The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Neuronal ambient RNA contamination causes misinterpreted and masked cell types in brain single-nuclei datasets
Open accessPublished version of a preprint
Abstract
Ambient RNA contamination in single-cell and single-nuclei RNA sequencing (snRNA-seq) is a significant problem, but its consequences are poorly understood. Here, we show that ambient RNAs in brain snRNA-seq datasets have a nuclear or non-nuclear origin with distinct gene set signatures. Both ambient RNA signatures are predominantly neuronal, and we find that some previously annotated neuronal cell types are distinguished by ambient RNA contamination. We detect pervasive neuronal ambient RNA contamination in all glial cell types unless glia and neurons are physically separated prior to sequencing. We demonstrate that this contamination can be removed in silico and show that previous single-nuclei RNA-seq-based annotations of immature oligodendrocytes are glial nuclei contaminated with ambient RNAs. After ambient RNA removal, we detect rare, committed oligodendrocyte progenitor cells not annotated in most previous adult human brain datasets. Together, these results provide an in-depth analysis of ambient RNA contamination in brain single-nuclei datasets.
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“For example, ambient RNA from lysed cells, a ubiquitous issue of scRNA- and snRNA-seq datasets, can lead to false positives in gene expression profiles, obscuring true cell type-specific markers[34, 35].”
From Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing (2026) · cites this paper as [34]
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“This contamination may distort transcriptomic profiles, leading to the underrepresentation of specific cell types or misclassification of cellular states[34, 37].”
From Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing (2026) · cites this paper as [34]
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Research works printed but not linked: 1
- Fleming, 2019, CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets, Preprint at bioRxiv
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Fleming, S. J., Chaffin, M. D., Arduini, A., Akkad, A.-D., Banks, E., Marioni, J. C., Philippakis, A. A., Ellinor, P. T., & Babadi, M. (2023) · Nature Methods · doi:10.1038/s41592-023-01943-7 · Cited by 867
Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics
Gulati, G. S., D'Silva, J. P., Liu, Y., Wang, L., & Newman, A. M. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00768-2 · Cited by 347
“Nuclei isolation for transcriptional profiling is susceptible to contamination with ambient RNA 22 and excludes key cytoplasmic transcripts, including those involved in microglial activation 23 , regulatory RNA stored in processing bodies 24 , and nuclear RNA during mitosis (when cells lack a…”
Self-citationFleming, S. J., Chaffin, M. D., Arduini, A., Akkad, A.-D., Banks, E., Marioni, J. C., Philippakis, A. A., Ellinor, P. T., & Babadi, M. (2019) · doi:10.1101/791699 · Cited by 164
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Astrocyte transcriptomic changes along the spatiotemporal progression of Alzheimer’s disease
Serrano-Pozo, A., Li, H., Li, Z., Muñoz-Castro, C., Jaisa-aad, M., Healey, M. A., Welikovitch, L. A., Jayakumar, R., Bryant, A. G., Noori, A., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01791-4 · Cited by 140
Metadata
- Authors
- Caglayan, E., Liu, Y., & Konopka, G.
- Affiliations
- The University of Texas Southwestern Medical Center (US)
- Corresponding author
- Caglayan, Emre
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2022-12
- Volume
- 110
- Issue
- 24
- Pages
- 4043-4056.e5
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC9789184
- OpenAlex
- W4305016438
- Cited by
- 123
- References
- 60
- Citations by year
- 2026: 35 · 2025: 32 · 2024: 28 · 2023: 25 · 2022: 3
- FWCI
- 7.58
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 385 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Neuron (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- James S. McDonnell Foundation
- American Heart Association (915654)
- University of Texas Southwestern Medical Center
- National Institute of Mental Health (MH103517, MH126481, R01MH103517, R01MH126481)
- National Human Genome Research Institute (R01HG011641, HG011641)
- National Institute of Neurological Disorders and Stroke (NS115821, UF1NS115821)
- 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 › Molecular Biology Techniques and Applications
- Keywords
- single-nucleus RNA sequencing, brain cell types, oligodendrocyte precursor cells, cell type annotation, human brain
- MeSH terms
- Adult, Cell Nucleus, Humans, Neurons, RNA, RNA, Small Nuclear, Sequence Analysis, RNA, Solitary Nucleus, Single-Cell Analysis
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