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Neuronal ambient RNA contamination causes misinterpreted and masked cell types in brain single-nuclei datasets
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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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Additional file 3 of scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing
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Reviewer #1 (Public Review): Sleep need driven oscillation of glutamate synaptic phenotype
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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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