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Cited by 230Single-cell and spatial transcriptomicsGenomics and Phylogenetic StudiesGene expression and cancer classification

The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index.

Single-cell transcriptome sequencing: recent advances and remaining challenges

Open access
Liu, Serena, Trapnell, Cole·2016·F1000Research
DOI 10.12688/f1000research.7223.1PMC PMC4758375
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Abstract

Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling.

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Figure 1. Common applications of single-cell RNA sequencing. ( a) Deconvolving heterogeneous cell populations. Clustering by single-cell transcriptomic profiles can reveal population substructure and enable the identification of cell subtypes and rare cell species (e.g. red cells above). Clusters may be tight and well defined (purple, red) or diffuse (blue). ( b) Trajectory analysis of cell state transitions. Single-cell RNA sequencing time-series data can be used to map cell developmental trajectories over the course of dynamic processes such as differentiation or signaling responses to an external stimulus. Some computational suites (e.g. Monocle 6) can also accommodate branching trajectories, enabling identification of lineage-specific gene expression and key genes that drive branching events. ( c) Dissecting transcription mechanics. Genes’ expression profiles across many cells can be compared to study transcriptional bursting and to model the kinetics of stochastic gene expression. ( d) Network inference. Genes can be clustered by expression profile to identify modules of putatively co-regulated genes, and gene-gene covariation relationships can be used to infer gene regulatory networks or subnetworks.
Figure 1. Common applications of single-cell RNA sequencing. ( a) Deconvolving heterogeneous cell populations. Clustering by single-cell transcriptomic profiles can reveal population substructure and enable the identification of cell subtypes and rare cell species (e.g. red cells above). Clusters may be tight and well defined (purple, red) or diffuse (blue). ( b) Trajectory analysis of cell state transitions. Single-cell RNA sequencing time-series data can be used to map cell developmental trajectories over the course of dynamic processes such as differentiation or signaling responses to an external stimulus. Some computational suites (e.g. Monocle 6) can also accommodate branching trajectories, enabling identification of lineage-specific gene expression and key genes that drive branching events. ( c) Dissecting transcription mechanics. Genes’ expression profiles across many cells can be compared to study transcriptional bursting and to model the kinetics of stochastic gene expression. ( d) Network inference. Genes can be clustered by expression profile to identify modules of putatively co-regulated genes, and gene-gene covariation relationships can be used to infer gene regulatory networks or subnetworks.CC BY · PubMed Central

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Metadata

Authors
Liu, S., & Trapnell, C.
Affiliations
University of Washington (US)
Corresponding author
Trapnell, Cole
ORCID
Venue
F1000Research
Publisher
F1000 Research Ltd
Published
2016-02-17
Volume
5
Pages
182
ISSN
2046-1402 (ISSN-L)
DOI
10.12688/f1000research.7223.1
PMC
PMC4758375
OpenAlex
W2288153033
Cited by
230
References
110
Citations by year
2026: 14 · 2025: 12 · 2024: 14 · 2023: 22 · 2022: 13 · 2021: 21 · 2020: 35 · 2019: 27 · 2018: 32 · 2017: 34 · 2016: 6
FWCI
2.99
Type
journal article
Language
en
Open access
Licence
Locations
  • F1000Research (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Science Foundation (1258485)
  • Damon Runyon Cancer Research Foundation
  • National Institutes of Health (NIH DP2)
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (DP2HD088158)
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 RNA sequencing, single-cell transcriptomics, cellular heterogeneity, cell state dynamics, developmental trajectories, transcriptional dynamics, single-cell data analysis, single cell testing

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