Transcriptomics

Dataset Information

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Single cell RNA-sequencing of E8.5 stage embryonic neural tissue from mouse


ABSTRACT: We describe a so far uncharacterized, embryonic and self-renewing Neural Plate Border Stem Cell (NBSC) population with the capacity to differentiate into central nervous and neural crest lineages. NBSCs can be obtained by neural transcription factor-mediated reprogramming (BRN2, SOX2, KLF4, and ZIC3) of human adult dermal fibroblasts and peripheral blood cells (induced Neural Plate Border Stem Cells, iNBSCs) or by directed differentiation from human induced pluripotent stem cells (NBSCs). Moreover, human (i)NBSCs share molecular and functional features with primary Neural Plate Border Stem Cells (pNBSCs) isolated from neural folds of E8.5 mouse embryos. Here we provide single cell RNA-sequencing data of neural tissue derived from two E8.5 mouse embryos. After manual isolation and enzymatic separation E8.5 neural tissue was single cell sorted and RNA sequencing was performed following the Smart-seq2 protocol. In sum, cultured pNBSCs and E8.5 neural tube cells share a similar regional identity and expression signature suggesting that pNBSCs might correspond to an endogenous progenitor in this area of the developing brain.

INSTRUMENT(S): Illumina HiSeq 2500

ORGANISM(S): Mus musculus  

SUBMITTER: Andreas Trumpp   Marc Christian Thier  

PROVIDER: E-MTAB-6912 | ArrayExpress | 2018-12-20

SECONDARY ACCESSION(S): ERP112246

REPOSITORIES: ArrayExpress, ENA

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Publications

Pooling across cells to normalize single-cell RNA sequencing data with many zero counts.

Lun Aaron T L AT   Bach Karsten K   Marioni John C JC  

Genome biology 20160427


Normalization of single-cell RNA sequencing data is necessary to eliminate cell-specific biases prior to downstream analyses. However, this is not straightforward for noisy single-cell data where many counts are zero. We present a novel approach where expression values are summed across pools of cells, and the summed values are used for normalization. Pool-based size factors are then deconvolved to yield cell-based factors. Our deconvolution approach outperforms existing methods for accurate nor  ...[more]

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