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

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-base... Full description

Journal Title: Science (New York N.Y.), April 13, 2018, Vol.360(6385), pp.176-182
Main Author: Rosenberg, Alexander B
Other Authors: Roco, Charles M , Muscat, Richard A , Kuchina, Anna , Sample, Paul , Yao, Zizhen , Graybuck, Lucas T , Peeler, David J , Mukherjee, Sumit , Chen, Wei , Pun, Suzie H , Sellers, Drew L , Tasic, Bosiljka , Seelig, Georg
Format: Electronic Article Electronic Article
Language: English
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ID: E-ISSN: 1095-9203 ; DOI: 1095-9203 ; DOI: 10.1126/science.aam8999
Link: http://search.proquest.com/docview/2014948109/?pq-origsite=primo
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recordid: proquest2014948109
title: Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding.
format: Article
creator:
  • Rosenberg, Alexander B
  • Roco, Charles M
  • Muscat, Richard A
  • Kuchina, Anna
  • Sample, Paul
  • Yao, Zizhen
  • Graybuck, Lucas T
  • Peeler, David J
  • Mukherjee, Sumit
  • Chen, Wei
  • Pun, Suzie H
  • Sellers, Drew L
  • Tasic, Bosiljka
  • Seelig, Georg
subjects:
  • Animals–Growth & Development
  • Brain–Genetics
  • Cell Nucleus–Methods
  • Gene Expression Profiling–Metabolism
  • Gene Expression Regulation, Developmental–Methods
  • Hek293 Cells–Growth & Development
  • Humans–Growth & Development
  • Mice–Growth & Development
  • NIH 3t3 Cells–Growth & Development
  • Neurons–Growth & Development
  • Sequence Analysis, RNA–Growth & Development
  • Single-Cell Analysis–Growth & Development
  • Spinal Cord–Growth & Development
  • Transcriptome–Growth & Development
ispartof: Science (New York, N.Y.), April 13, 2018, Vol.360(6385), pp.176-182
description: 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 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.
language: eng
source:
identifier: E-ISSN: 1095-9203 ; DOI: 1095-9203 ; DOI: 10.1126/science.aam8999
fulltext: fulltext
issn:
  • 10959203
  • 1095-9203
url: Link


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titleSingle-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding.
creatorRosenberg, Alexander B ; Roco, Charles M ; Muscat, Richard A ; Kuchina, Anna ; Sample, Paul ; Yao, Zizhen ; Graybuck, Lucas T ; Peeler, David J ; Mukherjee, Sumit ; Chen, Wei ; Pun, Suzie H ; Sellers, Drew L ; Tasic, Bosiljka ; Seelig, Georg
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descriptionIdentifying 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 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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authorRosenberg, Alexander B ; Roco, Charles M ; Muscat, Richard A ; Kuchina, Anna ; Sample, Paul ; Yao, Zizhen ; Graybuck, Lucas T ; Peeler, David J ; Mukherjee, Sumit ; Chen, Wei ; Pun, Suzie H ; Sellers, Drew L ; Tasic, Bosiljka ; Seelig, Georg
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