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Mapping microglia states in the human brain through the integration of high-dimensional techniques

An Author Correction to this article was published on 30 May 2022

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Abstract

Microglia are tissue-resident macrophages of the CNS that orchestrate local immune responses and contribute to several neurological and psychiatric diseases. Little is known about human microglia and how they orchestrate their highly plastic, context-specific adaptive responses during pathology. Here we combined two high-dimensional technologies, single-cell RNA-sequencing and time-of-flight mass cytometry, to identify microglia states in the human brain during homeostasis and disease. This approach enabled us to identify and characterize a previously unappreciated spectrum of transcriptional states in human microglia. These transcriptional states are determined by their spatial distribution, and they further change with aging and brain tumor pathology. This description of multiple microglia phenotypes in the human CNS may open promising new avenues for subset-specific therapeutic interventions.

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Fig. 1: Identification of microglia transcriptional clusters in the healthy human brain by scRNA-seq.
Fig. 2: GO term enrichment analysis of microglia states in the healthy human brain.
Fig. 3: Spatial and temporal expression of microglia clusters in the non-diseased human brain.
Fig. 4: Identification of human GAMs by scRNA-seq.
Fig. 5: In situ validation of protein expression in GAMs.
Fig. 6: Characterization of human brain-tumor-linked microglia clusters by CyTOF.

Data availability

The raw data for this project are available at the Gene Expression Omnibus under accession code GSE135437. The data can be explored in a browser widget at https://single-cell.shinyapps.io/human-microglia/.

Code availability

Computer code for this project can be provided upon request and found at https://github.com/rsankowski/sankowski-et-al-microglia.

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Acknowledgements

The authors thank J. Bodinek-Wersing, T. El Gaz, E. Barleon and A. Frömming for excellent technical support. R.S. is supported by the Berta-Ottenstein Programme for Clinician Scientists. M.P. is supported by the Sobek Foundation, the Ernst-Jung Foundation, the German Research Foundation (SFB 992, SFB1160, Reinhart-Koselleck-Grant) and the Ministry of Science, Research and Arts, Baden-Wuerttemberg (Sonderlinie “Neuroinflammation”) and by the BMBF-funded competence network of multiple sclerosis (KKNMS). This study was supported by the German Research Foundation (DFG) under Germany’s Excellence Strategy (CIBSS, EXC-2189, project ID 390939984). The authors would also like to acknowledge the assistance of the BCRT Flow Cytometry Lab (Charité—Universitätsmedizin Berlin, Germany). C.B., M.P. and J.P. are supported by the German Research Foundation (SFB/TRR167 “NeuroMac”). J.P. received additional funding from the Berlin Institute of Health (CRG2aSP6) and the UK DRI (Momentum Award). D.G. was supported by the Max Planck Society, the German Research Foundation (DFG) (SPP1937 GR4980/1-1, GR4980/3-1 and GRK2344 MeInBio), by the DFG under Germany’s Excellence Strategy (CIBSS, EXC-2189, project ID 390939984), by the ERC (818846, ImmuNiche, ERC-2018-COG) and by the Behrens-Weise-Foundation.

Author information

Authors and Affiliations

Authors

Contributions

R.S., C.B., T.M., L.G. and E.S. conducted the experiments and analyzed the data. C.S., M.J.S., D.H.H. and O.S. performed brain surgeries for tissue acquisition. R.S. and S. analyzed the scRNA-seq data (under the supervision of D.G.). R.S., C.B., T.S., A.M., D.G., J.P. and M.P. supervised the project and wrote the manuscript.

Corresponding authors

Correspondence to Dominic Grün, Josef Priller or Marco Prinz.

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Competing interests

A.M. and T.S. are shareholders of AC Immune. All the other authors declare no competing interests.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised. In the version of this article initially published, Fig.3b,c, Fig. 5a–d, Extended Data Fig. 1 and Supplementary Fig. 8b contained errors. Fig. 3b and c showed incorrect immunohistochemistry quantifications. Fig. 5a contained a duplicate image for the TMEM119 control sample, and panels b and c showed incorrect immunohistochemistry quantifications. Fig. 5d contained an incorrect image for the t-SNE map of P2RY12; it was a duplicate of the map for HLA-DRA. Extended Data Fig. 1 contained incorrect images in panel a for Pat 7 (TMEM119), Pat 12 (TMEM119, CD74) and Pat 15 (CD68) and in panel b for Pat 3 (GFAP), Pat 12 (TMEM119 and P2YR12) and Pat 13 (TMEM119). Supplementary Fig. 8b contained an incorrect image for the CSF2 t-SNE panel. In addition, the legend for Fig. 5b as well as the description of Fig. 1b in Methods incorrectly said that the test used was a paired t-test; it should read ‘unpaired t-test’ in both places. In the Results section, the sentence “Normalization for the number of IBA1+ cells revealed lower expression of the bona fide microglial markers P2RY12 and TMEM119, and higher expression of HLA-DR (Fig. 5c)” should read “no change of expression” instead of “lower expression”.

Extended data

Extended Data Fig. 1 Representative immunohistochemical pictures of control samples.

(a) grey and (b) white matter from each subject included in the study. Each image is representative of n=10 independent images taken per specimen.

Extended Data Fig. 2 Pregating Strategy and FACS plots – control samples.

Overview of FACS plots for the control samples included in the study. The boxplot whiskers represent 1.5 times the interquartile range. Each sample represents an independent experiment.

Extended Data Fig. 3 Cell signatures and microglia cell-to-cell-distance heatmap – control samples.

a) Cell signatures of microglial and non-microglial cells. Aggregate transcript counts of genes enriched in the given cell type (indicated in parentheses) are color-coded on t-SNE maps. b) Unsupervised clustering of microglia after exclusion of non-microglial cells divided microglia in 9 major populations, that is clusters. The heatmap displays pairwise cell-to-cell distances with clusters color-coded on the bottom. C4 (grey color) was removed from subsequent analysis due to upregulation of cell-stress genes indicating dissociation-induced artefacts. Minor clusters containing individual cells that comprise less than 1 % of all cells were also removed (white ‘x’ on black background), leading to n = 4,116 microglia that passed quality control. The color scale represents the pairwise distance between cells.

Extended Data Fig. 4 t-SNE maps and line plots of selected genes - control samples.

t-SNE maps color-coded for transcript counts with the corresponding line plots.

Extended Data Fig. 5 Dataset comparison with published data.

a) Workflow for the neural network classifier. b) Heatmap depicting the comparison between the true (y-axis) and the predicted (x-axis) cluster assignments on the test data. Microglia that were assigned correctly are lying on the diagonal. The color-coding indicates the ratio between the number of microglia in a given cell divided by the sum of all cells in this row. Absolute numbers are provided in each cell.

Extended Data Fig. 6 Line plots of selected GO terms - control samples.

Aggregate transcript counts of genes enriched in the respective GO term are visualized using line plots.

Extended Data Fig. 7 SPP1 immunohistochemistry and clusterwise SPP1 expression across age bins - control samples.

a) Immunohistochemistry of SPP1 and Iba1 in control grey and white matter. Each image is representative of n=5 independent images taken per specimen. b) Violin and box plots showing the transcript counts of SPP1 across age bins and. Statistically significant p-values are indicated resulting from two-way analysis of variance followed by a Tukey post-hoc test. Violin plots represent kernel density estimates of expression values from n=4,396 cells from 15 samples. The boxplot spans the 25th to 75th percentiles and the whiskers represent 1.5 times the interquartile range. The bold line indicates the median.

Extended Data Fig. 8 FACS plots of GBM samples. Overview of FACS plots for the GBM samples included in the study.

Each row represents a separate experiment.

Extended Data Fig. 9 Cell signatures and cell-to-cell distance heatmap – GBM samples.

a) Cell signatures of microglial and non-microglial cells. Aggregate transcript counts of genes enriched in the given cell type (indicated in parentheses) are color-coded on the respective t-SNE maps. b) Unsupervised clustering of microglia after exclusion of non-microglial cells resulted in 14 major populations, that is clusters. The heatmap displays pairwise cell-to-cell distances. The color scale represents the pairwise distance between cells.

Extended Data Fig. 10 t-SNE maps and line plots of selected genes - GBM samples.

t-SNE maps color-coded for transcript counts with the corresponding line plots.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8.

Reporting Summary

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Excel sheet with all Supplementary Tables 1–12.

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Sankowski, R., Böttcher, C., Masuda, T. et al. Mapping microglia states in the human brain through the integration of high-dimensional techniques. Nat Neurosci 22, 2098–2110 (2019). https://doi.org/10.1038/s41593-019-0532-y

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