Abstract
Human microglia play a pivotal role in neurological diseases, but we still have an incomplete understanding of microglial heterogeneity, which limits the development of targeted therapies directly modulating their state or function. Here, we use single-cell RNA sequencing to profile 215,680 live human microglia from 74 donors across diverse neurological diseases and CNS regions. We observe a central divide between oxidative and heterocyclic metabolism and identify microglial subsets associated with antigen presentation, motility and proliferation. Specific subsets are enriched in susceptibility genes for neurodegenerative diseases or the disease-associated microglial signature. We validate subtypes in situ with an RNAscope–immunofluorescence pipeline and high-dimensional MERFISH. We also leverage our dataset as a classification resource, finding that induced pluripotent stem cell model systems capture substantial in vivo heterogeneity. Finally, we identify and validate compounds that recapitulate certain subtypes in vitro, including camptothecin, which downregulates the signature of disease-enriched subtypes and upregulates a signature previously associated with Alzheimer’s disease.
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Data availability
Raw scRNA-seq data (fastq files) generated from CD45+ cells isolated from autopsy samples were deposited to the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE204702. Bulk RNA-seq data from compound-treated HMC3 cells were deposited to the GEO under accession number GSE202556. Bulk proteomic data from compound-treated HMC3 cells were deposited to ProteomeXChange (http://www.proteomexchange.org/) under accession number PXD033844. Data repurposed for label transfer was retrieved from the GEO under accession numbers GSE133432, GSE178317 and GSE103224.
Code availability
Code used to perform preprocessing, clustering, cluster validation and label transfer of scRNA-seq data in the current study is available publicly at https://github.com/jtuddenham/single-cell-microglia-v2/. The CellProfiler pipeline used to analyze joint immunofluorescence–RNAscope data is available as Supplementary Information (Supplementary Table 5), and in the aforementioned GitHub repository. Code for visualization, analysis of bulk RNA-seq/proteomic data and downstream analysis of CellProfiler outputs is available from the corresponding author upon request.
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Acknowledgements
We thank the individuals and their families who donated the brain samples used in this project. The work was supported by the Chan-Zuckerberg Initiative’s Neurodegeneration Challenge Network grant CS-02018-191971. Some of the work also emerged from support from National Institutes of Health (NIH)/National Institute on Aging (NIA) grants R01 AG070438, U01 AG061356, RF1 AG057473 and R01AG048015. Research reported in this publication was supported by the National Institute of General Medical Sciences of the NIH under award number T32GM007367 and by the National Cancer Institute of the NIH under award number F30CA261090. The Parkinsonism brain bank at Columbia University is supported by the Parkinson’s Foundation. R.A.H. was supported by grant funding from the Huntington Disease Society of America and Hereditary Disease Foundation and was a Columbia University Irving Medical Center ADRC Research Education Component trainee (P30AG066462). We are grateful to the Banner Sun Health Research Institute Brain and Body Donation Program of Sun City, Arizona for the provision of human brain tissue. The Brain and Body Donation Program has been supported by the National Institute of Neurological Disorders and Stroke (U24NS072026 National Brain and Tissue Resource for Parkinson’s Disease and Related Disorders), the NIA (P30AG19610 and P30AG072980 Arizona Alzheimer’s Disease Core Center), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer’s Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05-901 and 1001 to the Arizona Parkinson’s Disease Consortium) and the Michael J. Fox Foundation for Parkinson’s Research. The Rocky Mountain Multiple Sclerosis Tissue Bank is supported by the National Multiple Sclerosis Society. The Genomics Shared Resource is supported by NIH/NCI P30CA013696. ROSMAP is supported by P30AG10161, P30AG72975, R01AG15819, R01AG17917, U01AG46152 and U01AG61356. ROSMAP resources can be requested at https://www.radc.rush.edu/. Research reported in this publication was partially performed in the Columbia Center for Translational Immunology Flow Cytometry Core, supported in part by the Office of the Director, NIH under award S10OD020056. All figures were created using BioRender.com and Figs. 2–8 and Supplementary Fig. 1 were generated in Inkscape (https://inkscape.org).
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Conceptualization, J.F.T. and P.L.D.; Methodology, J.F.T., M.T., Y.Z., P.A.S., V.M. and P.L.D.; Software, J.F.T. and M.T.; Validation, J.F.T., M.T., V.H. and P.L.D.; Formal analysis, J.F.T., V.M., H.K., C.W. and P.A.S.; Investigation, J.F.T., M.T., V.H., A.K., M.O. and P.L.D.; Resources, B.H., T.R., A.J.L., G.G., G.E.S., N.H., M.F., S.H., T.G.B., J.C., R.K.S., A.F.T., R.A.H., R.N.A., N.S., J.S. and D.A.B.; Data curation, J.F.T., M.O. and V.M.; Writing–original draft, J.F.T. and P.L.D.; Writing–review and editing, all authors; Visualization, J.F.T. and V.H.; Supervision, P.L.D.; Project administration, P.L.D.; Funding acquisition, P.L.D.
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R.N.A. is funded by the NIH, DoD, the Parkinson’s Foundation and the Michael. J. Fox Foundation. R.N.A. received consultation fees from Avrobio, Caraway, GSK, Merck, Ono Therapeutics and Genzyme/Sanofi. P.L.D. has served as a consultant for Biogen, Merck-Serono and PureTech. All other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Proportions of overarching cell types in our dataset.
(A) Different cell types are discriminable in UMAP space or by marker genes. Unsupervised Jaccard-Louvain clustering on a kNN neighbor graph delineates distinct cell types, including adaptive immune cells, monocytes, glial/neuronal cells, and erythrocytes. UMAP plots are binned in hexagons: each single hexagon represents a merged representation of all cells falling within the region. The central UMAP plot is colored by the majority cell type. Different cell types are easily distinguishable in 2-D UMAP plots. The other schex-UMAP plots show gene expression values of selected characteristic marker genes projected onto cells. The color gradient bar represents log-normalized gene expression values. Yellow represents the maximal expressed value, while purple represents the lowest expression values. Markers of distinct immune subpopulations are detected in our data: CD8 T-cells (CD8A), NK cells (GZMB), B cells (MS4A1). Similarly, different non-neuronal cells can be detected in our analysis: astrocytes (GFAP), neurons (SNAP25), and oligodendrocytes (OLIG2). Monocytes (LYZ) localize close to our microglial cells and were used for comparative expression of marker genes in Fig. 2b. Red blood cells (HBB) were also easily discriminable. (B) Microglia are the predominant cell type recovered across regions and diseases. Bar plots showing the relative representation of different cell types across different metadata parameters, with each bar summing to 100%. Overall, 95.7% of cells are microglial, 2.2% are adaptive immune, 1.5% are glial/neuronal, 0.4% are monocytic, and 0.3% are erythrocytes. The upper bar plot shows proportion of each overarching cell group across regions, while the lower plot shows the same across diseases. Mono monocytes, RBC red blood cells, LOAD late-onset Alzheimer’s disease, EOAD early onset Alzheimer’s disease, MCI mild cognitive impairment, CNTRL control, DLBD-PD diffuse Lewy body disease-Parkinson’s disease, PSP progressive supranuclear palsy, TLE temporal lobe epilepsy, MS multiple sclerosis, ALS amyotrophic lateral sclerosis, FTD frontotemporal dementia, HD Huntington’s disease, DNET dysembryoplastic neuroepithelial tumor, BA Brodmann area, AWS anterior watershed, OC occipital cortex, TNC temporal neocortex, H hippocampus, TH thalamus, SC spinal cord, SN substantia nigra, FN facial nucleus.
Extended Data Fig. 2 Quality control metrics across our data after downsampling to account for 10x chemistry differences.
(A-F). Violin plots showing the distribution of our cellular data with overlaid boxplots. The center of boxplots is the median, and the hinges of the box span the 25% to 75% percentiles. Whiskers represent 1.5 IQR from the nearest hinge. Outliers are not shown in this visualization, nor are minima or maxima. Further information about metadata traits and number of cells included in each violin plot may be found in Supplementary Table 1 under ‘QC_’ tabs. The distributions of unique molecular identifiers (UMIs) and genes detected on a per-cell level after downsampling are similar across donors (A), clusters (B), genders (C), 10x chemistry versions (D), regions, (E), and diagnoses (F). Notably, after downsampling, differences between 10x chemistry versions in these metrics are largely eliminated. (G) Validation of population stability by resampling and reclustering demonstrates that overlap of gene expression is largely observed for clusters with similarly related families, such as 2 and 4, or for intermediate subsets such as 5 and 3. To evaluate clustering stability, we randomly sampled ¾ of the cells from our dataset and ran our clustering pipeline with identical parameters. We recorded the frequency of ‘misclassification’, where cells were re-clustered into clusters different from the one that contained most cells with the same original classification. This process was repeated between pairs of cells, and repeated 50 times for each comparison. Cells were considered to be classified into the ‘correct’ class if they were assigned correctly in ¾ of classification runs. Otherwise, they were considered ‘misclassified’ into a different cluster. Classification frequency is visualized in a heatmap here. LOAD late-onset Alzheimer’s disease, EOAD early onset Alzheimer’s disease, MCI mild cognitive impairment, CNTRL control, DLBD-PD diffuse Lewy body disease-Parkinson’s disease, PSP progressive supranuclear palsy, TLE temporal lobe epilepsy, MS multiple sclerosis, ALS amyotrophic lateral sclerosis, FTD frontotemporal dementia, HD Huntington’s disease, DNET dysembryoplastic neuroepithelial tumor, BA Brodmann area, AWS anterior watershed, OC occipital cortex, TNC temporal neocortex, H hippocampus, TH thalamus, SC spinal cord, SN substantia nigra, FN facial nucleus.
Extended Data Fig. 3 Microglial proportions across individual donors and donor-region pairings.
(A) Proportions of microglial subtypes across single donors. Proportions of microglial subtypes are plotted by donor, with selected metadata annotated in a header bar above. Each bar represents a single donor and sums to 100%. Samples are clustered hierarchically based on proportions of each subtype. Donors have variability in the exact proportions of different subtypes but exhibit consistent amounts of the most common subtypes in our dataset, clusters 1 through 6. (B) Proportions of microglial subtypes across region-donor pairings. Samples are aggregated to donor-region pairings (for example, AD1-BA9) to give a proportion of different clusters for each region for each individual. Boxplots are computed for specific region-disease pairings showing the median (center), 25% (left hinge), and 75% (right hinge), for the proportion of cells across all samples for which that combination of disease and region was sampled. Whiskers represent 1.5 IQR from the nearest hinge, and outliers are not shown, nor are minima or maxima. Proportions are shown on the x-axis, and the scale varies depending on the cluster in question. [Number of independent samples per category: TNC_TLE (6), TNC_PSP (1), TH_MS (2), SN_PSP (1), SN_LOAD (3), SN_DLBD-PD (5), SN_CNTRL (1), SC_ALS/FTD (2), SC_ALS (9), OC_TLE (1), OC_Stroke_lesion (1), Lesion_MS (1), H_TLE (2), H_PSP (1), H_LOAD (14), H_HD (1), H_FTD (1), H_EOAD (2), H_CNTRL (1), FN_ALS (4), DNET_DNET (1), BA9_Stroke_lesion (1), BA9_PSP (1), BA9_MS (2), BA9_MCI (4), BA9_LOAD (35), BA9_HD (1), BA9_FTD (1), BA9_EOAD (2), BA9_DLBD-PD (5), BA9_CNTRL (1), BA9_ALS/FTD (2), BA9_ALS (8), BA4_CNTRL (1), BA4_ALS/FTD (2), BA4_ALS (9), BA20_LOAD (9), BA20_HD (1), BA20_EOAD (2), AWS_MS (2), AWS_MCI (3), AWS_LOAD (13)].
Extended Data Fig. 4 Further exploration of microglial phenotypes with pseudotime analysis and GO annotation validates our trajectory map and reveals subsets associated with motility, lipid trafficking, and proliferation.
(A) Cluster 5, an intermediate cluster, shows association with motility. On the left, the size of the circle represents the percentage of cells in a cluster that express the gene, with no circle plotted if less than 10% of cells in a cluster express the gene. The color of the circle represents the z-scored expression of the gene. Cluster 5 expresses a transcriptional signature partially overlapping with the core homeostatic or transitional clusters, 2 and 3, but expresses unique sets of genes associated with motility. GO annotation was performed with topGO and summarized with rrvgo. Parent terms are shown in white, overlaid over child terms. Terms associated with motility are enriched in cluster 5. (B) Cluster 12 is associated with oxidative phosphorylation and proliferation. (C) Cluster 11 interfaces with lipids and beta-amyloid. (D) GO annotation of clusters 8/10 parallels results of Reactome pathway analysis, highlighting common immunological activation but divergence in other aspects of phenotype. (E) Trajectories of state shift in pseudotime analysis parallel those seen in other analyses. Monocle3 was used to build a pseudotime trajectory across our dataset, setting the root point at the boundary of clusters 2 and 3. Shifts in pseudotime from this root point reinforces the directionality laid out in the constellation diagram, suggesting that a broad intermediate gradient between a series of terminal points exists, with pseudotime scores in 6-7, 4, and 10 showing most divergence from the root point. GO gene ontology.
Extended Data Fig. 5 Additional representative images from our joint RNAscope/IF and CellProfiler measures highlight morphological differences between expression-defined subtypes.
Representative images are shown for both panel 1 (A) and panel 2 (B) across different diseases. (C) Compactness is highest in the medium classes of CD74, GPX1, and SPP1-defined expression groups. Compactness (a measure of ramification, where high values indicate high ramification) is shown across CD74-, GPX1-, and SPP1-expressing IBA1+ microglial cells quantified using CellProfiler. For this and following panels, significance was calculated with two-sided, two-sample Welch’s t-tests. Multiple testing correction was performed with Holm-Bonferroni correction. For boxplots in these visualizations, the center is the median, and the hinges of the box span the 25% to 75% percentiles. Whiskers represent 1.5 IQR from the nearest hinge. Outliers are shown as circles, but minima and maxima are not explicitly depicted. Significance thresholds for p-values: >0.05 = ns, <0.05 = *, <0.01 = **, <0.005 = ***. (D) Compactness is higher in the CXCR4+ class. (E) Eccentricity is highest in the low classes for CD74 and GPX1. Eccentricity (a measure of shape, where 0 is a circle and 1 is a line), is shown across CD74- and GPX1- expressing Iba1+ microglia. (F) CD74 distance is highest in the CD74 medium group, but also in the CXCR4+ group. CD74 distance (calculated as the median of all puncta for a given cell from the cellular centroid) is shown across CD74-, and CXCR4-expressing Iba1+ microglia. Number of cells per expression class are as follows. CD74: low (3756), medium (3333), high (329), GPX1: low (1404), medium (1653), high (329), SPP1: low (3216), medium (388), high (125), CXCR4: positive (322), negative (7096). 16 tissue sections were stained with panel 1 (CD74/CXCR4) and eight were stained with panel 2 (GPX1/SPP1).
Extended Data Fig. 6 In situ merFISH validation of microglia subtypes.
(A) Projection of microglial cells into the established scRNAseq model. UMAP space showing predicted cluster subtypes within a projected UMAP space (established model shown in greyed-out background). Seven out of twelve microglial subtypes were identified across AD (blue) and non-AD (yellow) cortex tissue, with different observed proportions. Clusters 8/10 show depletion in AD cortex ( < 1%) compared to non-AD cortex (35.7%). (B) Expression signatures of predicted clusters in situ. Microglia predicted to belong to clusters 8/10 show a greater average expression and percent expression of CXCR4, SRGN, and CD74. Showing clusters with at least 5 predicted microglia.
Extended Data Fig. 7 Performance metrics across models trained for different datasets.
Each row contains a different performance metric, while each column represents a single dataset. Training and validation sets were identical, but mNN correction incorporates the query dataset, slightly modifying input data. Accuracy metrics are derived from analysis of the holdout validation set, consisting of approximately 50% of the original dataset not used for training either SVM or XGB models (104902 cells). The first row presents histograms of XGBoost classification confidence for cells in the validation set, highlighting cells below 70% confidence in yellow and below 50% in red (the latter cells are dropped). Most cells in the validation set are classified with high confidence. Row 2 contains a UMAP visualization of classification confidence, revealing higher confidence for cells at the UMAP periphery and lower confidence for intermediate cells. Row 3 shows confusion matrices for the validation set. Row 4 presents sensitivity and specificity per class, which are comparable across different datasets. Row 5 shows boxplots for XGB classification confidence across the 4 classes. Boxplots represent the median (center), 25% (lower hinge), and 75% (upper hinge) percentiles. Whiskers extend to 1.5 times the IQR from the nearest hinge, with more extreme values represented as circles. Minima and maxima are not explicitly depicted. Classification confidence varies substantially depending on the data, with the ROSMAP data being the only dataset where classification confidence for families 167 and 24 is generally comparable to that for 3 and 5. Row 6 contains histograms of XGBoost classification confidence for the query cells. Notably, the glioblastoma and xenograft data have similar classification confidence to the validation set, but the ROSMAP data, and to a lesser extent, the Dräger data, diverge noticeably. Finally, row 7 shows marker gene expression across assigned labels in the query datasets. The size of the circle represents the percentage of cells in each cluster expressing the gene (no circle plotted if less than 10% of cells in a cluster express the gene). The color of the circle represents z-scored expression of the gene. Despite systematic differences, label transfer aligns expression profiles effectively.
Extended Data Fig. 8 Screening of in silico predictions identifies successful hits and compounds that fail to drive predicted signatures.
(A) Schematic overview of workflow for compound treatment. To explore the correct dosage for downstream studies, we conducted dose titration to examine viability of cells after treatment with varying dosages of our drugs. After choosing optimal concentrations, we conducted initial screening with qPCR to select candidates for final validation, then conducted final validation with bulk RNA-seq and proteomics. (B)-(D) qPCR results for different cluster families. Results not shown in Fig. 8b-d are shown here. Some compounds had effects on specific marker genes, but these did not pass our criteria for further study. Bars represent mean fold change expression, and error bars represent SD. All replicates are biological. Number of replicates per experiment as follows - Dorsomorphin: 6hrs: CXCR4 - n = 6, SRGN – n = 7; 24hrs: both n = 6, BX-795: 6hrs: CXCR4 - n = 5, SRGN – n = 8; 24hrs: CXCR4 - n = 3, SRGN – n = 5, BMS-2455421: 6hrs: both - n = 4; 24hrs: CXCR4 - n = 3, SRGN – n = 4, BRD: 6hrs: both - n = 7; 24hrs: TYROPB - n = 6, GPX1 – n = 7, Budesonide: 6hrs: n = 3; 24hrs: n = 3, Naltrexone: 6hrs: n = 3; 24hrs: n = 3, Cytochalasin b: 6hrs: SRGAP2 - n = 6, MEF2A – n = 5; 24hrs: both n = 6.
Extended Data Fig. 9 Different compounds modulate different aspects of the cluster 1/6 signature at the transcriptomic level.
(A) Camptothecin downregulates the cluster 1/6 signature. Bulk RNA-seq was generated from HMC3 cells treated with our candidate drugs for 24 h. Data was analyzed with DESeq2, which fits a negative binomial model to the data then uses Wald significance tests with Benjamini-Hochberg correction, and fold change shrinkage was performed with ashr. To examine the genes associated with cluster families, we took the top 20 non-overlapping genes for each individual cluster in our overarching groupings that were present in the differentially expressed gene list for each compound, irrespective of directionality and plotted them in volcano plots. FDR threshold was set to 0.01 and fold change threshold was set at 1.5. (B) Narciclasine does not upregulate the cluster 1/6 signature. (C) Narciclasine upregulates GO processes also found in cluster 1/6. GO annotation was computed on differentially expressed genes that passed an FDR threshold of 0.01 and a fold change threshold of 1.5. Terms were grouped based on similar etiology and parent terms were overlaid. Notably, Narciclasine drives metabolic shifts such as in nitrogen-containing metabolism, heterocyclic metabolism, and nucleic acid metabolism, that are strongly enriched in clusters 1/6 (Fig. 3a). (D) Narciclasine and Torin-2 drive distinct modules of cluster 1/6 marker genes. Cluster 1/6 genes were selected and shown in a row-scaled, zero-centered heatmap. Columns are individual replicates, and rows are genes. These two compounds appear to drive separate modules of genes associated with cluster 1/6. Camptothecin downregulates almost all 1/6 associated genes.
Extended Data Fig. 10 Representative flow gating images.
Cells that were stained with anti-CD11b and anti-CD45 antibodies and 7AAD were sorted by flow cytometry. Flow gates demonstrate selection of live singlets that are CD45-positive.
Supplementary information
Supplementary Information
Supplementary Fig. 1
Supplementary Table 1
Overview of demographics, hashing strategy and QC.
Supplementary Table 2
Pairwise marker genes across clusters. Differential expression calculations are down with MAST, which fits a two-part generalized regression model using logistic and Gaussian components, and Bonferroni multiple-testing correction.
Supplementary Table 3
ROSMAP trait association results. Analysis was performed in DESeq2, which fits a negative binomial model with Wald tests and Benjamini–Hochberg correction.
Supplementary Table 4
Overview of samples used for in situ confirmation.
Supplementary Table 5
Training results of machine learning models. P values for evaluation calculated with one-sided exact binomial test without multiple-testing correction.
Supplementary Table 6
Results of in silico CMAP analysis by cluster. CMAP uses a two-sided, nonparametric similarity measure based on the weighted Kolmogorov–Smirnov enrichment statistic that includes multiple-testing correction to calculate significance. Tau scores show the percentage of reference perturbations that show stronger connectivity to the query.
Supplementary Table 7
In vitro validation results at the transcriptomic and proteomic levels. qPCR results were analyzed with two-way analysis of variance without multiple-testing correction. Bulk RNA-seq data were analyzed with DESeq2, fitting a negative binomial model with Wald tests and Benjamini–Hochberg correction. Bulk proteomic data were analyzed with pairwise differential testing between DMSO control and each treated condition used Welch’s t-test with Benjamini–Hochberg correction.
Supplementary Code 1
CellProfiler pipeline.
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Tuddenham, J.F., Taga, M., Haage, V. et al. A cross-disease resource of living human microglia identifies disease-enriched subsets and tool compounds recapitulating microglial states. Nat Neurosci 27, 2521–2537 (2024). https://doi.org/10.1038/s41593-024-01764-7
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DOI: https://doi.org/10.1038/s41593-024-01764-7
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