Figure 2. Lymphocytes influence microglia transcriptional signature.

(A) CD45⁺CD11b⁺ cells were sorted from the ipsilateral hemisphere in naïve mice or 5 days after stroke in wild-type (WT) and Rag1^−/− (3 mice per condition), and RNA was isolated for single cell RNA sequencing (10× Genomics). (B) Uniform manifold approximation and projection 2D space (UMAP) plots of 2345 CD45⁺CD11b⁺ cells colored by 15 distinct transcriptional clusters (Figure 2—figure supplement 1A). (C) Clustering of the microglia subset color-coded by homeostatic and reactive microglia (right) and by conditions (left). (D) Volcano plots of the differentially expressed genes in microglia in naïve and stroke condition. Dotted lines indicate an adjusted p-value≤0.05 and FC = 1.5. (E) Number of microglial genes regulated after stroke in comparison to naïve condition in Rag1^−/− and WT mice. 67 genes were common to both genotypes, 17 genes were specifically regulated in WT mice and 120 genes only in Rag1^−/− mice. Boxes indicate key microglial genes in each condition (genes indicated in gray were down-regulated after stroke). (F) Selected gene sets of highly correlated and anti-correlated genes based on trajectory inference analysis in stroke condition (Figure 2—figure supplement 2E–G). Mean gene set activation score in WT and Rag1^−/− cells, selected marker genes, and top enriched gene ontology pathways associated to each gene set. Gene sets were classified by p-value (the lowest p-value at the top, asterisks [*] indicate significant difference between genotype in stroke condition) and by similar pathways, such as: pathways related to inflammation (dark blue), pathways related to DNA/RNA regulation (blue), and lipid pathways (light blue).

Figure 2—figure supplement 1. Transcriptomic analysis of microglia isolated from wild-type (WT) and Rag1^−/− mice in naïve and stroke conditions.

(A) Uniform manifold approximation and projection (UMAP) plots showing expression of known CD45⁺CD11b⁺ myeloid cell marker genes and Louvain-clusters. (B and C) Manifold and clustering of microglia: (B) UMAP plots indicate Louvain-clusters of microglial cells. (C) Dot plots show gene expression distribution of marker genes split by Louvain-cluster, and their grouping into homeostatic (top) and reactive (bottom) microglia.

Figure 2—figure supplement 2. Microglia single cell trajectory inference in wild-type (WT) and Rag1^−/− mice in naïve and stroke conditions.

(A and B) Microglia single cell trajectory inference describes the evolution of microglia activation from naïve to stroke condition. (A) Partition-based graph abstraction graph (PAGA, right) shows cluster connectivity of Louvain-clusters (left) with a threshold of 0.1. Nodes represent subsets, and thicker edges indicate stronger connectedness between subsets. The trajectories path 1 and 2 are selected based on the connectivity (edge width) in the PAGA after the root and end paths were defined based on marker gene expression. Clusters along the paths from root-to-end were merged since these are consecutive. Trajectory path 1 (dark gray): root (cluster 0), path 1 (clusters 6, 1, and 3 merged), and end (cluster 7); trajectory path 2 (black): root (cluster 0), path 2 (cluster 4, 2, and 5 merged), and end (cluster 7). (B) Barplot shows cell frequencies split by condition in trajectories path 1 and path 2. (C and D) Differential microglial gene expression analysis between root and end clusters along trajectory path 2. (C) Heatmaps show scaled expression along path 2 trajectory of microglial genes specifically regulated in Rag1^−/− and compared in WT (left) and Rag1^−/− (right) in path 2 (Wilcoxon test, adjusted p<0.05). For balanced representation of cells along the trajectory root and path 2, cells were randomly subset to 100 cells before plotting. (D) Top enriched pathways of microglial genes up- and down-regulated in Rag1^−/− mice. (E–G) Microglia gene set expression in stroke condition. (E) Uniform manifold approximation and projection (UMAP) plots in path 2 and end cell clusters colored-coded by stroke condition (left), genotype (middle), and clusters (right). (F) Gene-gene correlation map of the correlated and anti-correlated genes extracted from clusters 2, 4, 5, and 7 identified 11 gene clusters. Due to low correlation, G8 was excluded of further analysis. (G) Dot plots show gene set expression distribution (G1–G11) of the stroke cell clusters (2, 4, 5, and 7).

Figure 2—figure supplement 3. Immune cell infiltration in wild-type (WT) and Rag1^−/− mice after stroke.

(A) Absolute cell count of CD3⁺ T cells and CD19⁺ B cells in the ischemic hemisphere (ipsilateral) 5 days after distal occlusion of the middle cerebral artery (dMCAO) in WT mice as quantified by flow cytometry. There is a 14-fold increase of T cells in comparison to B cells at this time point; Student t-test, ***p<0.001. (B) Absolute cell count of CD45^highCD11b⁺ myeloid cells: Ly6G⁺ Neutrophils, CD11c⁺MHCII⁺ dendritic cells, Ly6C⁺Ly6G^– monocytes, F4/80⁺Ly6G^– macrophages in the ischemic hemisphere (ipsilateral) 5 days after dMCAO in WT (red) and Rag1^−/− (pink) mice as quantified by flow cytometry; ANOVA with Šídák’s multiple comparisons test, NS: non significant. N=4 mice/condition; Bar graphs show the mean ± the standard deviation.