Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA–seq
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Cellular heterogeneity can emerge from the expression of only one parental allele. However, it has remained controversial whether, or to what degree, random monoallelic expression of autosomal genes (aRME) is mitotically inherited (clonal) or stochastic (dynamic) in somatic cells, particularly in vivo. Here we used allele-sensitive single-cell RNA-seq on clonal primary mouse fibroblasts and freshly isolated human CD8+ T cells to dissect clonal and dynamic monoallelic expression patterns. Dynamic aRME affected a considerable portion of the cells' transcriptomes, with levels dependent on the cells' transcriptional activity. Notably, clonal aRME was detected, but it was surprisingly scarce (<1% of genes) and mainly affected the most weakly expressed genes. Consequently, the overwhelming majority of aRME occurs transiently within individual cells, and patterns of aRME are thus primarily scattered throughout somatic cell populations rather than, as previously hypothesized, confined to patches of clonally related cells.
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Imperfections in capture, conversion, and amplification of RNA in individual cells lead to both false negatives and false positives for individual transcripts, which may confound understanding of biological variation.
“Imperfections in the capture, conversion, and amplification77,78 of the minute quantities of RNA in an individual cell into a cDNA library lead to challenges in regard to both false negatives (expressed but undetected transcripts) and false positives (transcripts with inflated expression levels) for individual transcripts, which may confound the understanding of biological variation.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [77]
False negatives can be erroneously interpreted as allele-specific expression or as evidence for a wild-type homozygote rather than a genetic variant.
“For example, false negatives can be erroneously interpreted as allele-specific expression38,75 or as evidence for a wild-type homozygote rather than a genetic variant (or mutation)77.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [77]
Low capture ratios coupled to massive amplification would lead to epihaplotypes, similarly to falsely allelic scRNA-seq, where a gene could be erroneously classified as monoallelically expressed because of dropouts, and to allelic dropouts in single-cell DNA-seq due to which heterozygous variants could be falsely classified as homozygous.
“Low capture ratios coupled to massive amplification would also lead to epihaplotypes, similarly to falsely allelic scRNA-seq, where a gene could be erroneously classified as monoallelically expressed because of dropouts38,75, and to allelic dropouts77 in single-cell DNA-seq due to which heterozygous variants could be falsely classified as homozygous.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [77]
Important strides have been made in single-cell DNA sequencing as well as in analysis of copy-number variations (CNVs) and point mutations from single-cell DNA data.
“While our focus has been on single-cell transcriptomics, important strides have also been made in single-cell DNA sequencing77,194 as well as in analysis of copy-number variations (CNVs)11 and point mutations15,195 from single-cell DNA data.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [77]
Experimental noise can strongly affect estimates of heterogeneity due to false positives, false negatives (including allelic dropouts), and data sparsity.
“Some of the challenges highlighted here, including experimental noise16,204, can strongly affect estimates of heterogeneity due to false positives, false negatives (including allelic dropouts77), and data sparsity; these will require new methods and adaptation of existing ones (e.g., in phylogenetic reconstruction205).”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [77]
Reinius et al. applied single-cell RNA-seq to clonal cell populations, showing that less than 1% of genes demonstrating aRME had conserved behaviour.
“Reinius et al (2016) applied single‐cell RNA‐seq to clonal cell populations, showing that less than 1% of genes demonstrating aRME had conserved behaviour; this is in contrast to previous bulk RNA‐seq work that observed aRME for over 7% of assayed genes (Gimelbrant et al, 2007).”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Reinius et al (2016)
The improved Smart-seq2 protocol has become widely used.
“and this improved Smart-seq2 protocol (Picelli et al., 2014b) has also become widely used (Gokce et al., 2016; Reinius et al., 2016; Tirosh et al., 2016).”
From Comparative Analysis of Single-Cell RNA Sequencing Methods (2017) · cites this paper as Reinius et al., 2016
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- Authors
- Reinius, B., Mold, J. E., Ramsköld, D., Deng, Q., Johnsson, P., Michaëlsson, J., Frisén, J., & Sandberg, R.
- Affiliations
- Karolinska Institutet (SE)
- Ludwig Cancer Research (SE)
- Karolinska University Hospital (SE)
- Corresponding author
- Sandberg, Rickard
- ORCID
- Venue
- Nature Genetics
- Published
- 2016-09-26
- Volume
- 48
- Issue
- 11
- Pages
- 1430-1435
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC5117254
- OpenAlex
- W2526187074
- Cited by
- 149
- References
- 47
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 11 · 2023: 9 · 2022: 15 · 2021: 22 · 2020: 17 · 2019: 19 · 2018: 16 · 2017: 15 · 2016: 1 · 2015: 1
- FWCI
- 13.72
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Syndromes and Imprinting
- 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 Chromatin Dynamics
- Keywords
- allele-specific expression, mitotic inheritance, cellular heterogeneity, mouse fibroblasts, CD8+ T cells, transcriptional activity
- MeSH terms
- Alleles, Animals, Cells, Cultured, Clone Cells, Female, Fibroblasts, Humans, Male, Mice, Inbred C57BL, Mice, Inbred Strains, Sequence Analysis, RNA, CD8-Positive T-Lymphocytes, Polymorphism, Single Nucleotide, Gene Expression Profiling, Mice
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