Single-nucleus transcriptomic profiling reveals temporal dynamics of neuroinflammation and myelin repair after intracerebral haemorrhage

Abstract

Background: Intracerebral haemorrhage (ICH) progresses rapidly with complex pathology and limited treatment options, making it a severe subtype of stroke. The extravasation of blood into the brain parenchyma triggers a cascade of inflammatory responses, contributing to secondary injury. Single-nucleus RNA sequencing (snRNA-seq) data have enabled more profound insights into the cellular heterogeneity and dynamic interactions within the haemorrhagic brain. Immune cells play a crucial role in shaping neuroinflammation. However, the lack of comprehensive longitudinal studies limits our understanding of the temporal evolution of these inflammatory processes, posing a challenge to the development of targeted therapeutic strategies.

Methods: We used snRNA-seq in collagenase-induced ICH mouse models at Days 1, 3, 7, 14 and 28 post-injury, alongside naive controls, to profile the dynamics of gene expression over time.

Results: We obtained 281 577 high-quality transcriptional profiles representing 21 distinct cell types. Co-expression network analysis revealed a prominent 'inflammation module' that remained active throughout ICH. Integrative single-cell transcriptomic and immunofluorescence staining suggested that the various Mif-expressing cells may contribute to local inflammation, potentially engaging macrophages via receptor-ligand pairs such as Cd44 and Cd74. Over time, microglia appeared to serve as key recipients of pro-inflammatory signals increasingly. During the resolution phase, oligodendrocytes exhibited transcriptional signatures consistent with enhanced maturation and remyelination, which T cell-mediated interactions may have facilitated.

Conclusions: These findings offer a systems-level perspective on cell-type-specific responses and immune-mediated interactions during ICH progression and resolution.

Key points: Establish intracerebral haemorrhage (ICH) mouse models at various time points (Days 1, 3, 7, 14, 28) and construct a high-quality single-nucleus RNA sequencing (snRNA-seq) atlas. Computational analyses suggest that macrophage recruitment in the early stage of ICH potentially involves migration inhibitory factor (MIF) signalling pathways. T cells may interact with myelin-forming oligodendrocytes during the resolution phase, potentially contributing to remyelination after ICH.

Keywords: T cell modulation; neuroimmune signalling; remyelination; single‐nucleus RNA sequencing.

FIGURE 1

The single‐cell landscape of mouse brain tissue surrounding the lesion in the ICH model and naive controls. (A) Schematic of the workflow for transcriptomic profiling of the ICH model using single‐nucleus RNA sequencing (snRNA‐seq). (B) The dot plot shows the mean expression levels of marker genes for the 21 significant cell populations. The colour represents the average scaled gene expression level (z‐score), and the dot size represents the percentage of cells in which the marker gene was detected for each population. (C) Uniform Manifold Approximation and Projection (UMAP) visualisation of clustering in the snRNA‐seq transcriptomic data from the ICH mouse brain lesion tissue. The outer circles represent the log10‐transformed cell numbers for each cell type. A total of 21 cell populations are identified: DGGRC (dentate gyrus granule neurons), MEGLU (di‐ and mesencephalon excitatory neurons), DEINH (di‐ and mesencephalon inhibitory neurons), GNBL (glutamatergic neuroblasts), NGNBL (non‐glutamatergic neuroblasts), PEP (peptidergic neurons), TEINH (telencephalon inhibitory interneurons), TEGLU (telencephalic glutamatergic neurons), MSN (telencephalon projecting inhibitory neurons), MAC (macrophages), T (T cells), AC (astrocytes), MGL (microglia), OPC (oligodendrocyte precursor cells), OLG (oligodendrocytes), EPEN (ependymal cells), CHOR (choroid plexus epithelial cells), EN (endothelial cells), PER (pericytes), FB (fibroblasts) and VLM (vascular leptomeningeal cells). (D) Line plots (left) show the changes in relative proportions of primary cell types across six time points (naive, Day 1, Day 3, Day 7, Day 14 and Day 28). Bar plots (right) show F‐statistics using analysis of variance (ANOVA), coloured by cell types. They are categorised into four major groups: neurons, immune cells, glial cells and vascular cells. (E) The scatter plot illustrates the DEGs of TEGLU_0, with the top 100 genes highlighted in light green. (F) The violin plot shows the programmed cell death pathway scores in TEGLU subtypes.

FIGURE 2

The co‐expression network illustrates changes in gene expression following ICH. (A) UMAP visualisation of the hub gene co‐expression network for the ICH model and Naive group. Nodes, coloured by module assignment, represent genes, with the top three hub genes annotated for each module. Edges, downsampled for clarity, represent co‐expression relationships. (B) Correlation plots show the correlation between each module based on their harmonised module eigengenes (hMEs), with purple indicating positive and green indicating negative correlations. The areas of the squares show the absolute value of the corresponding correlation coefficients. (C) UMAP plot of all cell populations from single‐nucleus RNA sequencing (snRNA‐seq), coloured by module assignment. (D) Bar plot showing the top three gene ontology (GO) enrichment pathways, with the y‐axis representing log10 p values (values over 20 were truncated for clarity) and bars coloured by module assignment. (E) Heatmap displaying differential module eigengene (DME) results comparing the ICH model to Naive across time points. The Wilcoxon rank‐sum test with Bonferroni correction was applied: not significant (p > .05); ∗p < .05; ∗∗p < .01; ∗∗∗p < .001; ∗∗∗∗p < .0001. (F) Dot plot showing the enrichment of differentially expressed genes (DEGs) of primary cell types in each module, coloured by module assignment. Dot size represents the logarithm of the odds ratio.

FIGURE 3

Cellular heterogeneity of neuroinflammation subtypes within the ICH model. (A) UMAP visualisation of subtypes in the snRNA‐seq transcriptomic data from astrocytes (AC), microglia (MGL), macrophages (MAC) and T cells. (B) Beeswarm plot showing differential abundance of neuroinflammation subtypes at each time point after ICH. Significant changes are highlighted in red and blue. (C) Dot plot showing DEGs of astrocytes, coloured by subtype. (D) Box plots showing the neuroinflammation scores of astrocyte subtypes. (E) The volcano plot illustrates the up‐regulated differentially expressed genes of the microglial subtype comparing MGL_1, coloured by subtype. (F) Bar plots (bottom) show the changes in relative proportions of subtypes across six time points (naive, Day 1, Day 3, Day 7, Day 14 and Day 28). Bar plots (up) show F‐statistics using analysis of variance (ANOVA), coloured by subtypes. (G) Dot plot showing DEGs of macrophages, coloured by subtype. (H) Bar plot showing gene ontology (GO) enrichment of up‐regulated DEGs in MAC. (I) Representative immunofluorescence images showing CD3⁺ T cells (red) in the lesion core at Day 7 and Day 14 post‐ICH. DAPI (blue) marks nuclei. (J) Box plots showing T cell counts at Day 7 and Day 14 after ICH. p value was determined by t‐test. (K) Line plots showing inferred T cell functional scores over time after ICH, including cytotoxicity (left) and regulatory (right) programs, as assessed by TCellSI.

FIGURE 4

Cell–cell interactions in neuroinflammation subtypes within the ICH model. (A) The number of predicted ligand–receptor (L–R) interactions in neuroinflammation subtypes of ICH, coloured by subtype. (B) The sum of interaction probability differences (relative information flow) for each interaction group in the ICH model, categorised as pro‐inflammatory (top) and anti‐inflammatory (bottom). (C) Hierarchy plot showing the migration inhibitory factor (MIF) signalling network on Day 1 (left), Day 3 (middle) and Day 7 (right). Nodes represent cell types, and edges denote interactions, with width proportional to interaction probability and colour by subtype. (D) The dot plot showing the mean expression levels of L–R of MIF signalling pathway in Days 1 and 3. The colour represents the average scaled gene expression level (z‐score), and the dot size represents the percentage of cells in which the marker gene was detected for each population. (E) Representative immunofluorescence staining shows MIF signalling. MIF is the ligand, and CD44 and CD74 are the receptors. (F) Circle plot showing the MIF signalling network on Day 14 (left) and Day 28 (right). Nodes represent cell types, and edges denote interactions, with width proportional to interaction probability and colour by subtype. (G) The dot plot showing the mean expression levels of ligand–receptor of CD226 signalling pathway in Days 14 and 28. The colour represents the average scaled gene expression level (z‐score), and the dot size represents the percentage of cells in which the marker gene was detected for each population.

FIGURE 5

T cells promote the remyelination of oligodendrocyte lineages. (A) UMAP visualisation of oligodendrocyte lineages in the single‐nucleus RNA sequencing (snRNA‐seq) transcriptomic data from OPC and OLG. DOL, disease‐associated oligodendrocytes; MFOL, myelin‐forming oligodendrocytes; MOLL, mature oligodendrocytes; NFOL, newly formed oligodendrocytes; OPCs oligodendrocyte precursor cells; POPCs, proliferative oligodendrocyte precursor cells. (B) Stacked‐violin plot showing the mean expression levels of marker genes for the oligodendrocyte lineages subtypes, coloured by the median expression. (C) Line plots (left) show the changes in relative proportions of subtypes across six time points (naive, Day 1, Day 3, Day 7, Day 14 and Day 28). Bar plots (right) show F‐statistics using analysis of variance (ANOVA), coloured by subtypes. (D) Streamline plot showing RNA velocity flow projected in the UMAP space. (E) Time‐resolved quantification of weighted RNA velocity scores across oligodendrocyte lineage transitions after ICH. (F) Violin plot showing the difference potential of oligodendrocyte lineages by the relative CytoTRACE score. The lower score represents the higher potential difference. (G) Heatmap showing ligand activity of T cells, coloured by AUPR (area under the precision–recall curve). (H) Heatmap plot showing the regulatory potential between T and MFOL. (I) Venn plot illustrating the overlap between remyelination trajectories genes and oligodendrocyte lineage cells DEGs in predicting target genes. (J) Scatterplot showing pseudotime dynamics of the expression of Vcan, Chn2, Eml1 and Larp6 in oligodendrocyte lineage cells. (K) Representative immunofluorescence staining showing CD3⁺ T cells (green) and MBP⁺ myelin (red) in the lesion core (right) versus contralateral hemisphere (left) at Day 14 post‐ICH. (L) Box plots showing quantification of myelin basic protein (MBP) signal in the ipsilateral and contralateral striatum. p value was determined by paired t‐test. (K) Cumulative frequency curves showing the proportion of myelinated fibres across myelin cross‐sectional areas in the lesion (L‐1, L‐2, L‐3) and contralateral (C‐1, C‐2, C‐3) hemispheres.