This paper introduces single-cell DNA–RNA sequencing (SDR-seq), a novel, high-throughput method designed to simultaneously profile hundreds of genomic DNA loci and gene transcripts within thousands of individual cells. The technology addresses a critical limitation in genomics: the difficulty of confidently linking endogenous genetic variants, especially noncoding ones, to their functional impact on gene expression. SDR-seq combines in situ reverse transcription with droplet-based multiplexed PCR, achieving high sensitivity and a low allelic dropout rate (~10%), which allows for the accurate determination of variant zygosity.
The method's scalability and sensitivity were validated through experiments on human induced pluripotent stem cells, where it successfully detected subtle gene expression changes introduced by CRISPR and base editing. Furthermore, when applied to primary B cell lymphoma samples, SDR-seq revealed that malignant cells with a higher mutational burden exhibit elevated B cell receptor signaling and tumorigenic gene expression. SDR-seq provides a powerful and scalable platform to systematically dissect the regulatory mechanisms of genetic variants, advancing the understanding of gene regulation and its role in disease.
Unveiling SDR-seq: A New Method to Link Genetic Variants with Gene Expression
The Challenge: Linking Genotype to Phenotype: Over 90% of disease-associated genetic variants are in noncoding regions, making it difficult to assess their impact on gene expression. Existing methods to study this link at the single-cell level are often inefficient, have low throughput, or suffer from high error rates, preventing accurate analysis.
Introducing SDR-seq: A Novel Multiomic Approach: The paper presents SDR-seq, a targeted, droplet-based method that simultaneously measures up to 480 genomic DNA loci and RNA transcripts in the same single cell. It works by fixing cells, performing in situ reverse transcription to capture RNA information, and then using a multiplexed PCR in droplets to amplify both the captured RNA (cDNA) and specific DNA targets.
Key Advantage: High Sensitivity and Low Error Rate: A major breakthrough of SDR-seq is its low allelic dropout (ADO) rate of approximately 10%. This is a vast improvement over other high-throughput methods where ADO rates can exceed 96%. This low error rate allows researchers to confidently determine the zygosity of a variant (whether a cell has one or two copies of a mutation) and link it directly to gene expression changes.
Proven Scalability: The study demonstrates that SDR-seq is highly scalable and reproducible. The method was successfully tested with panels of 120, 240, and 480 targets, showing that it can reliably detect both DNA variants and gene expression levels regardless of panel size, chromatin state, or expression level.
Detecting Functional Consequences: To prove its sensitivity, researchers used SDR-seq in conjunction with gene editing tools like CRISPRi and base editing in stem cells. The method was able to precisely measure both strong and subtle changes in target gene expression resulting from the introduced genetic edits, validating its use for functional genomic screens.
Application in Cancer Research: SDR-seq was applied to primary tumor samples from individuals with B cell lymphoma. The technology successfully identified tumor-specific variants, distinguished between malignant and non-malignant cells, and revealed distinct clonal structures within the tumors based on their genetic profiles.
Key Biological Insight from Lymphoma Study: The analysis of lymphoma samples revealed a crucial link between mutation accumulation and cancer progression. Cells with a higher mutational burden showed significantly elevated B cell receptor signaling and tumorigenic gene expression, suggesting that the accumulation of variants helps cancer cells evade apoptosis (programmed cell death) and promote their own survival.
Future Applications and Impact: SDR-seq is a versatile and powerful platform for studying the functional consequences of genetic variation. It can be applied to both genetically engineered cell lines and primary patient tissues, holding great potential to decipher the regulatory mechanisms underlying complex genetic disorders and advance the development of new therapeutic strategies.
Glossary
Variant Zygosity: Refers to the genetic makeup of a cell at a specific DNA locus. For a given variant, a cell can be homozygous, meaning it has two identical copies of the allele (e.g., both copies are the normal version, or both are mutated), or it can be heterozygous, meaning it has two different alleles (one normal and one mutated). Accurately determining zygosity is crucial for understanding a variant's effect on gene function.
Allelic Dropout (ADO) Rates: Allelic dropout is a common technical error in single-cell DNA sequencing where one of the two alleles present in a heterozygous cell fails to be amplified during the PCR process. This causes the cell to be incorrectly identified as homozygous. A high ADO rate makes it difficult to trust the zygosity calls. SDR-seq's low ADO rate is a significant advantage, ensuring more accurate data.