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Tailoring the resolution of single-cell RNA sequencing for primary cytotoxic T cells

ABSTRACT: Single-cell RNA sequencing in principle offers unique opportunities to improve the efficacy of contemporary T-cell based immunotherapy against cancer. The use of high-quality single-cell data will aid our incomplete understanding of molecular programs determining the differentiation and functional heterogeneity of cytotoxic T lymphocytes (CTLs) allowing for optimal therapeutic design. So far a major obstacle to high depth single-cell analysis of CTLs is the minute amount of RNA available leading to low capturing efficacy. To overcome this we tailor a droplet-based approach for high-throughput analysis (tDrop-seq) and a plate-based method for high-performance in-depth CTL analysis (tSCRB-seq). The latter gives on average a 15-fold higher number of captured transcripts per gene compared to droplet-based technologies. The improved dynamic range of gene detection gives tSCRB-seq an edge in resolution sensitive downstream applications such as graded high confidence gene expression measurements and cluster characterization. We demonstrate the power of tSCRB-seq by revealing the subpopulation-specific expression of co-inhibitory and co-stimulatory receptor targets of key importance for immunotherapy.

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE163089 | GEO | 2020/12/13

REPOSITORIES: GEO

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Project description:Similar to other droplet-based single cell assays, single nucleus ATAC-seq (snATAC-seq) data harbor multiplets that confound downstream analyses. Detecting multiplets in snATAC-seq data is particularly challenging due to data sparsity and limited dynamic range (0 reads: closed chromatin, 1: open on in one parental chromosome allele, 2: open on in both alleles chromosomes). Yet, these unique data features offer an opportunity to identify multiplets. ATAC-DoubletDetector (https://ucarlab.github.io/ATAC-DoubletDetector/) AMULET (Atac MULtiplet Estimation Tool) exploits these unique features to detect multiplets by studying enumerates the number of regions with >2 uniquely aligned reads across the genome to effectively detect multiplets - an effective alternative to methods based on artificially-generated multiplets. We evaluated the method by generating snATAC-seq data (e.g., state-of-the-art ArchR). For benchmarking we generated snATAC-seq data and generated data fromeasured the efficacy of AMULET inm in two primary human tissues: peripheral human blood mononuclear cells (PBMCs) and pancreatic islet samples. AMULET detects had high multiplets with an estimated precision (estimated via donor-based multiplexing) and high recall (estimated via simulated doublets) compared to alternatives 0.57 precision and achieves 0.85 recall. When and was the most effective when a certain read depth is achieved (a certain read depth per nucleus is achieved samples are sequenced deeply (e.g., median read count per nucleus >20K25K) reads per nucleus in PBMCs), ATAC-DoubletDetector captured 85% of simulated doublets (i.e., recall), significantly outperforming ArchR (24%). For lower read depth, ATAC-DoubletDetector and ArchR produced complementary results. Moreover, ATAC-DoubletDetector was equally effective in identifying homotypic multiplets (i.e., multiplets from the same cell type), which are missed by simulation-based methods. Cell-specific marker peaks enabled accurate (85%) tracing of cellular origins of snATAC-seq multiplets. Accordingly, more abundant cells within a tissue are more likely to form multiplets and the majority of multiplets are homotypic. ATAC-DoubletDetector is a fast and effective multiplet detection/annotation tool for improved single cell epigenomic data analyses across diverse biological systems and conditions.

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Tailoring the resolution of single-cell RNA sequencing for primary cytotoxic T cells

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