To resolve cellular heterogeneity, researchers from the University of Washington developed a combinatorial indexing strategy to profile the transcriptomes of single cells or nuclei, termed sci-RNA-seq (single-cell combinatorial indexing RNA sequencing). They applied sci-RNA-seq to profile nearly 50,000 cells from the nematode Caenorhabditis elegans at the L2 larval stage, which provided >50-fold “shotgun” cellular coverage of its somatic cell composition. From these data, they defined consensus expression profiles for 27 cell types and recovered rare neuronal cell types corresponding to as few as one or two cells in the L2 worm. The researchers integrated these profiles with whole-animal chromatin immunoprecipitation sequencing data to deconvolve the cell type–specific effects of transcription factors. The data generated by sci-RNA-seq constitute a powerful resource for nematode biology and foreshadow similar atlases for other organisms.
(A) Schematic of the sci-RNA-seq workflow. AAAAA, polyadenosine tail; NVTTTTT, polythymidine primer. (B) Schematic of sci-RNA-seq library amplicons for Illumina sequencing. bp, base pairs; R, annealing sites for Illumina sequencing primers; P, Illumina P5 or P7 adaptor sequence. (C) Scatter plot of unique molecular identifier (UMI) counts from human and mouse cells, determined by 384 × 384 sci-RNA-seq. Blue, inferred mouse cells (n = 5953). Red, inferred human cells (n = 3967). Gray, collisions (n = 884). (D) Scatter plot of UMI counts from human and mouse cells, determined by 96 × 96 sci-RNA-seq with an optimized protocol. Blue, inferred mouse cells (n = 129). Red, inferred human cells (n = 160). Gray, collisions (n = 5). In (C) and (D), only cells originating from wells containing mixed human and mouse cells are shown. (E) Correlation between gene expression measurements in aggregated sci-RNA-seq profiles of NIH/3T3 cells (n = 238) and nuclei (n = 124). (F) t-SNE plot of cells originating in wells containing HEK293T (red; n = 60), HeLa S3 (blue; n = 69), or a mixture (gray; n = 321). (G) Correlation between gene expression measurements from aggregated sci-RNA-seq data and bulk RNA-seq data obtained using a related protocol. In (E) and (G), the red line is the linear regression, and the black line is y = x.
Availability– The researchers have created a website to facilitate the further annotation of these data by the community: http://atlas.gs.washington.edu
More Press Coverage –