Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain


A key aspect of nearly all single-cell sequencing experiments is dissociation of intact tissues into single-cell suspensions. While many protocols have been optimized for optimal cell yield, they have often overlooked the effects that dissociation can have on ex vivo gene expression. Here, we demonstrate that use of enzymatic dissociation on brain tissue induces an aberrant ex vivo gene expression signature, most prominently in microglia, which is prevalent in published literature and can substantially confound downstream analyses. To address this issue, we present a rigorously validated protocol that preserves both in vivo transcriptional profiles and cell-type diversity and yield across tissue types and species. We also identify a similar signature in postmortem human brain single-nucleus RNA-sequencing datasets, and show that this signature is induced in freshly isolated human tissue by exposure to elevated temperatures ex vivo. Together, our results provide a methodological solution for preventing artifactual gene expression changes during fresh tissue digestion and a reference for future deeper analysis of the potential confounding states present in postmortem human samples.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Analysis of sorted microglia confirms profound effect of enzymatic digestion on microglial gene expression via scRNA-seq.
Fig. 2: Enzymatic dissociation induces cell-type-specific artifactual gene expression in mice.
Fig. 3: snRNA-seq of human postmortem tissue identifies enrichment of mouse dissociation gene signatures in human microglia and astrocytes.
Fig. 4: LIGER analysis independently identifies similar gene expression signatures in postmortem data that are enriched in microglia following altered sample processing.

Data availability

Raw sequencing data for all mouse samples were deposited in the NCBI GEO database under the SuperSeries GSE152184 which contains the following subseries: GSE152183 (Mouse microglia four dissociation protocols), GSE152182 (Mouse all CNS cells), GSE152210 (Mouse microglia PBS tail vein), GSE188441 (Mouse microglia 10X version comparison). Cell Ranger output files are available as supplementary files via GEO and raw fastq files can be accessed from SRA linked from GEO records. Raw sequencing data for postmortem human tissue were deposited in the NCBI GEO database under the SuperSeries GSE152184, in the subseries GSE157760. Cell Ranger output files are available as supplementary files via GEO and raw fastq files can be accessed from SRA linked from GEO records. Raw sequencing data for the acutely isolated human tissue were deposited in the European Phenome-Genome Archive (EGA) (accession ID: EGAD00001008541). Raw sequencing data for the mock ‘digestion’ of human PBMCs were deposited in the Database of Genotypes and Phenotypes (dbGaP) (accession ID: phs002222.v2.p1).

Code availability

All analysis code required to replicate Seurat or LIGER objects used in analysis can be found at:


  1. Svensson, V., Vento-Tormo, R. & Teichmann, S. A. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc. 13, 599–604 (2018).

    CAS  PubMed  Google Scholar 

  2. Svensson, V., da Veiga Beltrame, E. & Pachter, L. A curated database reveals trends in single-cell transcriptomics. Database (Oxford) 2020 (2020).

  3. Abuzakouk, M., Feighery, C. & O’Farrelly, C. Collagenase and Dispase enzymes disrupt lymphocyte surface molecules. J. Immunol. Methods 194, 211–216 (1996).

    CAS  PubMed  Google Scholar 

  4. Cardona, A. E., Huang, D., Sasse, M. E. & Ransohoff, R. M. Isolation of murine microglial cells for RNA analysis or flow cytometry. Nat. Protoc. 1, 1947–1951 (2006).

    CAS  PubMed  Google Scholar 

  5. Grange, C. et al. Phenotypic characterization and functional analysis of human tumor immune infiltration after mechanical and enzymatic disaggregation. J. Immunol. Methods 372, 119–126 (2011).

    CAS  PubMed  Google Scholar 

  6. Autengruber, A., Gereke, M., Hansen, G., Hennig, C. & Bruder, D. Impact of enzymatic tissue disintegration on the level of surface molecule expression and immune cell function. Eur. J. Microbiol. Immunol. (Bp) 2, 112–120 (2012).

  7. Botting, R. A. et al. Phenotypic and functional consequences of different isolation protocols on skin mononuclear phagocytes. J. Leukoc. Biol. 101, 1393–1403 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Schreurs, R. R. C. E. et al. Quantitative comparison of human intestinal mononuclear leukocyte isolation techniques for flow cytometric analyses. J. Immunol. Methods 445, 45–52 (2017).

    CAS  PubMed  Google Scholar 

  9. Reichard, A. & Asosingh, K. Best practices for preparing a single cell suspension from solid tissues for flow cytometry. Cytometry A 95, 219–226 (2019).

    CAS  PubMed  Google Scholar 

  10. Lake, B. B. et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586–1590 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Jaffe, A. E. Postmortem human brain genomics in neuropsychiatric disorders—how far can we go? Curr. Opin. Neurobiol. 36, 107–111 (2016).

    CAS  PubMed  Google Scholar 

  13. Gosselin, D. et al. An environment-dependent transcriptional network specifies human microglia identity. Science 356, (2017).

  14. Bohlen, C. J. et al. Diverse requirements for microglial survival, specification, and function revealed by defined-medium cultures. Neuron 94, 759–773.e8 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Hammond, T. R. et al. Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity 50, 253–271.e6 (2019).

    CAS  PubMed  Google Scholar 

  16. Hrvatin, S. et al. Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex. Nat. Neurosci. 21, 120–12 (2017).

    PubMed  PubMed Central  Google Scholar 

  17. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Mimitou, E. P. et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat. Biotechnol. 39, 1246–1258 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Crowell, H. L. et al. muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat. Commun. 11, 6077 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Squair, J. W. et al. Confronting false discoveries in single-cell differential expression. Nat. Commun. 12, 5692 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  22. Kiselev, V. Y., Andrews, T. S. & Hemberg, M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20, 273–282 (2019).

    CAS  PubMed  Google Scholar 

  23. Chari, T., Banerjee, J. & Pachter, L. The specious art of single-cell genomics. Preprint at bioRxiv, (2021).

  24. Gao, L. L., Bien, J. & Witten, D. Selective Inference for Hierarchical Clustering. Preprint at (2020).

  25. Lähnemann, D. et al. Eleven grand challenges in single-cell data science. Genome Biol. 21, 31 (2020).

    PubMed  PubMed Central  Google Scholar 

  26. Wallin, R. P. et al. Heat-shock proteins as activators of the innate immune system. Trends Immunol. 23, 130–135 (2002).

    CAS  PubMed  Google Scholar 

  27. Hammer, M. et al. Dual specificity phosphatase 1 (DUSP1) regulates a subset of LPS-induced genes and protects mice from lethal endotoxin shock. J. Exp. Med. 203, 15–20 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Doberentz, E. & Madea, B. in Regulation of Heat Shock Protein Responses: Heat Shock Proteins (eds Asea, A. A. A. & Kaur, P.) Ch. 18 (Springer International Publishing, 2018).

  29. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Li, Q. et al. Developmental heterogeneity of microglia and brain myeloid cells revealed by deep single-cell RNA sequencing. Neuron 101, 207–223.e10 (2019).

    CAS  PubMed  Google Scholar 

  31. Lacar, B. et al. Nuclear RNA-seq of single neurons reveals molecular signatures of activation. Nat. Commun. 7, 11022 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Wu, Y. E., Pan, L., Zuo, Y., Li, X. & Hong, W. Detecting activated cell populations using single-cell RNA-seq. Neuron 96, 313–329.e6 (2017).

    CAS  PubMed  Google Scholar 

  33. Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030.e16 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Mattei, D. et al. Enzymatic dissociation induces transcriptional and proteotype bias in brain cell populations. Int. J. Mol. Sci. 21 (2020).

  35. Satoh, J. & Kim, S. U. HSP72 induction by heat stress in human neurons and glial cells in culture. Brain Res. 653, 243–250 (1994).

    CAS  PubMed  Google Scholar 

  36. Mathys, H. et al. Temporal tracking of microglia activation in neurodegeneration at single-cell resolution. Cell Rep. 21, 366–380 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014.e22 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Zywitza, V., Misios, A., Bunatyan, L., Willnow, T. E. & Rajewsky, N. Single-cell transcriptomics characterizes cell types in the subventricular zone and uncovers molecular defects impairing adult neurogenesis. Cell Rep. 25, 2457–2469.e8 (2018).

    CAS  PubMed  Google Scholar 

  39. Mizrak, D. et al. Single-cell analysis of regional differences in adult V-SVZ neural stem cell lineages. Cell Rep. 26, 394–406.e5 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Plemel, J. R. et al. Microglia response following acute demyelination is heterogeneous and limits infiltrating macrophage dispersion. Sci. Adv. 6, eaay6324 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017).

    CAS  PubMed  Google Scholar 

  42. Pasciuto, E. et al. Microglia require CD4 T cells to complete the fetal-to-adult transition. Cell 182, 625–640.e24 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Van Hove, H. et al. A single-cell atlas of mouse brain macrophages reveals unique transcriptional identities shaped by ontogeny and tissue environment. Nat. Neurosci. 22, 1021–1035 (2019).

    PubMed  Google Scholar 

  44. van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).

    PubMed  Google Scholar 

  45. Denisenko, E. et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol. 21, 130 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Crinier, A. et al. High-dimensional single-cell analysis identifies organ-specific signatures and conserved NK cell subsets in humans and mice. Immunity 49, 971–986.e5 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887.e17 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Gao, C. et al. Iterative single-cell multi-omic integration using online learning. Nat. Biotechnol. 39, 1000-1007 (2021).

  49. Morabito, S., Miyoshi, E., Michael, N. & Swarup, V. Integrative genomics approach identifies conserved transcriptomic networks in Alzheimer’s disease. Hum. Mol. Genet. 29, 2899-2919 (2020).

  50. Leng, K. et al. Molecular characterization of selectively vulnerable neurons in Alzheimer’s disease. Nat. Neurosci. 24, 276–287 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Zhou, Y. et al. Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease. Nat. Med. 26, 131–142 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Concannon, C. G., Gorman, A. M. & Samali, A. On the role of Hsp27 in regulating apoptosis. Apoptosis 8, 61–70 (2003).

    CAS  PubMed  Google Scholar 

  53. Wang, Z. et al. Dexamethasone-induced gene 2 (dig2) is a novel pro-survival stress gene induced rapidly by diverse apoptotic signals. J. Biol. Chem. 278, 27053–27058 (2003).

    CAS  PubMed  Google Scholar 

  54. Ryu, K. Y. et al. The mouse polyubiquitin gene UbC is essential for fetal liver development, cell-cycle progression and stress tolerance. EMBO J. 26, 2693–2706 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Bianchi, M., Crinelli, R., Arbore, V. & Magnani, M. Induction of ubiquitin C (UBC) gene transcription is mediated by HSF1: role of proteotoxic and oxidative stress. FEBS Open Bio 8, 1471–1485 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Regev, A. et al. The Human Cell Atlas. eLife 6 (2017).

  57. Stubbington, M. J. T., Rozenblatt-Rosen, O., Regev, A. & Teichmann, S. A. Single-cell transcriptomics to explore the immune system in health and disease. Science 358, 58–63 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Taylor, D. M. et al. The Pediatric Cell Atlas: defining the growth phase of human development at single-cell resolution. Dev. Cell 49, 10–29 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Schier, A. F. Single-cell biology: beyond the sum of its parts. Nat. Methods 17, 17–20 (2020).

    CAS  PubMed  Google Scholar 

  61. Zhu, C., Preissl, S. & Ren, B. Single-cell multimodal omics: the power of many. Nat. Methods 17, 11–14 (2020).

    CAS  PubMed  Google Scholar 

  62. de Haas, A. H., Boddeke, H. W., Brouwer, N. & Biber, K. Optimized isolation enables ex vivo analysis of microglia from various central nervous system regions. Glia 55, 1374–1384 (2007).

    PubMed  Google Scholar 

  63. Grabert, K. & McColl, B. W. Isolation and phenotyping of adult mouse microglial cells. Methods Mol. Biol. 1784, 77–86 (2018).

    CAS  PubMed  Google Scholar 

  64. Bordt, E. A. et al. Isolation of microglia from mouse or human tissue. STAR Protoc. 1, (2020).

  65. Adam, M., Potter, A. S. & Potter, S. S. Psychrophilic proteases dramatically reduce single-cell RNA-seq artifacts: a molecular atlas of kidney development. Development 144, 3625–3632 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Machado, L. et al. Tissue damage induces a conserved stress response that initiates quiescent muscle stem cell activation. Cell Stem Cell 28, 1125–1135.e7 (2021).

    CAS  PubMed  Google Scholar 

  67. Sankowski, R. et al. Mapping microglia states in the human brain through the integration of high-dimensional techniques. Nat. Neurosci. 22, 2098–2110 (2019).

    CAS  PubMed  Google Scholar 

  68. Pombo Antunes, A. R. et al. Single-cell profiling of myeloid cells in glioblastoma across species and disease stage reveals macrophage competition and specialization. Nat. Neurosci. 24, 595-610 (2021).

  69. Krasemann, S. et al. The TREM2-APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity 47, 566–581.e9 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Kang, S. S. et al. Microglial translational profiling reveals a convergent APOE pathway from aging, amyloid, and tau. J. Exp. Med. 215, 2235–2245 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Hardy, J. A. et al. The patients dying after long terminal phase have acidotic brains; implications for biochemical measurements on autopsy tissue. J. Neural Transm. 61, 253–264 (1985).

    CAS  PubMed  Google Scholar 

  72. Harrison, P. J. et al. The relative importance of premortem acidosis and postmortem interval for human brain gene expression studies: selective mRNA vulnerability and comparison with their encoded proteins. Neurosci. Lett. 200, 151–154 (1995).

    CAS  PubMed  Google Scholar 

  73. Li, J. Z. et al. Systematic changes in gene expression in postmortem human brains associated with tissue pH and terminal medical conditions. Hum. Mol. Genet. 13, 609–616 (2004).

    CAS  PubMed  Google Scholar 

  74. Tomita, H. et al. Effect of agonal and postmortem factors on gene expression profile: quality control in microarray analyses of postmortem human brain. Biol. Psychiatry 55, 346–352 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Monoranu, C. M. et al. pH measurement as quality control on human post mortem brain tissue: a study of the BrainNet Europe consortium. Neuropathol. Appl. Neurobiol. 35, 329–337 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Durrenberger, P. F. et al. Effects of antemortem and postmortem variables on human brain mRNA quality: a BrainNet Europe study. J. Neuropathol. Exp. Neurol. 69, 70–81 (2010).

    PubMed  Google Scholar 

  77. Miller, J. A. et al. Neuropathological and transcriptomic characteristics of the aged brain. eLife 6, e31126 (2017).

    PubMed  PubMed Central  Google Scholar 

  78. Yates, C. M., Butterworth, J., Tennant, M. C. & Gordon, A. Enzyme activities in relation to pH and lactate in postmortem brain in Alzheimer-type and other dementias. J. Neurochem. 55, 1624–1630 (1990).

    CAS  PubMed  Google Scholar 

  79. Webster, M. J. Tissue preparation and banking. Prog. Brain Res. 158, 3–14 (2006).

    CAS  PubMed  Google Scholar 

  80. de Paiva Lopes, K. et al. Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nat. Genet. 54, 4-17 (2022).

  81. Liddelow, S. A., Marsh, S. E. & Stevens, B. Microglia and astrocytes in disease: dynamic duo or partners in crime? Trends Immunol. 41, 820–835 (2020).

    CAS  PubMed  Google Scholar 

  82. Papalexi, E. & Satija, R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18, 35–45 (2018).

    CAS  PubMed  Google Scholar 

  83. Yao, Z. et al. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex. Nature 598, 103–110 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Marsh, S. E. scCustomize: an R package for custom visualization & analyses of single cell sequencing. (2021).

  87. Marques, S. et al. Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system. Science 352, 1326–1329 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Bennett, F. C. et al. A combination of ontogeny and CNS environment establishes microglial identity. Neuron 98, 1170–1183.e8 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Lun, A. T., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).

    PubMed  Google Scholar 

  90. Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).

    PubMed  PubMed Central  Google Scholar 

  91. Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Heng, T. S., Painter, M. W. & Immunological, G. P. C. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008).

    CAS  PubMed  Google Scholar 

  93. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573-3587.e29 (2021).

Download references


Thanks to the Harvard Medical School’s O2 High Performance Compute Cluster for use in data preprocessing. Thanks to Harvard Medical School’s System Biology Flow Cytometry Facility for use of the FACSAria sorter. The results of a reanalysis of data (originally published by Zhou et al.51) published here are in whole or in part based on data obtained from the AMP-AD Knowledge Portal ( (Zhou et al.51: study ID: snRNA-seqAD_TREM2; study syn21670836). Study data were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. Data collection was supported through funding by NIA grants no. P30AG10161 (ROS), no. R01AG15819 (ROSMAP; genomics and RNA-seq), no. R01AG17917 (MAP), no. R01AG30146, no. R01AG36042 (5hC methylation, ATACseq), no. RC2AG036547 (H3K9Ac), no. R01AG36836 (RNA-seq), no. R01AG48015 (monocyte RNA-seq), no. RF1AG57473 (snRNA-seq), no. U01AG32984 (genomic and whole-exome sequencing), no. U01AG46152 (ROSMAP AMP-AD, targeted proteomics), no. U01AG46161 (TMT proteomics), no. U01AG61356 (whole-genome sequencing, targeted proteomics, ROSMAP AMP-AD); the Illinois Department of Public Health (ROSMAP); and the Translational Genomics Research Institute (genomic). Additional phenotypic data can be requested at For reanalysis of Morabito et al.49, processed study data were downloaded from Synapse as provided by V. Swarup, Institute for Memory Impairments and Neurological Disorders, University of California, Irvine. Data collection was supported through funding via UCI Startup funds and the American Federation of Aging Research. This work was supported in part by: the Cure Alzheimer’s Fund (B.S.), the UK Multiple Sclerosis Society (MS 50) (R.J.M.F.), Adelson Medical Research Foundation (R.J.M.F.), Wellcome and MRC to the Wellcome-Medical Research Council Cambridge Stem Cell Institute (grant no. 203151/Z/16/Z) (R.J.M.F.), the NIHR (Research Professorship, Cambridge BRC, Senior Investigator Award), the Royal College of Surgeons of England (P.J.H.), a Wellcome Trust Ph.D. for Clinicians fellowship (A.M.H.Y.) and the Lundbeck Foundation International Postdoc Fellowships (L.D.-O.).

Author information

Authors and Affiliations



S.E.M., A.J.W., L.D.-O., T.R.H. and B.S. conceived the study. S.E.M., A.J.W., L.D.-O., T.R.H., A.M.H.Y., S.H., E.Z.M. and B.S. designed the experiments. S.E.M., A.J.W., L.D.-O., T.R.H., T.Y.S., C.D. and S.H. performed mouse cell isolation, sorting and cell capture. A.M.H.Y., H.B., P.J.H., D.J.G. and R.J.M.F. provided acutely resected human tissue. A.A., N.N. and C.V. performed human nuclei isolation, sorting and nuclei capture. L.E.L. and D.A.H. performed human PBMC ‘digestion’, library generation and sequencing. S.E.M., A.A. and N.N. generated scRNA-seq/snRNA-seq libraries. S.E.M. performed library next-generation sequencing. S.E.M., T.R.H., C.D., S.M. and A.C.W. performed smFISH experiments. S.E.M. and T.K. performed scRNA-seq/snRNA-seq analyses with assistance from A.J.W., T.R.H., V.K., L.E.L. and E.Z.M. S.E.M. and B.S. wrote the paper with input from all authors.

Corresponding author

Correspondence to Beth Stevens.

Ethics declarations

Competing interests

The authors declare no competing interests. Though we believe that none of these relationships are conflicts of interest, D.A.H. has received research funding from Bristol Myers Squibb, Sanofi and Genentech for work unrelated to this project. He has been a consultant over the past 10 years for Bristol Myers Squibb, Compass Therapeutics, EMD Serono, Genentech, Juno Therapeutics, Novartis Pharmaceuticals, Proclara Biosciences, Sage Therapeutics and Sanofi Genzyme.

Peer review

Peer review information

Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Information Table of contents, Supplementary Figs. 1–16, Notes 1–9 and references.

Reporting Summary

Supplementary Table 1

Sample information (mouse experiments).

Supplementary Table 2

Sample information (human experiments).

Supplementary Table 3

Differential abundance (mouse microglia).

Supplementary Table 4

Gene module scoring gene lists.

Supplementary Table 5

Microglia meta cell individual comparisons summary.

Supplementary Table 6

DESeq2 sig. genes results DNC-NONE versus ENZ-NONE.

Supplementary Table 7

DESeq2 sig. genes results DNC-INHIB versus ENZ-NONE.

Supplementary Table 8

DESeq2 sig. genes results ENZ-INHIB versus ENZ-NONE.

Supplementary Table 9

DESeq2 sig. genes results DNC-NONE versus DNC-INHIB.

Supplementary Table 10

DESeq2 sig. genes results DNC-NONE versus ENZ-INHIB.

Supplementary Table 11

DESeq2 sig. genes results DNC-INHIB versus ENZ-INHIB.

Supplementary Table 12

Effect of inhibitors DESeq2 overlap.

Supplementary Table 13

Microglia homeostatic versus exAM MAST summary results.

Supplementary Table 14

Microglia homeostatic versus chemokine MAST summary results.

Supplementary Table 15

Microglia homeostatic versus Ifn-responsive MAST summary results.

Supplementary Table 16

Microglia homeostatic versus proliferative MAST summary results.

Supplementary Table 17

Microglia homeostatic versus Mac/Mono MAST summary results.

Supplementary Table 18

Differential abundance (all CNS; mouse).

Supplementary Table 19

All CNS cells DESeq2 sig. genes.

Supplementary Table 20

All CNS microglia versus exAM MAST summary results.

Supplementary Table 21

Comparison and overlap with other publications.

Supplementary Table 22

Postmortem LIGER factors related to mouse signature.

Supplementary Table 23

Fresh 0 h versus 6 h DEG microglia factor.

Supplementary Table 24

Fresh 0 h versus 6 h DEG astrocyte factor.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Marsh, S.E., Walker, A.J., Kamath, T. et al. Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain. Nat Neurosci 25, 306–316 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing