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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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“For validation purposes, we compared our result with that of a recently published study [22].”
MethodResultWalsh, J. T., Junker, I. P., Chen, Y., Chen, Y., Gifford, H., Chen, D. S., & Ding, Y. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2516083122 · Cited by 5
“Ambient RNA in single - cell suspensions can introduce contamination, leading to false - positive signals that appear as lowly expressed genes (36), artificially inflating the breadth of gene expression.”
Schupp, P. G., Shelton, S. J., Brody, D. J., Eliscu, R., Johnson, B., Mazor, T., Kelley, K. W., Potts, M. B., McDermott, M. W., Huang, E. J., et al. (2024) · Cancers · doi:10.3390/cancers16132429 · Cited by 5
“However, single-cell data are confounded by technical factors related to tissue dissociation, sampling bias, noise, contamination, and sparsity [26–30], which muddle the relationships between malignant cell genotypes and molecular phenotypes, particularly for cancers that lack consistent CNVs.”
Human CLOCK enhances neocortical function
Liu, Y., Fontenot, M. R., Kulkarni, A., Khandelwal, N., Park, S. H. E., Criswell, C. J., Harper, M. J., Xu, P., Gupta, N., Gibson, J. R., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01993-4 · Cited by 10
“Due to high levels of ambient RNA contamination 55 , one sample of each genotype was removed at P7.”
Self-citationWeber, L. M., Divecha, H. R., Tran, M. N., Kwon, S. H., Spangler, A., Montgomery, K. D., Tippani, M., Bharadwaj, R. A., Kleinman, J. E., Page, S. C., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.28.514241 · Cited by 5
Sex effects on gene expression across the human cerebral cortex at single cell resolutionPreprint
DeCasien, A. R., Auluck, P. K., Liu, S., Marenco, S., Cookson, M., & Raznahan, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.30.661781 · Cited by 5
“To minimize contamination in cell type identification and differential expression [35], we removed ambient RNA and called cells using CellBender [36] (Table S4).”
Weber, L. M., Divecha, H. R., Tran, M. N., Kwon, S. H., Spangler, A., Montgomery, K. D., Tippani, M., Bharadwaj, R. A., Kleinman, J. E., Page, S. C., et al. (2023) · eLife · doi:10.7554/elife.84628.1 · Cited by 3
Cheng, Z., Wei, S., Wang, Y., Wang, Y., Lu, Q. R., Wang, Y., & Yu, G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.12.589306 · Cited by 3
“For validation purposes, we compared our result with that of a recently published study ( 44 ).”
MethodResultA consensus single-cell transcriptomic atlas of dermal fibroblast heterogeneityPreprint
Ascensión, A. M., & Izeta, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.05.611379 · Cited by 4
Dobner, S., Kleißl, L., Tóth, F., Paxton, R. L., Yordanova, I., Brazdilova, K., Vanluyten, C., Aizenshtadt, A., Toth, H., Burton, J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.18.669055 · Cited by 4
Dissecting the evolving cellular landscape of a remyelinating microenvironment
Melchor, G. S., Baydyuk, M., Manavi, Z., Hu, J., & Huang, J. K. (2024) · doi:10.1101/2024.12.25.630253 · Cited by 3
Albanus, R. D., Tang, X., Taylor, H. J., Manickam, N., Erdos, M. R., Narisu, N., Han, Y., Orchard, P., Varshney, A., Liu, C., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.12.516291 · Cited by 2
The effect of background noise and its removal on the analysis of single-cell expression dataPreprint
Janssen, P., Kliesmete, Z., Vieth, B., Adiconis, X., Simmons, S., Marshall, J. L., Aubin, C., Heyn, H., Levin, J. Z., Enard, W., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.16.516780 · Cited by 3
An extra-circadian function for human CLOCK in the neocortexPreprint
Liu, Y., Fontenot, M. R., Kulkarni, A., Khandelwal, N., Park, S.-H. E., Douglas, C., Harper, M. J., Xu, P., Gupta, N., Gibson, J. R., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.13.531623 · Cited by 3
“Due to high levels of ambient RNA contamination (Caglayan et al., 2022), one sample of each genotype was removed at P07.”
BackgroundSelf-citationThe Tabulae Paralytica: Multimodal single-cell and spatial atlases of spinal cord injuryPreprint
Skinnider, M. A., Gautier, M., Teo, A. Y. Y., Kathe, C., Hutson, T. H., Laskaratos, A., de Coucy, A., Regazzi, N., Aureli, V., James, N. D., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.23.544348 · Cited by 3
“Droplet-based snRNA-seq experiments are known to be affected by ambient RNA contamination, whereby freely floating RNA molecules are encapsulated along with a cell or nucleus in a single droplet and spuriously attributed to the endogenous expression profile of the encapsulated cell 179 .”
BackgroundKwon, S. H., Guo, B., Fang, C., Tippani, M., Bach, S. V., Pertea, G. M., Miller, R. A., Maguire, S. E., Iatrou, A., Eagles, N. J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.16.706214 · Cited by 5
Stirparo, G. G., Xu, X., Toni, T., Page, K. J., Harvey, J. R. M., Cadwalladr, D., Lawrence, J. M. K., Burley, R., Juvvanapudi, J., Megan, R., et al. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1636787 · Cited by 2
“This may be due to the presence of ambient RNA (Caglayan et al., 2022; Gautier et al., 2023) originating from the high number of granule neurons, which comprise ∼ 70%–90% of total cells.”
Stalder, L., Köchl, F., Hahn, K., Sultan, M., & Prasad, M. K. (2023) · Journal of Visualized Experiments · doi:10.3791/65611 · Cited by 2
Sleep need driven oscillation of glutamate synaptic phenotype
Vogt, K. E., Kulkarni, A., Pandey, R., Dehnad, M., Konopka, G., & Greene, R. W. (2024) · eLife · doi:10.7554/elife.98280.4 · Cited by 10
Self-citationLi, T., Alvarez, M., Liu, C., Abuhanna, K. D., Sun, Y. F., Ernst, J., Plath, K., Balliu, B., Luo, C., & Zaitlen, N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.06.636969 · Cited by 2
Li, T., Alvarez, M., Liu, C., Abuhanna, K. D., Sun, Y., Ernst, J., Plath, K., Balliu, B., Luo, C., & Zaitlen, N. (2025) · eLife · doi:10.7554/elife.106769.1 · Cited by 2
Cancer-myeloid cell invasive program in pediatric-type diffuse high-grade gliomaPreprint
Ruiz-Moreno, C., Collot, R. V. U., van den Broek, T. J. M., Wehrens, E. J., Bessler, N., Dharmadhikari, G., Pas, B. M. T., Ibarra, I. L., Metselaar, D. S., Kranendonk, M. E. G., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.23.701142 · Cited by 3
Travaglini, K. J., Gabitto, M. I., Ding, Y., Agrawal, A., Postupna, N. O., Mahoney, J. T., Kaplan, E. S., Melief, E. J., Goldy, J., Chakka, A. B., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.01.734821 · Cited by 7
A Single-Cell Omics Technical Guide for Advancing Neuropsychiatric Research
Casmey, K., Zimmermann, M., Xie, Y., Codeluppi-Arrowsmith, S. A., & Turecki, G. (2025) · Genes · doi:10.3390/genes16121394 · Cited by 3
“Additionally, RNA of dissociated cells in nuclei suspensions (i.e., ambient RNA) can interfere with single-cell assessment and cause masking or misinterpretation of cell identity [98].”
Cell-type-resolved transcriptomic landscape of human focal cortical dysplasia
Fang, C., Liu, Y., Zhang, X., Wang, Z., Guo, R., Guo, J., Liu, X., Tan, Y., Lu, H., Zhao, R., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2513674123 · Cited by 2
Self-citationMicroglial state transitions are constrained by developmental and metabolic checkpoints
Mesaoudi, A. E., & Kim, D. W. (2026) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2026.1852176 · Cited by 1
“Current approaches rely largely on static transcriptional profiling (Thrupp et al., 2020; Caglayan et al., 2022; Marsh et al., 2022), which cannot distinguish a cell that has not received an activating signal from one that has received it but lacks the capacity to act.”
Mätlik, K., Pressl, C., & Heintz, N. (2025) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-112723-025516 · Cited by 1
The gene expression signature of electrical stimulation in the human brainPreprint
Chatterjee, S., Elsadany, M., Vanrobaeys, Y. S., Gleason, A. I., Park, B. J., Heiney, S. A., Rhone, A. E., Nourski, K. V., Basu, B., Mukherjee, U., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.21.558812 · Cited by 1
Weber, L. M., Divecha, H. R., Tran, M. N., Kwon, S. H., Spangler, A., Montgomery, K. D., Tippani, M., Bharadwaj, R. A., Kleinman, J. E., Page, S. C., et al. (2023) · eLife · doi:10.7554/elife.84628.2 · Cited by 1
Cell-type-specific effects of autism-associated chromosome 15q11.2-13.1 duplications in human brainPreprint
Dias, C., Mo, A., Cai, C., Sun, L., Cabral, K., Brownstein, C. A., Rockowitz, S., & Walsh, C. A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.22.595175 · Cited by 1
“%) was manually removed given signatures of ambient RNA contamination as previously reported 17 .”
MethodLucas, T. A., Novikova, G., Rao, S., Wang, Y., Laufer, B. I., Pandey, S., Webb, M. G., Jorstad, N. L., Friedman, B. A., Hanson, J. E., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.12.637903 · Cited by 1
Evolution of the cellular landscape in mammalian striatumPreprint
Büyükkahraman, G., Caglayan, E., Hoerpel, S. G., Zhang, Y., van Tussenbroek, I. A., Orozco, C. G., Oh, E., Hopkins, W. D., Sherwood, C. C., Roberts, T. F., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.20.649707 · Cited by 1
“To assess the health of the nuclei, we used intronic read ratios (Caglayan, Liu et al. 2022).”
Self-citationProtein-guided RNA barcoding links transcriptomes to synaptic architecturePreprint
Urke, A., Dolan, M.-J., Silverman, J., Kim, M. T., Pineda, J., Garcia, S., Luu, J., Buckley, A. R., Kumar, V., Zhao, B., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.26.705527 · Cited by 2
Schroeder, L., Gerber, S., & Ruffini, N. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.08.717130 · Cited by 1
Order Matters: Ambient RNA correction confounds doublet detection in single-cell RNA sequencing
Meier, M., Howitt, G., Grima, C. V., Dixit, G., King, H., Gubbels, L., Levi, E., Davies, K., Shanthikumar, S., Neeland, M. R., et al. (2026) · doi:10.64898/2026.10.13.758369
“Ambient RNA correction undoubtedly improves analysis outcomes in many settings [30,32,33,40].”
InfluentialCell-associated Transcriptional Alterations in the Retinal of Alzheimer’s DiseasePreprint
Ngolab, J., Mark, A., Buchanan, J., Korouri, S., Priessl, S., Rosenthal, S. B., Wang, A., Fisch, K. M., & Rissman, R. A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.18.502974
Weber, L. M., Divecha, H. R., Tran, M. N., Kwon, S. H., Spangler, A., Montgomery, K. D., Tippani, M., Bharadwaj, R. A., Kleinman, J. E., Page, S. C., et al. (2023) · eLife · doi:10.7554/elife.84628.1.sa0
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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