Repopulated spinal cord microglia exhibit a unique transcriptome and contribute to pain resolution

Abstract

Microglia are implicated as primarily detrimental in pain models; however, they exist across a continuum of states that contribute to homeostasis or pathology depending on timing and context. To clarify the specific contribution of microglia to pain progression, we take advantage of a temporally controlled transgenic approach to transiently deplete microglia. Unexpectedly, we observe complete resolution of pain coinciding with microglial repopulation rather than depletion. We find that repopulated mouse spinal cord microglia are morphologically distinct from control microglia and exhibit a unique transcriptome. Repopulated microglia from males and females express overlapping networks of genes related to phagocytosis and response to stress. We intersect the identified mouse genes with a single-nuclei microglial dataset from human spinal cord to identify human-relevant genes that may ultimately promote pain resolution after injury. This work presents a comprehensive approach to gene discovery in pain and provides datasets for the development of future microglial-targeted therapeutics.

Keywords: CP: Neuroscience; WGCNA; chronic pain; complex regional pain syndrome; human; microglia depletion; mouse; sex differences; spinal cord; weighted gene co-expression network analysis.

Figure 1.. Cx3CR1-Cre^ERT2-eYFP;R26-iDTR^LSL mice exhibit significant loss of microglia after diphtheria toxin (DT)-induced depletion, but these cells repopulate over time

(A) Schematic representation of the strategy used to selectively deplete microglia, and no other myeloid-lineage cells, using the Cx3CR1-Cre^ERT2-eYFP; R26-iDTR^LSL mouse. Mice were injected with tamoxifen (TAM; 100 mg/kg daily × 5 days), allowing excision of the floxed STOP codon, resulting in all Cx3CR1⁺ cells expressing the DTR. After 21–24 days, high-turnover macrophages no longer express DTR, while slow-turnover microglia continue to express DTR. Treatment with DT (1,000 ng daily × 3 days) resulted in selective death of microglia. (B) Lumbar spinal cord sections from Cx3CR1-Cre^ERT2-eYFP;R26-iDTR^LSL mice taken at multiple time points before (baseline) or after DT injection demonstrate a significant loss of Iba1⁺ microglia in the dorsal horn at day 1 post-DT by immunohistochemistry. Scale bars, 100 and 20 μm (insets). (C) Representative Sholl analysis profiles of microglia demonstrate changes in morphology over time following repopulation. The start radius of the rings is 0.5 mm with a step size of 1 μm. (D) Quantification of microglia number over time after depletion with DT. Four or five lumbar spinal cord dorsal horn sections were counted and averaged (n = 2–3 mice per group). **p < 0.01 by one-way ANOVA with Tukey’s post hoc test. (E) Analysis of intersections at a given radial distance from the soma demonstrates a drop in the number of intersections in repopulating microglia, which recovers, although not quite to baseline, over 28 days. Fifty-six to 185 microglia were analyzed per time point, with n = 2–3 mice per group. (F) Area under the curve (AUC) for number of intersections by radial distance from soma demonstrates a time-dependent increase after microglial depletion. **p < 0.01, ***p < 0.001, by one-way ANOVA with Dunnett’s post hoc test. Fifty-six to 185 microglia were analyzed per time point, with n = 2–3 mice per group. (G and H) Microglia depletion alone does not affect (G) mechanical sensitivity or (H) latency to withdrawal on a 52.5°C hot plate in male or female Cx3CR1-Cre^ERT2-eYFP;R26-iDTR^LSL mice (n = 3–6/sex, comparison by unpaired t test, in all cases p > 0.05). ns, not significant. Data are represented as the mean ± SEM.

Figure 2.. Microglia depletion at the acute-to-chronic phase results in a sustained change in the pain trajectory after peripheral injury

(A) Schematic of experimental timeline for acute-phase (day 0) depletion of microglia. (B) Mechanical threshold is decreased at the time of cast removal (3 weeks) in all groups; however, microglia depletion results in progressive, partial improvement in mechanical sensitivity in both males and females out to 9 weeks post-injury. n = 5–15 per group per sex. **p < 0.01, ***p < 0.001, ****p < 0.0001 by two-way ANOVA with Bonferroni’s post hoc test. (C) Schematic of experimental timeline for acute-to-chronic-phase depletion of microglia. (D) Mechanical threshold is decreased at the time of cast removal (3 weeks) in all groups; however, microglia depletion results in sustained improvement in mechanical sensitivity in both males and females out to 9 weeks post-injury. n = 6–11 per group per sex. ***p < 0.001, ****p < 0.0001 by two-way ANOVA with Bonferroni’s post hoc test. (E and F) Microglia depletion at the acute-to-chronic phase additionally results in improvement in thermal sensitivity, weight bearing, edema, and temperature changes in both (E) males and (F) females at 4 weeks post-injury. n = 6–11 per group per sex for hot plate, n = 4–5 per group per sex for unweighting, edema, and temperature. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 by two-tailed unpaired t test. Data are represented as the mean ± SEM.

Figure 3.. Differential gene expression analysis of female and male spinal cord microglia after peripheral injury with or without microglia depletion and repopulation identifies sex-specific and sex-independent signatures of pain recovery

(A) Differentially expressed microglial transcripts (DETs) and genes (DEGs) were identified using DESeq2 analysis in both males and females, with 112 sex-independent/shared upregulated DEGs and 72 sex-independent/shared downregulated DEGs. n = 4–7 mice per group per sex. (B) Volcano plots depicting DEGs from injured-repopulated microglia compared with injured-resident microglia in females (left) and males (right). Highly regulated, sex-independent, and significant DEGs are labeled in red. Thresholds for the x axis are set at |log₂(FC)| > 2.0 (female) and >0.83 (male). Threshold for the y axis is set at −log₁₀(adjusted p value) > 1.3, equivalent to p-adjusted < 0.05, for both sexes. (C and D) Significant gene ontology (GO) terms for (C) females and (D) males based on adjusted p < 0.05 for injured-repopulated vs. injured-resident microglia DEGs. (E) GO terms for the shared DEGs between male and female injured-repopulated vs. injured-resident microglia. Size of circles represents number of GO terms combined into the parent GO term shown. Color of circles represents log₁₀ p value.

Figure 4.. Weighted gene co-expression network analysis (WGCNA) reveals module-trait relationships that are distinct by sex and condition

(A and B) WGCNA identified (A) 53 unique modules in females and (B) 54 unique modules in males, which were then correlated with each of the four microglial comparisons (“traits”). Module-trait relationships that had significant correlations are marked with an asterisk, with heatmaps depicting the strength of the relationship (blue, negative correlation with the trait; red, positive correlation with the trait). Arrows indicate modules selected for further analyses. (C) The number of significant modules per sex as well as total number of transcripts within each module.

Figure 5.. Modules of interest for each sex highlight networks of genes that may contribute to pain resolution

(A) Gene ontology (GO) terms identified for female module blue. (B) Gene significance vs. module membership plot demonstrates correlation of member genes to the module eigengene. The 20 most extreme genes are highlighted in either red, for positive fold change, or blue, for negative fold change, in the injured-repopulated microglia group vs. the injured-resident microglia group. (C) List of top 20 most significant genes in gene significance vs. module membership correlation for female module blue. (D) GO terms for female module midnightblue. (E) Gene significance vs. module membership plot for female module midnightblue. (F) Top 20 most extreme genes for gene significance vs. module membership correlation for female module midnightblue. (G) GO terms for male module darkseagreen1. (H) Gene significance vs. module membership for male module darkseagreen1. (I) Top 20 most extreme genes for gene significance vs. module membership correlation for male module darkseagreen1. (J) GO terms for male module turquoise. (K) Gene significance vs. module membership for male module turquoise. (L) Top 20 most extreme genes for gene significance vs. module membership correlation for male module turquoise. Size of circles represents number of GOterms combined into the parent GO term shown. Color of circles represents log₁₀ p value.

Figure 6.. Single-nucleus RNA sequencing (snRNA-seq) of human microglia uncovers gene targets identified in repopulated microglia

(A) Diagram depicting experimental flow of isolation and enrichment of microglial nuclei from human spinal cord dorsal horn gray matter and subsequent 10× Chromium sequencing. (B) Percentages of cell-type-specific nuclei captured and sequenced, with microglia enriched to 40% of total nuclei. (C) Uniform manifold approximation and projection (UMAP) plot showing distribution of human spinal cord single-nucleus clusters by cell type. Dashed box indicates final microglial clusters that were further subclustered in (E). (D) Microglial-specific cell clusters are identified using known microglial gene markers. (E) Six distinct microglial nucleus clusters were identified after subclustering using Seurat. (F) Gene ontology (GO) analysis showing top 15 GO terms that capture differential microglia cell states. (G) Dot plot of selected top mouse genes that were significantly differentially expressed between human microglial subclusters (clusters 1–6). Top genes that overlap in male and female mouse datasets are shown in the gray zone, top female genes in the purple zone, and top male genes in the green zone. (H) UMAP plots showing genes significantly highly expressed in the human homeostatic cluster 1 and also upregulated in injured-repopulated mouse microglia. Adjusted p values from differential cluster-expression analysis are shown in parentheses below the gene name. Scale bar represents relative gene expression.