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Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex
Open accessAbstract
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.
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“When studying single cells, which have limited sample complexity, quantification is often carried out with just one million reads but may be done reliably for highly expressed genes with as few as 50,000 reads [8]; even 20,000 reads have been used to differentiate cell types in splenic tissue [9].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [8]
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“Deeper sequencing for scRNA-seq will do little to improve quantification as the number of individual mRNA molecules in a cell is small (in the order of 100–300,000 transcripts) and only a fraction of them are successfully reverse-transcribed to cDNA [8, 176]”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [8]
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Spatial reconstruction of single-cell gene expression data
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“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 .”
BackgroundMethodMassively parallel digital transcriptional profiling of single cells
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A survey of best practices for RNA-seq data analysis
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Metadata
- Authors
- Pollen, A. A., Nowakowski, T. J., Shuga, J., Wang, X., Leyrat, A. A., Lui, J. H., Li, N., Szpankowski, L., Fowler, B., Chen, P., Ramalingam, N., Sun, G., Thu, M., Norris, M., Lebofsky, R., Toppani, D., Kemp, D. W., Wong, M., Clerkson, B., Jones, B. N., Wu, S., Knutsson, L., Alvarado, B., Wang, J., Weaver, L. S., May, A. P., Jones, R. C., Unger, M. A., Kriegstein, A. R., & West, J. A. A.
- Affiliations
- University of California, San Francisco (US)
- Standard BioTools (United States) (US)
- Corresponding author
- Pollen, Alex A
- Kriegstein, Arnold R
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2014-08-03
- Volume
- 32
- Issue
- 10
- Pages
- 1053-1058
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4191988
- OpenAlex
- W2051658465
- Cited by
- 920
- References
- 38
- Citations by year
- 2026: 33 · 2025: 51 · 2024: 54 · 2023: 62 · 2022: 78 · 2021: 110 · 2020: 110 · 2019: 105 · 2018: 93 · 2017: 91 · 2016: 74 · 2015: 52 · 2014: 5
- FWCI
- 30.36
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS072630, R01NS075998)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- single-cell transcriptomics, low-pass sequencing, cellular heterogeneity, cortical development, microfluidic cell capture, cell type classification, biomarker discovery, Notch signaling, radial glia, EGR1, FOS, human brain
- MeSH terms
- Animals, Cerebral Cortex, Equipment Design, Humans, RNA, Messenger, Signal Transduction, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Microfluidic Analytical Techniques, Mice
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