The DOI matches its Crossref record; 167 of the 167 references it cites were found in a registry or an index.
Single-cell RNA-seq: advances and future challenges
Open accessAbstract
Phenotypically identical cells can dramatically vary with respect to behavior during their lifespan and this variation is reflected in their molecular composition such as the transcriptomic landscape. Single-cell transcriptomics using next-generation transcript sequencing (RNA-seq) is now emerging as a powerful tool to profile cell-to-cell variability on a genomic scale. Its application has already greatly impacted our conceptual understanding of diverse biological processes with broad implications for both basic and clinical research. Different single-cell RNA-seq protocols have been introduced and are reviewed here-each one with its own strengths and current limitations. We further provide an overview of the biological questions single-cell RNA-seq has been used to address, the major findings obtained from such studies, and current challenges and expected future developments in this booming field.
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“new methodologies, such as Hi-C and single-cell sequencing, have yet to be fully integrated at scale with brain genomics data (13–16).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [15]
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“This includes 43,181 proximal and 42,681 distal linkages involving 11,573 protein-encoding target genes (TF–to–target gene via promoter for proximal versus via enhancer–target gene connection for distal) (Fig. 5A) (15, 21).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [15]
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“Using it, we determined which modules were prioritized, as well as the sets of genes associated with latent nodes that were found at each hidden layer (Fig. 8A and table S10) (15, 21).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [15]
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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 [15]
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“This is particularly the case for specific brain cell substructures, such as axons and dendrites (15).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [15]
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Metadata
- Authors
- Saliba, A.-E., Westermann, A. J., Gorski, S. A., & Vogel, J.
- Affiliations
- University of Würzburg (DE)
- Corresponding author
- Vogel, Jörg
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-07-22
- Volume
- 42
- Issue
- 14
- Pages
- 8845-8860
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC4132710
- OpenAlex
- W2069295116
- Cited by
- 847
- References
- 167
- Citations by year
- 2026: 38 · 2025: 81 · 2024: 92 · 2023: 78 · 2022: 74 · 2021: 69 · 2020: 58 · 2019: 63 · 2018: 67 · 2017: 91 · 2016: 84 · 2015: 46 · 2014: 6
- FWCI
- 7.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
- Online Publication Service of Würzburg University (Würzburg University) (submittedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft
- Bundesministerium für Bildung und Forschung
- 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, cell-to-cell variability, transcriptional landscape
- MeSH terms
- Cell Separation, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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