Experimental Considerations for Single-Cell RNA Sequencing Approaches
Open accessAbstract
Single-cell transcriptomic technologies have emerged as powerful tools to explore cellular heterogeneity at the resolution of individual cells. Previous scientific knowledge in cell biology is largely limited to data generated by bulk profiling methods, which only provide averaged read-outs that generally mask cellular heterogeneity. This averaged approach is particularly problematic when the biological effect of interest is limited to only a subpopulation of cells such as stem/progenitor cells within a given tissue, or immune cell subsets infiltrating a tumor. Great advances in single-cell RNA sequencing (scRNAseq) enabled scientists to overcome this limitation and allow for in depth interrogation of previously unexplored rare cell types. Due to the high sensitivity of scRNAseq, adequate attention must be put into experimental setup and execution. Careful handling and processing of cells for scRNAseq is critical to preserve the native expression profile that will ensure meaningful analysis and conclusions. Here, we delineate the individual steps of a typical single-cell analysis workflow from tissue procurement, cell preparation, to platform selection and data analysis, and we discuss critical challenges in each of these steps, which will serve as a helpful guide to navigate the complex field of single-cell sequencing.
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Using Seurat integration, cells and nuclei from all six studies were spatially interposed in a UMAP visualization of principal component space and separated into groupings that each expressed a panel of well-established cell type markers.
“With this approach, the cells and nuclei from all six studies were spatially interposed in a UMAP visualization of principal component space (Fig. 1c) and separated into groupings that each expressed a panel of well-established cell type markers such as Snap25 (neurons), Mbp (oligodendrocytes), Aqp4 (astrocytes), and Ctss (microglia) (Fig. 1d, e).”
From A harmonized atlas of mouse spinal cord cell types and their spatial organization (2021) · cites this paper as [22]
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- Authors
- Nguyen, Q. H., Pervolarakis, N., Nee, K., & Kessenbrock, K.
- Affiliations
- University of California, Irvine (US)
- Corresponding author
- Kessenbrock, Kai
- Venue
- Frontiers in Cell and Developmental Biology
- Publisher
- Frontiers Media SA
- Published
- 2018-09-04
- Volume
- 6
- Pages
- 108-108
- ISSN
- 2296-634X (ISSN-L)
- PMC
- PMC6131190
- OpenAlex
- W2891826157
- Cited by
- 170
- References
- 57
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 18 · 2023: 17 · 2022: 32 · 2021: 31 · 2020: 37 · 2019: 12 · 2018: 2
- FWCI
- 2.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Frontiers in Cell and Developmental Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (4R00CA181490)
- 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, cellular heterogeneity, rare cell types, cell preparation, tissue procurement, stem/progenitor cells, immune cell subsets, platform selection, data analysis challenges
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