:48
like this we usually keep some sort
11:49
record of runtime cuz leads degrade over
11:51
time I couldn't get any software to work
11:52
through the USB face but then had a
11:54
thought probably going to be writing the
11:55
runtime periodically to the e s prom so
11:58
I just just scope up that and noticed it
12:00
was doing right every 5 minutes so I
12:02
just ran it for a while periodically
12:04
reading the E squ I just held the pick
12:05
in in reset and um put clip over to read
12:07
the square prom and found it was writing
12:10
one location per color every 5 minutes
12:12
so if one color was on it would write
12:14
that location every 5 minutes and just
12:16
increment it by one so after doing a few
12:18
tests with different colors of different
12:19
time periods it looked extremely
12:21
straightforward it's like a four bite
12:22
count for each color looking at the
12:24
original data that was in it all the
12:26
colors apart from Green were reading
12:28
zero and the green was reading four
12:30
indicating a total 20 minutes run time
12:32
ever if it was turned on run for a short
12:34
time then turned off that might not have
12:36
been counted but even so indicates this
12:37
thing wasn't used a great deal the whole
12:40
s process of doing a run can be several
12:42
hours but it'll only be doing probably
12:43
the Imaging at the end of that so you
12:46
wouldn't expect to be running for a long
12:47
time but say a single color for 20
12:50
minutes over its whole lifetime does
12:52
seem a little bit on the low side okay
12:55
let's look at the camera un fortunately
12:57
I managed to not record any sound when I
12:58
did this it's also a couple of months
13:00
ago so there's going to be a few details
13:02
that I've forgotten so I'm just going to
13:04
dub this over the original footage so um
13:07
take the lid off see this massive great
13:10
heat sink so this is a pel cool camera
13:12
we've got this blower fan producing a
13:14
fair amount of air flow through
13:16
it the connector here there's the ccds
13:19
mounted on the board on the
13:24
right this unplugs so we've got a bit of
13:27
power supply stuff on here
13:29
USB interface I think that's the Cyprus
13:32
microcontroller High speeded USB
13:34
interface there's a zyink spon fpga some
13:40
RAM and there's a couple of ATD
13:42
converters can't quite read what those
13:45
those are but anal
13:47
devices um little bit of bodgery around
13:51
here extra decoupling obviously they
13:53
have having some noise issues this is
13:55
around the ram chip quite a lot of extra
13:57
capacitors been added there
13:59
uh there's a couple of amplifiers prior
14:01
to the HD converter buffers or Andor
14:05
amplifiers taking the CCD
14:08
signal um bit more power spy stuff here
14:11
this is probably all to do with
14:12
generating the various CCD bias voltages
14:14
they uh need quite a lot of exotic
14:18
voltages next board down is just a
14:20
shield and an interconnect
14:24
boardly shielding the power supply stuff
14:26
from some the more sensitive an log
14:28
stuff
14:31
and this is the bottom board which is
14:32
just all power supply
14:34
stuff as you can see tons of capacitors
14:37
or Transformer in
14:42
there and this is the CCD which is a uh
14:47
very impressive thing this is a kf50 100
14:50
originally by true sense then codec
14:53
there ON
14:54
Semiconductor it's 50 megapixels uh the
14:58
only price I could find was this one
15:00
5,000 bucks and the architecture you can
15:03
see there actually two separate halves
15:04
which explains the Dual AZ converters
15:06
and two amplifiers it's literally split
15:08
down the middle and duplicated so it's
15:10
outputting two streams in parallel just
15:13
to keep the bandwidth sensible and it's
15:15
got this amazing um diffraction effects
15:18
it's got micro lenses over the pixel so
15:20
there's there's a bit more Optics going
15:22
on than on a normal
15:25
sensor few more bodges on the CCD board
15:28
including this wire which isn't really
15:29
tacked down very well which is a bit uh
15:32
bit of a mess quite a few bits around
15:34
this board where they've uh tacked
15:36
various bits on which is not super
15:38
impressive looks like CCD drivers on the
15:40
left with those 3 ohm um damping
15:43
resistors on the
15:47
output get a few more little bodges
15:50
around here some of
15:52
the and there's this separator the
15:54
silica gel to keep the moisture down but
15:56
there's this separator that actually
15:58
appears to be cut from piece of
15:59
antistatic
16:04
bag and this sort of thermal block on
16:06
top of this stack of three pel Cola
16:12
modules so as with any Stacks they get
16:16
um larger as they go back towards the
16:18
heat sink because each P's got to not
16:20
only take the heat from the previous but
16:21
also the waste heat which is quite
16:27
significant you see a little temperature
16:29
sensor here that copper block which
16:32
makes contact with the back of the
16:37
CCD and this's the back of the
16:40
pelas this then contacts the heat sink
16:44
on the uh rear there a few thermal pads
16:46
as well for some of the other power
16:47
components on this
16:51
PCB okay I've connected this uh camera
16:54
up I found some drivers on the disc that
16:56
seem to work under Windows 7 couldn't
16:58
get to install under Windows 11 though
17:01
um in the absence of any sort of lens or
17:03
being bothered to the proper amount I've
17:04
just put some f over it and put a little
17:06
pin in there to make a pinhole lens and
17:08
software gives a few options I'm not
17:11
entirely sure what all these are there's
17:12
obviously a clock frequency 22 MHz low
17:15
gain and with PFG no idea what that is
17:19
something something game programmable
17:20
Something game perhaps ver exposure
17:23
types I think focus is just like a
17:25
continuous grab until you tell it to
17:27
stop not entirely sure all these options
17:30
are obviously exposure time uh triggers
17:33
there ex external hardware trigger inut
17:35
you just trigger using a um thing on
17:37
screen so the resolution is 8176 by
17:40
6132 and you can actually bin those
17:42
where you combine multiple pixels to get
17:46
increased gain at the expense of lower
17:48
resolution down this is a 10sec exposure
17:51
obviously of the pin hole it's very uh
17:53
intensitive so we just stand still now
17:56
downloading it there's the uh exposure
17:59
so when it's
18:01
um there's a little status thing down
18:03
here so that tells you the um exposure
18:07
[Applause]
18:09
time it's this is just it
18:15
downloading um it is quite I'm seeing
18:18
quite a lot like smearing I think that I
18:20
don't know whether that's just due to
18:21
pixels overloading or something else I
18:24
mean yeah it's not it's not um out of
18:26
the question that there's something not
18:27
totally right about this camera
18:28
certainly was bodge wise on there um I
18:31
don't I'd imagine a camera like this
18:32
it's got a fairly narrow range of
18:34
intensities that it's happy with I'm not
18:36
going to spend a great deal of time on
18:38
this if you're interested in this camera
18:40
maybe for astronomy or something and
18:42
happy to sort of take the risk of it may
18:44
not be uh perfect I'll um I think I'll
18:47
stick this on eBay along with the
18:48
Illuminator I'll put a link down in the
18:50
description to the listing take your
18:52
chances to grab a bargain so for example
18:54
here we see this vertical streaking so
18:56
I'm not sure how normal that is this is
18:58
on fairly bright scene looking out the
19:02
window if I cut the exposure time down
19:04
on that it's now 1 second
19:07
exposure again most of the image
19:09
disappears again this is looks like it's
19:11
possibly over still overloading here go
19:14
that go down to say say quarter a
19:16
second so again I think there might be
19:19
some Auto gain control going on here um
19:21
this is with the PFG option let's try
19:23
turning that off and see what
19:25
happens so I'm not sure this is actually
19:27
more streaking or which just it's
19:29
cranked up the gain all the dis display
19:31
gray scale to show what um you know the
19:33
range of things that it's captured
19:36
there's one of one of 12 things in the
19:38
software there's um you can see of you
19:40
can't seem to read out the temperature
19:42
of the pelta cooler but you can set the
19:44
temperature and if you said it's a
19:46
different temperature you see the power
19:48
consumption jump up running the cooler
19:50
to get the temperature you requested but
19:52
I can't see anything anywhere that tells
19:54
you whether the cool is at the at the
19:56
temperature other than the power
19:57
consumption going down and there's no
19:59
temperature read out
20:03
here and just some yeah this is just
20:05
sort of very basic software I'm sure
20:07
there's like an API for more
20:09
sophisticated
20:10
applications but so if you know anything
20:12
more about these cameras please um stick
20:14
in the
20:15
comments um incidentally when I was
20:18
editing I didn't notice there was a bent
20:19
pin on the um CCD but I did fix that
20:22
before doing these tests and also
20:24
reactivated the um silica gel desicant
20:26
cuz I noticed it was uh I was getting
20:28
bit of condensation on the window but um
20:31
yeah so a couple of uh interesting but
20:34
maybe not particularly uh useful pieces
20:37
of Kit except for someone that's got a
20:38
very specific use so um I'll stick a
20:42
I'll stick these on eBay put a link in
20:44
the description and say hopefully
20:45
someone could actually make some uh good
20:47
use of these things
Example Output:
**Abstract:**
This video presents Part 2 of a teardown focusing on the optical components of a Fluidigm Polaris biotechnology instrument, specifically the multi-wavelength illuminator and the high-resolution CCD camera.
The Lumen Dynamics illuminator unit is examined in detail, revealing its construction using multiple high-power LEDs (430nm, 475nm, 520nm, 575nm, 630nm) combined via dichroic mirrors and filters. A square fiber optic rod is used to homogenize the light. A notable finding is the use of a phosphor-converted white LED filtered to achieve the 575nm output. The unit features simple TTL activation for each color, conduction cooling, and internal homogenization optics. Analysis of its EEPROM suggests extremely low operational runtime.
The camera module teardown showcases a 50 Megapixel ON Semiconductor KAF-50100 CCD sensor with micro-lenses, cooled by a multi-stage Peltier stack. The control electronics include an FPGA and a USB interface. Significant post-manufacturing modifications ("bodges") are observed on the camera's circuit boards. Basic functional testing using vendor software and a pinhole lens confirms image capture but reveals prominent vertical streaking artifacts, the cause of which remains uncertain (potential overload, readout artifact, or fault).
**Exploring the Fluidigm Polaris: A Detailed Look at its High-End Optics and Camera System**
* **0:00 High-End Optics:** The system utilizes heavy, high-quality lenses and mirrors for precise imaging, weighing around 4 kilos each.
* **0:49 Narrow Band Filters:** A filter wheel with five narrow band filters (488, 525, 570, 630, and 700 nm) ensures accurate fluorescence detection and rejection of excitation light.
* **2:01 Customizable Illumination:** The Lumen Dynamics light source offers five individually controllable LED wavelengths (430, 475, 520, 575, 630 nm) with varying power outputs. The 575nm yellow LED is uniquely achieved using a white LED with filtering.
* **3:45 TTL Control:** The light source is controlled via a simple TTL interface, enabling easy on/off switching for each LED color.
* **12:55 Sophisticated Camera:** The system includes a 50-megapixel Kodak KAI-50100 CCD camera with a Peltier cooling system for reduced noise.
* **14:54 High-Speed Data Transfer:** The camera features dual analog-to-digital converters to manage the high data throughput of the 50-megapixel sensor, which is effectively two 25-megapixel sensors operating in parallel.
* **18:11 Possible Issues:** The video creator noted some potential issues with the camera, including image smearing.
* **18:11 Limited Dynamic Range:** The camera's sensor has a limited dynamic range, making it potentially challenging to capture scenes with a wide range of brightness levels.
* **11:45 Low Runtime:** Internal data suggests the system has seen minimal usage, with only 20 minutes of recorded runtime for the green LED.
* **20:38 Availability on eBay:** Both the illuminator and camera are expected to be listed for sale on eBay.
Here is the real transcript. What would be a good group of people to review this topic? Please summarize provide a summary like they would:
Immune Booster #22: T cell signaling and selection with Leslie Berg (https://www.youtube.com/@MicrobeTV) MicrobeTV (https://www.youtube.com/@MicrobeTV) 141K subscribers 18 Share Ask Save 209 views Feb 9, 2026 Immune (https://www.youtube.com/playlist?list=PLGhmZX2NKiNkNlShZ2YuHH1GkwdsnH4pr) Leslie Berg talks about her career studying T cell receptor signaling, T cell development, and the importance of mentors in her career.Show notes at https://www.microbe.tv/immune/immune-... 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Transcript 0:00 [music] from Micro TV. This is a special episode 0:07 of Immune, [music] an immune booster recorded on May 3rd, 2025 at the 0:13 American Association of Immun Immunologist Conference in Honolulu, Hawaii. Um the these immune episodes are 0:22 really designed to be a special introduction into the scientists behind 0:27 the science and uh just give us a glimpse into who is actually doing the 0:33 science. I'm Cindy Lifer and you're listening to the podcast about the body's defenders against disease. If you 0:41 like what you hear and you want to help donate, we'd really appreciate your support for Immune and all the other 0:47 shows at Micro TV, you can go to microbe.tv/contribute. 0:53 So, joining me today is Leslie Berg. Um, she is professor and chair at the 0:59 University of Colorado School of Medicine. And I thought since we're here at AI, it would be pertinent to mention 1:06 that you are also a former counselor, a the former president of AI and um one of 1:12 the co- first editors and chief of the IO horizons journal which is the um open 1:19 access journal that's published by the American Association of Immunologists. So um thank you for coming today. 1:26 You're welcome. It's really a great pleasure to have you. Um, I've been really looking forward to this. Um, I I know that you 1:34 have made multiple contributions that have really defined how we ask some questions in 1:41 immunology. made one of the first TCR transgenic mice if not the first uh and 1:46 that has really enabled uh opening the field to ask lots of different questions and you followed that up with really 1:53 amazing observations and I love your work because you work on kinases and signaling because I also love kinases 2:00 and signaling and things that go on inside cells so a lot of people get scared of those signaling pathways so 2:06 when you put them up and there's like all these arrows and everything but I love it so I'm really excited to have you on. So I just thought it would be 2:13 really cool to start with you telling us a little bit about how your journey through science. So your career path and 2:19 maybe mentioning a couple of the key pivotal things that got you to where you are right now. 2:24 Yeah. Well, it's been a long journey, so it's it's [laughter] might take a couple minutes, but 2:29 that's fine. Uh, you know, I was thinking about it actually as I was walking over here. Um, so I think my real passion for for doing 2:38 science was driven by curiosity about how things work and and and the ability 2:44 to ask questions and do experiments that could answer those questions. And it 2:49 started, I think, when I was in college when I first learned molecular biology, which was very different than the kind 2:55 of science that I learned in high school and biology, which I thought was incredibly boring and just memorizing 3:03 names of species and all this stuff that I did not seem very interesting to me. 3:08 And so what really appealed to me about molecular biology was the fact that 3:14 people who were really really smart could design incredibly simple experiments like with bacterial genetics 3:21 or whatever and figure out these inc really important fundamental things like 3:26 the genetic code. And I just thought that was like amazing that they could have bacteria 3:33 whatever doing things in a petri dish and like figure out how DNA worked and what the you know how the genetic code 3:40 was um encoding for different amino acids and all these things. So that's really started. I went to graduate 3:47 school at UC Berkeley and was in the molecular biology PhD program which I 3:54 don't think actually I think they merged with other things that probably don't exist anymore but um I basically worked 4:01 in a molecular biology. I actually worked on viruses since your you worked on a veterinary [clears throat] virus. 4:07 I worked on yes papilloma virus but not the human ones. Um bovine papaloma virus. It was one of 4:14 the first uh papaloma viruses that was like the genome was sequenced and it was this 4:21 small circular DNA virus that did all these things that you know including 4:26 causing cancer and no one could figure out like it was I don't know the whole genome was like 8KB. How could that 4:32 happen? So that was really what um the lab was working on at the time trying to 4:37 understand what the different genes were in the virus and what their function was and how it transformed cells with this 4:44 small number of genes and things like that. And then when I finished my PhD, I 4:49 decided I wanted to work on something a bit more um 4:55 I don't know what I'm not even sure what what bit more bigger like not just so my 5:00 whole PhD was essentially the nucleus [laughter] of cells in a petri dish, right? You 5:06 know, tissue culture cells. And I just thought, no, I want to do something that has like animals and like real, you 5:13 know, biology. Um, and and so I for reasons that are still obscure to me and 5:20 and whoever, I decided to become an immunologist. And I didn't actually know any immunology. I'd never taken an 5:26 immunology course, but I thought it was like a really 5:31 interesting. I mean, the immune system, the little I knew about it, just seemed so incredibly cool and like a 5:37 complicated black box, like so much to figure out. And at the time, I think also the people 5:44 in the field were just starting to use molecular tools to understand the immune 5:49 system and you must have been able to see sort of like what you could do I guess I don't remember thinking about 5:56 it things no [laughter] I don't remember being incredibly insightful about it but I just thought 6:02 like this sounds really cool I'm going to do this and so I went to Mark Davis's 6:08 lab at Stanford good choice with must have been like a year or two after he cloned the T- center and so the 6:15 field was like hopping. I mean they were his lab went from like nobody to you 6:21 know 25 people in like 3 years. Wow. It was it was a huge lab and and 6:27 there were just so many good people in the lab and I learned all the immunology 6:33 that I knew from all the other people in the lab the posttos and students and um 6:40 people who actually were trained as immunologists unlike me. So that was really really fun. It was a great time. 6:47 What was it like to be in a big lab like that? It was as a posttock I'm not sure was it 6:54 would have been as fun as a student as it was as a posttock as a post it was 7:00 for me perfect because I got left alone like there were too many people doing 7:05 things um you know that were actually more 7:11 interest to Mark than what I was doing. And so, you know, he basically left me 7:18 alone and I had a project and I knew what I was trying to, you know, 7:23 accomplish. we were making these TCR transgenics and [sighs and gasps] and the whole question was like can you 7:30 can you actually see positive and negative selection and the thymus happening you know in this um uh system 7:37 with the with mice whose essentially all their tea cells had the same receptor um 7:44 and so it was really I I mean I just had to learn how what I was doing because no 7:51 one was holding my hand except the other people than that. The other posts taught me a lot. But um but it was it was a 7:59 great I think experience in in um 8:04 leading to the next step of having your own lab where you you're actually having to figure out what to do and if 8:11 something doesn't work, how do you solve the problem and you know what do you do next and all that kind of stuff. So it 8:18 was great. I I had a great time. Um and then I you know got a faculty 8:25 position and started a lab and um what happened was and since you mentioned 8:32 signaling my first lab was in the um essentially 8:37 basic cell and molecular biology department at Harvard and I had a 8:42 colleague down the hall who um worked on kynases. He actually had um been the 8:50 person that discovered that sarkc was a tyrros and kynise. [clears throat] So this guy was like a huge figure in the 8:57 field of kinases and for some reason just talking to him all the time I 9:03 started thinking like well what are the kinases in T- cells like LCK I think had been discovered already that was it like 9:10 that can't that can't be it that can't be the only kynise in T- cells that's important and so anyway we worked out 9:18 this whole strategy to try to identify new tyrrosin kinases and um and And you 9:24 know you in those days you had to like screen libraries and clone the genes and 9:30 sequence them and then we had to make an antibbody to things we thought were interesting to look at where it was 9:36 expressed and try to figure out what it was doing. And yeah, it was really um 9:41 that is really his influence that um 9:47 I think um made me think a lot about because I had been working about on 9:52 thyic selection and the whole mystery of how does the 9:57 same developing T- cell kill itself when it gets strong signals and get sur and 10:04 survive and differentiate when it gets weak signals. I'm like, how does that work? Yeah, we still don't really know. I mean, 10:11 [laughter] all these years later, I mean, we know more of it, but like I think having a, you know, putting it in 10:17 a sentence. Yeah. So, right now, I'm actually um one of the co-authors of the Janeway textbook. 10:24 Yep. And I just on the airplane was working on the revision for the next edition of the chapter on BNT cell development. M 10:32 and I'm reading like this stuff and thinking I still can't explain how 10:38 positive versus negative selection works like in in a simple you know really um 10:45 piffy kind of way right yeah as well as some of this and some of that and maybe it looked well anyway 10:52 yeah so I I think that is still a mystery but that was the question that 10:59 really made me get interested in signaling and and try to um figure it out. So 11:08 So is it really like who is around you profoundly influenced the type of 11:14 questions that you were asking even if you started your lab in one direction? Yes. 11:19 But then had this really fruitful Yeah. And I I you know I think um I've I 11:24 have it's not like I've used that specific example but when I when we recruit um new faculty in in our 11:33 department junior faculty especially and sometimes they're very worried about 11:38 um leaving their lab that they were very successful posttos in and what are they going to work on and how are they going 11:44 to differentiate themselves from their former PI and I always tell them like I 11:51 promise you it will not be a problem. In in a year and maybe at most two, you 11:57 will be doing completely different things than you ever expected to do because you get influenced by the people 12:03 around you. What what systems they have, what questions they're asking, what um 12:09 comments they make about your work when you present it, the ideas that come from 12:15 just having different people around you. And I think it's inevitable that you put 12:22 two scientists in different places, you know, different states or whatever, 12:27 different institutions, even if they started here, there they there's no way they don't end up doing 12:34 different things. I've I've heard this thing about like seed and soil, right? So the seed can be very successful, but how what happens to 12:41 that seed depends on the soil it's in. So it's kind of like that same idea. Yeah, that's a good analogy for it. 12:47 Yeah. Yeah. So, I think that is um is really true and it's something I 12:54 don't think people should worry about. But you didn't stay in the same place though. No, no, no. I've been in uh several 13:00 places. So, I was at Harvard for seven years. Then I moved to the University of Massachusetts Medical School. 13:07 And one of the things I learned from that move that I didn't appreciate before because as I said like I started 13:14 life as a molecular biologist. So, I kind of felt at home in a molecular 13:19 biology department, but I don't think I appreciated how much 13:25 nicer it is to have colleagues around me or around one that understand immunology 13:32 because it's not I mean, it's the same thing with Drosophila genetics. You know, when 13:38 people start talking to me about Drosophila genetics over Yes. Like, okay. you know, I kind of get 13:45 the point, but like I don't really know what you're talking about. And and so I was really the only 13:51 immunologist in this department, right? And I don't I didn't feel 13:56 like isolated really. But in retrospect, after I moved to UMass Medical School 14:03 where I was in a like a basic immunology division of a clinical department, 14:10 I all of a sudden realized how much more fun it was to have a bunch of immunologists around me that could like 14:16 I could go tell them my latest result and they would understand it right away. I didn't really have to explain and ask 14:22 questions and they would make comments or or I'd have some idea and someone I had one colleague in particular Jins Su 14:29 Kang whose office was next door to mine who would frequently tell me that's really stupid like [laughter] you know 14:35 there there's no way it works like that like what are you talking about you know and it's great to have people around you like that that are going to challenge 14:43 you. Yeah, cuz very often as a PI, the people in your lab 14:49 are reluctant to do that, right? They're not going to tell you you're wrong or be stupid. 14:54 Well, some will, but some will, [clears throat] but not not as many. Not not not all. And so, it's 15:00 great to have colleagues who will put you in your place and, you know, and and 15:06 make you defend your idea. Like, it's not like I would roll over and say, "Oh, yeah, okay, you're right." No, I would 15:11 like we'd argue. we talk about, you know, well, this paper says this and that's why, you know, I think it works 15:18 like this and, you know, it was it's really it it was really nice to have 15:23 that. And um and it was something I didn't realize I was missing until I had 15:29 it and then I was like, "Oh my god, I just spent seven years without that. This is really much better." 15:35 Do you wish that you had done that sooner or do you think that it was it happened at the right time? 15:40 I think it did. I I think one of I mean I actually interviewed at in a number of 15:46 immunology departments for my first faculty position and at the time you 15:52 know we had just made these TCR transgenic mice and they were kind of a hot commodity of course and we gave them 15:59 out it wasn't like they weren't available but there were a number of um 16:04 senior faculty that I interviewed with on these various job interviews who 16:10 essentially wanted to eat it. Yep. Yep. They wanted, it wasn't that they 16:16 wanted access to the mice cuz they could, we gave them to whoever asked, but they wanted 16:24 to sort of, I don't know, take take my project over, 16:30 you know, sort of um it it I felt like I would easily get 16:37 lost in some bigger person's like empire, you 16:42 know, like I would lose my ability to have my own identity or whatever because 16:48 people would want a piece of me, you know, here and a piece of me here. And so I actually 16:55 didn't take any of those offers. I went I went to this molecular biology 17:00 department on purpose to get away from all these [laughter] 17:06 very aggressive senior people, you know. So I don't think I mean who knows like 17:14 thing about life as you know you never get to go back and do the control right 17:19 so you don't know what would happen if you had made a different decision but it all worked out okay 17:25 I wonder though cuz uh a lot of when we interview a faculty we always want to know what can they can contribute to the 17:30 department and what what can they use what resources can they do they see that 17:36 we have that would build their program and it's almost like the first was what they were asking like what can you give 17:42 us right but not so much what could we give you whereas it sounds like uh when 17:47 you moved you were really looking for what can I get out of the environment more than what I can 17:52 I think that's probably a much clearer way to put it than what I just said [laughter] 17:59 were there any other pivotal moments though um that you can think of or like maybe a mentor who really was um 18:05 instrumental in your career development well I my my PhD advisor probably had 18:10 the biggest impact. Um he it's a guy named Mike Botchan. He's still at 18:17 Berkeley and he still works on you know um DNA replication and things 18:24 like that. He he was just a great I mean he was 18:29 frustrating sometimes but he was such a great mentor because one of the things he 18:36 the quality he had was he he treated anyone who would talk science to him 18:41 like if you would sit with him and talk about papers or science or ideas or 18:47 whatever he he didn't care if you were the the you know person cleaning the bathrooms right [laughter] 18:54 And so he treated students, PhD students, especially me, like I didn't, 18:59 you know, um I had very little experience, like lab experience when I went to graduate school, you know, and 19:06 he he he valued [clears throat] everybody's opinions like he was not he was totally 19:12 um prank agnostic. Yes. Like he didn't he didn't say, "Well, you know, I'm going to talk to 19:18 the posts, but I don't care about, you know, like I'm not going to talk to you until you 19:24 have something interesting to say. I mean, he would talk to anybody [laughter] and for probably hours. So, so I really 19:33 I think it gave me a lot of confidence and a lot of um 19:39 um belief that I could contribute, right? Because he treated us all with not not just respect, but 19:46 with a sense like that we were contributing something worthwhile. Mhm. And 19:52 yeah, and so that was um one of his really great I think really great 19:57 qualities and you know I don't I don't exactly know I 20:04 mean I again you know you you live through these experiences you don't realize it at the time but it's in 20:10 retrospect. So when I started my postto where I you 20:15 know didn't interact so much with the PI because the lab was so big and he was 20:20 traveling a lot he was famous and all this stuff you know it was it made me 20:25 appreciate how much I had gotten out of all those you know Mike would like come 20:31 in the lab and sit down and just like talk about stuff you know. Oh, the other 20:39 thing I learned from him that was really important was that he um he would he would take whatever data 20:47 from our experiments or other people's he would come up with these like hypothesis. Okay, this is how it works 20:55 and he would be convinced he was right. Right? So like he would tell you like 21:01 well this proves it and this proves it and this whatever and then somebody would do an experiment in the lab that 21:07 said he was totally wrong and he would okay he would like all right I'm wrong 21:15 we we have to come up with something and he would go home and the next day he would come back to lab he goes I got it 21:20 now I know like I mean he he never held on to ideas 21:25 where the data didn't support it like he could just throw them out like 21:31 pe you know and I think that is a really important quality because we all get emotionally 21:37 attached to our ideas right and we're sure that something works the way we think it does and then 21:43 when some data comes along that disagrees you know it's hard to to just give it up 21:49 and just go okay I guess I'm wrong um but he had no problem doing that he was perfectly happy to in fact he'd get even 21:56 more excited because he liked He, you know, he liked 22:01 when he had to really think about things again like, okay, my original idea based 22:07 on all this stuff is not true. I better really go back and think about this some 22:12 more. And that that's what, you know, drove him. So, so which pieces of that do you take then 22:18 into your mentoring? Because there's there's good things and bad things about each of those, right? 22:24 So I mean because the the first experience is very hands-on but the second experience gave you this 22:30 space that you could develop. So how do you how do you take what you learned in those experiences and create your own 22:37 mentoring model for your lab? Yeah, it's a good question. I mean one thing is I I try to 22:46 so when people come in people will come in my office like students or whoever posts and they'll go my experiment 22:53 didn't work and I'll say what do you mean it didn't work and I would say half 22:58 the time it's not that the experiment didn't work it's that they didn't get the result they wanted they thought they were going to get 23:04 right that they thought or that we all thought they were going to get right and I you know I'm a big believer in the 23:11 fact that if um if you do an experiment and it's a convincing experiment and you 23:17 the controls are there and technically everything is okay and it gives you an answer you were not expecting that's 23:24 when you actually learn more. Yeah. And I keep telling you we learn more from the experiments that don't if if 23:29 every experiment turned out the way we predicted we would already know everything and we wouldn't learn 23:35 anything. Right? That means we already figured the whole thing out and we're done. You know, there's nothing to 23:40 learn. The when it it's only when it comes out a different way, not how you 23:46 expect, then you really learn something because then you go, okay, there's something going on. I don't I haven't 23:51 figured out yet because this result isn't what I expected or predicted. And so I try to, 23:59 you know, instill in the students that every experiment that's solid or sound 24:07 teaches you something whether or not you like the answer, you know. Yeah, 24:13 it's it is that's the that's what the data is telling you. And you have to go you have to believe the data. You can't 24:19 just, you know, pick the data you like or whatever. So that's one thing. And I 24:25 think the you know being the department chair now okay so that isn't something 24:31 that I was my whole career but I have when I moved to Colorado and I um 24:40 students started you know asking about joining the lab or whatever I would tell them look I'm really busy I have a 24:48 department to run you have to be really independent if that's not for you and it it's not 24:53 for everybody study, then this probably isn't the right lab for you. But if you 24:58 want that kind of environment where I said, I'm not going to be in the lab like at your bench every day looking 25:04 over your shoulder, whatever, looking at you, you know, your latest result. Um, some people like want 25:12 that and some people don't want that. And so that's something you have to decide like what whether that is um the 25:21 kind of environment that will work for you and that you'll be successful in because 25:27 I don't have the time to hold anybody's hand. [laughter] But actually I never did. I mean it it's not that I never had the time. I never 25:34 was interested in it. I don't want to micromanage people's experiments like that's then they don't learn. 25:40 Yeah. So I think that your job as a PI or faculty member is to 25:50 teach train your students to be scientists not thinking scientists right not to um you know design other 25:58 experiments not to clone yourself too right yeah you had to give room for growth 26:03 I I like this idea because uh I'm a director of graduate studies and that's one of the really things that I talk 26:09 about with the students about when you're doing rotations, think about how you are responding to 26:15 the mentoring that you're getting so that you can find out which mentoring environment you want to be in. It's a lot of like what you were just saying. 26:21 Yeah. You know, it's you have to decide. It's not it's not up to the PI. It's the student needs to decide if that's the environment 26:27 they're in. The PI can kind of help as they're rotating through. Right. And I think I think also students 26:32 need to develop some kind of self-awareness about what is their what is it that would work for them? 26:39 Yes. Right. because there's it's not the same for everybody, right? And um 26:45 yeah, so I think that's probably really good advice, I think. So So what kind of research questions 26:51 are keeping you up at night now? Well, it's not climate selection anymore, [laughter] 26:59 although it's still every, you know, every once while I I do, you know, 27:05 especially when I'm working on this chapter, I have to think about it. But um I think it's it's not a very 27:11 different question. It's just that the focus is a bit different. So, we still 27:17 work on T- cell receptor signaling and I still feel like I don't understand 27:24 how a cell when it gets stimulated through its T- cell receptor 27:31 based on the strength of that signal, the affinity, how much antigens on the 27:36 antigen presenting cell, whatever it is, how that cell then does different things. Yeah. like you know so there's 27:44 just so much evidence to say that strong signals promote these and these behaviors and te- cells and weak signals 27:51 you know promote different pathways of differentiation how does that work so 27:57 that's what so it's not positive and negative selection thymus but it's a similar 28:02 thing where there's a cell that has potential to do a lot of things 28:08 and it's s the T- cell receptor signaling has a huge impact on which of 28:13 those paths that T- cell goes down. Mhm. And we want to what I really want to 28:20 know is the wiring inside the T- cell. What are the signaling pathways, the transcription 28:26 factors activated, the gene expression programs that are different when it gets strong signaling versus weak signaling. 28:34 So that's that's what we still do years later. I guess I'm uh not very 28:40 imaginative. Some people say like, "Oh, you know, it's really I'm working on 28:46 things I never thought I'd be working on, you know, 20 years ago, whatever. My interests have changed. My whatever." 28:53 And I'm still like trying to [laughter] figure out the same thing after all these years. Um 29:00 it's a hard question. It is. And I think I think the reason it's compelling is that 29:08 I have a now I'm at a medical school where there's a lot of people working on CART T cells 29:14 and um and they suck those things, right? They're they're uni dimensional 29:22 TE-C cells responses. So the CAR receptors don't work like a real TCR and 29:27 the cells can't do this whole 29:33 um heterogeneous set of fates, right? They do one thing and and then they drop 29:41 dead or whatever. So I I think that it's really important 29:48 you know I mean CART cells alone just to me was such a um 29:54 success story of studying signaling right exactly like if we didn't know about LAT and 30:00 ITAMS and all these things like there would be no cell right so understanding all those molecules and 30:07 how they work and what signals they elicit and and how they work together and all the you know was so instrumental 30:14 in the whole field of cartis cells but it's it's very suboptimal right now 30:21 you know they're they I we have joint lab meeting with several 30:27 people that um work on cartis cells and so I've seen their introductory slides 30:32 like their students and posttos talk you know over and over and over again and you know they always start out with the 30:39 same thing that you know for see me 19 cars to treat um pediatric alll 30:47 right 95% of the kids respond in a year their tumors are their leukemia is gone 30:55 but only 50% still have that response after a you 31:00 know more than a year so fi so half the kids relapse 31:06 so there's something right so there's a lot left to be done so you if I if I can put that together. 31:13 What you think is what we can learn from the signaling is to maybe 31:19 construct better CART tea cells that have a broader repertoire of functions and 31:26 maybe that would improve Yeah. the outcome. And it may actually be that you can't do it with one 31:32 color. I mean, that's what I keep telling these guys like you you probably can't do it with one card. 31:37 You probably need cells that get different versions because you want actor cells that are going to kill the 31:44 tumor. You want them fast. You want them aggressive, proliferate their brains out, kill all 31:50 the leukemia, whatever. But they're not going to make good memory cells that persist, right? 31:55 Yeah. And so you might actually need a different construct kind of like 32:00 mimicking the strong signaling and weak signaling of a normal TCR 32:06 which happens because the repertoire of T- cells is varied. So you in a in a 32:12 person you have high affinity and medium affinity low affinity for the virus or 32:17 whatever. Right. They're not monoconal. Right. Exactly. So I think that maybe 32:22 the solution is you have to have one kind of car, you know, to make the aector cells and 32:28 you need a different construct with different signaling domains to make memory cells. 32:34 I don't know if that's true, but it's not that hard. I mean, they split the 32:40 K, you know, the T- cells in the that they collect for the transduction, split them in half and half get this one and 32:46 half get that one and mix them back together. Yeah, they've tried to put some of the domains together that 32:52 generally make memory or generally don't, but you're kind of putting it all at the the same. It's all activated at 32:57 the same way, which yeah, if they were separated, maybe it would work better. It's interesting. 33:02 We'll see. [gasps and laughter] That's cool. So I was wondering also shifting gears 33:07 just slightly uh you know being a chair do what kind of uh recommendations do 33:13 you have like advice for either early career faculty or for trainees or both? 33:20 Yeah. I mean right now I would say the biggest message is don't give up. [laughter] Yeah. It's it's easy to give up at this 33:28 moment in time. Yeah. you know, there's a lot of obstacles right now, a lot of uncertainty, a lot 33:35 of scary things happening. Um, and I think I guess maybe I'm a an optimist. I 33:43 keep thinking it's going to have to be It's just so hard to picture, 33:49 you know, biomedical science just kind of crashing down in the US and never recovering. Yeah. 33:54 So, I refuse to believe that's going to happen. Um 34:00 but I do I think that um 34:06 it is true that right now there's a lot of 34:13 I think it's a much easier let's say path to work on things especially in 34:19 iminology or veriology or microbiologies my department has some of everybody if 34:25 you if you have some um not you don't have to do translational research but 34:31 there's some like the carti cell there's something directly clear about my work 34:36 will help this right so I think what we're seeing now with NIH at least is 34:42 that there there's it's been happening for more than 10 years this emphasis on 34:50 um studying problems where where there's a reason to think that they could have 34:56 translational impact. That doesn't mean people aren't doing, 35:01 you know, basic science or discovery science, you know, where you don't know what you're going to find. And, you 35:06 know, it's not like you're just titrating drugs to see what dose is better at killing the tumor than 35:12 another. But but I do think it's easier to argue for, you know, spending public 35:20 money, taxpayer money, if you're can explain, well, what I'm hoping to learn 35:26 is something that could help people that, you know, have these autoimmune diseases or have um, you know, things 35:35 like um infections that that can't be eliminated or whatever it is. So, you 35:43 know, that is very unpalatable to some people, but I think it's the reality 35:48 now. Yeah. I I worry a little bit because 35:54 take what you did for example where we were studying kynases for the sake of understanding the kinases. 36:00 But that actually contributed for example to our understanding about how the signaling domains work that are 36:06 being used to construct these cars. But you would have never been able to predict a car 36:12 back when you started your life. So how do we make room for those kinds of things where you you do need to 36:19 understand how things work without sometimes not knowing where they're going to go in the future. 36:24 Yeah. Right. I mean I think that you know we can all think about examples like crisper. 36:31 Yep. Which no one would have predicted or PCR or whatever you know you name it. So 36:37 yes, I think that's true. I think that it's um it is 36:42 it is hard and I don't know if the answer is I mean of course one the perfect answer 36:48 would be that NIH would leave room for 36:54 just you know fundamental science that we don't know where it's going to go. Um, I guess the alternative is in some 37:01 ways it's a little bit what NSF's job is, right? and 37:07 you know that it's it's a little I mean they've never had as much money 37:12 and they never had as big a portfolio and things but I I think that so I have 37:18 a a PhD student and she tried to apply for an NSF like predoc fellowship 37:24 [snorts] and her project was like all about basic 37:30 TCR signaling no translational anything and they told 37:36 No, we won't fund that because it's immunology and that's not what we do. 37:41 So, [laughter] so you know, she she was really confused 37:48 cuz there was, you know, it was a little bit like nobody wanted to, you 37:55 know, whatever. It was unclear why they had that view of it, right? because just 38:01 because it was immunology maybe it was viewed as too medical or 38:07 something but so I I do think what you're pointing out is a problem and there's a gap and I 38:13 don't I don't really know um 38:19 I mean I you know I guess my view is that the 38:24 saying you're studying and trying to justify it by its potential 38:30 um application in the future. It's doesn't take much. It doesn't have to be like, well, I know if I know if I learn 38:37 this, I can cure that disease. No, but you know, understanding how 38:42 um infections contribute to autoimmune diseases, like who knows what people are 38:48 going to find, but um if you knew that, maybe that would be 38:53 useful information. We don't know. So, I think I don't think it has to be like a direct path like connecting the dots, 39:01 but but I I just think the appetite of the 39:06 general public right now for things that are like, oh, I'm just curious about how this works, so I'm going to study it. 39:13 Yeah, it's dwindled. Yep. I definitely would agree with that. 39:20 Yeah. So, um, anything that we haven't talked about that you think would be 39:26 good to to mention? Uh, [clears throat] I can't think of anything that I mean 39:32 that anything obvious that we haven't talked about. I I have to say it's fun 39:37 being a department chair. People told me I would hate it and I shouldn't do it. I had plenty of colleagues told me like, 39:42 "You're making a mistake. Don't do it. It's a horrible job. just have people 39:47 complaining and you're gonna whatever be just worrying about money all the time and stuff like that. So it 39:55 turns out it's actually really fun. How so? Um well I think I'm lucky that I I'm at 40:03 a place an institution where the department has some resources so so we can do things that I think help 40:11 people. We for example when I first got there we I had some 40:17 discretionary money that I got from my startup and I we first thing I did is we hired a bioinformatics person for the 40:23 department. This is now six years ago when when it was now the students are all 40:29 learning how to do it themselves but at the time nobody who wasn't trained in computer science 40:34 like knew actually how to analyze their own data. if you if you had, you know, 40:40 RNA seek data or single cell data or whatever. Um, and that was great and 40:46 people were so grateful and like they got to use her services for free, right? Like the 40:51 grant and now we hired a sort of like I don't 40:57 know what to call her, grant writing consultant. She doesn't write people's grants, but she's really really good at 41:04 reading them and like helping people do a better job um with all this stuff like 41:10 the relevance and significance and all that um 41:15 stuff and and making sure that the aims make sense and that the experiments 41:21 actually address the questions. and um she'll she'll read people's critiques on their grants and 41:28 and tell them, you know, talk to them about what she thinks they should do to revise and and it's it's it's really 41:37 interesting because I wasn't sure this was going to work. I thought, okay, we'll give it a try. But I don't know 41:44 how many people in the department have sent me emails like, "I just have been working with Kim on this grant and she's 41:49 fantastic and it was great and my grant is so much better." And you know, the weather we'll see how much more, 41:58 but the outcomes actually are, but people feel better. [laughter] They feel better about the grants 42:04 they're submitting. they feel like they're they're better written, more um 42:10 put together, have have done a better job of arguing for their important the 42:17 importance of the questions and the approaches and um and so right now 42:22 anything you can do to help people's frame of mind, state of mind, whatever 42:29 is a good thing. [laughter] So, so that's been fun, you know, because like I guess it comes down to if 42:36 you have resources, you can actually help. You can do things to help. Yeah. So, one last thing. What's a fun 42:43 fact that people would be surprised to know about you? Fun fact. I went to Beverly Hills High 42:49 School 90210. That's cool. Did you Did you Anybody 42:55 famous that you went to school with? Yeah. You won't say who. [laughter] 43:00 Carrie Fiser. Oh. Oh, neat. A year ahead of me in school. Jamie. Um, 43:07 oh my god, now I'm going to forget their names. Oh, it's okay. So, the guy So, we used to carpool to school cuz my parents, we lived up in 43:15 the hills and so it was a pain to get to our school. We we couldn't walk or there 43:21 was no buses. You had to get driven. And so we had a carpool with um so you're 43:27 probably too way too young to remember this TV show. The original Mission Impossible, okay, 43:32 was like a TV series and the guy who played one of the characters, you know, 43:37 who was like the cool sexy guy um lived down the street and so we carpooled with 43:43 him and his kids. [laughter] That's fun. That's fun. 43:49 Yeah. Great. Well, thank you so much. Um, that, uh, is a special immune booster. 43:56 You can send questions and comments to immune microbe.tv. And again, consider supporting this and 44:03 all the other shows of microbet TV by going to micro.tv/contribute. 44:10 And remember that uh, Micro TV is a 501c3, so your contributions are taxdeductible. I'm Cindy Lifer from 44:17 Cornell University and I've been joined today by Dr. Leslie Berg from the University of Colorado School of 44:23 Medicine. Thank you. Thank you. Uh music on Immune is by Tatami. Thanks for listening to Immune, the podcast 44:30 that's infectious. We'll be back next month. [music] 44:41 [music]
references from video description:
(https://www.cuanschutz.edu/homepage) Webmail (http://myemail.ucdenver.edu/) UCD Access (https://portal.prod.cu.edu/UCDAccessFedAuthLogin.html) Canvas (https://ucdenver.instructure.com/) Quick Links (javascript:void(0)) Search Department of Immunology & Microbiology School of Medicine Home (https://medschool.cuanschutz.edu/immunology-and-microbiology) About Us Events (https://medschool.cuanschutz.edu/immunology-and-microbiology/events-new) Research Labs (https://medschool.cuanschutz.edu/immunology-and-microbiology/labs) Services Graduate Edu & Training (https://medschool.cuanschutz.edu/immunology-and-microbiology/grad-train) Dept. Resources (https://medschool.cuanschutz.edu/immunology-and-microbiology/dept-resources) Home (https://medschool.cuanschutz.edu/immunology-and-microbiology) About Us (https://medschool.cuanschutz.edu/immunology-and-microbiology/about-us) Faculty & Staff (https://medschool.cuanschutz.edu/immunology-and-microbiology/about-us/faculty) Primary Faculty (https://medschool.cuanschutz.edu/immunology-and-microbiology/about-us/faculty/primary-faculty) Leslie Berg, PhD Leslie Berg, PhD Chair & Professor Department of Immunology & MicrobiologyUniversity of Colorado Anschutz School of MedicineEMAIL (mailto: Leslie.Berg@cuanschutz.edu) T lymphocytes play a central role in protection against infectious diseases, in tissue damage caused during autoimmunity, and in the eradication of tumors. T cells develop their effector functions after their initial activation and differentiation in response to antigen stimulation. Multiple pathways contribute to the specific functions acquired by activated T cells, including T cell antigen receptor signals, costimulatory receptor signals and cytokine signals. Our lab focuses on understanding the T cell antigen receptor (TCR) signals that promote different outcomes following T cell activation. This process is particularly evident during T cell development in the thymus, where conventional CD4+ and CD8+ T cells receive weak TCR signals that promotes their maturation; in contrast, strong TCR signals lead to clonal deletion of self-reactive T cells, and intermediate signals promote the development of alternative T cell lineages, such as FoxP3+ regulatory T cells, iNKT cells, and CD8αα intra-epithelial lymphocytes. In peripheral recirculating CD8+ T cells, strong TCR stimulation leads to robustly proliferating cytotoxic T cell responses, whereas weak TCR stimulation promotes the development of memory T cells in responses to viral infections. We are interested in dissecting the TCR signaling pathways that are responsible for determining these distinct T cell fates. Much of our work has focused on understanding the contribution of ITK to this process. ITK is a Tec family tyrosine kinase known to modulate ‘TCR signal strength’; biochemically, ITK phosphorylates and activates the enzyme phospholipase C-γ 1. Using a combination of transcription factor activation assays, genomics assays, and protein expression analyses, we investigate the thresholds, kinetics, and magnitude of responses to variations in TCR signal strength. These studies have identified distinct modes of TCR downstream signaling responses and their ability to be modulated independently following TCR stimulation. These findings provide insight into how disparate gene expression patterns can be elicited within individual activated T cells, but determining how these gene expression patterns contribute to the differentiation of distinct T cell lineages and effector functions remains an ongoing effort in the lab. To complement these molecular and biochemical signaling experiments, we use a variety of mouse models to examine T cell functions in vivo. We have used several viral infection systems to dissect the TCR signaling pathways and their contribution to the differentiation of effector T cells versus memory T cells. We have also examined the role of TCR signal strength on the development of tissue-infiltrating pathogenic autoreactive T cells in models of Type I diabetes and colitis. Our goal is to combine the top-down studies of T cell functions in responses to infections or autoimmunity with bottom-up approaches examining signaling pathways and their impact on gene expression programs to fully elucidate the impact of TCR signaling on T cell development and activation. Click here for Dr. Berg's publications (https://www.ncbi.nlm.nih.gov/myncbi/leslie.berg.1/bibliography/public/) Leslie J. Berg, PhD, earned her Bachelor of Arts degree at Harvard University and her PhD at the University of California at Berkeley. Her PhD thesis work was performed with Michael Botchan, PhD, on the topic of bovine papilloma virus DNA replication. She then trained as a postdoctoral fellow with Mark M. Davis, PhD, at Stanford University School of Medicine. Dr. Berg joined the faculty at Harvard University in the Department of Molecular and Cellular Biology from 1990-1997, and then moved to the University of Massachusetts Medical School in 1998. In 2019, Dr. Berg moved to the University of Colorado-Anschutz School of Medicine, where she became the Chair of the Department of Immunology and Microbiology. 1980 Phi Beta Kappa, Harvard University 1980-1983 National Science Foundation Graduate Research Fellowship 1986-1987 Cancer Biology Postdoctoral Fellowship, Stanford University 1987-1990 Leukemia Society Postdoctoral Fellowship 1990-1994 Cancer Research Institute, Inc. Investigator Award 1993-1995 Harcourt General Charitable Foundation Young Investigator Award 2001 American Association of Immunologists/Pharmingen Investigator Award for Outstanding Contributions to the field of Immunology 2002 Outstanding Educator Award, Univ. Massachusetts Medical School 2002 The Medical Foundation’s Chestnut Hill Award for Excellence in Medical Research 2003 Educational Achievement “Star” Award, Univ. Massachusetts Medical School 2004 Women in Science and Health Achievement Award, Univ. Massachusetts Medical School 2006 Faculty Achievement Award for outstanding mentoring in the research setting, Univ. Massachusetts Medical School 2006 American Association of Immunologists Distinguished Service Award 2010 Dean’s Award: Lamar Soutter Award for Excellence in Medical Education, Univ. Massachusetts Medical School 2011-2012 President, American Association of Immunologists 2017 Educational Achievement “Star” Award, Univ. Massachusetts Medical School 2020 American Association of Immunologists Distinguished Fellow Award Current Lab Members Zoe BedrosianUddeep ChaudhuryJosh HunkinsMJ MichaelsAlayna Rosales Past Lab Members Loni PerrenoudJulianne RiggsJames Conley, PhDCinthia Wilkinson
Research Article (https://www.pnas.org/topic/type/research-article) Immunology and Inflammation (https://www.pnas.org/topic/immun) Free access Share on (https://www.pnas.org/#facebook) (https://www.pnas.org/#x) (https://www.pnas.org/#bluesky) (https://www.pnas.org/#linkedin) (https://www.pnas.org/#email) (https://www.pnas.org/doi/10.1073/pnas.2025825118#) Hierarchy of signaling thresholds downstream of the T cell receptor and the Tec kinase ITK Michael P. Gallagher (https://www.pnas.org/doi/10.1073/pnas.2025825118#con1) https://orcid.org/0000-0002-7799-0546 (https://orcid.org/0000-0002-7799-0546) , James M. Conley (https://www.pnas.org/doi/10.1073/pnas.2025825118#con2) https://orcid.org/0000-0001-8901-4713 (https://orcid.org/0000-0001-8901-4713) , Pranitha Vangala (https://www.pnas.org/doi/10.1073/pnas.2025825118#con3) , +2 , and Leslie J. Berg (https://www.pnas.org/doi/10.1073/pnas.2025825118#con6) https://orcid.org/0000-0003-0899-8541 (https://orcid.org/0000-0003-0899-8541) leslie.berg@cuanschutz.edu (mailto:leslie.berg@cuanschutz.edu) Authors Info & Affiliations (https://www.pnas.org/doi/10.1073/pnas.2025825118#tab-contributors) Edited by Anjana Rao, La Jolla Institute for Immunology, La Jolla, CA, and approved June 9, 2021 (received for review December 17, 2020) August 27, 2021 118 (35 ) e2025825118 https://doi.org/10.1073/pnas.2025825118 (https://doi.org/10.1073/pnas.2025825118) 9,72832 (https://www.pnas.org/doi/10.1073/pnas.2025825118#) Metrics Total Views 9,728 Last 12 Months 1,705 Total Citations 32 Last 12 Months 7 Information & Authors (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-info) Metrics & Citations (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-metrics) View Options (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-fulltext-options) References (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-references) Figures (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-figures) Share (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-share) Significance In order to help fight off pathogens and malignancies, CD8+ T lymphocytes send signals from the T cell receptor (TCR) through multiple transcription factor pathways. The strength of the TCR signal is modulated in part by the Tec kinase ITK, whose activity helps create graded amounts of T cell activation genes. We report that some signaling pathways rely more heavily on ITK support for robust activation. During suboptimal signaling conditions, we measured the reduced amount of NF-κB activation and early gene induction within digital NFAT- and Erk1/2-activated cells. Our work highlights the importance of signal strength in activating T cells and uncovers details about the supplemental role of ITK in proximal TCR signaling.Abstract The strength of peptide:MHC interactions with the T cell receptor (TCR) is correlated with the time to first cell division, the relative scale of the effector cell response, and the graded expression of activation-associated proteins like IRF4. To regulate T cell activation programming, the TCR and the TCR proximal interleukin-2–inducible T cell kinase (ITK) simultaneously trigger many biochemically separate signaling cascades. T cells lacking ITK exhibit selective impairments in effector T cell responses after activation, but under the strongest signaling conditions, ITK activity is dispensable. To gain insight into whether TCR signal strength and ITK activity tune observed graded gene expression through the unequal activation of distinct signaling pathways, we examined Erk1/2 phosphorylation or nuclear factor of activated T cells (NFAT) and nuclear factor (NF)-κB translocation in naïve OT-I CD8+ cell nuclei. We observed the consistent digital activation of NFAT1 and Erk1/2, but NF-κB displayed dynamic, graded activation in response to variation in TCR signal strength, tunable by treatment with an ITK inhibitor. Inhibitor-treated cells showed the dampened induction of AP-1 factors Fos and Fosb , NF-κB response gene transcripts, and survival factor Il2 transcripts. ATAC sequencing analysis also revealed that genomic regions most sensitive to ITK inhibition were enriched for NF-κB and AP-1 motifs. Specific inhibition of NF-κB during peptide stimulation tuned the expression of early gene products like c-Fos. Together, these data indicate a key role for ITK in orchestrating the optimal activation of separate TCR downstream pathways, specifically aiding NF-κB activation. More broadly, we revealed a mechanism by which variations in TCR signal strength can produce patterns of graded gene expression in activated T cells.Sign up for PNAS alerts. Get alerts for new articles, or get an alert when an article is cited. After T cell receptor (TCR) triggering, a single naïve CD8+ T cell has the potential to expand into millions of daughter effector cells, which use cytolytic factors to eradicate virus-infected cells. The strength of the interaction between the TCR and cognate peptide:MHC molecules on antigen-presenting cells (APCs) controls the rapidity, the response, and the ultimate scale of the effector pool. Stronger-affinity TCR interactions lead to prolonged periods of proliferation and longer times of engagement with APCs, thereby producing larger pools of CD8+ effector T cells (1 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r1) –3 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r3) ).TCR triggering and proximal signaling events exhibit noisy, switch-like behavior. The kinetic proofreading model of TCR signal initiation posits that ligand discrimination is determined by the accumulation of rate-limiting signaling intermediates, which elicit the committed activation of transcription factors. Strong peptide:MHC ligands bind frequently with the TCR and overcome these rate-limiting steps, while weak ligands bind less frequently and are less likely to accumulate signaling intermediates (4 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r4) –6 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r6) ). Individual downstream transcription factors pathways also display digital signaling behaviors. TCR-mediated, store-operated calcium (Ca2+ ) entry (SOCE) and the subsequent nuclear factor of activated T cells (NFAT) activation display probabilistic, digital triggering corresponding to the dose of peptide:MHC (7 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r7) –10 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r10) ). We recently demonstrated that NFAT1 nuclear translocation in response to TCR stimulation is a digital process, as ovalbumin (OVA) peptide concentration modulates the frequency of responder cells within a naïve, clonal OT-I T cell population, without affecting the amount of NFAT1 protein measured in individual, responding cell nuclei (10 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r10) ). Similar bimodal behavior is described for extracellular signal-regulated kinase (Erk) activation, as OVA peptide dose carefully tunes the number of digitally activated Erk responders in a pool of naïve OT-I cells (11 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r11) , 12 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r12) ). The behavior of these pathways is enforced through feedback mechanisms (Erk) and the rapid regulation of phosphorylation states (NFAT) (8 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r8) , 12 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r12) ). Nuclear factor (NF)–κB signaling in T cells has also been observed to behave digitally under some conditions (13 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r13) ). Following TCR engagement, these separate pathways activate in parallel. However, careful observation of the simultaneous and relative behavior of each pathway in T cells in response to peptide stimulation is lacking (14 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r14) ).Peptide concentration and avidity contribute to the probability of digital TCR triggering in individual cells, but at the same time, these same variables tune the graded expression of important, effector-associated factors, notably interferon regulatory factor (IRF)4 and CD25 (10 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r10) , 12 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r12) , 15 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r15) , 16 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r16) ). This disconnect prompted us to question how graded gene expression patterns emerge from digital signaling events. Interleukin-2–inducible T cell kinase (ITK) is a critical component for the optimal activation of phospholipase-C-gamma-1 (PLC-γ1), the enzyme that cleaves the membrane-embedded phosphatidylinositol bisphosphate into equimolar amounts of two second signaling messengers: inositol triphosphate (IP3 ) and diacylglycerol (DAG). IP3 and DAG are responsible for the robust activation of downstream TCR signaling pathways, including NFAT, Erk, and nuclear factor (NF)-κB (17 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r17) , 18 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r18) ). Although TCR signaling is not completely abolished in the absence of ITK, T cells from Itk −/− mice show inefficient, intracellular Ca2+ flux and have notable defects in Erk phosphorylation (p-Erk) (19 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r19) –22 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r22) ). Stimulation of naïve Itk −/− OT-I cells or treatment with a small molecule ITK inhibitor reduced the induction of IRF4 (15 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r15) ), indicating that ITK may not regulate gene expression in an all-or-none fashion but rather act as a rheostat to carefully tune TCR signaling.To determine whether ITK differentially regulates digital TCR responses to tune-graded gene expression, we measured simultaneous NFAT1, NF-κB, and Erk activation in single naïve OT-I T cells stimulated with peptides of variable affinity with or without the use of an inhibitor of ITK (and resting lymphocyte kinase, RLK). We found that NFAT1 and Erk activation consistently showed digital responses, even in weakly stimulated cells; however, each pathway had a different sensitivity to ITK/RLK inhibition. Importantly, NF-κB activation occurred incrementally; cells that digitally triggered NFAT1 had graded amounts of NF-κB activation that scaled with peptide affinity and was sensitive to ITK/RLK inhibition. We also measured the immediate transcriptional response following different TCR signaling conditions. These studies revealed that the expression of NF-κB gene targets and the induction of transcripts encoding AP-1 factors were most sensitive to ITK/RLK inhibition. Regions of dynamic DNA accessibility most sensitive to ITK/RLK inhibition were also enriched for NF-κB and AP-1 binding motifs. Inhibition of NF-κB activation in stimulated OT-I cells altered TCR-induced gene expression in a pattern that mirrored the effects ITK/RLK inhibition. Together, these data underscore a role for ITK as an amplifier of TCR signaling and demonstrate a critical role for ITK in tuning NF-κB signaling in digitally activated naïve CD8+ T cells.Results Signaling pathway activation after TCR engagement has largely been described as digital, in which TCR triggering is determined by a threshold for activation. When this threshold is exceeded, downstream responses of NFAT, NF-κB, and MAPK then “switch on” together (9 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r9) , 12 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r12) , 13 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r13) ). The probability that a TCR stimulus will digitally activate an individual naïve T cell within a stimulated, clonal population is measured by examining the fraction of cells that up-regulate the cell surface marker CD69 (11 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r11) , 12 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r12) , 16 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r16) , 18 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r18) ). Cells experiencing weakened TCR stimulation may sufficiently switch on digital CD69 expression but fail to maximally up-regulate important, effector-associated factors like IRF4 (16 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r16) ). Additionally, CD69 expression does not explicitly guarantee that a T cell will be sufficiently stimulated to commit to clonal expansion and effector programming (23 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r23) , 24 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r24) ). Thus, there are signaling behaviors underlying digital triggering that generate divergent gene expression programs and ultimately contribute to a naïve T cell’s fate. We hypothesized that the activity of the TCR proximal tyrosine kinase ITK is sensitive to the variable amount of TCR stimulation, in order to tune the intensity of downstream signaling pathways, and the graded transcription of a subset of genes induced in digitally activated cells.ITK/RLK Inhibition Differentially Dampens Gene Expression in Activating OT-I Cells. To test whether tunable ITK activity modulates TCR signaling during activation, we measured gene expression in CD8+ T cells treated with a covalent small molecule inhibitor (PRN694) (25 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r25) ). PRN694 is a compound highly selective for the active site of both ITK and RLK; RLK is a kinase coexpressed in naïve T cells structurally similar to ITK without clear function in TCR signaling (26 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r26) ). Thymically derived Itk −/− cells do not develop normally (22 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r22) ). Thus, PRN694 allows for the titrated control of ITK/RLK activity in naïve wild-type (WT) OT-I cells and assures both untreated and treated naïve cell transcriptional states are similar before stimulation. To regulate TCR signaling via differential ligand stimulation, we utilized OVA peptide plus altered peptide ligands, in which graded peptide potency is achieved using residue substitutions within the native “SIIN FEKL” OVA peptide (N4) (2 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r2) ). After stimulation with weaker affinity “SIIT FEKL” (T4) altered OVA peptide, a similar number of ITK/RLK inhibitor-treated and -untreated OT-I CD8+ T cells up-regulated CD69, but inhibitor-treated cells exhibited dampened expression of IRF4 (Fig. 1A (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig01) ). This confirmed that the ITK/RLK inhibitor has a differential effect on specific, activation-induced genes, similar to studies examining Itk −/− OT-I cells (16 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r16) ). Inhibitor-treated, T4-stimulated cells also displayed less proliferative potential measured at 48 h, with a large percentage of cells remaining undivided (Fig. 2B (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig02) ). Thus, under these conditions, ITK/RLK activity is dispensable for the switch-like induction of the activation marker CD69 but critical in tuning the intensity of other important genes that govern T cell activation programming and cell division.Fig. 1. TCR signal strength and PRN694 regulate-graded NF-κB activation within digitally NFAT1 and p-Erk active cells. (A and B ) Histograms depicting CD69 and IRF4 expression after 24 h (A ) or CellTrace Violet fluorescence after 48 h (B ) in OT-I cells stimulated with APCs plus 100 nM indicated peptide with or without 50 nM ITK/RLK inhibitor PRN694. (C ) Histograms of NFAT1 and NF-κB fluorescence in isolated OT-I nuclei or p-Erk1/2 fluorescence in OT-I cells after stimulation with APCs plus 100 nM N4 peptide stimulation for 1 h. (D ) Histograms of NFAT1 and NF-κB fluorescence in isolated OT-I nuclei or p-Erk1/2 fluorescence in OT-I cells after stimulation with APCs plus 100 nM T4 peptide for the indicated times. (E ) Histograms of NFAT1 and NF-κB (p65) fluorescence within OT-I nuclei isolated after 1-h stimulation with APCs plus 100 nM N4 OVA peptide or PMA (10 ng/mL)/Ionomycin (1 µM) addition. (F ) Line plots depicting either NFAT1 or NF-κB fluorescence within OT-I nuclei or p-Erk fluorescence in OT-I cells over 2-h stimulation with APCs plus 100 nM indicated peptide with or without 50 nM PRN694. Histograms to the right represent nuclear or whole-cell fluorescence patterns after 60 min stimulation. (G ) Histograms depicting NF-κB (p65) fluorescence within NFAT1+ nuclei after 1-h stimulation in conditions as in A , B , and E . (H ) Line plots of change in normalized NFAT1 MFI (percentage) or normalized NF-κB (p65) MFI (percentage) within NFAT1+ OT-I nuclei after 1-h stimulation with APCs plus 100 nM N4 or T4 OVA peptide, in the presence of titrated concentrations of PRN694. Histograms shown are representative of three or more independent experiments.Fig. 2. PMA and ionomycin supplementation during peptide stimulation reveals synergistic NF-κB signaling activation. (A ) Histograms depicting NFAT1 and NF-κB (p65) activation during 1 h 100 nM N4 stimulation with (red) or without (blue) 50 nM PRN694 or received no peptide stimulation (dark gray). A total of 1 µg/mL ionomycin or 12.5 ng/mL PMA supplemented to wells as indicated. (B ) Fluorescence-activated cell sorting contour plots with adjust histograms comparing NF-κB (p65) and NFAT1 fluorescence after the same stimulation conditions as in A but with 100 nM T4 peptide stimulation.NF-κB Activation Is Tunable in Digitally Activated Naïve OT-I Cells. Pathways downstream of the TCR, critical for robust T cell activation, are highly dependent on the activation of NFAT, NF-κB, and AP-1 transcription factor families (18 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r18) , 27 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r27) , 28 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r28) ). We hypothesized that each pathway may have different sensitivity to varied amounts of upstream TCR stimulation or ITK activity and that differential signaling patterns could contribute to the graded expression of activation-associated factors. Naïve cells have NFAT and NF-κB factors sequestered in the cytoplasm, such that TCR engagement induces their rapid translocation to the nucleus (28 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r28) , 29 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r29) ). To measure activation of NFAT and NF-κB in single cells, we performed flow cytometry with stimulated OT-I nuclei, as described previously (10 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r10) ). This technique allowed us to quantify both the proportion of OT-I cells responding to stimulation as well as the relative abundance of each factor within individual, stimulated nuclei.After strong peptide:MHC stimulation, we observed that NFAT1 and NF-κB (p65) quickly translocated to the nucleus (Fig. 1C (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig01) ). Erk-MAPK signaling was also rapidly activated in nearly all stimulated OT-I cells, as measured by conventional phospho-Erk1/2 (p-Erk) fluorescence. These rapid responses confirmed previously reported “switch-like” signaling behavior after TCR engagement (7 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r7) , 10 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r10) –13 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r13) ). However, in response to weaker peptide stimulation, differences between pathways became more evident. Stimulation with T4 (altered OVA peptide) produced a slower accumulation of the OT-I population that exhibited nuclear NFAT1 and p-Erk than stimulation with the high-avidity OVA peptide, N4. Stimulation with T4 reduced the maximum proportion of NFAT1 positive nuclei by 2 h of stimulation from ∼90% (with N4) to 75% (T4) (Fig. 1 D–F (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig01) ). Importantly, we observed minimal differences in NFAT1 or p-Erk median fluorescence intensity (MFI) within responding nuclei or cells, indicating that these pathways remained digitally triggered, even under weaker signaling conditions (Fig. 1D (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig01) ). In contrast, TCR stimulation with T4 peptide failed to optimally activate NF-κB, as evident by both the reduced number of responder cells and by a lower intensity of NF-κB fluorescence in single OT-I nuclei (Fig. 1D (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig01) ). Intermediate intensity of nuclear NF-κB (p65) suggested that TCR control of the NF-κB pathway did not behave digitally, as was observed for NFAT1 and p-Erk1/2 activation. Furthermore, compared with chemical activation with PMA and ionomycin, peptide:MHC-stimulated OT-I cells activated NF-κB with suboptimal intensity, while NFAT1 activation in the same cell nuclei was similar between the two stimulation conditions (Fig. 1E (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig01) ). These findings demonstrated that, under more physiological peptide stimulation conditions, NF-κB is more tunable to the level of TCR engagement than NFAT1 and p-Erk and revealed a dynamic range of activation states within a population of stimulated T cells.ITK/RLK Inhibition Selectively Dampens NF-κB Intensity in Activating OT-I Cells. To test whether NF-κB activation was tunable by relative levels of ITK activity, we treated OT-I cells with the ITK/RLK inhibitor PRN694 prior to stimulation with peptide:MHC and then measured patterns of NFAT1 and NF-κB (p65) translocation or p-Erk1/2 induction. During N4 OVA peptide stimulation, ITK/RLK inhibitor treatment had little effect on NFAT1 and p-Erk1/2 activation but led to a marked reduction in the intensity of NF-κB activation (Fig. 1 F and G (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig01) ). Notably, NF-κB MFI was reduced in cells that had a similar amount of NFAT1 fluorescence as those from the untreated samples (Fig. 1G (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig01) ). NF-κB MFI was also more sensitive to incremental doses of PRN694 within NFAT1-positive nuclei (Fig. 1H (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig01) ). During stimulation with T4 peptide, inhibitor-treated cells had weakened NFAT1, p-Erk1/2, and NF-κB activation (Fig. 1 F–H (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig01) ). Various concentrations of strong N4, weak T4, or even weaker G4 peptide indicated that NF-κB activation was consistently sensitive to PRN964 treatment, while NFAT1 activation was less sensitive to PRN694, especially during strong N4 stimulation conditions (SI Appendix, Fig. S1 A and B (https://www.pnas.org/lookup/doi/10.1073/pnas.2025825118#supplementary-materials) ). To confirm the specificity of PRN694 for ITK/RLK in T cells, we compared WT and Itk −/− OT-I cells stimulated in the presence or absence of PRN694 and measured NFAT1 and NF-κB (p65) nuclear translocation (SI Appendix, Fig. S1 C and D (https://www.pnas.org/lookup/doi/10.1073/pnas.2025825118#supplementary-materials) ). As shown, these data confirm that PRN694 has no discernible effect on the response of Itk −/− OT-I cells, indicating its lack of off-target effects in T cells. Furthermore, these data also show that the PRN694 treatment of WT OT-I cells produces a similar inhibition of NFAT1 and NF-κB (p65) activation as observed with Itk −/− OT-I cells, indicating the complete, or near complete, inhibition of ITK signaling with this compound. Based on these data, we conclude that the overall effect of the inhibitor during weaker TCR signaling is to extend the temporal window during which cells “switched on,” along with lowering the absolute probability, or threshold, of activation. Additionally, ITK/RLK inhibition mirrored the effect of varying TCR stimulation with weakened affinity peptides.While NFAT activation is directly influenced solely by the activation of calcineurin, NF-κB can be regulated by both the DAG activation of IκB kinase (IKK) complex proteins as well as Ca2+ activation of CaM kinases (27 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r27) , 30 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r30) , 31 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r31) ). As ITK activity is upstream of both DAG and Ca2+ , ITK could possibly have multifactorial control over NF-κB activation. To separate DAG and Ca2+ components of NF-κB activation in OT-I cells, PMA or ionomycin was supplemented into wells of OT-I cultures during N4 stimulation with or without PRN694. The ionomycin addition to cultures during N4 stimulation did not modify the NFAT1+ fraction (which was already >90% responders) or modulate the NFAT1 fluorescence intensity (Fig. 2A (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig02) ) but did increase the proportion of NFAT1 responders during T4 stimulation, as expected (Fig. 2B (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig02) ). Ionomycin alone (in the absence of peptide stimulation) was sufficient to maximally translocate NFAT1 but did not induce appreciable NF-κB translocation (Fig. 2A (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig02) ). Ionomycin supplementation during N4 or T4 peptide stimulation increased NF-κB (p65) fluorescence intensity within individual nuclei, indicating that the threshold of Ca2+ flux sufficient to translocate NFAT1 is not the same as the maximum contribution to NF-κB translocation. PMA supplementation maximized NF-κB (p65) translocation in N4- or T4-stimulated cells and easily recovered the defect in NF-κB signaling because of ITK inhibition with PRN694. These experiments demonstrated that, under physiological peptide stimulation, separate downstream pathways are differently sensitive to the secondary messengers generated from the results of ITK activity. They also highlighted the synergistic nature of Ca2+ and DAG signaling in NF-κB activation but showed a dominant role for the amounts of DAG, rather than Ca2+ , to tune translocation.These data demonstrated the complex behavior of concomitant signaling pathways in response to variable TCR inputs. We concluded that pathways such as NFAT1 and MAPK require a lower threshold of TCR stimulation to activate digitally, whereas NF-κB can be triggered rapidly (and appear digitally switched) under supraphysiological TCR engagement, but normal peptide stimulation produces a dynamic range of NF-κB activation states. Also, ITK activity is crucial in ensuring the optimal activation of graded NF-κB, which sheds light on the role of ITK as an amplifier of TCR signals. ITK/RLK Inhibition Dampens NF-κB–Associated Gene Expression Immediately Following TCR Engagement. As we observed that NF-κB activation was more sensitive to ITK/RLK inhibition, we hypothesized that variable NF-κB intensities within activated cells might contribute to the immediate transcriptional control of graded gene induction. Prior to the up-regulation of effector genes like IL-2 and IRF4, activating CD8+ T cells exiting a quiescent state undergo waves of primary and transient gene transcription (32 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r32) –34 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r34) ). To connect observed differential signaling behavior in naïve T cells to immediate transcriptional effects, we treated OT-I cells with or without ITK/RLK inhibitor, briefly stimulated with OVA N4, and altered the OVA T4 peptide presented on WT splenocytes. We then sorted OT-I cells and measured the transcript abundance with RNA sequencing (RNA-seq).To determine whether varying peptide affinity or modulating ITK activity regulated disparate transcriptional programming, we compared early up-regulated transcripts in N4- or T4-stimulated OT-I cells, with or without treatment, with ITK/RLK inhibitor at 30, 60, and 120 min of activation. We observed that each condition, testing different qualities of TCR signaling, induced sets of transcripts largely similar in composition but significantly different in abundance (Fig. 3 A and B (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig03) and SI Appendix, Figs. S2 and S3 (https://www.pnas.org/lookup/doi/10.1073/pnas.2025825118#supplementary-materials) ). While a subset of transcripts that were identified in N4-stimulated cells were not significantly up-regulated in T4-stimulated cells, we found very few genes up-regulated in T4-stimulated cells that were not seen in N4-stimulated cells (SI Appendix, Fig. S3 (https://www.pnas.org/lookup/doi/10.1073/pnas.2025825118#supplementary-materials) ). We clustered genes induced over 2 h into six groups (Fig. 3 A and B (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig03) ). Gene clusters that exhibited peak expression 30 min after TCR contact with peptide:MHC were enriched for TCR signaling-related ontology terms, including “AP-1 signaling,” “NF-κB signaling,” and “NFAT signaling” (Fig. 3C (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig03) ). This suggested that TCR downstream signaling pathways directly regulate the transcription of these immediate early genes. Clusters of delayed genes first detectable at times greater than 30 min were enriched for terms linked to T cell effector functions “Myc targets” and cell cycle regulation, representing secondary gene transcription responses beyond immediate TCR control and likely regulated by a mix of continuing TCR signaling and first wave gene transcription. These experiments allowed us to interrogate the immediate transcriptional response to TCR engagement and revealed that the modulation of upstream TCR signal strength activates overall, similar transcriptional programming.Fig. 3. Inhibition of ITK dampens immediate TCR signaling-induced transcripts. (A ) Heatmap depicting mean variance-stabilized transformed normalized expression values of top 357 genes (Log2 fold change > 2, mean expression > 1,000, and P adjusted [p -adj.] < 0.1) induced in OT-I CD8+ T cells at three timepoints (30, 60, and 120 min) after stimulation with APCs plus 100 nM N4 OVA or T4-altered OVA peptide with or without 50 nM PRN694. Genes were grouped with k -means clustering into six clusters. (B ) Line plots depicting z -score of 2-h expression time course of gene clusters identified in A . Scores for mean expression values for each cluster are grouped by conditions in A : N4 (blue), N4 + PRN694 (red), T4 (orange), and T4 + PRN964 (green). Total expression data for all conditions from three replicates are drawn in gray. (C ) Enriched Molecular Signatures Database (MSigDB) signatures in gene clusters identified in A . Log10 transformed adjusted P values (false discovery rate) of (up to) the top five terms (p -adj. ≤ 0.05) from both “Hallmark Gene Sets” and “Immunologic Signatures” MSigDB collections. Cluster VI did not contain gene set enrichments with these constraints. Data represent three separate biological replicate experiments each utilizing pooled splenocytes from three or more OT-I mice.To determine whether weakened NF-κB activation during ITK/RLK inhibition differentially regulated the abundance of immediately induced transcripts, we compared differentially expressed transcripts in OT-I cells treated with or without inhibitor after 30 min of strong N4 peptide stimulation. Among transcripts significantly diminished by the ITK/RLK inhibitor were induced AP-1 family members (Fos , Fosl1 , Fosb , and Jun ) and NF-κB response genes (Nfkbia , Nfkbid , Nfkbiz , and Tnfaip3 ) (Fig. 4A (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig04) ). We interpret these results as reflecting the effect of weakened NF-κB signaling in ITK-inhibited cells. Genes that were up-regulated, compared with unstimulated cells, but were less sensitive to ITK/RLK inhibition included Egr family transcripts (Egr1 , Egr2 , and Egr3 ) and the activation marker CD69 (Fig. 4A (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig04) ). After 2 h stimulation, ITK/RLK inhibitor-treated OT-I T cells also had a significantly lower expression of cytokines (IL2 and Ifng ) and effector-associated transcripts (Irf4 ), consistent with the known importance of ITK signaling in promoting robust effector T cell functions (SI Appendix, Fig.S4 (https://www.pnas.org/lookup/doi/10.1073/pnas.2025825118#supplementary-materials) ) (35 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r35) –37 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r37) ). Independent of TCR stimulus, most immediately induced transcripts (e.g., Fos , Fosb , Fosl1 , Egr1 , and Egr2 ) peaked in abundance 30 min after contact with peptide:MHC (Figs. 3 A and B (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig03) and 4A (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig04) and SI Appendix, Fig. S4 (https://www.pnas.org/lookup/doi/10.1073/pnas.2025825118#supplementary-materials) ). This window of transcription is a known pattern of immediate early gene regulation (33 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r33) , 34 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r34) ). Our RNA-seq experiments did not suggest that the ITK/RLK inhibitor caused a delayed induction of these genes in strongly stimulated OT-I cells, but effects of this type might be revealed by further single-cell expression analysis approaches.Fig. 4. Immediate transcripts are differentially sensitive to PRN694 treatment. (A and B ) Volcano plots identifying transcripts sensitive to 50-nM PRN694 treatment measured after 30 stimulation of OT-I cells with APCs plus 100 nM N4 peptide (A ) or 100 nM T4 peptide (B ). Labeled gene names represent select early induced transcripts significantly expressed, compared with unstimulated controls, extracted from either clusters I or II from Fig. 3 (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig03) . Plots depict the Log2 fold change of normalized expression of untreated samples over PRN694-treated samples against −Log10 transformed false discovery rate (FDR) values. Data points and gene names significantly above fold change cutoffs (Log2 fold change > 1 and −Log10 FDR < 0.1) are drawn in red; genes that were not significantly differentially expressed because of PRN694 treatment are drawn in gray.Changes in DNA Accessibility Sensitive to ITK/RLK Inhibitor Are Enriched for NF-κB and AP-1 Motifs. To connect individual TCR signaling pathway behavior with the ITK control of immediate gene expression patterns, we measured immediate, genome-wide DNA accessibility changes with an assay for transposase-accessible chromatin and sequencing (ATAC-seq). (38 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r38) ). Cells for ATAC-seq analysis were sorted at the same time as those used in RNA-seq experiments to better compare accessibility changes with gene expression. Principal component analysis of ATAC-seq replicates indicated that variance was attributed to differences in stimulation (SI Appendix, Fig. S5 (https://www.pnas.org/lookup/doi/10.1073/pnas.2025825118#supplementary-materials) ). Compared with naïve, unstimulated OT-I cells, strong N4 stimulation induced the most significant differences in DNA accessibility, most evident after 120 min (Fig. 5A (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig05) ). After only 30 min of N4 stimulation, about 10,000 DNA regions displayed differential accessibility compared with unstimulated cells (Fig. 5B (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig05) ). To evaluate differential accessibility due to ITK inhibition, N4-stimulated cells with and without ITK inhibition were compared. After 30 min stimulation, the most significant changes in accessibility due to PRN694 were found near many gene loci also identified in early transcriptional data, including AP-1 factors (Fosb and Jun ), NF-κB response genes (Nfkbia and Nfkb1 ), and genes encoding transcription factors important in regulating effector function (Rel , Nfatc1 , and Irf4 ) (Fig. 5C (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig05) ). This indicated that while many genomic regions change in accessibility similarly during activation, the few that were most sensitive to PRN694 were near early genes that echoed the most sensitive gene sets found in the transcriptional analysis.Fig. 5. Early ITK-dependent changes in DNA accessibility are associated with NF-κB and AP-1 regulation. (A ) Distribution of ATAC-seq fragment distance from peak centers. Comparison of 120-min stimulation with N4 peptide (blue) and unstimulated OT-I nuclei (gray). (B ) Volcano plot of differentially accessible genomic peak regions after 30 min stimulation with N4 peptide. (C ) Volcano plot of differentially accessible peak regions due to PRN694 treatment after 30 min of N4 peptide stimulation. Annotations of the closest gene TSS are labeled for select regions. For B and C , significantly differentially accessible regions (Log2 fold change < −1 or > 1 and P adjusted [p -adj.] < 0.1) are drawn blue. (D ) Heatmap depicting k -means clustering (k = 3) of row-scaled coverage for dynamic peaks (|Log2 fold change| > 1 and p- adj. < 0.01) after 30 min. A total of 100 nM N4 or T4 peptide stimulation with or without 50 nM PRN694 (N4 untreated, blue; N4 + PRN694, red; T4 untreated, orange; and T4 + PRN694, green). To the right are the calculated Fisher’s Exact Test odds ratios indicating the amount of overlap of peak regions within each ATAC cluster with public ChIP-seq datasets, including NFAT1 immunoprecipitation (IP) from either WT NFAT1 mouse CD8+ T cells (WT) or NFAT1 KO CD8+ T cells transduced with constitutively active mutant NFAT1 unable to interact with AP-1 (CA-RIT-NFAT1) after 1-h PMA and ionomycin stimulation (40, GSE64409) and p65 IP after 3-h anti-CD3/CD28 simulation of mouse T cells (67, GSE82078). Also adjacent to each cluster is HOMER de novo motif enrichment analysis, presenting top (p- adj. < 10−50 ) motifs for each cluster. (E and F ) ATAC-seq genomic tracks for regions around Irf4 and Fosb loci, comparing different peptide and PRN694 stimulation conditions after 30 min. Also plotted are ionomycin or ionomycin + PMA stimulation ATAC-seq peaks and NF-κB ChIP-seq peaks (67, GSE82078 and 69, GSE93014). Select, differential, and experimental ATAC-seq peaks are highlighted in yellow.Like the RNA-seq experiments, the strength of the TCR stimulation or ITK/RLK inhibition did not regulate an independent set of genomic regions but rather regulated the intensity of a shared set of activation-associated pileups (Fig. 5 E and F (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig05) ). k -means clustering of all dynamic ATAC-seq peaks (differentially accessible compared with unstimulated control) after 30 min stimulation revealed the regulation of regions that had increased dependency on ITK activity (Fig. 5D (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig05) ). We performed Hypergeometric Optimization of Motif EnRichment (HOMER) analysis to identify motif enrichments unique within each cluster, compared with a background of reciprocal clusters (39 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r39) ). Cluster 1, which appeared to represent regions that were less accessible during treatment with the ITK/RLK inhibitor, was significantly enriched for AP-1 (Fosl2) motifs and NF-κB (p65) motifs. Gene ontology analysis using gene annotations nearest to each ATAC peak revealed that Cluster 1 was enriched for genes associated with NF-κB and Stat5 signaling (SI Appendix, Table S1 (https://www.pnas.org/lookup/doi/10.1073/pnas.2025825118#supplementary-materials) ). Cluster 2, which contained genomic regions less sensitive to PRN694 treatment, were significantly enriched for NFAT motifs (Fig. 5D (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig05) ).We performed Fisher’s exact test to calculate the amount of overlap of genomic regions in each ATAC-seq cluster with publicly available transcription factor chromatin immunoprecipitation sequencing (ChIP-seq) datasets (Fig. 5D (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig05) ). This revealed that Cluster 2 had a strong association (3.23) with WT NFAT1 ChIP-seq, while Cluster 1 (0.83) and Cluster 3 (0.01) did not. Cluster 2 also had a strong overlap (2.57) with ChIP-seq peaks generated from NFAT1 knockout mice transferred with constitutively active, mutant NFAT1 that cannot bind with AP-1 (CA-RIT-NFAT1) (40 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r40) ). NF-κB (p65) ChIP-seq peaks had a similar, strong association (>3.0) with Clusters 1 and 2. Overall, ATAC-seq analysis revealed that specific DNA genomic regions least sensitive to varied signal strength via peptide avidity or PRN-694 treatment were associated with NFAT regulation, while regions most sensitive to PRN-694 were associated with AP-1 and NF-κB regulation.TCR Stimulus and ITK Regulate-Graded Selective Expression of Early Gene Products. To determine whether ITK/RLK inhibitor-specific effects on immediate transcription also led to reduced protein product accumulation during activation, we measured Egr2, c-Fos, and c-Myc intracellular content in stimulated cells via flow cytometry (Fig. 6 A–C (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig06) ). Both c-Fos and Egr2 proteins were detectable within 30 min after OVA stimulation. Treatment with the ITK/RLK inhibitor dampened the amount of c-Fos expression in OVA-stimulated OT-I cells but had little effect on modulating Egr2 expression. After approaching a peak expression at 60 min, the expression of both proteins was stable out to 6 h. This indicated that ITK/RLK-inhibited OT-I cells did not “catch up” in c-Fos expression during the time course of this experiment, and the RNA-seq experiments measured dampened expression and not an average of asynchronous cells. Cells that expressed both CD69 and Egr2 also displayed a graded amount of c-Fos expression dependent on the TCR stimulation strength and ITK/RLK inhibition (Fig. 6D (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig06) ). These results also confirmed that ITK/RLK signaling contributes to graded gene expression in response to variations in TCR signal strength.Fig. 6. TCR signaling and ITK control-graded accumulation of early gene product c-Fos. (A ) Line plots of mean-normalized expression counts of select induced transcripts measured at 0, 30, 60, and 120 min. (B and C ) Line plots (B ) and representative histograms (C ) of flow cytometry results for gene products of transcripts profiled in A . Experiments were conducted under similar stimulation conditions and previous figures and stimulated up to 6 h. Results are plotted as percentage positive or normalized to maximum MFI (percent max MFI) where indicated. (D ) Representative histograms depicting graded c-Fos fluorescence in CD69+ Egr2+ OT-I cells in response to different stimulation conditions measured after 6 h. Labeling is consistent: N4—blue (circles), N4 + PRN—red (squares), T4—orange (diamonds), and T4 + PRN694—green (triangles). Data represent three biological replicates. Error bars indicate SEM.NF-κB Inhibition Produces Graded Immediate Gene Expression. To test whether a selected gene exhibiting graded induction during the first 30 min after stimulation was NF-κB dependent, we treated OT-I cells with either an inhibitor of the IKK complex, IKK-16, or for comparison, an MEK inhibitor (PD325901) to control activation of MAPK (Erk1/2). As expected, neither IKK-16 nor PD325901 had an effect on NFAT1 translocation in OT-I cells (SI Appendix, Fig. S6 A and B (https://www.pnas.org/lookup/doi/10.1073/pnas.2025825118#supplementary-materials) ), but treatment with the IKK-16–tuned NF-κB (p65) translocation and treatment of PD325901 effectively inhibited p-Erk (SI Appendix, Fig. S6 A and B (https://www.pnas.org/lookup/doi/10.1073/pnas.2025825118#supplementary-materials) ). Examination of c-Fos expression revealed that OT-I cells treated with moderate concentrations of IKK-16 or PD325901 showed dampened c-Fos expression (SI Appendix, Fig. S6 A, C, and D (https://www.pnas.org/lookup/doi/10.1073/pnas.2025825118#supplementary-materials) ). In contrast, expression of Egr2, a gene with an all-or-nothing expression profile, was inhibited by PD325901 treatment but was largely unaffected by IKK-16 treatment. For instance, at 1 h poststimulation, Egr2 expression was unchanged in the presence of IKK-16, whereas c-Fos expression was reduced twofold (SI Appendix, Fig. S6 C and E (https://www.pnas.org/lookup/doi/10.1073/pnas.2025825118#supplementary-materials) ). These experiments indicate that the induction of select, immediate genes, such as c-Fos, reflect graded amounts of NF-κB during activation.Discussion Our results provide evidence for a hierarchy of signaling thresholds for pathways downstream of the TCR and ITK. NFAT, MAPK, and NF-κB signaling all exhibit varying patterns of activation in single, naïve OT-I cells after stimulation with OVA peptide or altered OVA peptide ligands. We found that NF-κB p65 translocation is uniquely sensitive to the quality of peptide:MHC interaction and ITK/RLK activity (Fig. 7 (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig07) ). Our findings also demonstrated that diminished NF-κB signaling due to ITK/RLK inhibition has transcriptional effects in stimulated T cells, in that NF-κB response genes and induced AP-1 gene family (Fos , Fosl1 , and Fosb ) members are diminished in abundance compared with untreated cells. While previous studies have shown a key role for BTK in activating NF-κΒ downstream of the BCR and BAFF receptor in B cells (41 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r41) –43 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r43) ), our studies reveal the additional, important element that graded NF-κB activation is regulated by the magnitude of ITK kinase activity in T cells.Fig. 7. Data summary of TCR signal strength effects on the activation of separate signal pathways. (A ) Line plots depicting how ITK disproportionately shifts signaling thresholds. MAPK, NFAT, and NF-κB responses are drawn in blue, green, and, red, respectively. Activation response when ITK is present is drawn in a lighter shade; activation without ITK is drawn in a darker shade. Direction of the shift due to the presence of ITK is indicated with an arrow. TCR stimulation input is plotted against the resultant degree of activation. (B ) Summary of TCR signal strength control of pathway activation within a stimulated population. Groups of curves representing single-cell MAPK (blue), NFAT (green), and NF-κB (red) responses to either strong (light shade) or weak (dark shade) stimulation are presented over time. Histograms to the right represent the cumulative distribution of pathway activation of the populations in the line plots.Most models of TCR initiation describe receptor proximal signaling as digital and inherently noisy. In order for successful TCR signaling to result in changes to transcription factor activation in the nucleus, pathways must produce stable intermediates only after repeated and sustained engagements with peptide:MHC (5 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r5) , 11 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r11) , 44 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r44) ). TCR-induced MAPK activation has been shown to exhibit strong digital behavior due to the positive feedback regulation of son of sevenless (SOS), which sustains active Ras (12 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r12) , 44 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r44) ). To measure activation of MAPK signaling, we observed p-Erk1/2 fluorescence in single cells with flow cytometry. Consistent with previous reports, we measured strongly digital Erk activation in response to TCR ligation with peptide:MHC (11 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r11) , 12 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r12) ). The fraction of cells positive for p-Erk after PRN694 treatment remained largely unchanged, but the p-Erk fraction was sensitive to peptide affinity (Fig. 1F (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig01) ). We expected p-Erk fluorescence to be sensitive to ITK activity because DAG production stabilizes SOS activation (12 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r12) ). However, it is possible that upstream phosphorylation of LAT after TCR triggering is the rate-limiting step for MAPK activation rather than the ITK-dependent production of DAG. Furthermore, initial experiments with Itk −/− mice reported decreased p-Erk in pooled whole CD8+ T cell lysates after stimulation with anti-CD3 antibody (21 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r21) ). In contrast, our experiments measure TCR responses after engagement with peptide:MHC ligands, which show markedly different kinetics compared with those elicited by α-CD3 stimulation.A separate study previously reported NF-κB behaves digitally after TCR stimulation. Direct antibody-mediated stimulation of the TCR in C57BL/6 mouse CD4+ and CD8+ T cells or OVA peptide stimulation of CD4+ OT-II cells displayed evidence of digital NF-κB activation (13 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r13) ). In the current study, we provide a carefully tuned examination of physiological NF-κB responses in naïve CD8+ T cells. Given sufficiently strong peptide stimulation (native N4 OVA peptide) or PMA/Ionomycin, T cells can quickly and completely translocate p65 with similar kinetics as NFAT1, appearing “all or none” (Figs. 1 C and E (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig01) and 2A (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig02) ). Our results reveal, however, the strength of TCR stimulation the amount of ITK activity produces intermediate, graded states of p65 activation during T cell priming.As we observed that NF-κB signaling was specifically sensitive to PRN694 treatment, these findings highlighted a role for ITK in amplifying signals induced by weaker TCR inputs that could not sufficiently trigger NF-κB on their own, even under conditions that stimulate NFAT and Erk. Later during activation, after initial priming, combined TCR, CD28, and other costimulatory receptors become increasingly important in amplifying NF-κB signaling via PI3K (45 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r45) ). It may be advantageous for naïve cells to limit NF-κB by making it more difficult to trigger with TCR stimulus alone. Our results reveal, however, that weaker TCR stimulation or ITK/RLK inhibition with PRN694 produces intermediate states of p65 activation, which do not appear digital. Robust p65 nuclear translocation requires ITK and strong TCR interaction; in contrast, NFAT1 and p-Erk remain digitally responsive during weaker TCR stimulation or PRN694 treatment, with the amounts of nuclear NFAT1 and p-Erk equivalent to those observed after strong TCR signaling. These results indicate discrete, analog levels of NF-κB activation during T cell priming.NFAT translocation is exclusively dependent on SOCE activation of calcineurin, while NF-κB activation is layered with multiple signaling inputs (28 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r28) , 46 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r46) , 47 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r47) ). ITK and the subsequent PLC-ɣ1–induced production of DAG activates NF-κB p65 through PKC-theta (17 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r17) , 28 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r28) ). PLC-ɣ1 production of IP3 generates a calcium flux and activates calmodulin and subsequently calcineurin, which dephosphorylates sequestered NFAT1. Calmodulin also activates calmodulin-dependent protein kinases, which can stabilize the CARMA complex and ultimately assist in NF-κB activation (48 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r48) ). Thus, ITK influences the activity of NF-κB by both DAG and IP3 production. Experiments with ionomycin alone stimulate modest NF-κB p65 activation in naïve T cells, albeit lessened compared with TCR stimulation with peptide:MHC (Figs. 1E (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig01) and 2 (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig02) ). We and others describe digital NFAT1 translocation and calcium flux in single cells, in which a discrete threshold of activation governs an all-or-none response (Fig. 1 C and D (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig01) ) (7 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r7) , 9 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r9) , 10 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r10) ). We attribute the sensitivity and analog qualities of NF-κB activation, in response to ITK activity, to the combinatorial effects of simultaneous Ca2+ and DAG signaling (Fig. 2 (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig02) ). Indeed, higher NF-κB MFI correlates with “switched on” NFAT1 fluorescence, indicating that digital TCR initiation and Ca2+ flux may precede NF-κB activation (Fig. 1 C, E, and F (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig01) ).One of the questions driving our transcriptomics and genomics experiments was to discern whether ITK activity and, broadly, the strength of TCR signal directs diverging transcriptional programs or rather tunes the abundance of transcripts within one activation-associated gene set. RNA-seq results revealed that ITK inhibition or weaker TCR interactions with lower-affinity peptide:MHC induce similar genes as strong TCR signaling (Fig. 3 (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig03) ). A recent single-cell, transcriptomic study thoroughly concluded that peptide-stimulated OT-I cells activate a single-effector transcriptional program, and resultant effector cells have similar cytolytic capacity, independent of TCR signal strength (24 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r24) ). With single-cell RNA-seq analysis, the same study also showed that weaker TCR signaling delays early gene transcription. We identified the temporally induced early gene clusters within 2 h TCR stimulation but did not detect similar shifts in transcriptional kinetics due to ITK inhibition or weaker peptide affinity, but this is likely a limitation of our pooled RNA-seq experiments. In contrast, our analyses of signaling events in naïve OT-I cells do show weaker stimulation and ITK inhibition delays onset of peak fractional NFAT responders, and NF-κB is only appreciably detectable after 60 mins. Additionally, cytometric analysis of CD69, c-Myc, Egr2, and c-Fos display reduced kinetics of expression when stimulated with weak peptide:MHC.Cooperation of NFAT, AP-1, and NF-κB transcription factors is required for many aspects of optimal transcription of T cell activation programming, including production of cytokines like IL-2, which was one of the most differentially expressed genes because of TCR signal strength or PRN694 treatment after 2 h (SI Appendix, Fig. S4 (https://www.pnas.org/lookup/doi/10.1073/pnas.2025825118#supplementary-materials) ). Early IL-2 signaling through the induced, high-affinity IL-2 receptor CD25 helps maintain levels of c-Myc, while also lowering the apparent TCR signaling threshold (49 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r49) , 50 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r50) ). Thus, CD8+ T cells that quickly transcribe large amounts of IL-2 can maximize their clonal expansion. The IL-2 promoter contains NF-κB binding sites and critical NFAT:AP-1 binding sites, in which both partners are required for transcription (33 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r33) ). In ITK-inhibited cells or during weak signaling, we measure reduced NF-κB activation, diminished production of AP-1 transcripts (Fos , Fosb , and Fosl1 ), and decreased Fos protein (Fig. 4 (https://www.pnas.org/doi/10.1073/pnas.2025825118#fig04) ). These conditions could contribute to the slower production of IL-2 transcripts, which are among the most sensitive to the strength of TCR within 2 h. There is evidence that T cells may continue to accumulate c-Fos proteins after serial encounters with APCs during the early periods of T cell priming, effectively summing their cumulative duration of signaling (51 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r51) , 52 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r52) ). TCR stimulation that induces efficient NFAT translocation, but not NF-κB, and only weakly induces AP-1 factors may suffer from the low accumulation of early gene products and weak IL-2 production. ChIP assays utilizing a constitutively active NFAT mutant, in which NFAT is permanently nuclear, shows that NFAT cannot bind the IL-2 promoter without its AP-1 binding partner; in the absence of AP-1, IL-2 transcription is abrogated (40 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r40) , 53 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r53) ).A better understanding of the biology of T cell exhaustion is crucial in treating chronic illness and maximizing the efficacy of T cell immunotherapies, such as CAR-T. Recent work has identified NFAT as an important TCR-dependent regulator of T cell exhaustion phenotypes (40 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r40) , 53 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r53) , 54 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r54) ). NFAT can bind to the promoter of select exhaustion-associated genes without its partner AP-1 (40 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r40) ) and drives the expression of NR4A family transcription factors, which further maintain exhaustion programming (53 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r53) , 54 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r54) ). Within the hierarchy of T cell signaling pathways we identify here, NFAT and MAPK appear to be more easily stimulated than NF-κB. Our data provide further evidence that CD8 T cells may signal through NFAT more easily without complete downstream pathway activation, which could contribute to exhaustion phenotypes.Materials and Methods Mice. Mice were bred and housed in a specific, pathogen-free facility at the University of Massachusetts Medical School and the University of Colorado-Anschutz School of Medicine, in accordance with Institutional Animal Care and Use Committee guidelines. OT-I transgenic Rag1 −/− mice (B6.129S7-Rag1tm1Mom Tg(TcraTcrb)1100Mjb N9+N1) and C57BL/6 WT mice were purchased from Taconic Biosciences. OT-I Itk −/− Rag1 −/− mice were generated by crossing OT-I Rag1 −/− and Itk −/− . Itk −/− mice have been described previously (19 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r19) ). CD45.1+ (B6.SJL-PtprcaPep3b/BoyJ) mice were purchased from the Jackson Laboratory. Unless otherwise noted, experimental cohorts consisted of age- and sex-matched littermates aged 6 to 12 wk.Stimulation of CD8+ T Cells.Freshly harvested OT-I Rag1 −/− mouse splenocytes were pooled, red blood cell (RBC) lysed, and enriched for CD8+ cells with an EasySep negatively selective magnetic isolation kit (STEMCELL Technologies). OT-I cells prepared for use in nuclei isolation experiments were then treated with CellTrace Violet reactive dye (Invitrogen) for 20 min to fluorescently label cells (including nuclei). OT-I cells were cultured at 2 × 105 cells per well (unless otherwise noted) and incubated with or without 50 nM (or otherwise noted) ITK/RLK inhibitor PRN694 (Principia Biopharma) for 30 min at 37 °C. In some experiments, OT-I cells were incubated with or without IKK inhibitor IKK-16 (Sigma) or MEK inhibitor PD325901 (Tocris Bioscience). For APCs, RBC-lysed splenocytes harvested from WT mice were cultured at 4 × 105 per well and incubated with indicated concentrations of OVA “N4” peptide (SIINFEKL), altered OVA “T4” peptide (SIITFEKL), and altered OVA “G4” peptide (SIIGFEKL) (21st Century Biochemicals) for 30 to 60 min at 37 °C. OT-I cells and peptide-loaded splenocytes were then combined and incubated at 37 °C for specific times. For cell preparations used for molecular analyses (e.g., RNA-seq and ATAC-seq analyses), splenocytes from CD45.1+ WT mice were used as peptide-presenting cells for easy exclusion from CD45.2+ OT-I cells via cell sorting.Nuclei Isolation. To measure the translocation of nuclear proteins, we isolated cell nuclei after stimulation for fixation and subsequent flow cytometry analysis. To do this, we utilized a sucrose buffer-based protocol that we and others have previously published (7 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r7) , 10 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r10) ). To summarize, stimulated cells were pelleted and washed with 200 µL of ice-cold “Buffer A” containing 320 mM sucrose, 10 mM Hepes (Life Technologies), 8 mM MgCl2 , 13 EDTA-free cOmplete Protease Inhibitor (Roche), and 0.1% (volume/volume) Triton X-100 (Sigma-Aldrich). After 15 min on ice, the plate was spun at 2,000× g and 4 °C for 10 min. This was followed by 2× 200 µL washes with “Buffer B” (Buffer A without Triton X-100) at 2,000× g and 4 °C.Antibodies and Flow Cytometry. Stimulated cells and isolated cell nuclei were fixed and permeabilized with the Foxp3/Transcription Factor Staining Buffer Set (eBioscience), except cells used for anti-p-Erk1/2 analysis, which were fixed with 4% paraformaldehyde (Electron Microscopy Services) and permeabilized with 90% ice-cold methanol (Fisher Scientific). Fluorescently labeled flow cytometry antibodies against IRF4 (3E4), CD69 (H1.2F3), and Egr2 (erongr2) were purchased from eBioscience. Antibodies against CD8a (53-6.7), CD8b (53-5.8), CD25 (3C7), CD90.2 (53-2.1), and p-Erk1/2 (4B11B69) were purchased from BioLegend. Antibodies against NFAT1 (D43B1), NF-κB p65 (D14E12), c-Fos (9F6), and c-Myc (D84C12) were purchased from Cell Signaling Technology. Anti-CD45.1 (A20) was purchased from BD Pharmingen. PE-conjugated F(ab')2-goat anti-rabbit IgG (H+L) cross-adsorbed secondary antibody was purchased from Invitrogen. Cell Sorting. Samples of stimulated CD45.2+ OT-I cells and CD45.1+ WT splenocytes mixtures were stained with CD8a and CD45.2 antibodies, and 7-AAD and OT-I cells were sorted using fluorescence-activated cell sorting (FACS) (BD FACSAria) into 100% fetal bovine serum (FBS) and pelleted.RNA-Seq Library Preparation. Total RNA from ∼300,000 OT-I cells per sample was collected with the RNeasy micro kit (Qiagen) with a 15-min on-column DNase digestion (Qiagen) to remove genomic DNA. Total RNA quality and quantity was determined with fragment analysis (University of Massachusetts Medical School Molecular Biology Core Laboratory) and Qubit Fluorometer (Invitrogen) analysis. Complementary DNA libraries were generated following a modified, paired-end switching mechanism at 5′ end of RNA template (SMART) sequencing protocol (55 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r55) , 56 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r56) ). Briefly, at least 20 ng input RNA was used for reverse transcription with SMARTscribe reverse transcriptase (Clontech). Whole transcriptome amplification (WTA) was performed with Advantage 2 polymerase (Takara Bio). WTA reactions were monitored with qPCR to determine optimal cycle number. WTA libraries were then size selected with AMPure XP DNA solid phase reversible immobilization (SPRI) beads (Beckman Coulter), tagmented with Tn5 transposases (Illumina Nextera XT), barcoded, and amplified with a cycle number determined via qPCR monitoring. Final libraries were further size selected with SPRI beads to an average size of 300 to 500 bp, and quality was assessed with fragment analysis and Qubit analysis. Libraries were pooled and sequenced on a NextSEq. 500 sequencer (Illumina).Processing and Analysis of RNA-Seq Reads. Adapter sequences were trimmed from quality raw sequencing reads with Trimmomatic-0.38 (57 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r57) ) and then aligned to mouse ribosomal RNA with Bowtie2 version 2.3.2 (58 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r58) ). Unaligned reads were retained, and gene expression was estimated (transcripts per million and expected counts) with RSEM version 1.2.29 (59 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r59) ) configured to align to an mm10 RefSeq transcriptome with Bowtie2 version 2.3.2. Samples were filtered to retain expressed genes (expected counts >200), and batch effects between replicates were corrected with limma version 3.42.2 (60 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r60) ). Differential expression analysis was performed with DESeq2 version 1.26.0 (61 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r61) ) to identify induced genes (stimulated conditions versus unstimulated controls) or condition-specific changes in expression (untreated versus PRN694 or N4 versus T4 OVA peptides). Hierarchical clustering and k -means clustering of differentially expressed genes was performed within R version 3.5. Heatmap visualizations of gene clusters were drawn with ComplexHeatmap (62 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r62) ).Gene Ontology. We utilized the R packages msigdbr version 7.0.1 and clusterProfiler version 3.18.1 (63 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r63) ) to compare clusters of differentially expressed genes with the Molecular Signatures Database Hallmark (H) and Immunologic (C7) gene sets (60 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r60) –62 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r62) ). The top five terms with false discovery rate <0.05 were displayed in Results (https://www.pnas.org/doi/10.1073/pnas.2025825118#sec-1) .ATAC-Seq Library Preparation. Precisely 50,000 stimulated OT-I T cells were FACS sorted (BD FACSAria) at the same time as RNA-seq samples and pelleted in 100% FBS. ATAC-seq libraries were generated similarly, as described in Buenrostro et al. (38 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r38) ). Briefly, cell nuclei were isolated and transposed with 8 µL Tn5 (Illumina Nextera) at 37 °C for 60 min. DNA fragments were isolated with a Clean and Concentrator Kit (Zymo) and then Illumina barcoded and amplified with NEBNext High-Fidelity 2× PCR Master Mix (New England Biolabs). A portion of the reaction was performed as a separate qPCR reaction to determine the ideal cycle number. Samples were then size selected with SPRI beads to include fragments up to 450 bp, ensuring to maintain small (<200 bp), nucleosome-free fragments. The quality of final ATAC-seq libraries was assessed with fragment analysis and Qubit analysis. Libraries were then pooled and sequenced on an Illumina NextSEq. 500.Alignment and Processing of ATAC-Seq Reads. Adapter sequences were trimmed from raw sequencing reads with Cutadapt version 1.3 (64 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r64) ) and then aligned to the mouse genome (mm10) with Bowtie2 version 2.1.0 with the parameter −X 2,000. PCR duplicates were removed with Picard’s markDuplicates version 2.17.8, and aligned reads were sorted and filtered with SAMtools version 1.4.1. For the visualization of fragment coverage, TDFs were generated with IGVTools version 2.3.31.Peak Calling. Aligned ATAC-seq reads were trimmed to 29 bases closest to the Tn5 cut site with bedtools version 2.26.0 (65 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r65) ), and then, peaks were called with MACS2 version 2.1.1 (66 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r66) ) using parameters—bw 29—nomodel −q 0.0001. Summits of called peaks across all samples were merged and “slopped” ±100 bp to create a master peak reference file using bedtools version 2.26.0. Peaks were annotated with the names of closest genes using a mouse OT-I TSS BED file and bedtools version 2.26.0 (closest −D ref −t all). Peaks with summits within 500 bp from TSSs were labeled promoter peaks and summits further than 500 bp were labeled enhancer peaks. Peak coverage for each ATAC-seq sample was calculated using bedtools version 2.26.0 (intersect; coverage).ATAC Peak Analysis. Calculated peak coverage values for two ATAC-seq replicate experiments were used as input for differential analysis using DESeq2 version 1.26.0. Peaks were filtered to those significantly differential (|Log2FC| ≥ 1, p-adj < = 0.01), compared with unstimulated controls, and were clustered using hierarchical and k -means clustering methods within R. Heatmaps were generated with ComplexHeatmap. The amount of overlap of ATAC cluster peaks with published NFAT1 (Gene Expression Omnibus [GEO]: GSE64407) (40 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r40) ) and NF-κB p65 (GEO: GSE82078) (67 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r67) ) ChIP-seq datasets were calculated with bedtools (bedtools intersect), and significant enrichment was estimated with Fisher’s exact test (fisher.test within R). To do this, published NFAT ChIP-seq data originally aligned to the mouse mm9 genome assembly were first lifted to mm10 using the University of California Santa Cruz LiftOver tool (http://genome.ucsc.edu (http://genome.ucsc.edu/) ) (68 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-bib70) ). Then peaks were called on NFAT and p65 ChIP-seq data with MACS2 bdgpeakcall. Each cluster of annotated, differentially accessible peak regions was tested for de novo motif enrichment using HOMER version 4.10.3 (findMotifsGenome.pl −size 200) (39 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r39) ). For each HOMER test, peaks in all other clusters were used as background.Genomic Visualizations. Genomic tracks were created by plotting BigWig files with help of Gviz (69 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r68) ) PMA and ionomycin ATAC-seq tracks and NF-κB ChIP-seq tracks were extracted from publicly available sources (67 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r67) , 70 (https://www.pnas.org/doi/10.1073/pnas.2025825118#core-collateral-r69) ).Data Availability Raw RNA-seq data, processed RNA-seq reads, raw ATAC-seq data, and processed ATAC-seq reads generated in this study have been deposited in the Gene Expression Omnibus and the Sequence Read Archive (GSE167304 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE167304) ). Publicly available datasets were used for this work (GSE64407 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64407) ; GSE82078 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE82078) ; and GSE64409 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64409) ).Acknowledgments We thank Regina Whitehead, Sharlene Hubbard, and Loni Perrenoud for their technical assistance. We thank members of the A.R. laboratory and Joonsoo Kang’s laboratory for the sharing of laboratory space. We thank Principia Biopharma for providing PRN694. We thank the Department of Animal Medicine at the University of Massachusetts Medical School and University of Colorado Anschutz for maintaining our mouse colony. This work was supported by NIH National Institute of Allergy and Infectious Diseases Grants AI132419 and AI043976 (to L.J.B.).
Fac Rev . 2023 Oct 16;12:25. doi: 10.12703/r/12-25 (https://doi.org/10.12703/r/12-25) Recent advances in understanding TCR signaling: a synaptic perspective Michael L Dustin (https://pubmed.ncbi.nlm.nih.gov/?term=%22Dustin%20ML%22[Author]) 1, * Author information Article notes Copyright and License information PMCID: PMC10608137 PMID: 37900153 (https://pubmed.ncbi.nlm.nih.gov/37900153/) () AbstractThe T cell receptor is a multi-subunit complex that carries out a range of recognition tasks for multiple lymphocyte types and translates recognition into signals that regulate survival, growth, differentiation, and effector functions for innate and adaptive host defense. Recent advances include the cryo-electron microscopy-based structure of the extracellular and transmembrane components of the complex, new information about coupling to intracellular partners, lateral associations in the membrane that all add to our picture of the T cell signaling machinery, and how signal termination relates to effector function. This review endeavors to integrate structural and biochemical information through the lens of the immunological synapse- the critical interface with the antigen-presenting cell. Keywords: Immunology, affinity, microscopy, receptors, allostery () IntroductionThe T cell receptor (TCR) is restricted in its expression to T lymphocytes, but this is hardly a restriction. T lymphocytes carry out diverse functions in innate and adaptive recognition, and this is important to keep in mind when thinking about the signaling capabilities of the TCR1 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-1) . TCRs are made up of one of two pairs of variable subunits, one pair being between TCRα-TCRβ and the other TCRγ-TCRδ. The genes encoding the TCR mRNAs are generated by somatic gene rearrangement of germline coding segments with additional random sequence information introduced during the joining process; this leads to a huge potential repertoire for both αβ and γδ receptors2 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-2) . This variability is the cornerstone of adaptive immunity through recognition of peptide-MHC complexes by αβ TCR but is also used to build a number of stereotyped receptor rearrangements that enable innate-like TCR αβ and TCR γδ T cells that participate in immediate responses to pathogens in parallel with other effectors of innate immunity. The TCR αβ innate-like receptors include examples that recognize lipid antigens associated with CD1d3 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-3) , referred to as invariant NK T cells (iNKT), and B vitamin metabolites associated with MR14 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-4) , referred to as mucosal activated invariant T cells (MAIT). A large subset of blood T cells using variable segments γ2 and γ9 recognize cholesterol metabolites through butyrophilins5 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-5) . These recognition processes can be superimposed on different effector programs, including cell-mediated killing and help for immune responses against intracellular, extracellular or multicellular pathogens.Signal transduction through TCR is based on a tyrosine kinase cascade. Both TCRαβ and TCRγδ subunits have a short cytoplasmic domain with no known signaling potential. Signaling through a tyrosine kinase cascade is carried out by the invariant subunits of the complex. The variable TCR heterodimers complex with three invariant dimers with the composition CD3ε-CD3δ, CD3ε-CD3γ and CD247 (ζ-homodimer) through a well-ordered process driven by transmembrane domain interactions6 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-6) . The invariant subunits have cytoplasmic domains with pairs of tyrosine residues spaced to provide binding sites for the non-receptor tyrosine kinase ζ-associated protein of 70 kDa (ZAP70) after phosphorylation by lymphocyte kinase (LCK)7 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-7) . ZAP70 then phosphorylates linker of activated T cells (LAT), which recruits a number of additional adapters, phospholipase Cγ (PLCγ) and complexes containing son of sevenless (SOS) to enable downstream signaling based on cytoplasmic Ca2+ increase, diacylglycerol and mitogen-activated kinase cascade initiation via the small GTPase RAS8 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-8) –10 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-10) . While different types of TCR bind diverse ligands, these are uniformly recognized on the surface of other cells rather than in solution. TCR signaling, therefore takes place in the context of synapsis between the T cell and an antigen-presenting cell11 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-11) . Recent work on how this signaling cascade is initiated and sustained in the context of an immunological synapse will be the focus of this review. () Synapse sensing by CD45An important early concept for T cell antigen recognition was that the interaction of all types of TCR with ligands is typically low-affinity (with Kd values in the µM range) and requires a close approach of the T cell and antigen-presenting cells to within 13–14 nm, referred to as close contacts. This process is facilitated by adhesion molecules that reach out over greater distances and others that appear to fit into this same 13–14 nm space, leading to a proposal of a complex synapsis with at least two intermembrane spacings12 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-12) (Figure 1 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#fig-001) ). Kupfer and colleagues captured the first images of large-scale segregation of the TCR-pMHC interactions from the larger adhesion systems like LFA1-ICAM1 in a bull’s eye pattern13 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-13) and studies with supported lipid bilayers revealed striking segregation of the small and large adhesion systems in interfaces formed by activated T cells14 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-14) . The ~13–14 nm spacing is a sweet spot for optimizing the interactions of low-affinity receptors in interfaces15 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-15) . In addition, close contacts were proposed to exclude the transmembrane tyrosine phosphatase CD45, which has a large extracellular domain > 16 nm in length16 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-16) , facilitating activation of the tyrosine kinase cascade12 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-12) ,17 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-17) , and precise CD45 exclusion was demonstrated at TCR signaling sites18 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-18) .Figure 1. The TCR-pMHC under pressure. Open in a new tab (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/figure/fig-001/) TCR-pMHC interaction spans ~14 nm between membranes and this is supported by the fact that this is the median distance between membranes in TCR-pMHC-mediated contacts22 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-22) . The schematic shows this TCR-pMHC in between larger integrins and CD45 or the slightly smaller CD2-CD58 interactions. That the LFA1-ICAM1 and CD45 extracellular domains are larger than the TCR-pMHC complex is supported by lateral segregation of these interactions and the exclusion of CD45 from TCR microclusters18 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-18) . While the TCR-pMHC and CD2-CD58 interactions could be fit into a similar intermembrane spacing and have been shown to co-localize in actin-dependent microclusters, the reduction in 2D affinity for the TCR-pMHC interaction23 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-23) is consistent with a small size mismatch. This may put pressure on the TCR to undergo conformational changes to fit in a closed intermembrane gap generated by CD2-CD58 trans interactions (see below about cis interactions). The figure was made in Biorender.Manipulation of CD45 in T cells is complicated by the essential role of CD45 to activate Lck through de-phosphorylation of the C-terminal tyrosine, which is generated through action of C-terminal src kinase (CSK)19 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-19) . Expression of CD45 with intact phosphatase domains, but truncated extracellular domains, which would not be excluded from close contacts, has been shown to either rescue TCR signaling or inhibit it20 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-20) ,21 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-21) . CD45 can be seen as a gatekeeper19 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-19) that promotes ligand discrimination while ensuring high sensitivity18 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-18) . A model of CD45 exclusion from close contacts formed by TCR and other immunoreceptors combined with kinetic proofreading, referred to as kinetic segregation (KS), has become an important framework for immunoreceptor signaling across innate and adaptive immunity24 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-24) ,25 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-25) . The time frames involved in kinetic segregation are on the order of a few seconds, which also represents the time frame of initial T cell triggering. Immunological synapses may last from minutes to hours, and immune responses involving innate and memory T cells can be mobilized in hours, but the first adaptive response to a pathogen, involving proliferation of rare naïve precursors, takes days to weeks to develop. CD45 is expressed on most hematopoietic cells, which thus are all equipped with a system for activation of tyrosine kinase cascades upon close contact with adjacent cells or objects - a synapse sensor!T cells can be activated without CD45 exclusion through IS formation. For example, nanoscale engineering to generate stimulatory substrates that prevented exclusion of endogenous CD45 from sites of TCR clustering revealed that CD45 exclusion was not essential for TCR tyrosine phosphorylation if the TCR were clustered to within less than 50 nm, but the ability to exclude CD45 was required for TCR phosphorylation when TCR were > 50 nm apart26 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-26) . Cross-linking of TCR with soluble ligands spaced less than 9 nm apart triggers T cells27 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-27) ,28 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-28) . There are a number of mechanisms by which TCR clustering could lead to CD45 exclusion on a ~20 nm length scale without an IS. One possibility is lipid phase separation around clustered TCR that leads to highly localized CD45 exclusion based on mismatch of transmembrane domains29 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-29) . It has also been noted that acute selective inhibition of CSK can lead to TCR triggering without any manipulation of CD4530 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-30) . TCR clustering involved a phase separation process driven by interaction of the CD3ε tail with LCK. This phase separation process is self-limiting in that phosphorylation of CD3ε by LCK leads to recruitment of CSK, which favors dispersal of the TCR clusters in the absence of CD4531 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-31) . At higher pMHC density, the clusters may become resistant to dispersal, leading to graded activation of phosphatidylinositol 3-kinase (PI3K) and ITK32 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-32) . The ability of ITAM-based signaling to progress in the absence of CD45 exclusion is relevant in the setting of chimeric antigen receptors and bispecific T cell engagers, which can be designed to establish close contacts or greater intermembrane separation33 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-33) . Furthermore, CSK recruitment through CD3ε phosphorylation may be less inhibitory in settings where CD45 is not as effectively excluded34 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-34) . () Allosteric models for the TCRConformational changes in the TCR have been invoked in the context of TCR engagement by ligands in the absence or presence of ~10 pN forces acting in the interface35 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-35) –37 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-37) . Catch bonds increase the duration of the TCR-pMHC interaction at ~ 10 pM force38 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-38) ,39 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-39) . An interesting dilemma inherent to reconciling KS and mechanical models is that the mode of prolongation of binding can involve a significant increase in the length of the complex36 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-36) ,37 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-37) , such that the binding is prolonged, but CD45 exclusion may be impaired through a local increase in membrane separation. In contrast, allosteric models for TCR signaling, in which ligand binding causes changes in conformation that are transmitted through the extracellular and transmembrane regions to change the conformation of the cytoplasmic tails, may operate in parallel with CD45 exclusion to alter the outcome of ligand binding. The cytoplasmic domains of the CD3ε and CD247, and the costimulatory CD28, all interact at rest with the inner leaflet of the plasma membrane through hydrophobic and charge-based interactions at the lipid bilayer40 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-40) ,41 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-41) . These interactions are released during activation due in part to translocation of some of the acidic phospholipids to the outer leaflet of the plasma membrane42 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-42) . Different allosteric models have been proposed, for example, based on exposure of cryptic epitopes in the receptor complex35 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-35) , effects of small molecules that bind to the TCR on signaling43 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-43) , or mutations that destabilize the transmembrane domain packing44 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-44) . Conformational changes in the TCR could potentially change the local phospholipid environment, eject cholesterol, or promote dissociation of the cytoplasmic domains to become accessible to kinases and phosphatases45 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-45) ,46 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-46) . () Lessons from Cryo-structures of TCRThe structural picture of the TCR has evolved from the prior state-of-the-art crystallographic information on the interaction of variable chains of the TCR with pMHC and other ligands47 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-47) to a cryo-electron microscopy (cryo-EM) structure of the TCRαβ-CD3-CD247 complex in a detergent micelle48 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-48) and, most recently, to an engineered, high-affinity TCRαβ-CD3-CD247 complex in a detergent micelle interacting with its pMHC49 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-49) . At face value, the earlier crystal structures and newer cryo-EM structures support a rigid body model for the TCR. In all cases, there have been minimal changes in the TCR structures with or without interaction with the pMHC. The cryo-EM structure extends this observation to the CD3 extracellular domains and the transmembrane helices. However, a recent study demonstrates a cholesterol binding-dependent change in the conformation of the transmembrane helices TCRαβ-CD3-CD247 detergent micelle complex50 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-50) . This resonates with earlier data mentioned above that cholesterol binding helps keep the TCRαβ-CD3-CD247 complex in an inactive conformation45 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-45) ,46 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-46) . Other multi-transmembrane-spanning proteins in detergent micelles display different structures than observed in phospholipid bilayers51 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-51) . Thus, it will still be valuable to determine TCRαβ-CD3-CD247 structure in a bilayer environment with and without monovalent and multivalent ligation, in addition to collecting additional examples of TCRαβ-CD3-CD247 structures and complexes in detergent micelles. Extension to TCRγδ-CD3-CD247 would also be exciting.In the meantime, the TCRαβ-CD3-CD247-pMHC interaction can be modeled into an interface, and molecular dynamics simulations performed to model dynamics. The TCRαβ-CD3-CD247-pMHC complex fits within a 14 nm intermembrane separation at a 30-degree angle from vertical for the TCRαβ-pMHC axis49 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-49) . This fits with the notion that the TCR-pMHC interaction should fit into an interface stabilized by the CD2-CD58 adhesion system, as proposed earlier12 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-12) ,17 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-17) . However, another recent study showed that the CD2-CD58 interaction actually decreased the 2D affinity of the TCR-pMHC in the same interface by nearly 2-fold. This suggests that the CD2-CD58 and TCR-pMHC would have slightly different intermembrane spacings23 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-23) . Direct measurements on the CD2-CD48 interaction, which is very similar to CD58, show that it prefers a spacing of 12.8 nm15 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-15) . Direct measurements of the intermembrane spacing in contacts dominated by TCR-pMHC averaged 13.1 nm with a modal distance of 14 nm (Figure 1 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#fig-001) ). Molecular dynamics simulations of the complete TCR in a phospholipid bilayer52 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-52) suggest that the TCRαβ-CD3-CD247-pMHC can tilt more than observed in the cryo-EM structures to accommodate shorter intermembrane spacings. The simulations suggest that forcing the TCR to have a greater bending angle changes the conformation of the transmembrane helices, which suggests the potential for transmission of information across the membrane. The small mismatches between CD2-CD58 and TCRαβ-CD3-CD247-pMHC in the interface may also drive membrane bending that may create additional signaling opportunities53 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-53) . () F-actin requirementsEarly studies recognized a distinct requirement for F-actin for pMHC recognition and speculated about initial contacts mediated by actin-based protrusions54 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-54) –57 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-57) , but tools to study this further were limited. Variable angle total internal reflection, expansion microscopy, and lattice light sheet microscopies have enabled closer analysis of these structures in recent years58 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-58) –61 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-61) . Studies with fixed cells show that some protrusions show concentration of TCR and partial exclusion of CD45, even before ligand binding. Live imaging suggests that many of these configurations are transient and don’t persist long enough to trigger TCR signaling. Projections with TCR enriched appear to be explained by patchiness of the TCR distribution on T cells with the F-actin-based protrusions sampling the patches randomly61 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-61) . Approaches that generate snapshots of this dynamic situation emphasize the patchiness of receptor distribution60 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-60) ,62 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-62) , whereas single-molecule approaches emphasize the transience of any observed structure and the autonomy of individual receptors over time63 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-63) . The sources of the lateral patchiness of the T cell plasma membrane - for example, distinct domains with TCR and LAT - include lipid nanodomains64 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-64) ,65 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-65) , and protein-protein interactions62 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-62) ,66 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-66) . These effects may sensitize the T cell to respond upon stabilization of favorable configurations, such as CD45 segregation from the TCR, upon ligation by agonist pMHC67 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-67) or insertion of projections into confined spaces that lead to CD45-depleted protrusions68 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-68) . () Cis interactions and auto-costimulationT cells often express combinations of costimulatory receptors and ligands that can become functionally important in cell-cell and even intracellular cis complexes. Three exemplars are based on LFA1-ICAM1, CD2-CD48/58 and CD28-CD80/86 activation of integrin LFA1 and upregulation of its ligand ICAM1 upon T cell activation results in T cell-T cell aggregation just before the cells commit to cell division and differentiation69 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-69) . This enables collective decision-making in response to cytokines70 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-70) . LFA1-ICAM1 interactions contribute more to T cell-T cell interaction and T cell-B cell interactions than to T cell-dendritic cell interaction69 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-69) ,71 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-71) ,72 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-72) .Immunoglobulin superfamily costimulatory systems have been studied assuming trans-interaction between receptors on T cells and ligands on antigen-presenting cells, although T cells often express the ligands constitutively or upon activation. Evidence for cis interactions between receptors over the past 20 years has been revisited in a number of cases recently and confirmed for costimulators CD2-CD48/5873 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-73) –75 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-75) and CD28-CD80/8676 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-76) ,77 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-77) , and negative regulators LILR2B-HLA-ABC78 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-78) , PD1-PDL179 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-79) , and CTLA4-CD80/8680 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-80) .CD2-CD48 interactions undergo constitutive cis interactions that are detected based on competition for binding with soluble CD2-Fc or CD48-Fc comparing T cells from wild type or CD48 or CD2 knockout mice, respectively73 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-73) ,75 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-75) . T cells from CD48 or CD2 knockout mice had a significantly impaired response to anti-CD3 antibodies or pMHC presented that were independent of trans-interactions with ligands on the APCs. It was proposed that CD2-CD48 cis interactions recruited liquid-ordered lipid domains to the vicinity of the active TCR74 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-74) (Figure 2 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#fig-002) ). GPI-anchored proteins like CD48 have long acyl chains that enable transbilayer coupling to lipids in the cytoplasmic leaflet, which enables communication to lipid-anchored kinases and transmembrane proteins81 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-81) . CD2-CD58 interaction in trans has recently been shown to organize a number of other costimulatory receptors, including CD28, ICOS, DNAM, SLAMF1, and the checkpoint receptor PD182 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-82) . This synapse compartment, referred to as the corolla, strongly recruited phosphorylated LAT and active LCK in human T cells engaging CD58 in supported lipid bilayers also presenting ICAM-1 and anti-CD3 or pMHC. Interestingly, when human CD2 was transiently expressed in mouse T cells, it could mediate formation of a corolla, but was unable to enhance LCK activation in the mouse T cells. Human CD2 cannot bind mouse CD48, and this suggests that the CD2 corolla’s signaling function is dependent on forming a cis network even as it binds to ligands in trans to mediate adhesion and organization of other costimulatory/checkpoint-type receptors. The CD2-CD58 interaction is highly dynamic with an off rate > 7 s-1 83 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-83) , so this type of cis-trans multitasking may be possible. It is not known if CD2 can interact with its ligands on a flat surface. In the case of LILRB1 cis interactions with HLA-ABC, it has been proposed that this requires bending back of the two N-terminal Ig-domains so that they can adopt a pseudo-trans configuration with respect to the pMHC78 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-78) . It is not clear if CD2 or CD48/58 is capable of this kind of interdomain flexion. Alternatively, the cis interactions may take place in sites with negative membrane curvature, as proposed recently for CD28-CD80/86 interactions.Figure 2. Cis costimulation amplifies TCR signals. (https://www.ncbi.nlm.nih.gov/core/lw/2.0/html/tileshop_pmc/tileshop_pmc_inline.html?title=Click%20on%20image%20to%20zoom&p=PMC3&id=10608137_facrev-12-25-g002.jpg) Open in a new tab (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/figure/fig-002/) A recent analysis of CD2 and CD48 knockout mice supported earlier reports that CD2-CD48 cis interactions inhibit adhesion mediated by CD2 interaction with CD48 in trans. However, it is not clear how CD2 and CD48, or the human ligand CD58, interact in cis. CD28-CD80/86 can also undergo cis interactions, and this has been shown to require PI3K-dependent recruitment of SNX9, leading to membrane tubule formation from the TCR/CD28 microclusters. It is within these tubules that CD28 and CD80 can interact in cis and signal for PKCθ activation and downstream signaling. We speculate here that CD2-CD58 interactions may also depend upon membrane invaginations, but this has not been demonstrated. CD48 and CD58 are GPI anchored such that they recruit liquid-ordered lipid phases (Lo) that are enriched in LCK. The size mismatch between TCR-pMHC and CD2-CD58 (or CD28-CD80) is not shown for simplicity. The figure was made in Biorender. Auto-costimulation by CD28-CD80 interactions was reported in the context of T cell over-expression of CD8076 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-76) , but the requirements for this process were not studied in detail. CD80/CD86 expression is low in naïve T cells, but is up-regulated upon T cell activation. Activation of PI3K through CD28 recruits the bar domain protein SNX9, leading to formation of inward membrane tubules with an appropriate diameter to allow CD28-CD80/86 interactions that activate protein kinase C-θ across the tubule lumen77 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-77) (Figure 2 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#fig-002) ). In this context, it is important to point out that parallel work has connected SNX9 to T cell exhaustion84 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-84) . While SNX9 may have other roles, perhaps including CD2-CD48/58 auto-costimulation, these results suggest that auto-costimulation contributes to exhaustion in the context of chronic antigen exposure.In humans, both CD58 and CD80/86 are not expressed on naïve T cells and are expressed following activation85 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-85) . Thus, priming of naïve T cells may be initiated with costimulation from the APC, but exposure of previously activated T cells to agonist pMHC may recruit auto-costimulation. Sufficiently strong pMHC signals may also recruit auto-costimulation over a period of hours following initial TCR stimulation. Thus, the molecular basis of “TCR signaling” in effector and memory T cells will likely incorporate components of auto-costimulation in specialized membrane compartments. () LCK- instigator and networkerLCK plays a critical role in initiating TCR and costimulatory signaling. LCK has four interaction motifs - the N-terminal palmitoylation sites to interact with the inner leaflet of the plasma membrane, the membrane-proximal Zn2+ clasp that enables association with co-receptors, the SH3 domain that mediates intra and intermolecular interactions, and the SH2 domain that binds to the inhibitory C-terminal phosphorylation and to other tyrosine-phosphorylated proteins. LCK associates efficiently with co-receptors that are laterally recruited to the TCR through interactions with pMHC, MHC class II for CD4 and MHC class I for CD8. It has been suggested that CD8 may help to constrain the orientation of TCR binding to pMHC, as only the canonical orientation positions LCK for phosphorylation of the CD3 and CD247 tails86 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-86) . For CD4, it has been further suggested that LCK bound to CD4 has access to the cytoplasmic domain of TCR bound to adjacent self-pMHC87 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-87) . Perhaps due to these constraints, “free” LCK, not associated with co-receptors, is also involved in initial TCR signaling88 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-88) . LCK interacts with sequences in CD3ε through its N-terminus to facilitate phosphorylation of the CD3 and CD247 phosphotyrosine motifs that recruit ZAP-7089 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-89) ,90 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-90) . LCK can additionally use its SH3 domain to bridge the TCR-CD3-CD247-ZAP-70 complexes to LAT91 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-91) , which is important for PLCγ activation, and CD28 to protein kinase C-θ92 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-92) , which is important for AP-1 and NF-kB activation. These activities have generally been described in the context of CD2-CD48/CD58 cis interactions, which may help optimize local lipid organization and LCK availability. () Termination and re-birthDoes the central accumulation of TCR in the IS sustain signaling or terminate it? Signal termination by TCR endocytosis and either recycling or degradation has long been observed93 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-93) ,94 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-94) after a few minutes of signaling. TCR microclusters undergo a maturation process as they are transported by distinct F-actin transport networks95 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-95) and consolidation of TCR at the center of the interface serves to focus lipid modifications associated with central F-actin reorganization to generate a fenestrated secretory zone96 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-96) . TCR transport within this central compartment is dependent upon the endosomal complexes required for transport (ESCRT)97 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-97) , and some of the TCR are released into the central IS by ectocytosis98 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-98) –100 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-100) . The choice between ectocytosis to release an extracellular vesicle versus endocytosis leading to degradation or recycling is based on different clathrin adapters, HRS for ectocytosis, recruited first, and EPN1 for endocytosis, recruited later99 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-99) (Figure 3 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#fig-003) ). In helper T cells, the TCR-positive synaptic ectosomes also bear CD40 ligand (CD40LG) in response to CD40 on the antigen-presenting surface101 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-101) ,102 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-102) . CD40L is a critical signal for T cell help of dendritic cells and B cells, so the transfer of CD40L in synaptic ectosomes could be critical to deliver this signal. In the context of CD8 T cells, the release of ectosomes with a high diglyceride content has been proposed to allow release of the T cells from targets for serial killing100 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-100) . Lipidomics analysis of extracellular vesicles from CD8+ T cells confirms high (20%) diglyceride content103 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-103) , which favors hexagonal over bilayer lipid phases and may lead to unstable vesicles104 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-104) . Consistent with this, at 90 minutes after initiation of IS formation by CD8+ CTL, no lipid bilayer-based vesicles were recovered from the target side of the synaptic cleft, but only glycoprotein particles with a shell of thrombospondins containing cytotoxic proteins105 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-105) . Lipid vesicles were recovered only when FAS was incorporated into the target membrane, which captures vesicles containing FASLG105 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-105) ,106 (https://pmc.ncbi.nlm.nih.gov/articles/PMC10608137/#ref-106) . Thus, TCR signaling is terminated through a process of ectocytosis, but this same process gives birth to important effectors and serial killing.
Leslie J. Berg, Ph.D. Brief Bio Leslie J. Berg is the chair of and professor in the Department of Immunology and Microbiology at the University of Colorado School of Medicine on the Anschutz Medical Campus . Dr. Berg was president of The American Association of Immunologists from 2011 to 2012 and served on the AAI Council from 2006 to 2013. She is served as the first co-editor-in-chief of ImmunoHorizons (https://www.immunohorizons.org/) with Michael S. Krangel (https://www.aai.org/About/History/Past-Presidents-and-Officers/Michael-S-Krangel) from 2017 to 2019. She was awarded the AAI Distinguished Service Award in 2006 and the AAI-PharMingen Investigator Award (now the AAI-BD Biosciences Investigator Award) in 2001, and was elected a Distinguished Fellow of AAI in 2020. Oral History - Full Interview Interview Date: Thursday, November 1, 2012Location: Worcester, MAClick here (https://www.aai.org/About/History/Oral-History.aspx) to see other interviews from the Oral History Project Oral History - Transcript Click here (https://www.aai.org/AAISite/media/About/History/OHP/Transcripts/Trans-Inv_013-Berg_Leslie_J-2012_Final.pdf) to download the transcript of this interview Oral History - Clips Family Background (http://youtu.be/rNu98e0Mwss) (5:21)Early Interest in Science (http://youtu.be/s4NtxOGBMEs) (2:11)Undergraduate Years at Harvard and Interest in Molecular Biology (http://youtu.be/R8WG-zwz_Rw) (6:26)"Weird Criteria" that Led to Immunology (http://youtu.be/anvFqY0iobY) (5:21)Postdoc at Stanford (http://youtu.be/-68DTGCsOKA) (2:55)Research Highlights (http://youtu.be/sB_OW9n95Ig) (3:52)AAI Presidency (http://youtu.be/meqwNC5DJrg) (4:33)Balancing Professional and Family Life (http://youtu.be/erlXYDJjqpY) (4:36)What Do You Do for Fun? (http://youtu.be/Hlb_WVe7cp8) (1:10)Women in Science (http://youtu.be/-SuHv7fr_0M) (4:24) AAI Service History Joined: 1994President: 2011–2012Vice President: 2010–2011Councillor: 2006–2010The Journal of Immunology Associate Editor: 1994–1999Section Editor: 2000–2004ImmunoHorizons Co-Editor-in-Chief: 2017–2019Committees Education Committee: 1996–2000, 2000–2002 (chair)Program Committee: 2003–2006 (chair)Other Service Director, AAI Introductory Course: 2002Director, AAI Advanced Course: 2012–2015 President's Address " Signaling Pathways That Regulate T Cell Development and Differentiation (http://www.jimmunol.org/content/189/12/5487.full.pdf+html) ," Delivered May 4, 2012 The Journal of Immunology 189, no. 12 (2012): 5487-5488 President's Message "AAI President’s Message," (https://www.aai.org/About/History/Past-Presidents-Messages/Leslie-J-Berg.aspx) July 26, 2011AAI Newsletter (August 2011), 3–5. Awards and Honors AAI-PharMingen Investigator Award (https://www.aai.org/Awards/AAI-Career-Awards/AAI-BD-Biosciences-Investigator-Award.aspx) , 2001AAI Distinguished Service Award (https://www.aai.org/Awards/AAI-Career-Awards/AAI-Distinguished-Service-Award.aspx) , 2006Lamar Soutter Award for Excellence in Medical Education (http:/#) , University Massachusetts Medical School, 2010Distinguished Fellow (https://www.aai.org/Awards/Career/Distinguished-Fellows-of-AAI) , AAI, 2020 Institutional/Biographical Links University of Colorado School of Medicine, Anschutz Medical Campus profile (https://profiles.ucdenver.edu/display/21001250)
Cell (https://pubmed.ncbi.nlm.nih.gov/?term=%22Cell%22%5Bjour%5D&sort=date&sort_order=desc) (https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Cell%22%5BTitle+Abbreviation%5D) (https://pubmed.ncbi.nlm.nih.gov/2476238/#) . 1989 Sep 22;58(6):1035-46. doi: 10.1016/0092-8674(89)90502-3. Antigen/MHC-specific T cells are preferentially exported from the thymus in the presence of their MHC ligand L J Berg (https://pubmed.ncbi.nlm.nih.gov/?term=Berg+LJ&cauthor_id=2476238) 1 (https://pubmed.ncbi.nlm.nih.gov/2476238/#full-view-affiliation-1) , A M Pullen (https://pubmed.ncbi.nlm.nih.gov/?term=Pullen+AM&cauthor_id=2476238) , B Fazekas de St Groth (https://pubmed.ncbi.nlm.nih.gov/?term=Fazekas+de+St+Groth+B&cauthor_id=2476238) , D Mathis (https://pubmed.ncbi.nlm.nih.gov/?term=Mathis+D&cauthor_id=2476238) , C Benoist (https://pubmed.ncbi.nlm.nih.gov/?term=Benoist+C&cauthor_id=2476238) , M M Davis (https://pubmed.ncbi.nlm.nih.gov/?term=Davis+MM&cauthor_id=2476238) Affiliations Expand PMID: 2476238 DOI: 10.1016/0092-8674(89)90502-3 (https://doi.org/10.1016/0092-8674(89)90502-3) Abstract Transgenic mice expressing a T cell receptor heterodimer specific for a fragment of pigeon cytochrome c plus an MHC class II molecule (I-Ek) have been made. We find that H-2k alpha beta transgenic mice have an overall increase in the number of T cells and express a 10-fold higher fraction of cytochrome c-reactive cells than H-2b mice. Surface staining of thymocytes indicates that in H-2b mice, T cell development is arrested at an intermediate stage of differentiation (CD4+8+, CD310). Analyses of mice carrying these T cell receptor genes and MHC class II I-E alpha constructs indicate that his developmental block can be reversed in H-2b mice by I-E expression on cortical epithelial cells of the thymus. These data suggest that a direct T cell receptor-MHC interaction occurs in the thymus in the absence of nominal antigen and results in the enhanced export of T cells, consistent with the concept of "positive selection".
Nature (https://pubmed.ncbi.nlm.nih.gov/?term=%22Nature%22%5Bjour%5D&sort=date&sort_order=desc) (https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Nature%22%5BTitle+Abbreviation%5D) (https://pubmed.ncbi.nlm.nih.gov/2528070/#) . 1989 Aug 17;340(6234):559-62. doi: 10.1038/340559a0. Phenotypic differences between alpha beta versus beta T-cell receptor transgenic mice undergoing negative selection L J Berg (https://pubmed.ncbi.nlm.nih.gov/?term=Berg+LJ&cauthor_id=2528070) 1 (https://pubmed.ncbi.nlm.nih.gov/2528070/#full-view-affiliation-1) , B Fazekas de St Groth (https://pubmed.ncbi.nlm.nih.gov/?term=Fazekas+de+St+Groth+B&cauthor_id=2528070) , A M Pullen (https://pubmed.ncbi.nlm.nih.gov/?term=Pullen+AM&cauthor_id=2528070) , M M Davis (https://pubmed.ncbi.nlm.nih.gov/?term=Davis+MM&cauthor_id=2528070) Affiliations Expand PMID: 2528070 DOI: 10.1038/340559a0 (https://doi.org/10.1038/340559a0) Abstract T-cell differentiation in the thymus is thought to involve a progression from the CD4-CD8- phenotype through CD4+CD8+ intermediates to mature CD4+ or CD8+ cells. There is evidence that during this process T cells bearing receptors potentially reactive to 'self' are deleted by a process termed 'negative selection' One example of this process occurs in mice carrying polymorphic Mls antigens, against which a detectable proportion of T cells are autoreactive. These mice show clonal deletion of thymic and peripheral T-cell subsets that express the autoreactive V beta 3 segment of the T-cell antigen receptor, but at most a two-fold depletion of thymic cells at the CD4+CD8+ stage. By contrast, transgenic mice bearing both alpha and beta chain genes encoding autoreactive receptors recognizing other ligands, show severe depletion of CD4+CD8+ thymocytes as well, suggesting that negative selection occurs much earlier. We report here the Mls 2a/3a mediated elimination of T cells expressing a transgene encoded V beta 3-segment, in T-cell receptor alpha/beta and beta-transgenic mice. Severe depletion of CD4+CD8+ thymocytes is seen only in the alpha/beta chain transgenic mice, whereas both strains delete mature V beta 3 bearing CD4+ and CD8+ T cells efficiently. We conclude that severe CD4+CD8+ thymocyte deletion in alpha/beta transgenic mice results from the premature expression of both receptor chains, and does not reflect a difference in the timing or mechanism of negative selection for Mls antigens as against the allo- and MHC class 1-restricted antigens used in the other studies. PubMed Disclaimer (https://pubmed.ncbi.nlm.nih.gov/disclaimer/)
Mol Cell Biol (https://pubmed.ncbi.nlm.nih.gov/?term=%22Mol+Cell+Biol%22%5Bjour%5D&sort=date&sort_order=desc) (https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Mol+Cell+Biol%22%5BTitle+Abbreviation%5D) (https://pubmed.ncbi.nlm.nih.gov/3266655/#) . 1988 Dec;8(12):5459-69. doi: 10.1128/mcb.8.12.5459-5469.1988. Expression of T-cell receptor alpha-chain genes in transgenic mice L J Berg (https://pubmed.ncbi.nlm.nih.gov/?term=Berg+LJ&cauthor_id=3266655) 1 (https://pubmed.ncbi.nlm.nih.gov/3266655/#full-view-affiliation-1) , B Fazekas de St Groth (https://pubmed.ncbi.nlm.nih.gov/?term=Fazekas+de+St+Groth+B&cauthor_id=3266655) , F Ivars (https://pubmed.ncbi.nlm.nih.gov/?term=Ivars+F&cauthor_id=3266655) , C C Goodnow (https://pubmed.ncbi.nlm.nih.gov/?term=Goodnow+CC&cauthor_id=3266655) , S Gilfillan (https://pubmed.ncbi.nlm.nih.gov/?term=Gilfillan+S&cauthor_id=3266655) , H J Garchon (https://pubmed.ncbi.nlm.nih.gov/?term=Garchon+HJ&cauthor_id=3266655) , M M Davis (https://pubmed.ncbi.nlm.nih.gov/?term=Davis+MM&cauthor_id=3266655) Affiliations Expand PMID: 3266655 PMCID: PMC365649 (https://pmc.ncbi.nlm.nih.gov/articles/PMC365649/) DOI: 10.1128/mcb.8.12.5459-5469.1988 (https://doi.org/10.1128/mcb.8.12.5459-5469.1988) Abstract To examine the influences responsible for shaping the T-cell repertoire in vivo, we have introduced T-cell receptors of defined specificity into mice. In this report, we analyze transgenic mice carrying a T-cell receptor alpha-chain gene from a pigeon cytochrome c-reactive T-cell line. A variant of this construct, which has the immunoglobulin heavy-chain enhancer inserted into the JC intron, was also introduced into mice. Addition of the enhancer increased the steady-state level of transgene-encoded mRNA three- to fivefold in cultured T cells, leading to a two- to threefold increase in surface expression. In vivo, the difference between these two constructs was even more significant, increasing the number of transgene-positive cells from approximately 5 to 70% and the T-cell receptor surface density two- to threefold. Surprisingly, while surface expression of either type of transgene was limited to T cells, we found little tissue specificity with respect to transcription. In T cells expressing the alpha chain from the enhancer-containing construct, immunoprecipitation with a 2B4 alpha-specific monoclonal antibody revealed the expected disulfide-linked dimer. Costaining of these T cells with the 2B4 alpha-specific monoclonal antibody versus anti-CD3 indicated that expression of the transgene-encoded alpha chain precludes expression of endogenous alpha chains on the majority of cells; in contrast, 2B4 alpha-chain expression from the construct lacking the enhancer is inefficient at suppressing endogenous alpha-chain expression. In mice of the enhancer lineage, Southern blot analysis indicated suppression of endogenous alpha-chain rearrangements in T-cell populations, consistent with the observed allelic exclusion at the cellular level. Interestingly, newborn, but not adult, mice of this lineage also showed an increase in retention of unrearranged delta-chain loci in thymocyte DNA, presumably resulting from the suppression of alpha-chain rearrangements. This observation indicates that at least a fraction of alpha:beta-positive T cells have never attempted to produce functional delta rearrangements, thus suggesting that alpha:beta and gamma:delta T cells may be derived from different T-cell compartments (at least during the early phases of T-cell differentiation). PubMed Disclaimer (https://pubmed.ncbi.nlm.nih.gov/disclaimer/)
Mol Cell Biol (https://pubmed.ncbi.nlm.nih.gov/?term=%22Mol+Cell+Biol%22%5Bjour%5D&sort=date&sort_order=desc) (https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Mol+Cell+Biol%22%5BTitle+Abbreviation%5D) (https://pubmed.ncbi.nlm.nih.gov/3266655/#) . 1988 Dec;8(12):5459-69. doi: 10.1128/mcb.8.12.5459-5469.1988. Expression of T-cell receptor alpha-chain genes in transgenic mice L J Berg (https://pubmed.ncbi.nlm.nih.gov/?term=Berg+LJ&cauthor_id=3266655) 1 (https://pubmed.ncbi.nlm.nih.gov/3266655/#full-view-affiliation-1) , B Fazekas de St Groth (https://pubmed.ncbi.nlm.nih.gov/?term=Fazekas+de+St+Groth+B&cauthor_id=3266655) , F Ivars (https://pubmed.ncbi.nlm.nih.gov/?term=Ivars+F&cauthor_id=3266655) , C C Goodnow (https://pubmed.ncbi.nlm.nih.gov/?term=Goodnow+CC&cauthor_id=3266655) , S Gilfillan (https://pubmed.ncbi.nlm.nih.gov/?term=Gilfillan+S&cauthor_id=3266655) , H J Garchon (https://pubmed.ncbi.nlm.nih.gov/?term=Garchon+HJ&cauthor_id=3266655) , M M Davis (https://pubmed.ncbi.nlm.nih.gov/?term=Davis+MM&cauthor_id=3266655) Affiliations Expand PMID: 3266655 PMCID: PMC365649 (https://pmc.ncbi.nlm.nih.gov/articles/PMC365649/) DOI: 10.1128/mcb.8.12.5459-5469.1988 (https://doi.org/10.1128/mcb.8.12.5459-5469.1988) Abstract To examine the influences responsible for shaping the T-cell repertoire in vivo, we have introduced T-cell receptors of defined specificity into mice. In this report, we analyze transgenic mice carrying a T-cell receptor alpha-chain gene from a pigeon cytochrome c-reactive T-cell line. A variant of this construct, which has the immunoglobulin heavy-chain enhancer inserted into the JC intron, was also introduced into mice. Addition of the enhancer increased the steady-state level of transgene-encoded mRNA three- to fivefold in cultured T cells, leading to a two- to threefold increase in surface expression. In vivo, the difference between these two constructs was even more significant, increasing the number of transgene-positive cells from approximately 5 to 70% and the T-cell receptor surface density two- to threefold. Surprisingly, while surface expression of either type of transgene was limited to T cells, we found little tissue specificity with respect to transcription. In T cells expressing the alpha chain from the enhancer-containing construct, immunoprecipitation with a 2B4 alpha-specific monoclonal antibody revealed the expected disulfide-linked dimer. Costaining of these T cells with the 2B4 alpha-specific monoclonal antibody versus anti-CD3 indicated that expression of the transgene-encoded alpha chain precludes expression of endogenous alpha chains on the majority of cells; in contrast, 2B4 alpha-chain expression from the construct lacking the enhancer is inefficient at suppressing endogenous alpha-chain expression. In mice of the enhancer lineage, Southern blot analysis indicated suppression of endogenous alpha-chain rearrangements in T-cell populations, consistent with the observed allelic exclusion at the cellular level. Interestingly, newborn, but not adult, mice of this lineage also showed an increase in retention of unrearranged delta-chain loci in thymocyte DNA, presumably resulting from the suppression of alpha-chain rearrangements. This observation indicates that at least a fraction of alpha:beta-positive T cells have never attempted to produce functional delta rearrangements, thus suggesting that alpha:beta and gamma:delta T cells may be derived from different T-cell compartments (at least during the early phases of T-cell differentiation). PubMed Disclaimer (https://pubmed.ncbi.nlm.nih.gov/disclaimer/)