转录组
生物
计算生物学
基因表达谱
细胞
DNA微阵列
单细胞分析
基因
基因表达
遗传学
作者
Alexander Rosenberg,Charles M. Roco,Richard A. Muscat,Anna Kuchina,Paul Sample,Zizhen Yao,Lucas T. Graybuck,David J. Peeler,Sumit Mukherjee,Wei Chen,Suzie H. Pun,Drew L. Sellers,Bosiljka Tasic,Georg Seelig
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2018-03-15
卷期号:360 (6385): 176-182
被引量:1513
标识
DOI:10.1126/science.aam8999
摘要
To facilitate scalable profiling of single cells, we developed split-pool ligation-based transcriptome sequencing (SPLiT-seq), a single-cell RNA-seq (scRNA-seq) method that labels the cellular origin of RNA through combinatorial barcoding. SPLiT-seq is compatible with fixed cells or nuclei, allows efficient sample multiplexing, and requires no customized equipment. We used SPLiT-seq to analyze 156,049 single-nucleus transcriptomes from postnatal day 2 and 11 mouse brains and spinal cords. More than 100 cell types were identified, with gene expression patterns corresponding to cellular function, regional specificity, and stage of differentiation. Pseudotime analysis revealed transcriptional programs driving four developmental lineages, providing a snapshot of early postnatal development in the murine central nervous system. SPLiT-seq provides a path toward comprehensive single-cell transcriptomic analysis of other similarly complex multicellular systems.
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