Comprehensive single-cell transcriptional profiling of a multicellular organism
Comprehensive single-cell transcriptional profiling of a multicellular organism
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DOI：
10.1126/science.aam8940
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发表时间：
2017-08-18
期刊：
影响因子：
56.9
通讯作者：
Shendure, Jay
中科院分区：
文献类型：
--
作者：
Cao, Junyue;Packer, Jonathan S.;Shendure, Jay
为了解决细胞异质性，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L2幼虫阶段的线虫秀丽隐杆线虫的近50，000个细胞，其提供了其体细胞组成的>50倍“鸟枪”细胞覆盖。根据这些数据，我们定义了27种细胞类型的共识表达谱，并回收了与L2蠕虫中少至一两个细胞相对应的罕见神经元细胞类型。我们整合了这些配置文件与整个动物染色质免疫沉淀测序数据去卷积转录因子的细胞类型特异性的影响。sci-RNA-seq产生的数据构成了线虫生物学的强大资源，并预示着其他生物的类似图谱。
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 Caenorhabditis elegans 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.