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.
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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).
All analysis code required to replicate Seurat or LIGER objects used in analysis can be found at: https://github.com/samuel-marsh/Marsh_et-al_2022_scRNAseq_Dissociation_Artifacts
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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 (https://adknowledgeportal.synapse.org) (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 www.radc.rush.edu. 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.).
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.
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Supplementary Information Table of contents, Supplementary Figs. 1–16, Notes 1–9 and references.
Sample information (mouse experiments).
Sample information (human experiments).
Differential abundance (mouse microglia).
Gene module scoring gene lists.
Microglia meta cell individual comparisons summary.
DESeq2 sig. genes results DNC-NONE versus ENZ-NONE.
DESeq2 sig. genes results DNC-INHIB versus ENZ-NONE.
DESeq2 sig. genes results ENZ-INHIB versus ENZ-NONE.
DESeq2 sig. genes results DNC-NONE versus DNC-INHIB.
DESeq2 sig. genes results DNC-NONE versus ENZ-INHIB.
DESeq2 sig. genes results DNC-INHIB versus ENZ-INHIB.
Effect of inhibitors DESeq2 overlap.
Microglia homeostatic versus exAM MAST summary results.
Microglia homeostatic versus chemokine MAST summary results.
Microglia homeostatic versus Ifn-responsive MAST summary results.
Microglia homeostatic versus proliferative MAST summary results.
Microglia homeostatic versus Mac/Mono MAST summary results.
Differential abundance (all CNS; mouse).
All CNS cells DESeq2 sig. genes.
All CNS microglia versus exAM MAST summary results.
Comparison and overlap with other publications.
Postmortem LIGER factors related to mouse signature.
Fresh 0 h versus 6 h DEG microglia factor.
Fresh 0 h versus 6 h DEG astrocyte factor.
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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). https://doi.org/10.1038/s41593-022-01022-8