Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex

Abstract

Large-scale surveys of single-cell gene expression have the potential to reveal rare cell populations and lineage relationships, but require efficient methods for cell capture and mRNA sequencing1–4. Although cellular barcoding strategies allow parallel sequencing of single cells at ultra-low depths5, the limitations of shallow sequencing have not been directly investigated. By capturing 301 single cells from 11 populations using microfluidics and analyzing single-cell transcriptomes across downsampled sequencing depths, we demonstrate that shallow single-cell mRNA sequencing (~50,000 reads per cell) is sufficient for unbiased cell-type classification and biomarker identification. In developing cortex we identify diverse cell types including multiple progenitor and neuronal subtypes, and we identify EGR1 and FOS as previously unreported candidate targets of Notch signaling in human but not mouse radial glia. Our strategy establishes an efficient method for unbiased analysis and comparison of cell populations from heterogeneous tissue by microfluidic single-cell capture and low-coverage sequencing of many cells.

Somatosensory neuron types identified by high-coverage single-cell RNA-sequencing and functional heterogeneity

“…By single-cell PCR, Chiu et al identified six subgroups of DRG neurons [25]. Single-cell RNA-seq enables a better understanding of a cell's transcriptome [26][27][28][29][30][31][32][33]. Usoskin et al performed low-coverage single-cell RNA-seq (3 574 ± 2 010 genes per neuron) and classified the mouse DRG neurons into two PEP types, three NP types, TH type and five NF200-positive types within the traditional classification framework [34].…”

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

“…However, methods for identifying subpopulations of cells and modeling their gene regulatory landscapes are only now beginning to emerge 18,19 . To fully exploit single-cell RNA-seq data, we have to account for the random noise inherent to such data sets 20 and, equally important, to account for different hidden factors that might result in gene expression heterogeneity.…”

“…Some of these subpopulations may represent previously unidentified cell types. Additionally, by studying patterns of gene expression in different single cells, insights into the regulatory landscape of each cell population can be obtained.However, methods for identifying subpopulations of cells and modeling their gene regulatory landscapes are only now beginning to emerge 18,19 . To fully exploit single-cell RNA-seq data, we have to account for the random noise inherent to such data sets 20 and, equally important, to account for different hidden factors that might result in gene expression heterogeneity.…”

The Human Cell Atlas: from vision to reality

“…New cell types have been found in the brain [3][4][5][6][7] , gut 8 , retina 9 and immune system 10 , and these discoveries have yielded new insight -into how the immune system 11 functions, for example, and into the dynamics of tumour ecosystems 12 . Yet, to take the next step -to build a human cell atlas that is truly useful -requires taking the long view and addressing various systemic and organizational challenges, as well as technical and scientific ones.…”