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 accessAbstract
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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“In addition, current single-cell techniques suffer from low sensitivity and dropouts; thus, it remains challenging to reliably quantify low-abundance transcripts (15, 53).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [53]
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Cites
RNA-Seq: a revolutionary tool for transcriptomics
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Self-citationMicroRNAs: small RNAs with a big role in gene regulation
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Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
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Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
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Self-citationA practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
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Metadata
- Authors
- Liu, S., & Trapnell, C.
- Affiliations
- University of Washington (US)
- Corresponding author
- Trapnell, Cole
- Venue
- F1000Research
- Publisher
- F1000 Research Ltd
- Published
- 2016-02-17
- Volume
- 5
- Pages
- 182
- ISSN
- 2046-1402 (ISSN-L)
- 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
- gold
- doi.org
- 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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