Single cell profiling of the developing mouse brain and spinal cord with split-pool barcoding

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. Over 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 towards comprehensive single-cell transcriptomic analysis of other similarly complex multicellular systems. Single-cell/nucleus RNA-seq was performed using SPLiT-seq This code explains how to read the data from the paper into python: https://gist.github.com/Alex-Rosenberg/5ee8b14ea580144facad9c2b87cebf10为实现单细胞的规模化转录组谱型分析，我们开发了基于裂分连接的转录组测序技术（Split Pool Ligation-based Transcriptome sequencing，简称SPLiT-seq）——这是一种通过组合条形码标记RNA细胞来源的单细胞RNA测序（single-cell RNA-seq，简称scRNA-seq）方法。 该技术兼容固定细胞或细胞核，可实现高效的样本多路复用，且无需定制化实验设备。 我们利用SPLiT-seq分析了出生后第2天和第11天的小鼠大脑与脊髓共计156049个单细胞核转录组。 本次分析共鉴定出超过100种细胞类型，其基因表达模式与细胞功能、区域特异性以及分化阶段高度对应。 拟时间分析揭示了驱动四种发育谱系的转录调控程序，为小鼠中枢神经系统的出生后早期发育提供了全景快照。 SPLiT-seq为其他类似复杂的多细胞系统的全面单细胞转录组分析提供了可行路径。 本研究采用SPLiT-seq技术完成单细胞/细胞核RNA测序。以下代码可用于将论文中的数据读取至Python环境：https://gist.github.com/Alex-Rosenberg/5ee8b14ea580144facad9c2b87cebf10