Cao Junyue, Packer Jonathan S, Ramani Vijay, Cusanovich Darren A, Huynh Chau, Daza Riza, Qiu Xiaojie, Lee Choli, Furlan Scott N, Steemers Frank J, Adey Andrew, Waterston Robert H, Trapnell Cole, Shendure Jay
Department of Genome Sciences, University of Washington, Seattle, WA, USA.
Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA.
Science. 2017 Aug 18;357(6352):661-667. doi: 10.1126/science.aam8940.
To resolve cellular heterogeneity, we developed a combinatorial indexing strategy to profile the transcriptomes of single cells or nuclei, termed sci-RNA-seq (single-cell combinatorial indexing RNA sequencing). We applied sci-RNA-seq to profile nearly 50,000 cells from the nematode at the L2 larval stage, which provided >50-fold "shotgun" cellular coverage of its somatic cell composition. From these data, we 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. We 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.
为了解决细胞异质性问题，我们开发了一种组合索引策略来分析单细胞或细胞核的转录组，称为sci-RNA-seq（单细胞组合索引RNA测序）。我们应用sci-RNA-seq分析了线虫L2幼虫阶段近50000个细胞的转录组，这提供了其体细胞组成超过50倍的“散弹枪式”细胞覆盖。从这些数据中，我们定义了27种细胞类型的一致性表达谱，并找回了L2线虫中少至一两个细胞对应的罕见神经元细胞类型。我们将这些谱与全动物染色质免疫沉淀测序数据整合，以反卷积转录因子的细胞类型特异性效应。sci-RNA-seq产生的数据构成了线虫生物学的强大资源，并预示着其他生物也会有类似的图谱。
