The DOI matches its Crossref record; 51 of the 52 references it cites were found in a registry or an index.
Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells
Open accessAbstract
Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.
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“Finally, while we focus here on modeling technical variation due to differences in cellular sequencing depth, we note that our approach can be easily extended to model alternative “nuisance” parameters, including cell cycle [31], mitochondrial percentage, or experimental batch, simply by adding additional covariates to the model.”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [31]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“For 709 genes, estimates of fold change in response to IFN-β stimulation in myeloid and CD4+ cells are consistent with estimates in monocyte derived dendritic cells28 and CD4+ T cells27, respectively (Supplementary Fig. 17) and correlated with qPCR results of sorted CD4+ T cells (Supplementary Fig. 18).”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [28]
Experimental — accuracy not yet measuredIn the citing paper
“We performed latent variable regression to mitigate heterogeneity resulting from cell-cycle state10,11 (Extended Data Fig. 1), preventing subsequent analysis from being dominated by mitotic phase-specific gene expression”
From Developmental diversification of cortical inhibitory interneurons (2018) · cites this paper as [10]
In the citing paper
“Other biological factors such as cell‐cycle stage can also lead to structure that can mask the signal of interest; computational strategies exist to identify and remove these effects (Buettner et al, 2015).”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Buettner et al, 2015
In the citing paper
“Other biological factors such as cell‐cycle stage can also lead to structure that can mask the signal of interest; computational strategies exist to identify and remove these effects (Buettner et al, 2015).”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Buettner et al, 2015
In the citing paper
“Previous work showed that in scRNA-seq analysis, cell cycle genes can overload the major principal components that underlie cell-to-cell variations, thereby masking other, more functionally relevant, differences between cells (Buettner et al., 2015).”
From Developmental Heterogeneity of Microglia and Brain Myeloid Cells Revealed by Deep Single-Cell RNA Sequencing (2019) · cites this paper as Buettner et al., 2015
In the citing paper
“We propose that coincident expression of counteracting regulatory network components manifests as dynamic instability34.”
From Single-cell analysis of mixed-lineage states leading to a binary cell fate choice (2016) · cites this paper as [34]
In the citing paper
“While removing cell cycle effects can improve the inference of developmental trajectories (Buettner et al, 2015; Vento‐Tormo et al, 2018), cell cycle signals can also be informative of the biology.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Buettner et al, 2015
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Citations
Cites
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Posttranscriptional Control of T Cell Effector Function by Aerobic Glycolysis
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Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells
Taniguchi, Y., Choi, P. J., Li, G.-W., Chen, H., Babu, M., Hearn, J., Emili, A., & Xie, X. S. (2010) · Science · doi:10.1126/science.1188308 · Cited by 1,982 · cited 3×
“We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells.”
BackgroundMethodCapturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis
Leek, J. T., & Storey, J. D. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030161 · Cited by 1,930
Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells
Yan, L., Yang, M., Guo, H., Yang, L., Wu, J., Li, R., Liu, P., Lian, Y., Zheng, X., Yan, J., et al. (2013) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2660 · Cited by 1,740
The IL-4 receptor: signaling mechanisms and biologic functions
Nelms, K., Keegan, A. D., Zamorano, J., Ryan, J. J., & Paul, W. E. (1999) · Annual Review of Immunology · doi:10.1146/annurev.immunol.17.1.701 · Cited by 1,545 · cited 1×
“This set was heavily enriched for genes that have important roles in T H 2 cell differentiation— Il4ra 35 .”
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Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Self-citationHafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479
“Finally, while we focus here on modeling technical variation due to differences in cellular sequencing depth, we note that our approach can be easily extended to model alternative “nuisance” parameters, including cell cycle [31], mitochondrial percentage, or experimental batch, simply by adding additional covariates t…”
BackgroundOn the Dependency of Cellular Protein Levels on mRNA Abundance
Liu, Y., Beyer, A., & Aebersold, R. (2016) · Cell · doi:10.1016/j.cell.2016.03.014 · Cited by 3,221
Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., Slichter, C. K., Miller, H. W., McElrath, M. J., Prlic, M., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0844-5 · Cited by 3,159
Metadata
- Authors
- Buettner, F., Natarajan, K. N., Casale, F. P., Proserpio, V., Scialdone, A., Theis, F. J., Teichmann, S. A., Marioni, J. C., & Stegle, O.
- Affiliations
- European Bioinformatics Institute (GB)
- Helmholtz Munich (DE)
- Corresponding author
- Marioni, John C
- Stegle, Oliver
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2015-01-19
- Volume
- 33
- Issue
- 2
- Pages
- 155-160
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W2010653277
- Cited by
- 1,136
- References
- 52
- Citations by year
- 2026: 27 · 2025: 54 · 2024: 74 · 2023: 79 · 2022: 93 · 2021: 119 · 2020: 133 · 2019: 119 · 2018: 130 · 2017: 144 · 2016: 112 · 2015: 50
- FWCI
- 63.14
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion)
- Site cant be reached (submittedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- cellular heterogeneity, single-cell RNA sequencing, cell subpopulations, latent variable models, confounding factors, cell cycle, gene expression heterogeneity, naive T cell differentiation, Th2 cells
- MeSH terms
- Mouse Embryonic Stem Cells, Animals, Cell Differentiation, Models, Theoretical, RNA, Sequence Analysis, RNA, Th2 Cells, Gene Expression Regulation, Developmental, Genetic Heterogeneity, Cell Lineage, Computational Biology, Mice, Single-Cell Analysis, Transcriptome
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