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Analysis of brain and blood single-cell transcriptomics in acute and subacute phases after experimental stroke

Nature Immunology volume 25, pages 357–370 (2024)Cite this article

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Abstract

Cerebral ischemia triggers a powerful inflammatory reaction involving peripheral leukocytes and brain resident cells that contribute to both tissue injury and repair. However, their dynamics and diversity remain poorly understood. To address these limitations, we performed a single-cell transcriptomic study of brain and blood cells 2 or 14 days after ischemic stroke in mice. We observed a strong divergence of post-ischemic microglia, monocyte-derived macrophages and neutrophils over time, while endothelial cells and brain-associated macrophages showed altered transcriptomic signatures at 2 days poststroke. Trajectory inference predicted the in situ trans-differentiation of macrophages from blood monocytes into day 2 and day 14 phenotypes, while neutrophils were projected to be continuously de novo recruited from the blood. Brain single-cell transcriptomes from both female and male aged mice were similar to that of young male mice, but aged and young brains differed in their immune cell composition. Although blood leukocyte analysis also revealed altered transcriptomes after stroke, brain-infiltrating leukocytes displayed higher transcriptomic divergence than their circulating counterparts, indicating that phenotypic diversification occurs within the brain in the early and recovery phases of ischemic stroke. A portal (https://anratherlab.shinyapps.io/strokevis/) is provided to allow user-friendly access to our data.

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Fig. 1: Microglia show altered transcriptional states through the acute and subacute phases of stroke.
Fig. 2: Transcriptional changes of BAMs after stroke.
Fig. 3: Inflammatory blood monocytes give rise to infiltrating brain MdCs after stroke.
Fig. 4: EC transcriptional changes and Igf1r signaling after stroke.
Fig. 5: Granulocyte transcriptional changes through ischemia–reperfusion.
Fig. 6: Comparison of the cellular composition and transcriptome signatures of brain and blood cells in aged and young stroke mice.
Fig. 7: Comparison of post-stroke transcriptomic profiles between mouse and human blood leukocytes by KEGG pathway analysis.

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Data availability

The raw and processed data and metadata of all scRNA-seq datasets included in this study are available in the GEO repository (GSE225948). A publicly accessible interactive web portal for exploring the scRNA-seq data included in this study has been developed (https://anratherlab.shinyapps.io/strokevis/). Source data are provided with this paper.

Code availability

Code that supports the findings of this study are available from the corresponding author upon request.

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Acknowledgements

This work was supported by NIH grants R01NS081179 (J.A.), R01NS34179 (C.I.), the Leducq Foundation (StrokeIMPaCT Network; J.A.) and the Sackler Brain and Spine Institute Research Grant (L.G.B.). We thank C. Mason for helpful discussions. The generous support of the Feil Family Foundation is gratefully acknowledged. All libraries were sequenced at the Genomics Core of the Cornell Institute of Biotechnology (RRID: SCR_021727).

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Authors and Affiliations

  1. The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA

    Lidia Garcia-Bonilla, Ziasmin Shahanoor, Rose Sciortino, Omina Nazarzoda, Gianfranco Racchumi, Costantino Iadecola & Josef Anrather

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  1. Lidia Garcia-Bonilla
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Contributions

J.A. and L.G.B. conceived the study with input from C.I. L.G.B., Z.S., R.S., O.N. and G.R. performed experiments and analyzed data. J.A. performed bioinformatic analyses. L.G.B. and J.A. wrote the original draft; C.I. revised the manuscript; all authors read and approved the final manuscript.

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Correspondence to Lidia Garcia-Bonilla or Josef Anrather.

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Competing interests

C.I. serves on the scientific advisory board of Broadview Ventures. The other authors declare no competing interests.

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Nature Immunology thanks Louise McCullough and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. S. Houston was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Single-cell transcriptomic profiling of mouse brain and blood cells after transient focal cerebral ischemia.

a, Schematic representation of Drop-Seq scRNA-seq pipeline used to analyze brain and blood cells isolated from either control surgery (Sham) or stroke mice 2 and 14 days (D02, D14) after injury. Brain cells were dissociated by enzymatic digestion with papain. Infiltrating leukocytes (CD45hi), microglia (Mg) and endothelial cells (EC) were isolated by flow cytometry sorting. Blood leukocytes were purified after erythrocyte removal. Brain and blood single cell suspensions were subjected to Drop-Seq, sequencing and analysis. b, c, Left: UMAP plot representing color-coded cell clusters identified in merged brain (b) or blood (c) single-cell transcriptomes; Middle: UMAP of 3 color-coded time point overlay of brain (b) or blood (c) single-cell transcriptomes; Right: bar graph showing relative frequencies of each cell type across Sham, D02 and D14 groups of either brain (b) or blood (c) identified cell type clusters. d, Left: UMAP plot of the combined brain (Br) and blood (PB) dataset showing cell clustering similarities between brain and blood Gran, Tc, Bc and brain myeloid cells (BAM, MdC, DC) with blood monocytes (left). Right: Same UMAP plot annotated by tissue. Border-associated macrophages (BAM), monocyte-derived cells (MdC), granulocytes (Gran), mast cells (MaC), dendritic cells (DC), T cells (Tc), NK cells (NK), B cells (Bc), vascular mural cells (MC), epithelial-like cells (Epi), oligodendrocytes (OD); Eosinophils-Basophils (EosBas); Monocytes (Mo); hematopoietic precursors (pre); unclassified (UC).

Extended Data Fig. 2 Histological validation of microglia marker genes (related to Fig. 1).

a, left: Representative immunofluorescence (IF) image of a whole brain section from a Cx3cr1CreERT2:R26Tdomato mouse subjected to 2 days of MCAo (D02) showing the distribution of Ki67+ cells (white, binary mask) and nuclear DAPI staining (blue); middle and right panels: IF images of magnified areas showing Ki67 expression by Td+(red) Iba1+(green) microglia in the peri-infarct area. Arrowheads indicate Ki67 staining. The border of the ischemic lesion is indicated by yellow dash outline and was traced based on DAPI, Iba1 and Tomato labels. b, top: RNAscope fluorescence in situ hybridization (FISH) validating Cst7 (white) expression in D02 Td+ microglia (red). Left: Representative whole brain section image of Cst7 expression (binary mask) and nuclear DAPI staining; Middle and right panels: FISH-IF images of magnified areas showing upregulation of Cst7 in microglial cells surrounding the ischemic lesion. Bottom: FISH-IF images validating Cst7 (white) expression in D14 mice. Left: Representative whole brain section image of Cst7 expression (binary mask) and nuclear DAPI staining; Middle and right top panels: FISH-IF images of magnified areas showing upregulation of Cst7 in microglia (Iba1+, green) surrounding the ischemic lesion. c, FISH-IF images validating Cxcl10 (white) expression in Cx3cr1-Td+ mice 2 and 14 days after MCAo. Left (D02): Representative whole brain section image of Cxcl10 expression (binary mask) and images of magnified areas showing localization of Cxcl10 in microglial cells (Td+, red) outside of the ischemic lesion. Right (D14): Representative whole brain section image of Cxcl10 expression (binary mask) and images of magnified areas showing localization of Cxcl10 in microglial cells (Td+, red) on the border of the ischemic lesion. d, IF images validating IGF1 (white) expression by Cx3cr1-Td+(red) MHCII–(green) microglia 14 days after MCAo in the ischemic region.

Extended Data Fig. 3 Cellular composition and transcriptomics of brain dendritic cells.

a, UMAP plots of brain dendritic cells (DC) transcriptomes for each studied time point identifies 9 clusters (DC1-9). b, Bar graph showing relative frequencies of DC clusters across Sham, D02 and D14 groups. c, UMAP of 3 color-coded time point overlay of brain DC. Classification of clusters into DC subtypes is based on marker gene expression (d,f): cDC1 (Xcr1, Clec9a), cDC2 (Cd209a, Sirpa), monocyte derived-DC (moDC; Sirpa, Ms4a7), migratory (migDC; Ccr7), and plasmacytoid DC (pDC; Ly6d, Ccr9). d, UMAP plots displaying expression of marker genes for each identified DC cluster in the brain. Scale bars represent log of normalized gene expression. e, Heatmap displaying differential expression of the top 10 upregulated genes in each DC cluster. Scale bar represents Z-score of average gene expression (log). f, Flow cytometry analysis validating brain cDC1 (XCR1+), cDC2 (CD172a+) and migDC (CCR7+) subtypes identified by scRNA-seq after stroke. g, Left: FISH of Cd209a (red) expression in the brain, combined with IF for Iba1 (green) and nuclear staining with DAPI (blue), showing Cd209a+Iba1+ cells around blood vessel (dotted line). Cx: cortex; St: striatum. Right: Flow cytometry analysis showing double positive CD209a+CD172a+ DC (CD11c+MHCII+).

Extended Data Fig. 4 Cellular composition and transcriptomics of brain lymphoid cells.

a, UMAP plots of brain lymphoid cells (Tc) transcriptomes for each studied time point identifies 7 clusters (Tc1-7). b, Bar graph showing relative frequencies of Tc clusters across Sham, D02 and D14 groups. c, Heatmap displaying expression of the top 10 upregulated genes in each Tc cluster. Scale bar represents Z-score of average gene expression (log). d, UMAP of merged Sham, D02 and D14 Tc transcriptomes. Classification of clusters into T cell types is based on marker gene expression (c,e): CD4 (Cd3d,Cd4), Treg (Cd3d,Cd4,Foxp3), CD8 (Cd3d, CD8b1), NKT (Cd3d, Gzma), γδT (Cd3d, Trdc), interferon stimulated T cells (ISG; Cd3d, Ifit3), innate lymphoid cells type 2 (ILC2; Gata3, Hes1) and proliferating T cells (prol; Cd3d, Top2a). e, Density plots in the UMAP space showing the expression of selected marker genes used for lymphoid cell type identification. Scale bars represent densities based on kernel density estimation of gene expression using. f, Chord plot showing cell-cell interactions between Cxcr3 and Cxcl10 in grouped Sham, D02 and D14 stroke mice. The strength of the interaction is indicated by the edge thickness. The color of the chord matches the cell cluster color sending the signal (Cxcl10). The number of cell recipient clusters (Cxcr3) and their weight in the interactions is indicated by the color-matched stacked bar next to each sender.

Supplementary information

Supplementary Information

Supplementary Figs. 1–15.

Reporting Summary

Peer Review File

Supplementary Table 1

Kegg analysis.

Supplementary Table 2

Reagents.

Supplementary Table 3

Mouse census and Drop-seq sample-specific information.

Supplementary Table 4

Brain cell number downsample.

Supplementary Table 5

Module score gene list.

Supplementary Table 6

Statistical source data for Supplementary Figs. 5c, 6b, 7 and 14.

Source data

Source Data Fig. 6

Statistical source data.

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Garcia-Bonilla, L., Shahanoor, Z., Sciortino, R. et al. Analysis of brain and blood single-cell transcriptomics in acute and subacute phases after experimental stroke. Nat Immunol 25, 357–370 (2024). https://doi.org/10.1038/s41590-023-01711-x

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