The Technology and Biology of Single-Cell RNA Sequencing
Open accessAbstract
The differences between individual cells can have profound functional consequences, in both unicellular and multicellular organisms. Recently developed single-cell mRNA-sequencing methods enable unbiased, high-throughput, and high-resolution transcriptomic analysis of individual cells. This provides an additional dimension to transcriptomic information relative to traditional methods that profile bulk populations of cells. Already, single-cell RNA-sequencing methods have revealed new biology in terms of the composition of tissues, the dynamics of transcription, and the regulatory relationships between genes. Rapid technological developments at the level of cell capture, phenotyping, molecular biology, and bioinformatics promise an exciting future with numerous biological and medical applications.
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tRFs have been implicated in neurological and neurodegenerative conditions including Alzheimer's disease, Parkinson's disease, psychological stress, and neuronal ferroptosis.
“tRFs have likewise been implicated in a growing number of neurological and neurodegenerative conditions 21,22 , including Alzheimer's disease 23,24 (AD), Parkinson's disease 25 (PD), psychological stress 26 , and neuronal ferroptosis 27 .”
From Human brain microRNAs exhibit cell-type specificity and age dependence (2026) · cites this paper as [21]
Emerging spatial transcriptomics platforms allow high-resolution, multiplexed mapping of RNA molecules within intact 3D cultures.
“Emerging spatial transcriptomics platforms, such as MERSCOPE (Vizgen), FISSEQ, ExSeq, Slide-seq, and 10x Genomics Visium, now allow for high-resolution, multiplexed mapping of RNA molecules within intact 3D cultures [199,200,201].”
From Modeling Tumor Microenvironment Complexity In Vitro: Spheroids as Physiologically Relevant Tumor Models and Strategies for Their Analysis (2025) · cites this paper as [200]
Technical variation in scRNA-seq is due to factors such as differences in cell integrity and lysis, RNA capture and cDNA conversion, and detection.
“The first is technical variation, which is due to factors such as differences in cell integrity and lysis, RNA capture and cDNA conversion, and detection38,39.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [39]
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References 86 · Cited by 1,487
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- Authors
- Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C., & Teichmann, S. A.
- Affiliations
- European Bioinformatics Institute (GB)
- Corresponding author
- Teichmann, Sarah A.
- ORCID
- Venue
- Molecular Cell
- Publisher
- Elsevier BV
- Published
- 2015-05
- Volume
- 58
- Issue
- 4
- Pages
- 610-620
- ISSN
- 1097-2765 (ISSN-L)
- OpenAlex
- W1580018887
- Cited by
- 1,487
- References
- 86
- Citations by year
- 2026: 115 · 2025: 214 · 2024: 194 · 2023: 157 · 2022: 145 · 2021: 133 · 2020: 152 · 2019: 112 · 2018: 99 · 2017: 96 · 2016: 58 · 2015: 9 · 2014: 1
- FWCI
- 8.46
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Molecular Cell (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Biotechnology and Biological Sciences Research Council (1300642)
- 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 capture, cell phenotyping, transcriptional dynamics, gene regulatory networks, tissue composition
- MeSH terms
- Animals, Humans, Models, Genetic, Genetic Variation, Alternative Splicing, Sequence Analysis, RNA, Cell Lineage, Gene Expression Profiling, Gene Regulatory Networks, Single-Cell Analysis
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