Microglia Heterogeneity in the Single-Cell Era
Open accessAbstract
Microglia are resident immune cells in the central nervous system (CNS) that are capable of carrying out prominent and various functions during development and adulthood under both homeostatic and disease conditions. Although microglia are traditionally thought to be heterogeneous populations, which potentially allows them to achieve a wide range of responses to environmental changes for the maintenance of CNS homeostasis, a lack of unbiased and high-throughput methods to assess microglia heterogeneity has prevented the study of spatially and temporally distributed microglia subsets. The recent emergence of novel single-cell techniques, such as cytometry by time-of-flight mass spectrometry (CyTOF) and single-cell RNA sequencing, enabled scientists to overcome such limitations and reveal the surprising context-dependent heterogeneity of microglia. In this review, we summarize the current knowledge about the spatial, temporal, and functional diversity of microglia during development, homeostasis, and disease in mice and humans.
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Another 4-9% of the sorted nuclei were classified as CNS-associated macrophages, while the remaining cells represented various other brain cell types.
“Interestingly, another 4-9% of the nuclei were classified as CNS-associated macrophages (CAMs; Figures 3A and S4B), a recently identified class of brain-resident macrophages with high expression of MS4A7 and MRC139, while the remaining cells represented various other brain cell types.”
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Clustering analysis of snRNA-seq reveals three distinctive transcriptional states of microglia that segregate from homeostatic microglial population and correspond to previously identified DAM, IFN-associated and neurodegenerative microglial states associated with ageing and neurodegenerative conditions.
“Clustering analysis revealed three distinctive transcriptional states of microglia displaying a unique transcriptional profile that segregated them from the main homeostatic microglial population (Fig. 4e and Extended Data Fig. 9b,c).”
From cGAS–STING drives ageing-related inflammation and neurodegeneration (2023) · cites this paper as [41]
The microglia population is highly heterogeneous, comprised of cells in discrete transcriptional dynamic substates that appear in defined contexts and brain regions.
“Indeed, the microglia population is highly heterogeneous, comprised of cells in discrete transcriptional dynamic “substates” that appear in defined contexts and brain regions [333,334,335,336].”
From Revisiting the Pathogenesis of X-Linked Adrenoleukodystrophy (2025) · cites this paper as [333]
Peripheral monocytes may infiltrate the brain and give rise to microglia-like cells, partially replacing resident microglia under pathological conditions.
“peripheral monocytes may infiltrate the brain and give rise to microglia-like cells, partially those that replace resident microglia under pathological conditions [37,38].”
From Clonal Hematopoiesis of Indeterminate Potential as an Emerging Interdisciplinary Risk Factor in Alzheimer’s Disease: Current Evidence and Future Directions (2026) · cites this paper as [38]
A similar percent area of CLEC7A-positive microglia was observed in both PLCG2 M28L and PLCG2 P522R mice.
“Moreover, we observed a similar percent area of C-type lectin domain containing 7A (CLEC7A)-positive microglia, a distinct amoeboid microglia subset 29 , in both PLCG2 M28L and PLCG2 P522R mice.”
From Genetic variants of phospholipase C-γ2 alter the phenotype and function of microglia and confer differential risk for Alzheimer’s disease (2023) · cites this paper as [29]
Single-cell RNA sequencing has revealed significant functional heterogeneity among microglial subtypes and their inflammatory profiles in the neurodegenerative setting.
“single-cell RNA sequencing, which enables the profiling of individual microglial cells with high-throughput datasets, has revealed significant functional heterogeneity among microglial subtypes and their inflammatory profiles in the neurodegenerative setting [109, 119].”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [109]
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A loss of mature microglial markers without immune activation in schizophrenia
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- Authors
- Masuda, T., Sankowski, R., Staszewski, O., & Prinz, M.
- Affiliations
- University of Freiburg (DE)
- Corresponding author
- Prinz, Marco
- Venue
- Cell Reports
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 30
- Issue
- 5
- Pages
- 1271-1281
- ISSN
- 2211-1247 (ISSN-L)
- OpenAlex
- W3004544639
- Semantic Scholar
- 23314e9c4f8ee430f423cc272a28e90eb1b1c573
- Cited by
- 784
- References
- 79
- Citations by year
- 2026: 115 · 2025: 103 · 2024: 161 · 2023: 101 · 2022: 139 · 2021: 112 · 2020: 51 · 2013: 1 · 2012: 1
- FWCI
- 10.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cell Reports (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Deutsche Forschungsgemeinschaft (390939984, EXC-2189, SFB 992, SFB1160)
- Japan Society for the Promotion of Science
- Bundesministerium für Bildung und Forschung
- Kanae Foundation for the Promotion of Medical Science
- Roman, Marga und Mareille Sobek Stiftung
- 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 › Pathology and Forensic Medicine › Multiple Sclerosis Research Studies
- Keywords
- single-cell RNA sequencing, mass cytometry, CNS homeostasis, microglial development
- MeSH terms
- Aging, Animals, Brain, Homeostasis, Humans, Macrophages, Microglia, Single-Cell Analysis
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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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