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The Human Microglia Atlas (HuMicA) unravels changes in disease-associated microglia subsets across neurodegenerative conditions
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Abstract
ABSTRACT Dysregulated microglia activation, leading to neuroinflammation, is crucial in neurodegenerative disease development and progression. The initial M1/M2 dual activation classification for microglia is outdated. Even the ‘disease-associated microglia’ (DAM) phenotype, firstly described in mice, falls short in representing the diverse microglia phenotypes in pathology. In this study, we have constructed a transcriptomic atlas of human brain immune cells by integrating single-nucleus (sn)RNA-seq datasets from multiple neurodegenerative conditions. Sixteen datasets were included, comprising 295 samples from patients with Alzheimer’s disease, autism spectrum disorder, epilepsy, multiple sclerosis, Lewy body diseases, COVID-19, and healthy controls. The integrated Human Microglia Atlas ( HuMicA ) dataset included 60,557 nuclei and revealed 11 microglial subpopulations distributed across all pathological and healthy conditions. Among these, we identified four different homeostatic clusters as well as pathological phenotypes. These included two stages of early and late activation of the DAM phenotype and the disease-inflammatory macrophage (DIM) phenotype, which was recently described in mice, and is also present in human microglia, as indicated by our analysis. The high versatility of microglia is evident through changes in subset distribution across various pathologies, suggesting their contribution in shaping pathological phenotypes. Our analysis showed overall depletion of four substates of homeostatic microglia, and expansion of niche subpopulations within the DAM and DIM spectrum across distinct neurodegenerative pathologies. The HuMicA is invaluable in advancing the study of microglia biology in both healthy and disease settings.
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![Fig. 4 Differential distribution of microglia populations. a Differential cluster composition in each group in relation to “No Neuropathology” individuals using sccomp represented by the mean of the posterior distribution for the composition parameter, and the 2.5 and 97.5% quantiles. (FDR < 0.05 is highlighted in red). b Proportion of the clusters showing altered distribution (Cluster 0: Homeos1; Cluster 3: Ribo.DAM1; Cluster 6: Lipo.DAM; Cluster 7: MAC), individualized by sample, per group. The blue boxes represent the posterior predictive check and the red triangles outliers (*FDR < 0.05). In panels (a, b) the groups include 60 Alzheimer’s Disease (AD), 7 autism spectrum disorder (ASD), 15 COVID-19, 17 epilepsy, 23 Lewy Body Diseases (LBD), 10 multiple sclerosis (MS) and 109 control samples. c Maximum projection of an immunostaining of the pan-macrophage marker IBA1 in green combined with RNAscope in situ hybridization for P2RY12 (red) and GPNMB (magenta), counterstained with DAPI. Images correspond to human postmortem temporal cortex tissue sections from controls (n = 7), AD (n = 6), MS (n = 8) and Parkinson’s disease (PD) (n = 6). The yellow arrows indicate the blue autofluorescence of amyloid senile plaques in AD samples. The red and magenta arrows indicate P2RY12-high and GPNMB-high cells, respectively. Scale 15 µm. d Fraction of IBA1 + GPNMB-high cells, considered for a number of GPNMB dots equal or superior to 15. Each dot represents a donor sample. e Fraction of IBA1 + P2RY12-high cells, considered for a number of P2RY12 dots equal or superior to 3. Each dot represents a donor sample. The significance in panels (d, e) was calculated using the non-parametric Kruskal-Wallis test followed by the Dunn’s test [*adjusted p (Bonferroni) < 0.05]. f Comparison of the mean number of branches between P2RY12-high (n = 509) and P2RY12-low IBA1+ cells (n = 1275) and between GPNMB-high (n = 49) and GPNMB-low IBA1+ cells (n = 1735). Each dot represents a cell. The significance was calculated using the non-parametric Mann-Whitney test (*p < 0.05). For box plots, the hinges correspond to the first and third quartiles, the whiskers denote minimum and maximum values (excluding outliers) and the horizontal line to the median. All tests used were two-sided. Source data are provided as a Source Data file.](/paper-figures/fcacdb2d658c423d9872e1d7af2955ba/the-human-microglia-atlas-humica-unravels-changes-in-disease-associated-figure-4.jpg)
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“Many disease-associated microglial programs are spatially enriched around plaques, which suggests that microglial state transitions are not only disease-stage-dependent but also anatomically localized.”
From Lymphoid-like Suppressive Microglia in Alzheimer’s Disease: A New Neuroimmune Regulatory Axis? (2026) · cites this paper as [47]
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“Large human microglial atlases and state-dynamics studies are beginning to show that microglia in Alzheimer’s disease occupy multiple transcriptional trajectories rather than discrete and permanent categories [22,47].”
From Lymphoid-like Suppressive Microglia in Alzheimer’s Disease: A New Neuroimmune Regulatory Axis? (2026) · cites this paper as [47]
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“Instead, microglia in Alzheimer’s disease appear to acquire hybrid programs: they remain tissue-resident myeloid cells, but they can express genes normally associated with professional antigen presentation, immune communication, and inflammatory coordination.”
From Lymphoid-like Suppressive Microglia in Alzheimer’s Disease: A New Neuroimmune Regulatory Axis? (2026) · cites this paper as [47]
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“Single-cell and single-nucleus studies have repeatedly identified Alzheimer ’s-associated microglial states enriched for immune activation, antigen-presentation genes, lipid-processing pathways, and inflammatory mediators.”
From Lymphoid-like Suppressive Microglia in Alzheimer’s Disease: A New Neuroimmune Regulatory Axis? (2026) · cites this paper as [47]
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Metadata
- Authors
- Martins-Ferreira, R., Calafell-Segura, J., Leal, B., Rodríguez-Ubreva, J., Martínez-Saez, E., Mereu, E., Pinho E Costa, P., Laguna, A., & Ballestar, E.
- Affiliations
- Universidade do Porto (PT)
- Josep Carreras Leukaemia Research Institute (ES)
- Universitat Autònoma de Barcelona (ES)
- Corresponding author
- Ballestar, Esteban
- ORCID
- Venue
- Nature Communications
- Published
- 2025-01-16
- Volume
- 16
- Issue
- 1
- Pages
- 739-739
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC11739505
- OpenAlex
- W4406437527
- Cited by
- 123
- References
- 77
- Citations by year
- 2026: 79 · 2025: 41
- FWCI
- 43.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- Funders
- Centres de Recerca de Catalunya (501100011033, MCIN/AEI/10.13039/501100011033)
- European Commission (MCIN /AEI/ 10.13039/501100011033, 10.13039/501100011033, 13039/501100011033, 501100011033, /AEI/10.13039/501100011033)
- Generalitat de Catalunya (10.13039/501100011033, 501100011033, MCIN /AEI/10.13039/501100011033)
- Ministerio de Ciencia e Innovación (MCIN/AEI/10, .13039/501100011033, 10.13039, / AEI/10, MCIN/AEI/10.13039/501100011033,, 10.13039/501100011033/, PID2020117212RB-I00, 501100011033, RYC2021, MCIN /AEI /10.13039, /AEI/10.13039/ 501100011033)
- Fundació Institut de Recerca Hospital Universitari Vall d’Hebron
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas
- Fundação para a Ciência e a Tecnologia (UIDB/00215/2020 and UIDP/00215/2020, MCIN/AEI/10.13039/501100011033/, LA/P/006/2020, SFRH/BD/137900/2018, 10.13039/501100011033, UIDB/00215/2020, UIDP/00215/2020)
- Agència de Gestió d'Ajuts Universitaris i de Recerca (MCIN/AEI/10.13039/501100011033/, 10.13039/501100011033, 13039/501100011033)
- Instituto de Salud Carlos III (MCIN/ AEI /10.13039/501100011033, /10.13039/501100011033, PI21/01603, AEI/10.13039/501100011033)
- Agencia Estatal de Investigación (AEI//10.13039/501100011033/, 501100011033, 10.13039/501100011033, MCIN/AEI/10.13039/501 100 011 033, 13039, .13039/501100011033, 10.13039, AEI/10)
- Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto
- Institut de Recerca contra la Leucèmia Josep Carreras
- NextGenerationEU
- Topics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Clinical Neurology › Multiple Sclerosis Research Studies
- Keywords
- microglia, disease-associated microglia, microglial subtypes, neuroinflammation, neurodegenerative diseases, Alzheimer's disease, multiple sclerosis, autism spectrum disorder, epilepsy, Lewy body disease, COVID-19 pandemic, GPNMB, single-nucleus RNA sequencing, single-cell RNA sequencing
- MeSH terms
- Autism Spectrum Disorder, RNA-Seq, COVID-19, Aged, Alzheimer Disease, Animals, Atlases as Topic, Brain, Female, Humans, Macrophages, Male, Middle Aged, Multiple Sclerosis, Microglia, Neurodegenerative Diseases, Lewy Body Disease, Single-Cell Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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