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A single-cell mass cytometry-based atlas of the developing mouse brain

Nature Neuroscience volume 28, pages 174–188 (2025)Cite this article

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Abstract

Development of the mammalian brain requires precise molecular changes across diverse cell lineages. While single-cell RNA abundances in the developing brain have been characterized by single-cell RNA sequencing (scRNA-seq), single-cell protein abundances have not been characterized. To address this gap, we performed mass cytometry on the whole brain at embryonic day (E)11.5–E12.5 and the telencephalon, the diencephalon, the mesencephalon and the rhombencephalon at E13.5–postnatal day (P)4 from C57/BL6 mice. Using a 40-antibody panel to analyze 24,290,787 cells from two to four biological replicates per sample, we identify 85 molecularly distinct cell clusters from distinct lineages. Our analyses confirm canonical molecular pathways of neurogenesis and gliogenesis, and predict two distinct trajectories for cortical oligodendrogenesis. Differences in protein versus RNA expression from mass cytometry and scRNA-seq, validated by immunohistochemistry and RNAscope in situ hybridization (ISH), demonstrate the value of protein-level measurements for identifying functional cell states. Our findings show the utility of mass cytometry as a scalable platform for single-cell profiling of brain tissues.

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Fig. 1: Classification of cells in the developing mouse brain by mass cytometry.
Fig. 2: Corroboration of mass cytometry protein measurements by IHC.
Fig. 3: Spatiotemporal profile of cell abundances in the developing mouse brain.
Fig. 4: Comparison of protein and mRNA expression patterns in the developing mouse brain.
Fig. 5: Differentiation trajectories of SOX2+nestin+ cells in the developing mouse brain.
Fig. 6: Differentiation trajectories and molecular dynamics in the telencephalon.
Fig. 7: Microglia and macrophage expansion and putative phagocytic cargoes in the developing mouse brain.
Fig. 8: Overview of key processes in mouse brain development.

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Data availability

Requests for reagents and other resources will be fulfilled by E.R.Z. (ezunder@virginia.edu). Raw single-cell mass cytometry FCS files have been deposited at FlowRepository (https://flowrepository.org/) under accession number FR-FCM-Z7CQ. FCS files for each individual sample (debarcoded, normalized and batch corrected) and clean-up gating used for sample preprocessing are available on Cytobank (https://cytobank.org/cytobank/experiments/) under experiment IDs 105280–105284. The scRNA-seq dataset from La Manno et al.42 is available at the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) under accession PRJNA637987. Source data are provided with this paper.

Code availability

Code used to perform analysis of mass cytometry data (as detailed in the Methods) was adapted from standard R and Python packages (for example, UMAP version 0.5.3, LeidenAlg version 0.8.2, Seurat version 4.3.0, URD version 1.1.1 and ggplot2 version 3.4.0) and is available on GitHub at https://github.com/zunderlab/VanDeusen-et-al.-CNS-Development-Manuscript. More detailed information is available upon request.

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Acknowledgements

Research reported in this publication was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number R01NS111220 to E.R.Z. and C.D.D. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Further support was provided by the 3Cavaliers Pilot research program to C.D.D. and E.R.Z. A.L.V.D. was supported by a UVA BRAIN Presidential Fellowship. S.K. was supported by the Owens Family Foundation. S.M.G. was supported by the National Institute of General Medical Sciences of the National Institutes of Health under the Systems and Biomedical Data Science Training Program, award number T32GM145443. C.M.W. was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under the Cardiovascular Research Training Program, award number T32HL007284. A.B.K. was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number F32GM125147. K.I.F. was supported by the National Institute of General Medical Sciences of the National Institutes of Health under the Systems and Biomedical Data Science Training Program, award number T32GM136615. C.D.D. was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number R01NS091617. We thank A. Spano for contributing protein chemistry expertise and resources. We thank H. Zong (University of Virginia) for providing cell lines to validate antibodies and for providing feedback on the manuscript. We thank S. Kucenas (University of Virginia) for providing the anti-SOX10 antibody and feedback on the manuscript. We thank T. Müller and C. Birchmeier (Max Delbrück Center) for providing the anti-FABP7 antibody. We thank B. Condron, A. Güler, M. Hunter-Chang, A. Pathak, J. Sewell and E. Stepanova for providing feedback on the manuscript. We thank A. Hirt for assistance with coding and data analysis, K. Warner for assistance with antibody validation and L. Jin for assistance with mouse dissections. We thank the University of Virginia Flow Cytometry Core, RRID SCR_017829 for technical assistance with the CyTOF mass cytometer instrument. We acknowledge Research Computing at the University of Virginia (https://rc.virginia.edu) for providing computational resources and technical support that have contributed to the results reported within this publication.

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Authors and Affiliations

  1. Department of Biology, College of Arts and Sciences, University of Virginia, Charlottesville, VA, USA

    Amy L. Van Deusen, Sushanth Kumar, O. Yipkin Calhan, Jiachen Shi, Austin B. Keeler, Irene C. Gadani & Christopher D. Deppmann

  2. Neuroscience Graduate Program, School of Medicine, University of Virginia, Charlottesville, VA, USA

    Amy L. Van Deusen, Sushanth Kumar, Sarah M. Goggin, Irene C. Gadani, Christopher D. Deppmann & Eli R. Zunder

  3. Department of Biomedical Engineering, School of Engineering, University of Virginia, Charlottesville, VA, USA

    Amy L. Van Deusen, Sarah M. Goggin, Corey M. Williams, Kristen I. Fread, Christopher D. Deppmann & Eli R. Zunder

  4. Department of Neuroscience, School of Medicine, University of Virginia, Charlottesville, VA, USA

    Christopher D. Deppmann

  5. Department of Cell Biology, School of Medicine, University of Virginia, Charlottesville, VA, USA

    Christopher D. Deppmann

  6. Program in Fundamental Neuroscience, College of Arts and Sciences, University of Virginia, Charlottesville, VA, USA

    Christopher D. Deppmann & Eli R. Zunder

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Contributions

A.L.V.D., C.D.D. and E.R.Z. planned all experiments. A.L.V.D., A.B.K., I.C.G. and J.S. collected tissue and performed single-cell dissociations. A.L.V.D. performed all antibody conjugations. A.L.V.D. validated all antibodies by performing titration experiments on positive and negative control cell samples. A.L.V.D. and E.R.Z. performed the mass cytometry measurements. S.K. and O.Y.C. performed IHC and RNAscope ISH measurements. A.L.V.D., S.K. and J.S. performed microscopy. A.L.V.D., S.M.G., C.M.W, A.B.K., K.I.F. and E.R.Z. wrote scripts for data analysis. A.L.V.D., S.M.G. and E.R.Z. performed data analysis. E.R.Z. and C.D.D. conceived and supervised all aspects of the project. A.L.V.D., S.M.G. and E.R.Z. prepared figures. A.L.V.D., E.R.Z. and C.D.D. wrote the manuscript with input from all authors.

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Correspondence to Christopher D. Deppmann or Eli R. Zunder.

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Extended data

Extended Data Table 1 Antibodies used for mass cytometry
Full size table

Supplementary information

Supplementary Table 1

Antibodies used for mass cytometry.

Supplementary Fig. 1

Validation of single-cell processing of mouse brain tissue and titration of antibodies for mass cytometry.

Supplementary Fig. 2

Pre-processing of mass cytometry data for the developing mouse brain.

Supplementary Fig. 3

Classification of cells in the developing mouse brain by mass cytometry.

Supplementary Fig. 4

Comparison of protein expression in mouse brain measured by mass cytometry and immunohistochemistry.

Supplementary Fig. 5

Spatiotemporal profile of cell abundances in the developing mouse brain.

Supplementary Fig. 6

Comparison of protein and mRNA expression patterns in the developing mouse brain.

Supplementary Fig. 7

Differentiation trajectories of SOX2+Nestin+ cells in the developing mouse brain.

Supplementary Fig. 8

Differentiation trajectories and molecular dynamics in the telencephalon.

Supplementary Fig. 9

Microglia/macrophage expansion and putative phagocytic cargoes in the developing mouse brain.

Supplementary Data 1

Statistical source data for Supplementary Fig. 2.

Supplementary Data 2

Statistical source data for Supplementary Fig. 9.

Reporting Summary

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Source Data Fig. 3

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Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 7

Statistical source data.

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Van Deusen, A.L., Kumar, S., Calhan, O.Y. et al. A single-cell mass cytometry-based atlas of the developing mouse brain. Nat Neurosci 28, 174–188 (2025). https://doi.org/10.1038/s41593-024-01786-1

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