Identifying single-cell types in the mouse brain The recent development of single-cell genomic techniques allows us to profile gene expression at the single-cell level easily, although many of these methods have limited throughput. Rosenberg et al. describe a strategy called split-pool ligation-based transcriptome sequencing, or SPLiT-seq, which uses combinatorial barcoding to profile single-cell transcriptomes without requiring the physical isolation of each cell. The authors used their method to profile >100,000 single-cell transcriptomes from mouse brains and spinal cords at 2 and 11 days after birth. Comparisons with in situ hybridization data on RNA expression from Allen Institute atlases linked these transcriptomes with spatial mapping, from which developmental lineages could be identified. Science, this issue p. 176 Single-cell analyses with SPLiT-seq (split-pool ligation-based transcriptome sequencing) elucidate development of the mouse nervous system. 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.

中文翻译：

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使用分池条形码对发育中的小鼠大脑和脊髓进行单细胞分析


识别小鼠大脑中的单细胞类型单细胞基因组技术的最新发展使我们能够轻松地在单细胞水平上分析基因表达，尽管其中许多方法的通量有限。罗森伯格等人。描述了一种称为基于分割池连接的转录组测序（SPLiT-seq）的策略，该策略使用组合条形码来分析单细胞转录组，而不需要对每个细胞进行物理隔离。作者使用他们的方法对出生后 2 天和 11 天的小鼠大脑和脊髓的 >100,000 个单细胞转录组进行了分析。与艾伦研究所图集的 RNA 表达原位杂交数据进行比较，将这些转录组与空间图谱联系起来，从中可以识别发育谱系。科学，本期第 14 页。 176 使用 SPLiT-seq（基于分池连接的转录组测序）进行的单细胞分析阐明了小鼠神经系统的发育。为了促进单细胞的可扩展分析，我们开发了基于分池连接的转录组测序 (SPLiT-seq)，这是一种单细胞 RNA 序列 (scRNA-seq) 方法，可通过组合条形码标记 RNA 的细胞起源。 SPLiT-seq 与固定细胞或细胞核兼容，可实现高效的样品复用，并且无需定制设备。我们使用 SPLiT-seq 分析了出生后第 2 天和第 11 天小鼠大脑和脊髓的 156,049 个单核转录组。鉴定了 100 多种细胞类型，其基因表达模式与细胞功能、区域特异性和分化阶段相对应。 伪时间分析揭示了驱动四个发育谱系的转录程序，提供了小鼠中枢神经系统出生后早期发育的快照。 SPLiT-seq 提供了一条对其他类似复杂多细胞系统进行全面单细胞转录组分析的途径。