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

By Alex A. Pollen, T. Nowakowski, Joe Shuga, Xiaohui Wang, A. Leyrat, Jan H. Lui, Nianzhen Li, Lukasz Szpankowski, Brian Fowler, Peilin Chen and 20 more. Nature Biotechnology, 2014

899 citations recorded by the source corpus.

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 sequencing. Although cellular barcoding strategies allow parallel sequencing of single cells at ultra-low depths, the limitations of shallow sequencing have not been investigated directly. 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 the 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.

Subfields: Genetics, Molecular Biology, Neurology

View Paper: https://www.semanticscholar.org/paper/c4e56a84a3932245540fbb15e7b5aee47f4b7a9b

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Citations in the corpus

Adult Mouse Cortical Cell Taxonomy by Single Cell Transcriptomics (2016, positive): “Recently, robust and scalable transcriptomic single cell profiling has emerged as a powerful approach to characterization and classification of single cells including neurons 14–17 .”
Generalizable and Scalable Visualization of Single-Cell Data Using Neural Networks (2018, background): “…cells rather than bulk samples, has been a key tool in dissecting the intercellular variation in a wide range of domains, including cancer biology (Wang et al., 2014), immunology (Stubbington et al., 2017), and metagenomics (Yoon et al., 2011). scRNA-seq also enables the de novo identification of…”
Spatial reconstruction of single-cell gene expression (2015, methodology): “We pooled and sequenced libraries to an average depth of 530,000 reads per sample, where single-cell gene expression tends to saturate 7,28,29 .”
Modelling dropouts for feature selection in scRNASeq experiments (2016, methodology): “(C) Michaelis-Menten (M3Drop) consistently fits full-transcript datasets (Kolo, Shalek, Deng, Buettner, Pollen, Biase) despite having fewer parameters than the logistic regression, whereas DANB consistently fits UMI-tagged datasets (Tung, Klein, Zeisel).”
Design and Analysis of Single-Cell Sequencing Experiments. (2015, methodology): “This Fluid-igm C1 autoprep system is particularly suited to single-cell sequencing (Islam et al., 2014; Pollen et al., 2014).”
The Human Cell Atlas (2017, methodology): “The first single-cell genomic characterization method to become feasible at large-scale is trancriptome analysis by single cell RNA-Seq (Box 1) (Hashimshony et al., 2012; Jaitin et al., 2014; Picelli et al., 2013; Ramskold et al., 2012; Shalek et al., 2013).”
Human cerebral organoids recapitulate gene expression programs of fetal neocortex development (2015, background): “The immediate early genes FOS and EGR1 are Notch signaling targets recently reported to be expressed in human but not mouse radial glia (24).”
Molecular Identity of Human Outer Radial Glia During Cortical Development (2015, positive): “We anticipated that cell cycle would be the major source of transcriptional variation across proliferative populations (Pollen et al., 2014).”
A general and flexible method for signal extraction from single-cell RNA-seq data (2017, positive): “Dimensionality reduction is used in the literature as a preliminary step prior to clustering [3, 13, 14], the inference of developmental trajectories [15–18], spatio-temporal ordering of the cells [5, 19], and, of course, as a visualization tool [20, 21].”
The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016, background): “…transcriptional signatures of different transient zones and the cell types within them, revealing both conserved patterns and species differences (Figures 4D and 4E) (Fietz et al., 2012; Ip et al., 2010; Johnson et al., 2009; Miller et al., 2014; Pletikos et al., 2014; Pollen et al., 2014).”
Design and computational analysis of single-cell RNA-sequencing experiments (2016, background): “Pollen et al. [14] have shown, for example, that 50,000 reads is sufficient for cell-type classification in a sample of 301 cells containing diverse cell types.”