RNA-Seq Blog Transcriptome Research & Industry News
Single-cell measurements of cellular characteristics have been instrumental in understanding the heterogeneous pathways that drive differentiation, cellular responses to signals, and human disease. Recent advances have allowed paired capture of protein abundance and transcriptomic state, but a lack of epigenetic information in these assays has left a missing link to gene regulation. Using the heterogeneous mixture of cells in human peripheral blood as a test case, we developed a novel scATAC-seq workflow that increases signal-to-noise and allows paired measurement of cell surface markers and chromatin accessibility: integrated cellular indexing of chromatin landscape and epitopes, called ICICLE-seq. Researchers from the Allen Institute for Immunology extended this approach using a droplet-based multiomics platform to develop a trimodal assay that simultaneously measures transcriptomics (scRNA-seq), epitopes, and chromatin accessibility (scATAC-seq) from thousands of single cells, which we term TEA-seq. Together, these multimodal single-cell assays provide a novel toolkit to identify type-specific gene regulation and expression grounded in phenotypically defined cell types.
Trimodal measurement of transcription, epitopes, and accessibility
(a) Workflow diagram for the major steps in transcription, epitopes, and accessibility (TEA-seq). (b) Scatterplot comparing unique single-cell assays for transposase-accessible chromatin (scATAC-seq) fragments and scRNA-seq unique molecule indexes (UMIs) for each TEA-seq cell barcode. In (a, b, d, and e), n = 227,390,390 barcodes are displayed in total; 29,264 passing QC criteria are represented by purple points. (c) Scatterplot comparing unique scATAC-seq fragments and antibody-derived tags UMIs for each cell barcode. (d) scATAC-seq QC scatterplot comparing unique scATAC-seq fragments and fraction of reads in peaks scores for each cell barcode. n = 34,757,757 total cells are displayed (those with >1,000,000 unique ATAC fragments); 29,264 passing QC criteria are represented by purple points. (e) Uniform manifold approximation and projection (UMAP) projections generated using each of the three modalities separately. Only cells passing QC (n = 7,939,939 barcodes) are presented in (e, f). (f) A joint UMAP projection generated using three-way weighted nearest neighbors that leverages all three of the measured modalities. (g) Scatterplot showing the peak-to-RNA correlations (x-axis) and peak-to-protein (y-axis) correlation values for each peak (points) that was found to be correlated with the CCR2 gene or CD192 antibody in TEA-seq data. Histograms at the margins show the distribution of scores for RNA correlations (top) and protein correlations (right). Dashed line shows 1:1 correspondence. Peaks not found to be correlated in each method were assigned a score of 0. (h) Genome tracks showing links between peaks (red hashes) and the CCR2 gene based on protein expression (above peaks) and gene expression (below peaks). Correlations are represented by arcs colored based on the correlation score (color scale for both panels to the left). The bottom panel shows the gene neighborhood around CCR2. All coordinates are from the Hg38 genome assembly. (i), as in (g), for the CD38 gene and correlated peaks. (j), as in (h), for the CD38 gene locus.
