Single-cell sequencing-based technologies will revolutionize whole-organism science

By Ehud Y. Shapiro, Tamir Biezuner, S. Linnarsson. Nature reviews genetics, 2013

1,141 citations recorded by the source corpus.

Subfields: Biophysics, Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/16101e04e5d8f403b899a7f1ac3ec62ee7f18952

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Citations in the corpus

Clustering single-cell RNA-seq data with a model-based deep learning approach (2019, background): “ingle-cell RNA sequencing (scRNA-seq) can reveal heterogeneity and diversity among cell populations and has helped researchers to better understand complex biological questions 1,2 .”
The Human Cell Atlas (2017, neutral): “, 2007; Richmond and Su, 2008), or can be inferred from in vivo samples by measuring the DNA differences between individual sampled cells, arising from random mutations during cell division, and using the genetic distances to construct cellular phylogenies, or lineages (Behjati et al., 2014; Shapiro”
The origin and evolution of cell types (2016, background): “Before diversification, two new modules had arisen (3, 4) that became synapomeres after the split.”
Single-cell RNA-seq: advances and future challenges (2014, neutral): “The analysis of single cells by global approaches has the potential to change our understanding of whole organisms since cell lineages can be traced and heterogeneity inside an organ be described with unprecedented resolution (1).”
Exponential scaling of single-cell RNA-seq in the past decade (2017, methodology): “(Islam et al., 2011) to develop methods explicitly targeted at assaying many cells in parallel using unbiased RNA sequencing, with the long term goal of eventually cataloging all neuronal cell types (Shapiro et al., 2013; Zeisel et al., 2015). In the past few years, many sensitive and accurate singl”
DrImpute: imputing dropout events in single cell RNA sequencing data (2017, neutral): “Bulk RNA 63 sequencing (bulk RNA-seq) technology performs high throughput sequencing 64 using bulk populations of millions of input cells, which implies that the resulting 65 expression value for each gene is the average expression value of a large 66 population of input cells ([1], [2]).”
Gene co-expression analysis for functional classification and gene–disease predictions (2017, neutral): “In large-scale singlecell RNA-seq experiments, mutations could be used to separate cells based on their origin, or to group cells based on the mutations they harbour [173].”
Technologies for Single-Cell Isolation (2015, background): “generation sequencing (NGS) of single cells [82], isolation of circulating tumor cells for diagnostic purposes [83], or single-cell proteomics [84].”