Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation
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Embryonic stem cell (ESC) culture conditions are important for maintaining long-term self-renewal, and they influence cellular pluripotency state. Here, we report single cell RNA-sequencing of mESCs cultured in three different conditions: serum, 2i, and the alternative ground state a2i. We find that the cellular transcriptomes of cells grown in these conditions are distinct, with 2i being the most similar to blastocyst cells and including a subpopulation resembling the two-cell embryo state. Overall levels of intercellular gene expression heterogeneity are comparable across the three conditions. However, this masks variable expression of pluripotency genes in serum cells and homogeneous expression in 2i and a2i cells. Additionally, genes related to the cell cycle are more variably expressed in the 2i and a2i conditions. Mining of our dataset for correlations in gene expression allowed us to identify additional components of the pluripotency network, including Ptma and Zfp640, illustrating its value as a resource for future discovery.
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The computed measure of gene expression noise is independent of RNA abundance across individuals.
“We computed a measure of gene expression noise that is independent of RNA abundance across individuals4647.”
From Batch effects and the effective design of single-cell gene expression studies (2017) · cites this paper as [46]
Specific functional groups of genes were more or less variable in each mESC culture condition.
“Although global levels of gene expression variability were equivalent between environments, specific functional groups of genes were more or less variable in each condition.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Kołodziejczyk et al, 2015
In mESCs, global levels of gene expression variability were equivalent between serum+LIF and 2i environments.
“Although global levels of gene expression variability were equivalent between environments, specific functional groups of genes were more or less variable in each condition.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Kołodziejczyk et al, 2015
2i-treated cells showed greater variability in the expression of cell-cycle genes.
“Gene ontology terms such as “organ development” were more variably expressed in the serum condition, where differentiation is less repressed, while 2i‐treated cells showed greater variability in the expression of cell‐cycle genes.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Kołodziejczyk et al, 2015
Gene ontology terms such as 'organ development' were more variably expressed in the serum condition, where differentiation is less repressed.
“Gene ontology terms such as “organ development” were more variably expressed in the serum condition, where differentiation is less repressed, while 2i‐treated cells showed greater variability in the expression of cell‐cycle genes.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Kołodziejczyk et al, 2015
Whole-transcriptome comparisons revealed that different treatments produce distinct transcriptome profiles.
“Whole‐transcriptome comparisons additionally revealed that the different treatments produce distinct transcriptome profiles, suggesting no overlap between subpopulations of serum‐treated and 2i‐treated cells, as was previously thought to be the case.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Kołodziejczyk et al, 2015
Technical noise for genes with very low mean expression is high.
“We eliminated all genes with a mean expression lower than µ=0.1, as technical noise for genes with very low mean expression is known to be high (Kolodziejczyk et al., 2015) (supplementary Materials and Methods).”
From Aging induces aberrant state transition kinetics in murine muscle stem cells (2020) · cites this paper as Kolodziejczyk et al., 2015
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- Authors
- Kolodziejczyk, A. A., Kim, J. K., Tsang, J. C. H., Ilicic, T., Henriksson, J., Natarajan, K. N., Tuck, A. C., Gao, X., Bühler, M., Liu, P., Marioni, J. C., & Teichmann, S. A.
- Affiliations
- European Bioinformatics Institute (GB)
- Wellcome Sanger Institute (GB)
- Friedrich Miescher Institute (CH)
- Corresponding author
- Marioni, John C.
- Teichmann, Sarah A.
- ORCID
- Venue
- Cell Stem Cell
- Publisher
- Elsevier BV
- Published
- 2015-10
- Volume
- 17
- Issue
- 4
- Pages
- 471-485
- ISSN
- 1875-9777 (ISSN-L)
- 1934-5909
- PMC
- PMC4595712
- OpenAlex
- W2111882541
- Cited by
- 570
- References
- 74
- Citations by year
- 2026: 25 · 2025: 31 · 2024: 48 · 2023: 66 · 2022: 57 · 2021: 60 · 2020: 63 · 2019: 76 · 2018: 51 · 2017: 60 · 2016: 29 · 2015: 4
- FWCI
- 20.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Cell stem cell (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion, cc-by-nc-nd)
- Funders
- Wellcome Trust (103977/Z/14/Z, 103977)
- Cancer Research UK (22231)
- Biotechnology and Biological Sciences Research Council (1300642)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Pluripotent Stem Cells Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Keywords
- embryonic stem cells, pluripotency, transcriptome heterogeneity, pluripotency gene expression, cell cycle genes, prothymosin alpha, pluripotency network, self-renewal
- MeSH terms
- Mouse Embryonic Stem Cells, Animals, Cell Differentiation, Cells, Cultured, Repressor Proteins, RNA, Glycogen Synthase Kinase 3, MAP Kinase Kinase 1, Mice, Single-Cell Analysis, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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