SPLiT-seq reveals cell types and lineages in the developing brain and spinal cord

By Alexander B. Rosenberg, Charles M. Roco, Richard A. Muscat, Anna Kuchina, Paul J. Sample, Zizhen Yao, Lucas T. Graybuck, David J. Peeler, S. Mukherjee, Wei Chen and 4 more. Science, 2018

1,182 citations recorded by the source corpus.

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.

Subfields: Immunology, Molecular Biology, Neurology

View Paper: https://www.semanticscholar.org/paper/13f31ae36cdea2d755cd2f377e13d7b60b035e3d

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Citations in the corpus

Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin (2020, methodology): “To create a chromatin accessibility and mRNA expression co-profiling approach that is scalable and sensitive, we built on SPLiT-seq and Paired-seq (Rosenberg et al., 2018; Zhu et al., 2019) to develop SHARE-seq, which uses multiple rounds of hybridization blocking to uniquely and simultaneously…”
The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution (2020, neutral): “, 2017), and combinatorial indexing (Rosenberg et al., 2018; Vitak et al., 2017).”
The single cell transcriptional landscape of mammalian organogenesis (2019, neutral): “A conceptually identical method was recently termed Split-Seq 20 .”
DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors (2018, background): “However, genomic and cellular barcoding techniques for identifying doublets have only recently been developed (Stoeckius et al., 2017; Kang et al., 2018; Gehring et al., 2018; Guo et al., 2018; Rosenberg et al., 2018).”
A benchmark of batch-effect correction methods for single-cell RNA sequencing data (2020, methodology): “[42] to test the batch correction methods on a big dataset [9].”
Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain (2018, background): “High-throughput single-cell RNA sequencing (scRNA-seq) now enables RNA profiling in thousands of individual cells in complex tissue (Han et al., 2018; Klein et al., 2015; Macosko et al., 2015; Rosenberg et al., 2018; Zheng et al., 2017).”