Defining cell types and states with single-cell genomics

By Cole Trapnell. 2015

761 citations recorded by the source corpus.

A revolution in cellular measurement technology is under way: For the first time, we have the ability to monitor global gene regulation in thousands of individual cells in a single experiment. Such experiments will allow us to discover new cell types and states and trace their developmental origins. They overcome fundamental limitations inherent in measurements of bulk cell population that have frustrated efforts to resolve cellular states. Single-cell genomics and proteomics enable not only precise characterization of cell state, but also provide a stunningly high-resolution view of transitions between states. These measurements may finally make explicit the metaphor that C.H. Waddington posed nearly 60 years ago to explain cellular plasticity: Cells are residents of a vast “landscape” of possible states, over which they travel during development and in disease. Single-cell technology helps not only locate cells on this landscape, but illuminates the molecular mechanisms that shape the landscape itself. However, single-cell genomics is a field in its infancy, with many experimental and computational advances needed to fully realize its full potential.

Subfields: Biomedical Engineering, Biophysics, Molecular Biology

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Citations in the corpus

Exponential scaling of single-cell RNA-seq in the past decade (2017, background): “classify and characterise cells at the molecular level (Kolodziejczyk et al., 2015; Trapnell, 2015).”
A Single-Cell Transcriptome Atlas of the Human Pancreas (2016, neutral): “With the advent of single-cell transcriptomics, it is possible to determine the transcriptome of individual cells, allowing the identification of cell types in an unbiasedmanner (Grün and vanOudenaarden, 2015; Kolodziejczyk et al., 2015; Trapnell, 2015; Wang and Navin, 2015).”
Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes and progenitors (2017, methodology): “To overcome some of these limitations, we used single-cell RNA sequencing (scRNA-seq) (4, 5) to better assess the diversity of blood DCs and monocytes, leading us to identify new subtypes of DCs and monocytes, refine their existing classification, and pinpoint a precursor of cDCs in the blood.”
CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data (2016, background): “For example, clusters can be used as covariates in differential expression analysis [6], or co-expression analysis can be conducted within each of the clusters separately [23].”
scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data (2019, background): “, via fluorescence-activated cell sorting (FACS), these markers may not reflect the overall heterogeneity at a transcriptomic and phenotypic level from mixed cell populations [13, 14].”
SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation (2016, background): “In analyses by scRNA-Seq, the reconstruction of cellular differentiation processes attracts attention as a novel approach to revealing differentiation mechanisms [8].”
Design and computational analysis of single-cell RNA-sequencing experiments (2016, background): “Bulk RNA-seq studies have enabled numerous insights, but averaging over thousands of cells obscures, and in some cases mis-represents, signals of interest [79].”
Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq. (2016, positive): “The ﬁrst is the complexity of biological circuits, which include redundancies and nonlinear crosstalk between pathways, and the second is cellular plasticity and heterogeneity within both in vivo and in vitro models (Junker and van Oudenaarden, 2014; Trapnell, 2015).”