This presentation by Dr. Fei Chen of the Broad Institute and Harvard University explores recent advancements in high-resolution spatial genomics, specifically highlighting Slide-seq, Slide-tag, and in situ sequencing technologies. The talk emphasizes the critical role of spatial context in understanding tissue organization, cell-cell interactions, gene expression, and cell fate decisions in both development and disease, with a particular focus on the tumor microenvironment. Dr. Chen details the technical evolution from spatial RNA/DNA capture (Slide-seq) to single-cell spatial profiling (Slide-tag), demonstrating their application in dissecting clonal heterogeneity and environmental influences on gene expression in melanoma. He also introduces a novel computational approach to reconstruct spatial data solely from sequencing information, promising scalable spatial genomics for whole organs. The presentation underscores the transformative potential of these tools for unraveling fundamental biological principles and informing therapeutic strategies.
Understanding Tissue Architecture: Advances in Spatial Genomics
0:00 Introduction to Spatial Genomics: Anoja Perera introduces Dr. Fei Chen, highlighting his pioneering work in developing genomic and computational tools, including co-inventing expansion microscopy and Slide-seq, to understand tissue organization and disease.
2:46 The Revolution in Genomics: Dr. Chen frames the current era as a revolution in genomics, where advanced technologies now allow high-resolution, genome-wide monitoring of gene expression and spatial localization within tissues, fulfilling predictions made decades ago.
4:56 Cells as Communities: The core premise is that cellular states and gene expression programs are profoundly influenced by their spatial context and interactions within multicellular circuits in tissues.
9:09 Spatial Capture Technologies (Slide-seq): The lab developed Slide-seq, an early high-resolution spatial transcriptomics technology that uses spatially barcoded microbeads (10-micron resolution) to capture RNA and map its origin within tissue sections.
13:25 Spatial DNA Capture (Slide-DNA-seq): Extending Slide-seq, the team developed methods to capture DNA, enabling the mapping of clonal genomic variations (e.g., chromosomal alterations) within tissues, providing insights into tumor evolution and heterogeneity.
17:15 Decomposing Gene Expression Drivers: By comparing RNA and DNA spatial data, the researchers can decompose gene expression changes into components driven by intrinsic clonal assignments and extrinsic factors like local cell density, revealing how the environment influences cancer cell behavior.
22:50 Bridging Single-Cell and Spatial Data (Slide-tags): To overcome challenges like cell mixtures in capture methods, Slide-tags was developed. This approach spatially barcodes cells before single-cell sequencing, allowing high-quality single-cell (or nucleus) data to be recovered with precise spatial coordinates.
27:38 Versatility of Slide-tags: Slide-tags is compatible with various single-cell assays, including RNA expression, TCR sequencing, copy number inference, and chromatin accessibility, enabling multi-omic spatial profiling.
28:30 Melanoma Case Study: Applying Slide-tags to metastatic melanoma revealed distinct tumor subclones with unique transcriptomic states and spatial segregation. TCR sequencing linked specific, expanded CD8 T-cell clones to particular tumor subclones, suggesting tumor cell lineage's role in shaping immune interactions.
37:05 Recurrent Neighborhoods and Signaling: A large melanoma dataset (300k nuclei, 27 biopsies) identified recurrent "niches" or cellular neighborhoods (e.g., immune effector hubs, myeloid/stromal niches). These neighborhoods correlate with and predict specific gene expression programs in malignant cells and are associated with distinct soluble ligand signaling programs (e.g., interferon responses, neuro-crest differentiation).
48:45 Importance of Spatial Context: Understanding recurrent spatial neighborhoods and extracellular signaling programs is crucial for deciphering rules of tissue organization, developmental patterning, pathological processes, and guiding therapeutic interventions.
54:16 Overcoming Throughput Bottlenecks with Imaging-Free Reconstruction: To scale spatial genomics to whole organs, Dr. Chen introduces a novel computational method that reconstructs spatial coordinates solely from sequencing data of diffusing barcodes, eliminating the need for complex imaging steps during array production.
1:01:09 Validation of Imaging-Free Reconstruction: Validation studies show that the reconstructed spatial maps are virtually indistinguishable from ground truth, with errors comparable to typical histological stretching.
1:05:03 Scalability to Human Organs: This imaging-free reconstruction method allows for the creation of very large spatial arrays (e.g., 7 cm, similar to a human brain slab), enabling high-resolution spatial mapping of cell types and gene expression across entire organs.
1:09:09 Q&A Insights: Discussions confirm the ability to integrate molecular pathology data (e.g., TMB status) with spatial findings and explore the potential for identifying circulating biomarkers linked to specific microenvironments. The team is also working on improving cell recovery rates (aiming for >25-30%) and testing protocols for long-term tissue preservation (e.g., FFPE compatibility for RNA and DNA).