Dataset Information


Single cell RNA-sequencing of primary mouse Neural Plate Border Stem Cells

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 an endogenous NBSC population isolated from neural folds of E8.5 mouse embryos. Upon differentiation, iNBSCs give rise to either (1) radial glia-type stem cells, dopaminergic and serotonergic neurons, motoneurons, astrocytes, and oligodendrocytes or (2) cells from the neural crest lineage. Here we provide single cell RNA-sequencing data of two primary mouse Neural Plate Border Stem Cell Lines (pNBSCs). pNBSCs were single cell sorted and RNA sequencing was performed following the Smart-seq2 protocol. In sum, pNBSCs and iNBSCs share a similar regional identity, expression signature and analogous differentiation dynamics on the single-cell-level, suggesting the presence of a transient, NBSC-like progenitor during the neurulation stage of mouse and likely also human embryos.

INSTRUMENT(S): Illumina HiSeq 2500

ORGANISM(S): Mus musculus  

SUBMITTER: Andreas Trumpp   Marc Christian Thier  

PROVIDER: E-MTAB-6925 | ArrayExpress | 2018-12-20



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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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