Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. 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, 2., 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 Nat Biotechnol, 32(10):1053–1058, August, 2014.
abstract   bibtex   
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.
@ARTICLE{Pollen2014-ow,
  title    = "Low-coverage single-cell {mRNA} sequencing reveals cellular
              heterogeneity and activated signaling pathways in developing
              cerebral cortex",
  author   = "Pollen, Alex A and Nowakowski, Tomasz J and Shuga, Joe and Wang,
              Xiaohui and Leyrat, Anne A and Lui, Jan H and Li, Nianzhen and
              Szpankowski, Lukasz and Fowler, Brian and Chen, Peilin and
              Ramalingam, Naveen and Sun, Gang and Thu, Myo and Norris, Michael
              and Lebofsky, Ronald and Toppani, Dominique and Kemp, 2nd,
              Darnell W and Wong, Michael and Clerkson, Barry and Jones,
              Brittnee N and Wu, Shiquan and Knutsson, Lawrence and Alvarado,
              Beatriz and Wang, Jing and Weaver, Lesley S and May, Andrew P and
              Jones, Robert C and Unger, Marc A and Kriegstein, Arnold R and
              West, Jay A A",
  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 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.",
  journal  = "Nat Biotechnol",
  volume   =  32,
  number   =  10,
  pages    = "1053--1058",
  month    =  aug,
  year     =  2014,
  language = "en"
}

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