Skip to main content

Thank you for visiting nature.com. 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.

  • Perspective
  • Published:

A dynamic and multimodal framework to define microglial states

Nature Neuroscience volume 28, pages 1372–1380 (2025)Cite this article

Subjects

Abstract

The widespread use of single-cell RNA sequencing has generated numerous purportedly distinct and novel subsets of microglia. Here, we challenge this fragmented paradigm by proposing that microglia exist along a continuum rather than as discrete entities. We identify a methodological over-reliance on computational clustering algorithms as the fundamental issue, with arbitrary cluster numbers being interpreted as biological reality. Evidence suggests that the observed transcriptional diversity stems from a combination of microglial plasticity and technical noise, resulting in terminology describing largely overlapping cellular states. We introduce a continuous model of microglial states, where cell positioning along the continuum is determined by biological aging and cell-specific molecular contexts. The model accommodates the dynamic nature of microglia. We advocate for a parsimonious approach toward classification and terminology that acknowledges the continuous spectrum of microglial states, toward a robust framework for understanding these essential immune cells of the CNS.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Taxonomy of myeloid cell types in the CNS.
Fig. 2: Torus-like model of microglial activation.
Fig. 3: Multimodal approach to myeloid cell-state classification.

Similar content being viewed by others

References

  1. The Petilla Interneuron Nomenclature Group (PING). Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat. Rev. Neurosci. 9, 557–568 (2008).

    Google Scholar 

  2. Zeng, H. What is a cell type and how to define it? Cell 185, 2739–2755 (2022). This review discusses approaches to the definition of cell types.

    CAS  PubMed Central  Google Scholar 

  3. Deiters, O. & Schultze, M. J. S. Untersuchungen Über Gehirn Und Rückenmark Des Menschen Und Der Säugethiere (F. Veiweg, 1865).

  4. Golgi, C. Sulla sostanza connettiva del cervello (nevroglia). Rendiconti R. Inst. Lomb. Sci. E Lett. 3, 275–277 (1870).

    Google Scholar 

  5. Zeng, H. & Sanes, J. R. Neuronal cell-type classification: challenges, opportunities and the path forward. Nat. Rev. Neurosci. 18, 530–546 (2017).

    CAS  Google Scholar 

  6. BRAIN Initiative Cell Census Network (BICCN). A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 598, 86–102 (2021).

    PubMed Central  Google Scholar 

  7. Siletti, K. et al. Transcriptomic diversity of cell types across the adult human brain. Science 382, eadd7046 (2023).

    CAS  Google Scholar 

  8. Prinz, M., Masuda, T., Wheeler, M. A. & Quintana, F. J. Microglia and central nervous system-associated macrophages—from origin to disease modulation. Annu. Rev. Immunol. 39, 251–277 (2021).

    CAS  PubMed Central  Google Scholar 

  9. Amann, L., Masuda, T. & Prinz, M. Mechanisms of myeloid cell entry to the healthy and diseased central nervous system. Nat. Immunol. 24, 393–407 (2023).

    CAS  Google Scholar 

  10. Paolicelli, R. C. et al. Microglia states and nomenclature: a field at its crossroads. Neuron 110, 3458–3483 (2022). This is a consensus paper on transcriptional microglia states.

    CAS  PubMed Central  Google Scholar 

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

    CAS  Google Scholar 

  12. Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).

    CAS  PubMed Central  Google Scholar 

  13. Masuda, T. et al. Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 566, 388–392 (2019).

    CAS  Google Scholar 

  14. 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  Google Scholar 

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

    CAS  Google Scholar 

  16. Safaiyan, S. et al. White matter aging drives microglial diversity. Neuron 109, 1100–1117 (2021).

    CAS  Google Scholar 

  17. Tuddenham, J. F. 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).

    CAS  PubMed Central  Google Scholar 

  18. Chen, Y. & Colonna, M. Microglia in Alzheimer’s disease at single-cell level. Are there common patterns in humans and mice? J. Exp. Med. 218, e20202717 (2021).

    CAS  PubMed Central  Google Scholar 

  19. Masuda, T., Sankowski, R., Staszewski, O. & Prinz, M. Microglia heterogeneity in the single-cell era. Cell Rep. 30, 1271–1281 (2020).

    CAS  Google Scholar 

  20. Böttcher, C. et al. Human microglia regional heterogeneity and phenotypes determined by multiplexed single-cell mass cytometry. Nat. Neurosci. 22, 78–90 (2019).

    Google Scholar 

  21. Olah, M. et al. Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease. Nat. Commun. 11, 6129 (2020).

    CAS  PubMed Central  Google Scholar 

  22. Venteicher, A. S. et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science 355, eaai8478 (2017).

    PubMed Central  Google Scholar 

  23. Darmanis, S. et al. Single-cell RNA-seq analysis of infiltrating neoplastic cells at the migrating front of human glioblastoma. Cell Rep. 21, 1399–1410 (2017).

    CAS  PubMed Central  Google Scholar 

  24. Müller, S. et al. Single-cell profiling of human gliomas reveals macrophage ontogeny as a basis for regional differences in macrophage activation in the tumor microenvironment. Genome Biol. 18, 234 (2017).

    PubMed Central  Google Scholar 

  25. Jordão, M. J. C. et al. Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 363, eaat7554 (2019).

    Google Scholar 

  26. 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).

    Google Scholar 

  27. Sankowski, R. et al. Multiomic spatial landscape of innate immune cells at human central nervous system borders. Nat. Med. 30, 186–198 (2024).

    CAS  Google Scholar 

  28. Krasemann, S. et al. The TREM2-APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity 47, 566–581 (2017). This paper describes AD-associated microglia.

    CAS  PubMed Central  Google Scholar 

  29. 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 (2019). This study demonstrates the transcriptional similarity of developmental and aging-associated microglia.

    CAS  Google Scholar 

  30. Stadler, T., Pybus, O. G. & Stumpf, M. P. H. Phylodynamics for cell biologists. Science 371, eaah6266 (2021).

    CAS  Google Scholar 

  31. Tanay, A. & Sebé-Pedrós, A. Evolutionary cell type mapping with single-cell genomics. Trends Genet. 37, 919–932 (2021).

    CAS  Google Scholar 

  32. Arendt, D. et al. The origin and evolution of cell types. Nat. Rev. Genet. 17, 744–757 (2016).

    CAS  Google Scholar 

  33. Mrdjen, D. et al. High-dimensional single-cell mapping of central nervous system immune cells reveals distinct myeloid subsets in health, aging, and disease. Immunity 48, 380–395 (2018).

    CAS  Google Scholar 

  34. Schulz, C. et al. A lineage of myeloid cells independent of Myb and hematopoietic stem cells. Science 336, 86–90 (2012).

    CAS  Google Scholar 

  35. Kierdorf, K. et al. Microglia emerge from erythromyeloid precursors via Pu.1- and Irf8-dependent pathways. Nat. Neurosci. 16, 273–280 (2013).

    CAS  Google Scholar 

  36. Ginhoux, F. et al. Fate mapping analysis reveals that adult microglia derive from primitive macrophages. Science 330, 841–845 (2010).

    CAS  PubMed Central  Google Scholar 

  37. Prinz, M., Jung, S. & Priller, J. Microglia biology: one century of evolving concepts. Cell 179, 292–311 (2019).

    CAS  Google Scholar 

  38. Kierdorf, K., Masuda, T., Jordão, M. J. C. & Prinz, M. Macrophages at CNS interfaces: ontogeny and function in health and disease. Nat. Rev. Neurosci. 20, 547–562 (2019).

    CAS  Google Scholar 

  39. Masuda, T. et al. Specification of CNS macrophage subsets occurs postnatally in defined niches. Nature 604, 740–748 (2022).

    CAS  Google Scholar 

  40. Hickey, W. F. & Kimura, H. Perivascular microglial cells of the CNS are bone marrow-derived and present antigen in vivo. Science 239, 290–292 (1988).

    CAS  Google Scholar 

  41. Drieu, A. et al. Parenchymal border macrophages regulate the flow dynamics of the cerebrospinal fluid. Nature 611, 585–593 (2022).

    CAS  PubMed Central  Google Scholar 

  42. The Tabula Muris Consortium et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

    Google Scholar 

  43. The Tabula Muris Consortium et al. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590–595 (2020).

    Google Scholar 

  44. Yao, Z. et al. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain. Nature 624, 317–332 (2023).

    CAS  PubMed Central  Google Scholar 

  45. Chakarov, S. et al. Two distinct interstitial macrophage populations coexist across tissues in specific subtissular niches. Science 363, eaau0964 (2019).

    CAS  Google Scholar 

  46. Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 42, 293–304 (2024).

    CAS  Google Scholar 

  47. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    PubMed Central  Google Scholar 

  48. Sankowski, R., Monaco, G. & Prinz, M. Evaluating microglial phenotypes using single-cell technologies. Trends Neurosci. 45, 133–144 (2022).

    CAS  Google Scholar 

  49. Tay, T. L. et al. A new fate mapping system reveals context-dependent random or clonal expansion of microglia. Nat. Neurosci. 20, 793–803 (2017).

    CAS  Google Scholar 

  50. Réu, P. et al. The lifespan and turnover of microglia in the human brain. Cell Rep. 20, 779–784 (2017).

    PubMed Central  Google Scholar 

  51. Kracht, L. et al. Human fetal microglia acquire homeostatic immune-sensing properties early in development. Science 369, 530–537 (2020).

    CAS  Google Scholar 

  52. Yousef, H. et al. Aged blood impairs hippocampal neural precursor activity and activates microglia via brain endothelial cell VCAM1. Nat. Med. 25, 988–1000 (2019).

    CAS  PubMed Central  Google Scholar 

  53. Villeda, S. A. et al. Young blood reverses age-related impairments in cognitive function and synaptic plasticity in mice. Nat. Med. 20, 659–663 (2014).

    CAS  PubMed Central  Google Scholar 

  54. Hagemeyer, N. et al. Microglia contribute to normal myelinogenesis and to oligodendrocyte progenitor maintenance during adulthood. Acta Neuropathol. 134, 441–458 (2017).

    PubMed Central  Google Scholar 

  55. Lan, Y. et al. Fate mapping of Spp1 expression reveals age-dependent plasticity of disease-associated microglia-like cells after brain injury. Immunity 57, 349–363 (2024).

    CAS  Google Scholar 

  56. Barclay, K. M. et al. An inducible genetic tool to track and manipulate specific microglial states reveals their plasticity and roles in remyelination. Immunity 57, 1394–1412 (2024).

    CAS  PubMed Central  Google Scholar 

  57. De Vlaminck, K. et al. Differential plasticity and fate of brain-resident and recruited macrophages during the onset and resolution of neuroinflammation. Immunity 55, 2085–2102 (2022).

    Google Scholar 

  58. Stratoulias, V. et al. ARG1-expressing microglia show a distinct molecular signature and modulate postnatal development and function of the mouse brain. Nat. Neurosci. 26, 1008–1020 (2023).

    CAS  PubMed Central  Google Scholar 

  59. Escoubas, C. C. et al. Type-I-interferon-responsive microglia shape cortical development and behavior. Cell 187, 1936–1954 (2024).

    CAS  PubMed Central  Google Scholar 

  60. Datta, M. et al. Histone deacetylases 1 and 2 regulate microglia function during development, homeostasis, and neurodegeneration in a context-dependent manner. Immunity 48, 514–529 (2018).

    CAS  Google Scholar 

  61. Zierfuss, B. et al. Targeting foam cell formation in inflammatory brain diseases by the histone modifier MS‐275. Ann. Clin. Transl. Neurol. 7, 2161–2177 (2020).

    CAS  PubMed Central  Google Scholar 

  62. Hoeksema, M. A. et al. Targeting macrophage histone deacetylase 3 stabilizes atherosclerotic lesions. EMBO Mol. Med. 6, 1124–1132 (2014). This study shows the involvement of histone deacetylases in phagocyte polarization, suggesting a molecular point-of-no-return.

    CAS  PubMed Central  Google Scholar 

  63. Hayflick, L. & Moorhead, P. S. The serial cultivation of human diploid cell strains. Exp. Cell. Res. 25, 585–621 (1961). This study proposes an upper limit for the number of replication cycles for normal cells, known as the Hayflick limit.

    CAS  Google Scholar 

  64. Hu, Y. et al. Replicative senescence dictates the emergence of disease-associated microglia and contributes to Aβ pathology. Cell Rep. 35, 109228 (2021).

    CAS  PubMed Central  Google Scholar 

  65. Rachmian, N. et al. Identification of senescent, TREM2-expressing microglia in aging and Alzheimer’s disease model mouse brain. Nat. Neurosci. 27, 1116–1124 (2024).

    CAS  Google Scholar 

  66. Li, X. et al. Transcriptional and epigenetic decoding of the microglial aging process. Nat. Aging 3, 1288–1311 (2023).

    PubMed Central  Google Scholar 

  67. Wendeln, A. -C. et al. Innate immune memory in the brain shapes neurological disease hallmarks. Nature 556, 332–338 (2018).

    CAS  PubMed Central  Google Scholar 

  68. Kinker, G. S. et al. Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity. Nat. Genet. 52, 1208–1218 (2020). This study shows that tumor cells are present in recurrent states across different tumor entities.

    CAS  PubMed Central  Google Scholar 

  69. Neftel, C. et al. An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell 178, 835–849 (2019). This paper demonstrates the fluidity of glioblastoma transcriptional states.

    CAS  PubMed Central  Google Scholar 

  70. Sun, N. et al. Human microglial state dynamics in Alzheimer’s disease progression. Cell 186, 4386–4403 (2023).

    CAS  PubMed Central  Google Scholar 

  71. Mancuso, R. et al. Xenografted human microglia display diverse transcriptomic states in response to Alzheimer's disease-related amyloid-β pathology. Nat. Neurosci. 27, 886–900 (2024).

    CAS  PubMed Central  Google Scholar 

  72. Sala Frigerio, C. et al. The major risk factors for Alzheimer’s disease: age, sex, and genes modulate the microglia response to Aβ plaques. Cell Rep. 27, 1293–1306 (2019).

    CAS  Google Scholar 

  73. Shukla, A. K. et al. CD11a expression distinguishes infiltrating myeloid cells from plaque‐associated microglia in Alzheimer’s disease. Glia 67, 844–856 (2019).

    Google Scholar 

  74. Yin, Z. et al. Immune hyperreactivity of Aβ plaque-associated microglia in Alzheimer’s disease. Neurobiol. Aging 55, 115–122 (2017).

    CAS  Google Scholar 

  75. Grubman, A. et al. Transcriptional signature in microglia associated with Aβ plaque phagocytosis. Nat. Commun. 12, 3015 (2021).

    PubMed Central  Google Scholar 

  76. Marschallinger, J. et al. Lipid-droplet-accumulating microglia represent a dysfunctional and proinflammatory state in the aging brain. Nat. Neurosci. 23, 194–208 (2020).

    CAS  PubMed Central  Google Scholar 

  77. Gazestani, V. et al. Early Alzheimer’s disease pathology in human cortex involves transient cell states. Cell 186, 4438–4453 (2023).

    CAS  PubMed Central  Google Scholar 

  78. Bakken, T. E. et al. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598, 111–119 (2021).

    CAS  PubMed Central  Google Scholar 

  79. Wang, Z. et al. An immune cell atlas reveals the dynamics of human macrophage specification during prenatal development. Cell 186, 4454–4471 (2023).

    CAS  Google Scholar 

  80. McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016).

    PubMed Central  Google Scholar 

  81. Lareau, C. A. et al. Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling. Nat. Biotechnol. 39, 451–461 (2021). This paper demonstrates an approach to infer cell clonality from mitochondrial single-nucleotide variants.

    CAS  Google Scholar 

  82. Pei, W. et al. Polylox barcoding reveals haematopoietic stem cell fates realized in vivo. Nature 548, 456–460 (2017).

    CAS  PubMed Central  Google Scholar 

  83. Trapnell, C. Defining cell types and states with single-cell genomics. Genome Res. 25, 1491–1498 (2015).

    CAS  PubMed Central  Google Scholar 

  84. Morris, S. A. The evolving concept of cell identity in the single cell era. Development 146, dev169748 (2019).

    CAS  Google Scholar 

  85. 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 Central  Google Scholar 

  86. Grün, D. Revealing dynamics of gene expression variability in cell state space. Nat. Methods 17, 45–49 (2020).

    Google Scholar 

  87. Karczewski, K. J. & Snyder, M. P. Integrative omics for health and disease. Nat. Rev. Genet. 19, 299–310 (2018).

    CAS  PubMed Central  Google Scholar 

  88. Clarke, R. et al. The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nat. Rev. Cancer 8, 37–49 (2008).

    CAS  PubMed Central  Google Scholar 

  89. Hotelling, H. Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24, 417–441 (1933).

    Google Scholar 

  90. Wang, A. & Gehan, E. A. Gene selection for microarray data analysis using principal component analysis. Stat. Med. 24, 2069–2087 (2005).

    Google Scholar 

  91. Maaten van der, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  92. McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://doi.org/10.48550/arXiv.1802.03426 (2018).

  93. Silverman, J. D., Roche, K., Mukherjee, S. & David, L. A. Naught all zeros in sequence count data are the same. Comput. Struct. Biotechnol. J. 18, 2789–2798 (2020).

    CAS  PubMed Central  Google Scholar 

  94. Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).

    CAS  PubMed Central  Google Scholar 

  95. Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014).

    Google Scholar 

  96. Haque, A., Engel, J., Teichmann, S. A. & Lönnberg, T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. 9, 75 (2017).

    PubMed Central  Google Scholar 

  97. Luecken, M. D. & Theis, F. J. Current best practices in single‐cell RNA‐seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).

    PubMed Central  Google Scholar 

  98. Tjärnberg, A. et al. Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data. PLoS Comput. Biol. 17, e1008569 (2021).

    PubMed Central  Google Scholar 

  99. Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).

    CAS  Google Scholar 

  100. Welch, J. et al. Integrative inference of brain cell similarities and differences from single-cell genomics. Preprint at bioRxiv https://doi.org/10.1101/459891 (2018).

Download references

Acknowledgements

R.S. is supported by the IMMediate Advanced Clinician Scientist-Program, Department of Medicine II, Medical Center, University of Freiburg and Faculty of Medicine, University of Freiburg, funded by the Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research), 01EO2103. Furthermore, R.S. is supported by the Fritz Thyssen Foundation. M.P. is supported by the DFG (CRC/TRR167 project ID 259373024 ‘NeuroMac’). M.P. is further supported by the Sobek Foundation, the Ernst-Jung Foundation, the Klaus Faber Foundation, the Novo Nordisk Prize, the German Research Foundation (SFB 992 project ID 192904750, SFB 1160, SFB-1479 project ID 441891347, TRR 359 project ID 491676693 and Gottfried Wilhelm Leibniz Prize) and by the DFG under Germany’s Excellence Strategy (CIBSS EXC-2189 project ID 390939984). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Roman Sankowski, Marco Prinz.

Authors and Affiliations

  1. Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Roman Sankowski & Marco Prinz

  2. Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany

    Marco Prinz

Authors
  1. Roman Sankowski
    View author publications

    Search author on:PubMed Google Scholar

  2. Marco Prinz
    View author publications

    Search author on:PubMed Google Scholar

Corresponding authors

Correspondence to Roman Sankowski or Marco Prinz.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Neuroscience thanks Marta Olah and the other, anonymous, reviewer(s) 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.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sankowski, R., Prinz, M. A dynamic and multimodal framework to define microglial states. Nat Neurosci 28, 1372–1380 (2025). https://doi.org/10.1038/s41593-025-01978-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41593-025-01978-3

This article is cited by

Search

Advanced search

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