Treg cell-derived osteopontin promotes microglia-mediated white matter repair after ischemic stroke

Ligen Shi; Zeyu Sun; Wei Su; Fei Xu; Di Xie; Qingxiu Zhang; Xuejiao Dai; Kartik Iyer; T Kevin Hitchens; Lesley M Foley; Sicheng Li; Donna B Stolz; Kong Chen; Ying Ding; Angus W Thomson; Rehana K Leak; Jun Chen; Xiaoming Hu

doi:10.1016/j.immuni.2021.04.022

. Author manuscript; available in PMC: 2022 Jul 13.

Published in final edited form as: Immunity. 2021 May 19;54(7):1527–1542.e8. doi: 10.1016/j.immuni.2021.04.022

Treg cell-derived osteopontin promotes microglia-mediated white matter repair after ischemic stroke

Ligen Shi ^1,⁹, Zeyu Sun ^1,⁹, Wei Su ¹, Fei Xu ^1,², Di Xie ¹, Qingxiu Zhang ¹, Xuejiao Dai ¹, Kartik Iyer ¹, T Kevin Hitchens ³, Lesley M Foley ³, Sicheng Li ¹, Donna B Stolz ⁴, Kong Chen ⁵, Ying Ding ⁶, Angus W Thomson ⁷, Rehana K Leak ^1,⁸, Jun Chen ^1,², Xiaoming Hu ^1,^2,¹⁰

PMCID: PMC8282725 NIHMSID: NIHMS1697821 PMID: 34015256

Summary:

The precise mechanisms underlying the beneficial effects of regulatory T (Treg) cells on long-term tissue repair remain elusive. Here, using single-cell RNA sequencing and flow cytometry, we found that Treg cells infiltrated the brain 1–5 weeks after experimental stroke in mice. Selective depletion of Treg cells diminished oligodendrogenesis, white matter repair, and functional recovery after stroke. Transcriptomic analyses revealed potent immunomodulatory effects of brain-infiltrating Treg cells on other immune cells, including monocyte-lineage cells. Microglia depletion, but not T cell lymphopenia, mitigated the beneficial effects of transferred Treg cells on white matter regeneration. Mechanistically, Treg cell-derived osteopontin acted through integrin receptors on microglia to enhance microglial reparative activity, consequently promoting oligodendrogenesis and white matter repair. Increasing Treg cell numbers by delivering IL-2:IL-2 antibody complexes after stroke improved white matter integrity and rescued neurological functions over the long term. These findings reveal Treg cells as a neurorestorative target for stroke recovery.

Keywords: Regulatory T cells, microglia, white matter, oligodendrocytes, osteopontin, stroke recovery

Graphical Abstract

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In Brief

The mechanisms underlying the beneficial effects of Treg cells on stroke recovery remain unclear. Shi et al. report that brain-infiltrating Treg cells enhance brain repair after stroke. Treg cell-derived osteopontin promotes a tissue-reparative microglial response, thereby facilitating oligodendrocyte regeneration and remyelination at the chronic stages of stroke. Boosting Treg cell numbers with an IL-2:IL-2 antibody complex improves long-term stroke recovery.

Introduction

Elaborate systems of crosstalk between the central nervous system (CNS) and the immune system have evolved to mitigate the impact of brain injuries and promote recovery (Kamel and Iadecola, 2012). Both innate and adaptive immune cells are activated after cerebral ischemia and play crucial roles in debris clearance and inflammation resolution (An et al., 2014). Emerging evidence increasingly implicates additional types of immune cells in neurorepair and regeneration (Hu et al., 2018). However, excessive or indiscriminate immune activation leads to secondary injury and impedes reparative processes in injured brains (An et al., 2014). Thus, establishing an immune milieu that favors tissue repair is fundamentally important for neurological recovery after ischemic stroke.

Accumulating evidence supports multifaceted roles of regulatory T (Treg) cells in response to cerebral ischemia. Treg cells constitute a minor subpopulation of CD4⁺ T cells defined by the expression of an array of signature proteins, including CD25, forkhead box p3 (Foxp3), and Helios (Ferreira et al., 2019). Treg cells are dedicated to curtailing excessive immune responses and preserving immune homeostasis. A variety of molecular mechanisms, ranging from cell-to-cell signals and soluble mediators, are deployed by Treg cells to suppress the functions of innate and adaptive immune cells, including CD4⁺ T effector cells, CD8⁺ T cells, monocyte lineage cells and natural killer cells. The impact of Treg cells on ischemic brain injury at the acute (the first 48 hours after stroke) and subacute (3-6 days after stroke) stages has been investigated. Depletion of endogenous Treg cells yielded conflicting early effects on brain infarct sizes, which might be due to the variations in stroke severity and the dynamic nature of post-stroke immunity (Kleinschnitz et al., 2013; Li et al., 2013a; Liesz et al., 2015; Liesz et al., 2009). Adoptive transfer of syngenic Treg cells or increasing endogenous Treg cell numbers early after stroke protects the brain 1-7 days after ischemic injury (Li et al., 2013a; Zhang et al., 2018a). Gut microbiota also remotely impact acute ischemic stroke outcomes by regulating the balance between intestinal Treg cells and γδ T cells (Benakis et al., 2016). Recent research further supports a contribution of Treg cells in CNS recovery in the chronic stages (>1 week) after initial ischemic insult. The number of Treg cells is increased in the ischemic brain and continued to be high in number up to at least one month after stroke. These accumulating Treg cells are thought to promote post-stroke functional recovery by inhibiting astrogliosis (Ito et al., 2019). In addition, Treg cells may be involved in neurogenesis, as Treg cell depletion inhibits neural stem cell proliferation 4 days after stroke (Wang et al., 2015). These studies have focused on direct interactions between Treg cells and resident nonimmune CNS cells. However, whether and how the canonical immune checkpoint functions of Treg cells impact cerebral immunity during the chronic stage of stroke and contribute to long-term brain recovery remains unknown.

In this study, we used single-cell RNA sequencing (scRNAseq) to identify a prominent Treg cell cluster among brain infiltrating immune cells during the chronic stages after stroke in mice. Our results revealed that Treg cell deficiency impaired white matter (WM) integrity and retarded oligodendrocyte regeneration in post-stroke brains. Mechanistically, Treg cell-derived osteopontin (OPN) acted through integrin receptors to enhance the reparative activity of microglia, thereby promoting oligodendrogenesis and WM regeneration. Finally, boosting Treg cell numbers via administration of IL-2:IL-2 antibody complexes (IL-2:IL-2Ab) markedly improved WM integrity and long-term functional recovery after stroke. These findings help pave the way for a therapeutic approach to stroke and other neurological disorders that involve chronic neuroinflammation and WM pathology.

Results:

Treg cells accumulate in the injured brain over a long time frame after ischemic stroke

An assortment of peripheral immune cells enter the brain soon after ischemic injury and remain in the CNS for various periods. Dissecting the cellular complexity of these infiltrating cells and their dynamic changes over time may shed light on the function of individual immune cell subsets in the injured brain. We therefore performed scRNAseq of CD45^high infiltrating immune cells sorted from ischemic mouse brains at 5 days (5d) and 14 days (14d) after transient (60 min) middle cerebral artery occlusion (tMCAO) (Figure 1A, Figure S1A). The 5d time point after tMCAO was chosen to represent the subacute stage, which is characterized by prominent inflammatory responses and massive immune cell infiltration (Gelderblom et al., 2009). Brain repair processes are known to commence approximately 14d after tMCAO (Chu et al., 2012; Wahl and Schwab, 2014), and this survival timepoint is commonly employed (Clarkson et al., 2010; Ito et al., 2019) to represent the recovery phase of stroke. A total of 10,925 cells from 4 animals (2 biological replicates for each time point) were sequenced. After quality control, 4,629 (76.78% of 6,029) 5d cells and 3,779 (77.19% of 4,896) 14d cells were included in downstream analyses. The sequencing depths (~60,000 reads per cell), median unique molecular identifier (UMI) count (Figure S1B), and the percentages of mitochondrial genes (Figure S1C) were comparable in all four samples.

Unsupervised clustering analysis identified 14 distinct clusters in 5d cells (Figure S1D) and 11 distinct clusters in 14d cells (Figure 1B). Based on the enriched genes for each cluster (Figure 1C, such as microglia, neutrophils and specific populations of macrophages were dominant in 5d cells, consistent with a predominant innate immune response early after stroke (Figure 1D, Figure S1D). In contrast, the 14d cells were characterized by robust increases in adaptive immune cell numbers (Figure 1D). In particular, the number of αβ T cells increased robustly from 5d to 14d after stroke (Figure 1D), suggestive of the importance of this population in stroke recovery. We therefore subclustered and more deeply assessed the 14d αβ T cell population (Figure 1E). Seven subclusters were identified including CD4⁺ Treg cells, CD4⁺ memory T cells, natural killer T (NKT) cells, macrophage-like T cells and populations of CD8⁺ T effector cells (Figure 1E, Table S2). The distinct Treg cell subcluster expressed transcripts of canonical Treg cell markers such as Cd4, Il2ra (CD25), Foxp3 and Ikzf2 (Helios) (Figure S1E). In addition, this distinct cluster expressed Tnfrsf4, Ctla4, Nr4a1, Rora, Ass1, Maf and Gata3 transcripts (Figure S1F).

We ascertained temporal changes in the CD3⁺CD4⁺CD25⁺Foxp3⁺ Treg cell population in the ischemic brain using flow cytometry at different time points (3, 5, 7, 14 and 35 days) after stroke, thereby spanning the acute, subacute and chronic stages (Figure 1F). Small amounts of Treg cell infiltration were observed 3d and 5d after tMCAO. The numbers of infiltrating Treg cells significantly increased from 7d onwards, escalating until at least 35d post-tMCAO. The vi-SNE map of flow cytometry data revealed a prominent Treg cell population among all CD4⁺ T cells in single-cell brain suspensions collected 14d after stroke. These cells expressed CD25 and Foxp3 (Figure 1G). Consistent with recent studies (Ito et al., 2019), the majority of brain-infiltrating Treg cells were Helios⁺(Figure 1G, Figure S1G), a population recently identified as phenotypically active Treg cells (Thornton et al., 2019), and expressed CTLA4 (Figure S1G).

Treg cells underlie long-term functional recovery and enhance white matter integrity after stroke

To determine the role of Treg cells in long-term outcomes after stroke, we selectively depleted Treg cells by diphtheria toxin (DT) injections in Foxp3^DTR (DTR) transgenic mice that express the DT receptor under control of the Foxp3 promoter. DT was injected 3d prior to tMCAO and repeated every 3d until 21d after stroke or sham operation (Figure 1H). We have reported that 3-day DT dosing into DTR mice induces Treg cell depletion 3d after the final dosing (Zhang et al., 2018a). Repeated DT application resulted in significant reduction of Treg cells in the blood, spleen (Figure S2A-S2D), and in the ischemic brain 21d after stroke (Figure 1I). Treg cell depletion for 3 weeks did not impair behavioral performance in sham-operated mice (Figure 1J-1K). Treg cell-depleted and Treg cell non-depleted DTR mice exhibited comparable cerebral blood flow reduction and reperfusion, indicating that differences in outcomes cannot be attributed to variations in the magnitude of the initial ischemic insult (Figure S2E-S2F). Furthermore, the initial neurological deficits after stroke were similar between DTR mice with DT or PBS treatment (Figure 1J-1K). However, persistent Treg cell deficiency exacerbated long-term sensorimotor functions, as revealed by decreased time spent on a rotating bar (Figure 1J) and increased time until removal of adhesive tapes from the impaired paws (Figure 1K). These results support a critical contribution of endogenous Treg cells to functional recovery after stroke.

We then quantified neuronal tissue loss in DTR mice with or without DT injection by immunostaining brain slices for the neuronal marker MAP2. Treg cell-depleted and Treg cell non-depleted animals exhibited comparable neuronal tissue loss 21d after tMCAO (Figure S2G), indicating that endogenous Treg cells exert negligible long-term effects on macroscopic gray matter tissue loss. Treg cells have recently been implicated in remyelination processes in models of multiple sclerosis (Dombrowski et al., 2017). We observed a massive accumulation of Foxp3⁺ Treg cells in brain regions dense in MBP⁺ WM 35d after stroke (Figure 2A). The influence of Treg cells on WM integrity after stroke was therefore evaluated by selective Treg cell depletion and adoptive transfer (Figure 2B). Quantification of Luxol fast blue (LFB) staining areas 21d after stroke revealed a significant reduction in myelin coverage in Treg cell-depleted mice compared to non-depleted mice (Figure 2C). We then transferred wild-type (WT) Treg cells to Treg cell-depleted DTR mice 6h after stroke. The WT Treg cells are not affected by DT injections because they lack DT receptors and can therefore rescue the sustained depletion of endogenous Treg cells in DT-injected DTR mice. Adoptive transfer of WT Treg cells to Treg cell-depleted DTR mice 6h after stroke reinstated myelin coverage in the WM (Figure 2C). Treg cell depletion did not change WM integrity in sham DTR mice (Figure S3A). The same regimen of DT injections in WT mice did not affect WM coverage (Figure S3B), ruling out any effect of DT alone on long-term WM integrity during stroke recovery. No significant difference in LFB staining was detected between Treg cell-depleted and Treg cell non-depleted mice 5d after tMCAO (Figure S3C), indicative of comparable early WM injury. In addition, Treg cell depletion starting as late as 5d after stroke also aggravated WM lesions 21d after tMCAO (Figure S3D), suggesting that the long-term salutary effects of Treg cells might be partially attributed to enhanced late-stage tissue repair, rather than to early tissue preservation and protection.

Whole-brain ex vivo diffusion tensor imaging (DTI) scanning was performed 21d after tMCAO (Figure 2D). WM integrity was evaluated on fractional anisotropy (FA) maps and quantified as the ratio of FA(ipsilateral)/FA(contralateral) values. FA values were reduced 21d after stroke in WM-enriched areas, including the external capsule (EC) and internal capsule (IC), as indicated by an FA ratio<1 (Figure 2E). The decrease in FA value was augmented in Treg cell-deficient stroke mice, but mitigated by WT Treg cell replenishment (Figure 2E). In addition, WT Treg cell transfer corrected the otherwise elevated radial diffusivity (RD, λ_⫠) in the EC and IC (Figure 2F), a measure believed to reflect myelin damage (Song et al., 2002). No differences across groups were observed in axial diffusivity (λ_∥), a measurement of axonal integrity (not shown).

Transmission electron microscopic (TEM) analysis of ipsilesional EC further supported the role of Treg cells in maintaining WM structure after stroke (Figure 2G-2I). At 21d after tMCAO, many axons without myelin sheaths or with thin myelin sheaths were observed in stroke mice (Figure 2G). Treg cell depletion by DT further reduced myelin thickness in stroke mice. Myelin sheath defects, including split myelin layers, myelin discontinuity and myelin detachment were more frequently observed in Treg cell-depleted stroke mice (Figure 2G, red asterisks). Moreover, Treg cell-depleted mice had fewer axons with typical structures of remyelination, including thin and loosely wrapped myelin sheaths and ends of the spiraling cytoplasmic processes of oligodendrocytes (Figure 2G, purple arrows) (Deshmukh et al., 2013). Increases in the g-ratio (the ratio of the inner axonal diameter to the total outer diameter of myelinated fiber) were observed in low-, middle- and high-caliber axons in Treg cell-depleted compared to Treg cell non-depleted mice after tMCAO (Figure 2H). Scatter plots of g-ratio as a function of axon diameter revealed higher g-ratios in DTR+DT mice vs. DTR+PBS mice, suggesting a decrease in myelin thickness in Treg cell-depleted mice after stroke (Figure 2I).

Evoked compound action potentials (CAPs) were used to determine whether structural alterations in the WM-enriched corpus callosum (CC) and EC were associated with nerve fiber conduction deficits (Figure 2J-2M). Evoked CAPs display a biphasic wave with an early peak representing fast-conducting myelinated axons (N1), followed by a delayed peak representing slow-conducting unmyelinated axons (N2, Figure 2J) (Zhang et al., 2019). The amplitudes of the N1 and N2 segments were markedly reduced 21d after tMCAO (Figure 2K-2L), indicating impaired conduction through myelinated and unmyelinated axons, respectively. Treg cell depletion exacerbated the reduction in the N1 amplitude and worsened conduction deficits through myelinated axons (Figure 2K). The amplitude of the N2 component did not show a difference between Treg cell-competent and Treg cell-depleted mice (Figure 2L), suggesting no major influence of Treg cell depletion on unmyelinated axons. The conduction velocity of neither the N1 (Figure 2M) nor the N2 component (not shown) was affected by ischemic injury or Treg cell depletion.

Treg cells promote oligodendrogenesis contingent upon microglia

We then compared the regeneration of myelin-producing oligodendrocytes in Treg cell-depleted and non-depleted mice 14d after tMCAO. BrdU was injected 3d-5d after tMCAO to label newly generated cells (Figure 2B). Mature oligodendrocytes were recognized by immunostaining with the anti-APC (also known as CC1) antibody (Figure 3A). A reduction in the numbers of APC⁺BrdU⁺ newly generated oligodendrocytes was observed in the EC and striatum of Treg cell-depleted mice compared to Treg cell-competent mice (Figure 3B). The total numbers of APC⁺ cells and BrdU⁺ cells were also decreased in the EC of Treg cell-depleted mice (Figure 3B). Adoptive transfer of WT Treg cells into Treg cell-depleted DTR mice reinstated the numbers of newly generated oligodendrocytes after stroke (Figure 3B).

Figure 3. — (A) Representative images of BrdU (green) and APC (red) immunostaining in *Foxp3*^DTR (DTR) mice with or without DT injection and WT Treg cell transfer. The dotted red lines trace the boundaries of the EC. (B) Quantification of the numbers of APC⁺BrdU⁺ cells, APC⁺ cells and BrdU⁺ cells in the peri-infarct areas. n=6. *p<0.05, **p<0.01, ***p<0.001, one-way ANOVA & Bonferroni or Kruskal-Wallis test & Dunn’s. (C) CD4⁺CD25⁺Foxp3(GFP)⁺ Treg cells were isolated from the blood and ischemic brain 14d after tMCAO and from blood of sham mice for RNAseq. (D) Scatter plot showing comparison of log₂FPKM for extracellular and cell-released factors in brain Treg cells vs. sham blood Treg cells. Red dots highlight some DEGs that are known to promote oligodendrogenesis and are upregulated in brain Treg cells by more than 4-fold. The orange dashed line indicates a fold change of 1. **(E)** Protein expression of OPN and IL-10 by brain-infiltrating Treg cells and blood Treg cells 14 days after stroke or after sham operation. n=6. **p<0.01, ***p<0.001, one-way ANOVA & Bonferroni. (F) IPA analysis of DEGs identified key biological processes in brain infiltrating Treg cells compare to sham blood Treg cells, which was graphically displayed in bubble plots according to Z scores and significance [−log₁₀(adjusted P value)]. (G) Bar graphs indicating immunomodulatory effects of Treg cells in the ischemic brain. (**H-I**) *Rag1*^−/− mice (H, n=7-8) or microglia and macrophage depleted mice (I, n=6-7. by dietary intake of PLX5622 7d before tMCAO) received Treg cells (2 × 10⁶) or PBS 6h after tMCAO. The number of APC⁺BrdU⁺ cells in ischemic areas were quantified. *p<0.05, ***p<0.001, student’s t test (H) or one-way ANOVA & Bonferroni (I). Data are mean ± SD. (See also Figure S4 and Table S3)

To further elucidate the mechanisms whereby Treg cells contribute to post-stroke recovery, we performed RNAseq analysis on sorted CD4⁺CD25⁺Foxp3(GFP)⁺ Treg cells from the ischemic brain and blood of DTR mice 14d after tMCAO and from the blood of sham DTR mice (Figure 3C, Figure S4A). Principal component analysis (PCA) revealed that the overall transcriptomic profiles were markedly changed in brain and blood Treg cells after stroke compared to Treg cells from sham mice (Figure S4B). The differentially-expressed genes (DEGs, false discovery rate (FDR) < 0.05; and ∣fold change∣ > 4) between brain-infiltrating Treg cells and peripheral Treg cells under sham or stroke conditions were identified (Table S3). In the blood and brain-infiltrating Treg cells from stroke mice, we noted increased expression of genes related to inflammatory responses (C3ar1, C5ar1, Spp1, Tgfb2, etc.) and cell chemotaxis (Cxcr2, Ccr1, Ccr5, Ccl12, etc.) compared to blood Treg cells from sham mice (Figure S4C). Consistent with a recent report (Ito et al., 2019), brain-infiltrating Treg cells exhibited much higher expression of genes encoding immunoregulatory and trophic factors (Figure S4C). Scatter plots of the normalized average gene expression in brain Treg cells against sham blood Treg cells (Figure 3D) or stroke blood Treg cells (Figure S4D) highlighted extracellular and cell-released factors. Notably, several genes upregulated in brain-infiltrating Treg cells vs. blood Treg cells encode trophic factors known to stimulate oligodendrocyte precursor cell (OPC) differentiation (Igf1, Il1a, Osm, etc.) (Glezer and Rivest, 2010; Hsieh et al., 2004; Schmitz and Chew, 2008). In addition, robust increases in transcripts encoding cytokines (Spp1, Il1b, Il1a, Il10, etc.), chemokines (Ccl12, Ccl4, etc.) and other immunomodulatory factors were observed (Figure 3D, Figure S4D). When comparing fold changes in DEGs in brain-infiltrating Treg cells vs. sham blood Treg cells with those recently shown to be upregulated in brain Treg cells vs. sham spleen Treg cells (Ito et al., 2019), we found similarities in upregulation of some immune regulators and trophic factors, such as Spp1 and Il10 (Figure S4E). Flow cytometry analysis confirmed that greater percentages of brain-infiltrating Treg cells expressed OPN (the protein encoded by Spp1) and IL-10 compared to blood Treg cells (Figure 3E). The distinct patterns of gene expression of brain-infiltrating Treg cells exhibited an enrichment for genes with functions in immunoregulation and cell-cell interactions, especially the activation of phagocytes (Figure 3F-3G, Figure S4F-S4G).

To determine whether Treg cell-mediated WM repair relied on immunomodulation of effector T cells and other lymphocytes, we utilized Rag1^−/− lymphopenic mice (Figure 3H). Adoptive transfer of WT Treg cells into Rag1^−/− mice still increased the numbers of APC⁺BrdU⁺ newly generated oligodendrocytes in the ischemic brain (Figure 3H), suggesting that the presence of lymphocytes is not essential for the stimulating effect of Treg cells on oligodendrogenesis.

PLX5622, an inhibitor of colony-stimulating factor 1 receptor, was then utilized to test the role of microglia in Treg cell-induced oligodendrogenesis (Figure 3I). We recently reported that the PLX5622 diet achieves ~ 90% depletion efficacy within 7d and maintained microglia and macrophage depletion without major impact on other immune cells (Zhang et al., 2019). Depletion of microglia did not change the numbers of APC⁺BrdU⁺ cells compared to microglia-competent stroke mice (Figure 3I). Treg cell treatment increased the numbers of APC⁺BrdU⁺ cells in stroke mice fed with the normal control diet, but not in stroke mice fed with PLX5622 (Figure 3I). These results support the hypothesis that Treg cells facilitate post-stroke oligodendrogenesis through interaction with microglia.

Treg cells promote tissue-reparative microglia responses to ischemic brain injury

Given the potential influence of Treg cells on microglial functions during stroke recovery, we performed morphological analysis of Iba1⁺ microglia and macrophages in peri-infarct areas in Treg cell-depleted vs. non-depleted mice 14d after stroke (Figure 4A). Confocal 3D-reconstructions of Iba1⁺ cell images showed smaller cell surface areas and decreased cell volumes in Treg cell-depleted stroke mice (Figure 4B). Microglia and macrophage cell densities in the peri-infarct area were similar between Treg cell-depleted and non-depleted mice (Figure 4B). These data suggest that the presence of Treg cells could change activation status of microglia and macrophages in the post-stroke brain.

We then prepared Treg cells from mouse spleens and lymph nodes and established a coculture system to further dissect the impact of activated Treg cells on microglia. Treg cells in transwells were cocultured with brain slices collected 5d after tMCAO in the lower compartment for 24h. Treg cells in the transwells were then transferred to new wells with primary cultured microglia, and cocultured for 2d. Microglia were then collected for bulk RNAseq analyses (Figure 4C). Unstimulated microglia served as a control. We first applied Gene Set Enrichment Analysis (GSEA) to determine the biological processes in microglia that might be modulated by Treg cells. Indeed, several gene sets related to activation of innate immune responses and cytokine production and secretion were enriched in Treg cell-treated microglia (Figure 4D). A biological process termed ‘type 2 immune response’, which is thought to be related to tissue repair and acts as a counterbalance against tissue damaging inflammation (Lloyd and Snelgrove, 2018), was positively enriched in Treg cell-stimulated microglia compared to control microglia. In addition, the terms of ‘tissue remodeling’ and ‘stem cell differentiation’ were positively enriched in Treg cell-stimulated microglia. We then performed further analysis of the 1,173 DEGs (FDR < 0.05; and ∣fold change∣ > 2) identified between Treg cell-stimulated microglia and control microglia (Figure 4E, Table S4). Microglia cocultured with activated Treg cells displayed significant upregulation of genes related to an anti-inflammatory phenotype, such as Arg1, Fgl2, Mrc1, Il1rn, and Lgals3 (Figure 4F). Genes encoding proteins involved in brain repair, such as Ccr5, Tgm2, Vegfa, Fgf1 and Gdnf were upregulated concomitantly (Figure 4F). Many genes upregulated in Treg cell-stimulated microglia encode cell-released factors known to promote oligodendrogenesis, including Tgm2 (Giera et al., 2018), Vegfa (Hayakawa et al., 2011), Timp1 (Nicaise et al., 2019), Cxcl2 (Hosking et al., 2010), Fgf1 (Furusho et al., 2015), Ptn (Kuboyama et al., 2015), Inhbb (Miron et al., 2013) and Lif (Deverman and Patterson, 2012) (Figure 4G). Flow cytometry confirmed higher expression of TGM2, VEGFa, OPN, and ARG1 by Treg cell-stimulated microglia compared to unstimulated microglia (Figure 4H). These data suggest potential roles of Treg cells in polarizing microglia toward an anti-inflammatory and reparative phenotype.

We further tested the impact of Treg cell-microglia interactions on OPC differentiation using a primary coculture system (Figure 4I). While both Treg cell-conditioned and non-conditioned microglia promoted the differentiation of NG2⁺ OPCs into MBP⁺ mature oligodendrocytes, Treg cell-conditioned microglia exerted a more robust effect (Figure 4I). These results confirm that Treg cell-microglia interactions enhance a reparative microglial phenotype that promotes OPC differentiation.

OPN-mediated Treg cell-microglia interaction is essential for white matter restoration after stroke

Protein-protein interaction enrichment analyses using STRING identified potential ligand-receptor crosstalk between activated Treg cells and microglia (Figure 5A). Interactions between Spp1, which encodes the pleiotropic cytokine osteopontin (OPN), and Itgb1, Itga5 and Itgav, which encode the integrin subunits of the OPN receptor, were prominent during Treg cell-microglia interactions (Figure 5A). Immunostaining confirmed the expression of OPN in infiltrating Treg cells in the ischemic brain (Figure 5B). Flow cytometry confirmed that higher percentages of brain-infiltrating Treg cells were OPN⁺ compared to T effector cells 14d after tMCAO (Figure 5C).

Figure 5. — (A) STRING analysis predicts ligand-receptor pairs that mediate interactions between activated Treg cells and microglia. (B) OPN (red) in Foxp3-GFP⁺ (green) Treg cells in the ischemic brain 35d after tMCAO. (C) OPN expression by CD3⁺CD4⁺CD25⁺Foxp3⁺ Treg cells and CD3⁺CD4⁺CD25− T effector cells in the ischemic brains 14d after tMCAO. n=4. **p<0.01, Welch's t test. (D) Primary rat microglia were treated with 1 nM OPN (n=9), IL-1α (n=5) or IL-1β (n=6) or PBS (n=8) for 24h. Conditioned media (CM) were collected and applied to rat OPCs for 3d. To evaluate a direct effect, 1 nM OPN (n=4), IL-1α (n=4), IL-1β (n=3) or PBS vehicle control (n=10) were applied to OPCs for 3d. T3+CNTF (n=5) were used as a positive control. (E) Expression of *mbp* was assessed by RT-PCR. *p<0.05, one-way ANOVA & Dunnett’s. (F) Quantification of MBP⁺ cells (green) and NG2⁺ cells (red) as percentages of total cells. Five independent experiments. ***p<0.001 vs. control (a). ### p<0.001 vs. control Mi (microglia) CM (c). One-way ANOVA & Bonferroni. (G) Experimental design for WT or *Spp1*^−/− Treg cell transfer to Treg cell depleted DTR mice. (H) Quantification of APC⁺BrdU⁺ cells 14d after tMCAO. n=6-7. *p<0.05, **p<0.01, Student’s t test. (I) Quantification of myelin coverage by LFB staining. n=5. *p<0.05, **p<0.01, Student’s t test. (J) Microglia-specific Itgb1 deficient mice (*Cx3cr1*^CreER+/−*Itgb1*^f/f) or control mice (*Cx3cr1*^creER+/−) received WT Treg cells or PBS after tMCAO. (K) Flow cytometry confirmed deficiency of Itgb1 in brain microglia and blood macrophages 7d after 4-OH tamoxifen (TAM). n=3. *p<0.05, **p<0.01, Student’s t test (macrophage) and Mann Whitney test (microglia, box plot, 2.5-97.5 percentile). (L) Quantification of APC⁺BrdU⁺ cells. n=6. *p<0.05, ***p<0.001, ns: not significant, one-way ANOVA & Bonferroni. (M) Upper: Primary mouse microglia in inserts were cocultured with or without activated WT or *Spp1*^−/− Treg cells in the lower chamber for 2d, and then cocultured with WT OPCs for 3d. Lower: Microglia in inserts were cocultured with activated WT Treg cells in the lower chamber in the presence of anti-Itgb1 Ab (20μg/ml) or isotype control Ab (20μg/ml) for 2d, and then cocultured with WT OPCs for 3d. (**N-O**) Immunostaining of MBP (green) and NG2 (red). (P) MBP⁺ cells and NG2⁺ cells were quantified as percentages of total cells. Five independent experiments. *p<0.05, ***p<0.001, one-way ANOVA & Bonferroni. Data are mean ± SD unless otherwise specified. (See also Figure S5)

We then stimulated microglia in cultures with recombinant OPN or two other cytokines (IL-1α and IL-1β) that were released by activated Treg cells (Figure 5D). Conditioned media (CM) collected from OPN-stimulated microglia enhanced OPC differentiation toward mature oligodendrocytes, as revealed by elevated mRNA expression of the mature oligodendrocyte marker mbp, while CM from IL-1α- or IL-1β-stimulated microglia or direct exposure to OPN, IL-1α or IL-1β failed to increase OPC differentiation (Figure 5E). Immunostaining confirmed that OPN-stimulated microglia CM increased OPC differentiation (Figure 5F). We also noted that Treg cell-stimulated microglia in culture showed higher Spp1 mRNA expression (Figure 4F, Figure S5A) and higher OPN protein expression (Figure 4H) compared to non-stimulated microglia. In vivo studies confirmed that much fewer microglia in Treg cell-depleted mice expressed OPN than in Treg cell-competent mice 14 days after stroke (Figure S5B-S5C). These data suggest that Treg cell-derived OPN stimulates microglial OPN production upon Treg cell-microglia interaction.

To further evaluate the mechanistic involvement of OPN in Treg cell-enhanced oligodendrogenesis, we adoptively transferred Treg cells derived from WT or Spp1^−/− mice into DTR+DT mice 6h after tMCAO (Figure 5G). OPN-deficient Treg cells exhibited a reduced capacity to promote oligodendrogenesis 14d after stroke, as revealed by decreased numbers of APC⁺BrdU⁺ cells in mice with Spp1^−/− Treg cell transfer compared to that following WT Treg cell transfer (Figure 5H). Consequently, reduced myelination was detected in Spp1^−/− Treg cell-treated mice vs. WT Treg cell-treated mice after tMCAO (Figure 5I).

Integrin β1 is a common subunit for different subtypes of integrin receptors. Therefore, microglia and macrophage-specific integrin β1 conditional deficient mice (Cx3cr1^CreER+/−Itgb1^f/f) were bred from Itgb1^flox/flox and Cx3cr1^CreER+/− mice to disrupt the function of microglial OPN receptors and evaluate the role of these receptors in Treg cell-enhanced oligodendrogenesis after stroke (Figure 5J). Hemizygous Cx3cr1^CreER+/− mice served as age-matched controls. EYFP immunofluorescence could be detected in Cx3cr1-expressing microglia and macrophages in Cx3cr1^CreER+/−Itgb1^f/f mice. Flow cytometry confirmed the deficiency of integrin β1 specifically in CD45⁺EYFP⁺ microglia in the brain and in the CD3⁻EYFP⁺ macrophages in blood collected from Cx3cr1^CreER+/−Itgb1^f/f mice 7d after 4-hydroxytamoxifen (TAM) injections (Figure 5K). In contrast, expression of integrin β1 did not change in O4⁺ oligodendrocytes or GLAST⁺ astrocytes in the brain, or CD3⁺ T lymphocytes in blood (Figure S5D). Itgb1 deficiency in microglia and macrophages alone impaired oligodendrocyte replacement in the striatum after stroke, and reduced the capacity of WT Treg cells to increase the number of newly-generated APC⁺BrdU⁺ oligodendrocytes in Cx3cr1^CreER+/−Itgb1^f/f mice compared to control Cx3cr1^CreER+/− mice 21d after tMCAO (Figure 5L).

In vitro coculture experiments were performed to further confirm the direct interactions between Treg cells and microglia via the OPN-Itgb1 axis (Figure 5M-5P). First, Spp1^−/− Treg cell-stimulated microglia exhibited lower capacities than WT Treg cell-treated microglia to enhance OPC differentiation (Figure 5N and 5P). In the second experiment, blocking Itgb1 on microglia reduced the Treg cell-promoted trophic effect of microglia on OPCs (Figure 5O-5P).

These in vivo and in vitro data collectively support the importance of OPN-Itgb1 interactions in Treg cell-microglia crosstalk and subsequent promotion of oligodendrogenesis and myelin repair.

Boosting the numbers of endogenous Treg cells post-tMCAO improves long-term stroke outcomes

To assess the long-term influence of Treg cell boosting, we treated a cohort of WT mice with either IL-2:IL-2Ab (1 μg IL-2 and 5 μg IL-2Ab, ip) or the same amount of isotype-matched irrelevant antibody (IgG) starting 6h after stroke, and repeated once a day, on days 1, 2, 3, 10, 20 and 30 after tMCAO (Figure S6A). As expected, repeated IL-2:IL-2Ab injections increased the number of blood Treg cells and brain-infiltrating Treg cells compared to isotype IgG-injected controls (Figure S6B-S6C). IL-2:IL-2Ab treatment boosted the number of OPN⁺ Treg cells in the ischemic brain, but did not upregulate OPN expression in OPN⁺ Treg cells on a per-cell basis (Figure S6D). Sensorimotor functions, as measured by the adhesive removal test (Figure 6A) and Rotarod test (Figure 6B), were improved in IL-2:IL-2Ab-treated mice compared to IgG-treated mice, starting approximately 21d after stroke. IL-2:IL-2Ab treatment also improved post-stroke spatial cognitive functions, as manifested by a reduction in the latency to find the hidden platform (improved spatial learning) and increased time spent in the goal quadrant after the platform was removed (improved memory retention) in the Morris water maze (Figure 6C). There was no difference in swimming speed between different treatment groups, indicating similar swimming skills across groups.

Animals were euthanized 35d after stroke and the fixed brains subjected to DTI scanning (Figure 6D-6F). The ratio of FA(ipsilateral)/FA(contralateral) demonstrated WM injury (ratio<1) in the EC and IC in IgG-treated mice, which was attenuated in IL-2:IL-2Ab-treated mice (Figure 6E). IL-2:IL-2Ab treatment reduced RD values in EC and IC (Figure 6F), an indication of improved myelin integrity. Dual staining for MBP (a major myelin protein) and SMI32 (a marker of demyelinated axons) was performed to assess WM lesions 35d after tMCAO. There were significant increases in the MBP and SMI32 ratio in the infarct border in the EC, striatum, and cortex in IL-2:IL-2Ab-treated mice compared to control IgG-treated mice (Figure 6G), again indicating improved WM integrity after boosting Treg cells.

Myelin ultrastructure in the EC was evaluated using TEM 35d after tMCAO (Figure 6H). Many axons had lost their myelin sheath in IgG-treated mice (Figure 6H, red asterisks). Myelin sheath morphological defects were frequently observed in control stroke mice, but were reduced after IL-2:IL-2Ab treatment (Figure 6H, blue arrows). In contrast, IL-2:IL-2Ab-treated mice had more myelinated axons with typical structures of remyelination (Figure 6H, red arrow heads). IL-2:IL-2Ab treatment increased the thickness of myelin sheaths, as indicated by decreased g-ratios in axons of stroke mice (Figure 6I-6J). Taken together, these data reveal that boosting Treg cells by IL-2:IL-2Ab complexes improves WM integrity and rescues long-term neurological functions after tMCAO.

Discussion

The current study elucidates a mechanism whereby Treg cell-microglia interplay generates an OPN-enriched microenvironment to optimize microglial responses, facilitate oligodendrocyte regeneration and promote WM repair at the chronic stages of ischemic stroke. An improvement in WM integrity might be achieved by preserving existing anatomical structures or by enhancing tissue repair and functional recovery. Our data suggest the latter as the predominant mechanism of action of Treg cells in the context of stroke. First, we observed ultrastructural evidence of remyelination in the ischemic brain, including thinner and more loosely wrapped myelin segments than in normal myelin sheaths (Franklin and Ffrench-Constant, 2017) as well as visible ends of the spiraling cytoplasmic processes of oligodendrocytes (Deshmukh et al., 2013). These structural alterations were diminished by Treg cell depletion and enhanced by IL-2:IL-2Ab administration. Second, Treg cell depletion reduced, while Treg cell transfer increased the number of new, mature oligodendrocytes in the ischemic brain. Third, impairments in WM integrity and behavioral performance were only observed in Treg cell-depleted animals in the late stages of recovery, but not at the early stage after stroke. Similarly, the IL-2:IL-2Ab-mediated Treg cell increase led to behavioral improvements only during the late phase of stroke. In addition, Treg cell depletion delayed until 5d after tMCAO also exacerbated WM injury after stroke. These results strongly suggest a delayed effect of Treg cells on long-term WM repair, rather than preservation of WM structure and function shortly after ischemic injury.

Transcriptomic analyses revealed that brain-infiltrating Treg cells were reprogrammed in favor of the mobilization or activation of phagocytes. Further studies on lymphocyte-deficient and microglia-depleted mice confirmed that the capacity of Treg cells to augment post-stroke oligodendrogenesis was still prominent in the absence of mature lymphocytes, but was reduced in mice lacking microglia and macrophages. These data support the importance of Treg cell-microglia interactions in oligodendrocyte replacement and WM repair after stroke. Dombrowski et al. reported that Treg cell-released trophic factors directly enhanced OPC differentiation (Dombrowski et al., 2017). In our RNAseq analyses, brain-infiltrating Treg cells showed higher expression of factors known to promote OPC differentiation. Dombrowski and colleagues excluded the involvement of immunoregulation in Treg cell-enhanced oligodendrogenesis in the model of MS (Dombrowski et al., 2017). However, we found that Treg cells acquired their immunoregulatory effects on microglia in the ischemic brain milieu, driving microglia towards reparative phenotypes to steer WM repair. Indeed, modulatory effects of Treg cells on microglia have been reported in neurodegenerative diseases. For example, Treg cells exert neuroprotection by switching microglia phenotype in the early phase of amyotrophic lateral sclerosis (ALS) (Beers et al., 2011), and Treg cell-derived IL-4 suppresses microglial toxicity in the slowly progressive phase of ALS (Zhao et al., 2012). In addition, boosting the function or quantity of Treg cells ameliorates pathological changes in a model of Parkinson’s disease, in part by directly reducing microglial activation and suppressing reactive oxygen species production (Reynolds et al., 2007; Reynolds et al., 2009). Therefore, Treg cells appear to contribute to brain repair via both indirect immunomodulation of microglial responses as well as direct trophic effects.

Treg cells function through a variety of molecular mechanisms depending on their destinations and target cells (Hu et al., 2018). Our study has uncovered a role of the OPN-integrin receptor axis in Treg cell-microglia dialog. OPN is a multifaceted glycoprotein with immunomodulatory functions and bridges innate and adaptive immunity under pathological conditions (Moorman et al., 2020). OPN has been implicated in neuroimmune responses in different CNS diseases including ischemic stroke, intracerebral hemorrhage, and Alzheimer’s disease (AD) (Gong et al., 2018; Ladwig et al., 2017; Rentsendorj et al., 2018; Zhang et al., 2018b). Previous reports showed that OPN is expressed by peripheral Foxp3⁺ Treg cells, where it plays important roles in immune regulation (Leavenworth et al., 2015; Shen et al., 2018). Here, we found that higher percentages of infiltrating Treg cells in the ischemic brain expressed OPN compared to circulating Treg cells from sham or stroke mice. Bioinformatic analyses predicted strong interactions between Treg cells and microglia through OPN and integrin receptors, which was confirmed experimentally by a series of in vivo and in vitro studies. Although OPN-treated microglia exhibited potent oligodendrogenic capacity, OPN itself had minimal direct effects on OPC differentiation in vitro, supporting the concept that OPN may boost oligodendrogenesis indirectly by modifying microglial functions or with help from other factors. Microglia are also important cellular sources of OPN, particularly upon activation. Recent single-cell studies highlight increased transcription of Spp1 in disease-associated microglia in AD (Keren-Shaul et al., 2017) and injury-responsive microglia in demyelinated lesions (Hammond et al., 2019). In the present study, we found that Treg cells served as a trigger or enhancer of a surge of OPN production, as well as other trophic factors, in microglia, which might be important for sustained reparative capacity of microglia. Notably, OPN deficiency in Treg cells or Itgb1 blockade in microglia significantly reduced but did not abolish the capacities of Treg cells to enhance oligodendrogenic activity of microglia, suggesting the involvement of additional OPN-independent mechanisms in Treg cell-microglia crosstalk.

In conclusion, we have shown that CNS-infiltrating Treg cells, brain-resident microglia, and oligodendrocytes interact to manage WM repair and functional recovery in the chronic stage of stroke. We have discovered that Treg cells enhance post-stroke oligodendrogenesis, at least partially, in a microglia-dependent manner. OPN has been identified as a mediator between Treg cells and microglia. Boosting Treg cell numbers might be a practical and druggable approach to improve WM repair and functional recovery.

Limitations of Study

The mechanism by which OPN-stimulated microglia enhance OPC differentiation remains to be explored. OPN may function as an intracellular signaling molecule (iOPN) to regulate actin cytoskeletal rearrangement and participate in morphological alteration, cell movement and phagocytic clearance (Inoue and Shinohara, 2011; Shin et al., 2011). Alternatively, OPN may change the secretory profiles of microglia and enhance the release of trophic factors. For example, OPN reduces the release of proinflammatory cytokines from LPS-challenged microglia (Rabenstein et al., 2016). In our study, in vitro analyses using no-contact transwells confirmed that cell-released soluble factors or microvesicles are possible operating mechanisms for microglia-OPC crosstalk. Transcriptomic analyses suggest increased expression of multiple oligodendrogenic factors from Treg cell-stimulated microglia. The exact mechanisms adopted by OPN-stimulated microglia to enhance oligodendrogenesis await further elucidation. In addition, the human relevance of our murine findings needs to be verified. A recent clinical study reported that the percentage of CD4⁺ Treg cells within lymphocytes is highly correlated to long-term neurological functions after stroke and can therefore predict stroke outcomes (Li et al., 2021). Future studies are warranted to further assess the translational potential of Treg cell therapy or IL-2:IL-2Ab treatment in stroke patients.

STAR* METHODS:

RESOURCE AVAILABILITY

Lead Contact

Further information and request for resources and reagents should be directed to and will be fulfilled by the lead contact, Xiaoming Hu (hux2@upmc.edu).

Materials Availability

This study did not generate new unique reagents.

Data and Code Availability

Raw single cell and bulk RNAseq files for each animal and sample are in the NIH GEO database (GSE171171). This study did not generate any unique code. All software and algorithms used in this study are publicly available and listed in the Key Resource table.

Key Resources Table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
Anti-APC Antibody	Millipore Sigma	Cat#OP80; RRID: AB_2057371
Anti-BrdU antibody	Abcam	Cat#ab6326; RRID: AB_305426
Anti-CD28 Antibody	Thermo Fisher Scientific	Cat#14-0281; RRID: AB_467191
Anti-CD29 Antibody	Thermo Fisher Scientific	Cat# 14-0291-82; RRID: AB_657727
Anti-CD3e Antibody	Thermo Fisher Scientific	Cat#14-0031; RRID: AB_467050
Anti-GFP Antibody	Thermo Fisher Scientific	Cat#G10362; RRID: AB_2536526
Anti-Iba1Antibody	Abcam	Cat# ab5076; RRID: AB_2224402
Anti-IL-2 Antibody	Thermo Fisher Scientific	Cat#16-7022-85; RRID: AB_469207
Anti-MAP2 Antibody	Santa Cruz	Cat# sc-20172; RRID: AB_2250101
Anti-MBP Antibody	Abcam	Cat# ab40390; RRID: AB_1141521
Anti-NF-H Antibody (SMI32)	BioLegend	Cat# 801701; RRID: AB_2564642
Anti-NG2 Antibody	Millipore Sigma	Cat#AB5320; RRID: AB_11213678
Anti-Osteopontin Antibody	Abcam	Cat#ab8448; RRID: AB_306566
Anti-TGM2 Antibody	R&D Systems	Cat# AF4376; RRID: AB_10890213
Anti-VEGF-A Antibody	Biolegend	Cat# 512809; RRID: AB_2814439
Anti-Arg1 Antibody, APC	R&D Systems	Cat# IC5868A; RRID: AB_2810265
Anti-CD11b Antibody, APC	BioLegend	Cat# 101211; RRID: AB_312794
Anti-CD25 Antibody, Alexa Fluor® 700	BioLegend	Cat# 102024; RRID: AB_493709
Anti-CD25 Antibody, APC	BioLegend	Cat# 102012; RRID: AB_312861
Anti-CD25 Antibody, PE	Thermo Fisher Scientific	Cat# 12-0251; RRID: AB_465607
Anti-CD29 (Integrin beta 1) Antibody, PE	Thermo Fisher Scientific	Cat#12-0291-81; RRID: AB_657733
Anti-CD3 Antibody, APC-Cy7	BioLegend	Cat# 100221; RRID: AB_2057374
Anti-CD3 Antibody, FITC	Thermo Fisher Scientific	Cat# 11-0032-80; RRID: AB_2572430
Anti-CD4 Antibody, BUV395	BD Biosciences	Cat#563790; RRID: AB_2738426
Anti-CD4 Antibody, BV510	BioLegend	Cat # 100449; RRID: AB_2564587
Anti-CD4 Antibody, ef450	Thermo Fisher Scientific	Cat# 48-0042; RRID: AB_1272194
Anti-CD45 Antibody, PE-Cyanine5	Thermo Fisher Scientific	Cat#15-0451-81; RRID: AB_468751
Anti-CD45 Antibody, PE-CF594	BD Biosciences	Cat#562420; RRID: AB_11154401
Anti-CTLA4 Antibody, BV605	Biolegend	Cat# 106323; RRID: AB_2566467
Anti-Foxp3 Antibody, APC	Thermo Fisher Scientific	Cat#17-5773-82; RRID: AB_469457
Anti-Foxp3 Antibody, PE-Cy7	Thermo Fisher Scientific	Cat# 25-5773-82; RRID: AB_891552
Anti-GLAST Antibody, APC	Miltenyi Biotec	Cat# 130-123-555; RRID: AB_2811532
Anti-Helios Antibody, FITC	Biolegend	Cat# 137214; RRID: AB_10662745
Anti-Human and Sheep IgG Fc Antibody, APC-Cy7	Biolegend	Cat# 409313; RRID: AB_2561858
Anti-IL-10 Antibody, APC-Cy7	Biolegend	Cat# 505035; RRID: AB_2566330
Anti-O4 Antibody, APC	Miltenyi Biotec	Cat#130-119-155; RRID: AB_2751644
Anti-Osteopontin Antibody, PE	R&D Systems	Cat# IC808P; RRID: AB_10643832
Anti-Rat IgG Antibody, BV421	Biolegend	Cat# 405414; RRID: AB_10900808
IgG2a kappa Isotype antibody	Thermo Fisher Scientific	Cat#: 16-4321-82; AB_470156
Chemicals, Peptides, and Recombinant Proteins
Diphtheria Toxin	Sigma-Aldrich	Cat#D0564-1MG
IL-2, Recombinant Protein	Thermo Fisher Scientific	Cat#34-8021-85
Luxol® Fast Blue MBSN	Sigma-Aldrich	Cat#S3382-25G
Osteopontin Protein	R&D Systems	Cat#441OP050CF
Percoll	GE Healthcare	Cat#17-0891-01
PLX5622	Chemgood	Cat#C-1521
(Z)-4-Hydroxytamoxifen	Sigma-Aldrich	Cat#H7904
Critical Commercial Assays
CD4⁺CD25⁺ Regulatory T Cell Isolation Kit, mouse	Miltenyi Biotec	Cat# 130-091-041
Mouse on Mouse (M.O.M.) Basic Kit	Vector laboratories	Cat#BMK-2202
Neural Tissue Dissociation Kit (T)	Miltenyi Biotec	Cat#130-093-231
Nextera XT DNA Library Prep Kit	Illumina	Cat#15031942
RNeasy Plus Micro Kit	Qiagen	Cat#74034
SMART-Seq HT Kit	Takara Bio	Cat#634456
SMART-Seq Ultralow Input RNA Kit	Takara Bio	Cat#634890
UltraComp eBeads Compensation Beads	Thermo Fisher Scientific	Cat#01-2222-42
Deposited Data
Raw data files for RNA-seq	This paper	GEO: GSE171171
Experimental Models: Organisms/Strains
Mouse: C57BL/6J (WT)	The Jackson Laboratory	Stock No: 000664
Mouse: Cx3cr^cre/ER	The Jackson Laboratory	Stock No: 021160
Mouse: Foxp3^DTR/GFP	The Jackson Laboratory	Stock No: 016958
Mouse: Itgb1^flox/flox	The Jackson Laboratory	Stock No: 004605
Mouse: Rag1^−/−	The Jackson Laboratory	Stock No: 002216
Mouse: Spp1^−/−	The Jackson Laboratory	Stock No: 004936
Oligonucleotides
qPCR primer Mbp:	Thermo Fisher Scientific	F:CACAGAAGAGACCCTCACAGCGACA R:CCGCTAAAGAAGCGCCCGATGGA
qPCR primer Spp1:	Thermo Fisher Scientific	F:GAATCTCCTTGCGCCACAGAATG R:CATCTGTGGCATCAGGATACTGTTC
qPCR primer Gapdh:	Thermo Fisher Scientific	F: TGCTGGTGCTGAGTATGTCGTG R: CGGAGATGATGACCCTTTTGG
Software and Algorithms
Cell Ranger 3.0.1	10xGenomics	https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger
Chipster 3.15	CSC-IT Center for Science	https://chipster.csc.fi/
DESeq2	Bioconductor	http://bioconductor.org/packages/DESeq2/
DSI studio	(Yeh et al., 2013)	http://dsi-studio.labsolver.org/
edgeR 3.26.8	Bioconductor	http://bioinf.wehi.edu.au/edgeR
FastQC	Babraham Bioinformatics	http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
FlowJo 10.5.3	TreeStar FlowJo LLC.	https://www.flowjo.com/
GraphPad Prism 8.4.3	GraphPad Software	https://www.graphpad.com
GSEA 4.0.1	UC San Diego and Broad Institute	http://gsea-msigdb.org/gsea/index.jsp
HTSeq	(Anders et al., 2015)	http://htseq.readthedocs.io/
ImageJ	NIH	https://imagej.nih.gov/ij/
Imaris 9.3	Bitplane	https://imaris.oxinst.com/
Ingenuity Pathway Analysis	Qiagen	http://qiagen.force.com/KnowledgeBase/KnowledgeIPAPage
pClamp 10 software	Molecular Devices, LLC	https://www.moleculardevices.com/products/axon-patch-clamp-system
R 3.5	R	https://www.r-project.org/
Seurat 2.3.4	(Stuart et al., 2019)	https://cran.r-project.org/web/packages/Seurat
STAR 2.7.8	GitHub	https://github.com/alexdobin/STAR
STRING 11.0	STRING	https://string-db.org/

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EXPERIMENTAL MODEL AND SUBJECT DETAILS

Experimental animals

C57BL/6J, Spp1^−/−, Rag1^−/−, Foxp3^DTR, Itgb1^flox/flox, and Cx3cr1^CreER mice were purchased from the Jackson Laboratory. Cx3cr1^CreER+/−Itgb1^f/f mice were bred from Itgb1^flox/flox and Cx3cr1^CreER(+/−) mice. The depletion of Itgb1 in microglia and macrophages was induced in Cx3cr1^CreER+/−Itgb1^f/f mice (9 week-old males) by intraperitoneal injection of 4-hydroxytamoxifen (0.1 mg in 100 μL corn oil, daily for 5 consecutive days). Male mice (8-12 week-old) were used for in vivo experiments. All animal procedures were approved by the University of Pittsburgh Institutional Animal Care and Use Committee (approval number: 18032546), performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals, and reported in accordance with the Animal Research: Reporting In Vivo Experiments (ARRIVE) guidelines (Percie du Sert et al., 2020). All efforts were made to minimize animal suffering and the number of animals used. All animals were housed in a temperature and humidity-controlled facility with a 12-h light/dark cycle. Food and water were available ad libitum. Animals were randomly assigned to sham or stroke groups and received randomized treatments using a lottery drawing box. All treatments and analyses were performed by blinded investigators wherever feasible.

Primary microglia, OPC and oligodendrocyte cultures

Primary microglia and OPC cultures were prepared from mouse or rats with the differential attachment method (Dai et al., 2020; Huang et al., 2002; Yang et al., 2016; Zhang et al., 2019). Briefly, brains of postnatal day 1-2 mixed-sex pups were diced after removing the meninges. The dissociated cells were plated onto poly-D-lysine (PDL)-coated T-flasks filled with culture media (DMEM/F12 containing 10% heat-inactivated fetal bovine serum, 2 mM l-glutamine, 1 mM sodium pyruvate, 100μM nonessential amino acids, 50 U/mL penicillin, and 50 μg/mL streptomycin, adjust PH to 7.4 with NaOH). Cells were grown to confluence (12–14 days in vitro (DIV)) in a humidified incubator at 37 °C and with 5% CO₂. Microglia were collected by shaking the mixed glia-containing flasks for 1h at 180 rpm. After removing microglia, flasks were subjected to shaking at 200 rpm (for rat) and 250 rpm (for mouse) overnight to separate OPCs from the astrocyte layer. Cell suspension was transferred to untreated Petri dishes and incubated for 20 min, allowing astrocytes and microglia to attach to the surface, while OPCs remained suspended. OPCs were then plated in PDL-coated plates and maintained for 3-5 days in a serum-free basal defined medium (BDM for rat: DMEM, 0.1% bovine serum albumin, 50 μg/mL human apo-transferrin, 50 μg/mL insulin, 30 nM sodium selenite, 10 nM D-biotin, 10 nM hydrocortisone; BDM for mouse: DMEM/F12, 0.1% bovine serum albumin, 1% N2, 2% B27) containing 10 ng/mL PDGF and 10 ng/mL bFGF. For oligodendrocyte induction, OPCs were stimulated with T3 (50 ng/mL)+CNTF (10 ng/mL), microglia conditioned media, or cocultured with treated microglia for 3 days. The media (BDM:glia culture media=1:1) was exchanged every 2 days.

METHOD DETAILS

Murine models of transient cerebral ischemia

Transient cerebral ischemia was induced by intraluminal occlusion of the left middle cerebral artery (MCA) for 60 minutes, as described previously (Zhang et al., 2019). Sham-operated animals underwent the same anesthesia and exposure of arteries without MCA occlusion. Briefly, mice were anesthetized with 3% isoflurane in a 30% O₂/67% N₂O mixture until they were unresponsive in the tail pinch test. Mice were then fitted with a nose cone blowing 1.5% isoflurane in a 30% O₂/68.5% N₂O mixture under spontaneous breathing for anesthesia maintenance. An 8-0 monofilament with a silicon-coated tip was introduced into the common carotid artery, advanced to the origin of the MCA, and left in place to limit MCA blood flow for 60 min. Rectal temperature was controlled at 37.0 ± 0.5°C using a temperature-regulated heating pad during surgery. Regional cerebral blood flow (CBF) was measured in all stroke animals using laser Doppler flowmetry (LDF) or 2-dimensional laser speckle imaging system. Animals that did not display at least 70% reduction in regional CBF (using LDF) during tMCAO compared to pre-ischemia levels were excluded from further experimentation. Surgeries and quantification were performed by investigators blinded to animal genotypes and experimental grouping.

Cell Depletion

Diphtheria toxin (DT, ip, 0.05 μg/g body weight) was injected 3 days prior to 60 min tMCAO to deplete Treg cells, and repeated every 3 days to maintain Treg cell depletion until 14 or 21 days after stroke (Dombrowski et al., 2017; Galdino et al., 2018). Delayed DT injection started at 5 days after the tMCAO procedure and was repeated every 3 days until animal sacrifice.

For microglia and macrophage depletion, PFX5622 was supplied to mice (9-10 weeks old, 25-30 g body weight) in the diet (Research Diets) at 1200 PPM (1200 mg/kg of chow), starting 7 days prior to surgery and continued until the end of experiments (Zhang et al., 2019).

Treg cell isolation and adoptive transfer

Spleen, inguinal and axillary lymph nodes were harvested from uninjured mice (8-10-week-old) and pooled together to prepare single cell suspensions as we described (Li et al., 2013b; Zhang et al., 2018a). CD4⁺CD25⁺ Treg cells were isolated using a mouse Treg cell isolation kit (Miltenyi Biotec) according to the manufacturer’s instructions. The isolation was performed in a two-step procedure with a negative selection on CD4⁺ cells and a positive selection on CD25⁺ cells. For in vivo studies, 2×10⁶ freshly isolated Treg cells were transferred intravenously to recipient mice 6 hours after tMCAO through the lateral tail vein. Control mice received an equivalent volume of phosphate-buffered saline (PBS).

In vitro Treg cell cultures and cocultures with microglia

For Treg cell cocultures with microglia, isolated Treg cells were stimulated with soluble anti-CD3 (4 μg/ml), anti-CD28 (5 μg/ml) and IL-2 (100 ng/ml) for 3d in Treg culture media (RPMI1640 containing 2mM L-Glutamine, 10% FBS, 1% penicillin/streptomycin, 1mM pyruvate sodium, and 55μm β-mercaptoethanol) and then cocultured with primary microglia for 2d. For brain slice-Treg cell-microglia coculture, isolated Treg cells in a transwell insert were incubated with brain slices in the lower chamber in Treg cell culture media in the presence of soluble anti-CD3, anti-CD28 and IL-2 for 1d and then transferred to a new well with primary microglia for 2d.

Intraperitoneal injection of IL-2:IL-2 antibody complex

IL-2 protein was mixed with anti-IL-2Ab at a 2:1 molar ratio (1 μg of recombinant murine IL-2 protein and 5 μg of anti-IL-2 mAbs) (Shevach, 2012), and incubated at 37 °C for 30 minutes. IL-2:IL-2Ab complex or rat IgG isotype control was intraperitoneally injected into mice once per day for 3 consecutive days starting 6h after tMCAO, and repeated once per day on day 10, 20 and 30 after tMCAO.

BrdU Injections

In order to label proliferating cells, animals were intraperitoneally injected with the thymidine analogue 5′-bromo-2′-deoxy-uridine (BrdU, 50 mg/kg) twice a day (with an intertreatment interval of at least 8 hours) for 3 consecutive days, beginning at 3 days after tMCAO.

Behavioral tests

We used a battery of behavioral tests that has been shown in rodent models to be highly sensitive and powerful for assessing functional deficits after stroke (Xia et al., 2018; Zhang et al., 2019). Behavioral tests were performed by an individual blinded to experimental groups. The rotarod test: Briefly, mice were forced to run on a rotating drum (IITC Life Science Inc.) with speeds starting at 4 rpm and accelerating to 40 rpm within 300 seconds. Three consecutive trials were conducted for each mouse, with an interval of 15 minutes. The time at which a mouse fell off the drum was recorded as the latency to fall. Data were expressed as mean values from three trials per day. The adhesive removal test was performed to assess tactile responses and sensorimotor asymmetries. Two 2×3 mm adhesive tapes were applied to lesioned forepaws. Tactile responses were measured by recording the time to remove the adhesive tape, with a maximum observation period of 120 seconds.

Cognitive function was analyzed using the Morris water maze test, as described previously (Liu et al., 2016; Vorhees and Williams, 2006). A square platform (11×11 cm²) was submerged 2 cm beneath the water surface in a circular pool (diameter =109 cm) filled with opaque water. Mice were placed into the pool from one of the four locations and allowed to locate the hidden platform for 60 seconds. Each mouse was trained on 3 trials (with randomly assigned starting positions) per day to locate the platform for three consecutive days before tMCAO. At the end of each trial, the mouse was placed on the platform or allowed to stay on the platform for 30s with prominent spatial cues displayed around the room. Trials were recorded with Anymaze system (Stoelting Co.). In the learning test, three trials were performed on each day. The time spent to reach the platform was recorded to reflect spatial learning. In the memory test, the platform was removed and a single 60 second probe trial was conducted. Time spent in the goal quadrant (where the platform was previously located) was recorded to reflect spatial memory.

Measurement of tissue loss

Animals were euthanized and perfused with saline followed by 4% paraformaldehyde (Sigma-Aldrich) in PBS. Brains were cryoprotected in 30% sucrose in PBS. Coronal brain sections (25 μm) were sliced on a freezing microtome and six equally spaced coronal brain sections encompassing the MCA territory were stained with an anti-MAP2 antibody. The tissue loss was calculated as the volume of the contralateral hemisphere minus the non-infarcted volume of the ipsilateral hemisphere.

Measurement of myelin loss

Luxol fast blue (LFB) staining was used to demonstrate myelination (Koizumi et al., 2018). For LFB staining, sections were immersed in 95% ethanol for 5 min at room temperature before immersion in 0.1% LFB solution overnight. Next, sections were immersed in 95% and 70% hydrochloric acid ethanol to remove overstaining until grey and WM could be distinguished clearly. The images were captured in the peri-infarct areas. The regions of interest (ROI) were picked at the same approximate location in both striatum and external capsule across different brain slices. Myelin staining was measured by positive pixel identification using the same threshold cutoff in Image J across animals. To generate a percentage, the positive-stained area was divided by the total area of the ROI.

Immunohistochemistry and image analysis

Coronal brain sections were subjected to immunofluorescence staining. Briefly, floating brain sections were blocked with 5% donkey serum in 0.3% Triton X-100 in PBS (PBST) for 1h at room temperature, followed by overnight incubation with primary antibodies at 4°C. After three washes in 0.3% PBST, sections were incubated with the appropriate secondary antibodies for 1h at room temperature. The process was repeated once for double staining. Sections were then washed and mounted with DAPI Fluoromount-G or Fluoromount-G (Southern Biotech). For immunostaining using the mouse primary antibody, the M.O.M kit was applied before primary antibodies to block nonspecific signals, according to the manufacturer’s instructions. Primary antibodies and secondary antibodies used are listed in the Key Resources Table. All the secondary antibodies were diluted 1:1000. Confocal microscopy (FluoView FV1000; Olympus) was used to capture images.

Image analyses were performed on one or two randomly selected microscopic fields in the peri-infarct areas of the external capsule, cortex, and striatum of each section. Two sections covering the infarct area were assessed for each mouse brain. The recorded images were loaded into Image J (NIH) and were quantified by 2 independent observers blinded to grouping. Positively-stained cells were electronically labelled with the software to avoid duplicated counting. The infarct area was identified as the region in which the majority of DAPI-stained nuclei were shrunken. The border of the infarct was determined by the loss of MBP or LFB staining and the accumulation of Iba1⁺ microglia and macrophages or BrdU⁺APC⁺ oligodendrocytes. The peri-infarct area was defined as the tissue that covers a radial distance of 200-300 μm from the border of the infarct.

Image reconstruction and morphology study

The image processing software Imaris (Bitplane) was used to reconstruct three-dimensional images of Iba1⁺ cells as described (Shi et al., 2020). Z-stack confocal images were imported into Imaris, and the surface module was used to generate 3D structures of each color channel. A ROI was selected, and the absolute intensity of each source channel was used for reconstruction. Smoothing was set at 0.300 μm for all channels and images. A threshold was set to differentiate the target signal from background, and the same threshold value was used for all groups. Nonspecific signals were removed, and the 3D-rendered images were constructed. All images were captured using the same camera and microscope settings and processed with the same adjustments and parameters.

Flow Cytometry

Animals were euthanized and perfused with cold saline. Brains were dissected and the ipsilateral (left) and contralateral (right) hemispheres were collected. Brain homogenates were prepared with the Neural Tissue Dissociation Kit (T) using a gentle MACS dissociator with heaters (Miltenyi Biotec) following the manufacturer’s instructions. The suspension was passed through a 70-μm cell strainer (Thermo Fisher Scientific), and resuspended in 30% Percoll. Single cell suspensions were separated from myelin and debris by centrifugation (500g, 30min, 18°C) on a 30–70% Percoll gradient. Cells at the interface were collected and washed with Hank’s balanced salt solution (HBSS; Sigma-Aldrich) containing 1% fetal bovine serum (Sigma-Aldrich) and 2 mM EDTA (Sigma-Aldrich). Blood and spleen cells were prepared as described previously (Li et al., 2013a). Single cell samples were first incubated with antibodies to surface antigens for 30 minutes on ice at 4°C in the dark. After two washes, cells were fixed and permeabilized with Fixation/Permeabilization Diluent & Concentrate according to the manufacturer’s protocol. Appropriate isotype controls were used according to the manufacturer’s instructions (Thermo Fisher eBioscience). Fluorochrome compensation was performed with single-stained UltraComp eBeads. Flow cytometry was performed on the BD LSRII flow cytometer (BD Biosciences) or Aurora (Cytek). Data analyses were performed using FlowJo software. Furthermore, individual immune cells were plotted using viSNE to map high-dimensional cytometry data in two dimensions based on the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. Briefly, immune cells were gated as CD4 positive in FlowJo, and the relative intensity values of each fluorophore were exported and used to generate viSNE plots in a MATLAB-based tool CYT (Amir el et al., 2013). Data were transformed using hyperbolic arcsin with a cofactor of 150, and 6000 cells were selected from each group. viSNE plots were generated with 500 iterations, and subpopulations of cells were identified according to their expression of prototypic markers.

Compound action potential measurements

Compound action potentials (CAPs) were measured in the CC and EC as we have described (Zhang et al., 2019). Mice were decapitated after CO₂ euthanasia and brains were removed. Coronal slices (350 μm thick) were prepared −1.06 mm from bregma and transferred to an incubation chamber containing pre-gassed (95% O₂/5% CO₂) artificial cerebrospinal fluid (aCSF; 126 mmol/L NaCl, 2.5 mmol/L KCl, 1 mmol/L Na₂H₂PO₄, 2.5 mmol/L CaCl₂, 26 mmol/L NaHCO₃, 1.3 mmol/L MgCl₂, 10 mol/L glucose; pH 7.4) for 30 min at 34°C, and then for 1h at room temperature (25°C). Brain slices were then perfused with aCSF at a constant rate (3-4 mL/min) at 25 °C. For recording, a bipolar stimulating electrode (intertip distance, 100 μm) was placed across the CC at ~0.9 mm lateral to the midline. A glass extracellular recording pipette (5–8 MΩ tip resistance when filled with aCSF) was placed in the EC. The distance between the stimulus electrode and the recording electrode was 0.75 mm. The CAP signal was digitized (Digidata 1500B plus humsilencer; Molecular Devices), amplified (× 1K) and recorded by the Axoclamp 700B amplifier (Molecular Devices). The signal was then analyzed by pClamp 10 software. The input-output curves were generated by increasing the stimulating intensity up to 2 mA. The amplitudes of the N1 (representing myelinated fibers) and N2 (representing unmyelinated fibers) components of the CAP were calculated as the variance from the first and second peak, respectively, to the first trough. The conduction velocity = conduction distance (0.75 mm) / conduction time.

Transmission electron microscopy

Transmission electron microscopy (TEM) was used to measure myelin thickness in the CC/EC area (Zhang et al., 2019). Mice were perfused with ice-cold saline, followed by 4% PFA and 2.5% glutaraldehyde in 0.1 mol/L PBS buffer. The CC and EC tissue near the site of ischemia was micro-dissected into 1 mm³ blocks. These specimens were immersion-fixed for 24 h in 2% glutaraldehyde. Following fixation, tissue was washed 3 times in PBS and then post-fixed in aqueous 1% OsO₄, 1% K₃Fe(CN)₆ for 1h. Following three washes, the tissue was dehydrated through a graded series of 30-100% ethanol, 100% propylene oxide and then infiltrated in 1:1 mixture of propylene oxide: Polybed 812 epoxy resin (Polysciences) overnight at room temperature. After several changes of 100% resin over 24h, tissue was embedded in molds, cured at 37°C overnight, followed by additional hardening at 65°C for two more days. Ultrathin 60 nm sections were cut on a Leica UCT ultramicrotome with a diamond knife (Diatome), stained with uranyl acetate and lead citrate. Sections were imaged using a JEOL JEM 1400 transmission electron microscope (Peabody) at 80 kV fitted with a side-mount AMT digital camera (Advanced Microscopy Techniques). Two sections from each animal were analyzed. Three to 5 images (600 μm² each) were acquired in randomly selected areas within the CC and EC from each section at a magnification of 50,000× and analyzed with Image J by an investigator blinded to experimental groups. At least 30 random axons per animal were analyzed by tracing the axonal circumference and the whole fiber circumference in a blinded fashion. G-ratios were calculated as the ratio of the inner axonal diameter to the total outer diameter (axonal diameter + total myelin sheath thickness).

DTI

White matter integrity was evaluated using DTI as described (Cengiz et al., 2011; Dai et al., 2020; Zhang et al., 2019). Perfusion-fixed brains were left in the skull and imaged ex vivo using a Bruker AV3HD 11.7 Tesla/89 mm vertical-bore microimaging system equipped with a Micro2.5 gradient set capable of 1500 mT/m, a 20 mm quadrature RF resonator and ParaVision 6.0.1 (Bruker Biospin). DTI data were collected using a multislice spin-echo sequence with 5 A0 images and 30 non-colinear diffusion images with the following parameters: TE/TR 22/2800 ms, 2 averages, 160 × 160 matrix, 16 × 16 mm field of view, 25 slices, 0.5 mm slice thickness, b-value = 3000 s/mm², and Δ/δ = 11.0/5.0 ms. DTI data were analyzed with DSI Studio software (http://dsi-studio.labsolver.org/) (Yeh et al., 2013). Directionally-encoded color (DEC), FA, and RD maps were generated by DSI Studio software. ROIs were drawn manually in a blinded manner encompassing the EC and IC in the ipsilesional and contralesional hemispheres to determine FA and RD (Dai et al., 2020; Song et al., 2002; Zhang et al., 2019).

Real-time PCR

Total RNA was extracted from rat culture samples using the RNeasy Mini Kit (Qiagen) according to the manufacturer’s instructions. RNA (1 μg) was used to synthesize the first strand of cDNA using the Superscript First-Strand Synthesis System for RT-PCR (Thermo Fisher Scientific) according to the manufacturer’s protocols. The program for reverse transcription was 25°C 10 min, 50°C 30 min, 85°C 5 min, 4°C maintain. PCR was performed on the Opticon 2 Real-Time PCR Detection System (Bio-Rad) using corresponding primers (Key Resources Table) and SYBR green PCR Master Mix (Qiagen). The program for real-time PCR was 95°C 15 min, (94°C 20s, 59°C 30s, 72°C 30s) x 40 cycles, melting curve from 50°C to 92°C, read every 0.2°C, hold 2s, incubate at 8°C. The cycle time values were normalized to Gapdh in the same sample as an internal control.

Single cell RNA sequencing

Ischemic hemispheres were perfused with ice-cold saline and freshly harvested at 5 or 14 days after tMCAO. Single cell suspensions were isolated as described for flow cytometry. Single cells were incubated with anti-CD45-PE-Cy5 antibody for 30 minutes on ice at 4 °C in the dark. The CD45^high cells were sorted by FACSAriaTM (BD Bioscience). Cell viability were checked by Cellometer Auto 2000 Automatic Cell Viability Counter system (Nexcelom). The samples with > 85% viability were proceeded to single cell RNA sequencing library preparation. Single live cells were separated into Gel bead in emulsion (GEM)s formed by oil micro-droplets by the Chromium instrument (10X Genomics) using Single Cell 3' v2 reagents. Each GEM contains a gel bead and a cell. A greater than 1000-fold excess of partitions compared to cells assured that most GEMs were only one cell per GEM. The reaction mixture and emulsion with captured and barcoded mRNAs were removed from the Chromium instrument followed by reverse transcription. The cDNA samples were fragmented and amplified per 10X protocol. The libraries were then purified, quantified, and sequenced on an Illumina HiSeq.

RNA sequencing

RNA extraction, library preparation, and sequencing were performed at the Health Sciences Sequencing Core at UPMC Children’s Hospital of Pittsburgh. For cultured microglia, frozen cell pellet samples were disrupted in Trizol®. Chloroform was added to the reaction and mixed vigorously. Following phase separation by centrifuging for 15 minutes at 12,000g, the aqueous phase was removed to a clean microcentrifuge tube. RNA was precipitated by isopropanol and pelleted by centrifugation for 10 minutes at 12,000g. RNA integrity was assessed using the High Sensitivity RNA ScreenTape system on an Agilent 2200 TapeStation. The SMART-Seq HT Kit was used to generate cDNA from total RNA. For FACS-sorted CD4⁺CD25⁺Foxp3⁺ Treg cells, 500-1000 cells were sorted to a plate with lysis buffer. cDNA was generated from cell lysis using the SMART-Seq Ultralow Input RNA Kit (Qiagen), according to the manufacturer’s instructions. The cDNA product was checked by an Agilent Fragment Analyzer system for quality control. The sequencing library was constructed by following the Illumina Nextera XT Sample Preparation Guide. One nanogram of input cDNA was tagmented (tagged and fragmented) and amplified using the Illumina Nextrera XT kit. Sequence libraries of each sample were finally equimolarly pooled and sequenced on an Illumina Nextseq 500 system, using a paired-end 75-bp strategy. All RNA-seq data are deposited in the Gene Expression Omnibus database at the National Center for Biotechnology Information.

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical analyses

Sample sizes for animal studies were determined based on pilot studies or our previously published work. Results are presented as mean ± standard deviation (SD). GraphPad Prism software (version 8.4.3) was used for statistical analyses. The Student’s t test (equal variances) or Welch's t test (unequal variances) was used for comparison of two groups for continuous variables with normal distributions. The Mann-Whitney U rank sum test was used for continuous variables with non-normal distributions. The differences in means among multiple groups were analyzed using one-way or two-way analysis of variance (ANOVA) or its non-parametric version, the Kruskal-Wallis test. Differences in means across groups with repeated measurements over time were analyzed using the repeated measures ANOVA. When the ANOVA showed significant differences, pairwise comparisons between means were tested by post hoc Bonferroni (comparisons between all conditions), Dunnett (all conditions compared with a control group) or Dunn’s (following Kruskal-Wallis) tests. In all analyses, p<0.05 was considered statistically significant. Details of statistical analysis are summarized in Table S5.

Single cell RNA sequencing data analysis

Single cell RNA-seq raw data were processed by Cell Ranger version 3.0.1 (10X Genomics) and aligned to the GRCm38 (mm10) mouse reference genome. Seurat V2.3.4 was used for the downstream analysis, including tSNE, heatmap, and violin plots (Stuart et al., 2019). Multiplets and broken cells were excluded from analysis. Cells ranging from 200 to 5500 detected genes per cell with less than 5% of genes encoding mitochondria were included for further analysis. Variable genes were determined by iterative selection based on the dispersion versus average expression of the gene. Principal component analysis (PCA) was performed and top 20 PCA was used to reduce dimension. Smart local moving (SLM) algorithm implemented in Seurat V2.3.4 was performed for clustering with the resolution parameter value of 0.4. Sub-clustering analysis of the αβ T cells was performed using the same parameters as described above.

RNAseq data analysis

Preprocessing of the RNAseq data was completed using Chipster (Kallio et al., 2011). Fastq files were quality controlled using FastQC. All samples passed quality control criteria. Reads were mapped to the GRCm38 (mm10) mouse genome using STAR (Dobin et al., 2013) and counted by HTSeq (Anders et al., 2015). Genes were identified by Ensembl ID (Zerbino et al., 2018). Read counts were subsequently analyzed using R/Bioconductor. Fragments Per Kilobase of transcript per Million mapped reads (FPKM) was calculated based on the length of the gene and reads count mapped to this gene for estimating gene expression levels.

Differential expression analysis

The R package edgeR (v3.26.8) (Robinson et al., 2010) was performed for differential expression analysis between two conditions. The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the FDR. For PCA, variance-stabilizing transformation was performed on normalized counts for each sample. Heatmaps were generated using the R package pheatmap (v1.0.12). Volcano plots were generated using the R package ggplot2 (v3.2.1).

Ingenuity Pathway Analysis (IPA)

DEGs were submitted to IPA for functional analysis (Kramer et al., 2014) using the Ingenuity Knowledge Base (Qiagen Bioinformatics). The fold change and FDR for each gene were used to perform the core analysis. Diseases and functions were considered significantly enriched with an adjust P-value of overlap <0.01 and an activation z-score > 2 (predicted to be activated) or < −2 (predicted to be inhibited).

Gene Set Enrichment Analysis (GSEA)

GSEA analyses were performed using the normalized counts to identify the significantly enriched gene sets clusters in microglia (Mootha et al., 2003; Subramanian et al., 2005). The number of permutations was set to 1,000, and permutation type was set as gene set. The enrichment score (ES) curve is the running sum of the weighted enrichment score generated by the GSEA software. The normalized enrichment scores (NES) are the value of the ES curve at the leading edge where the statistic reaches its maximum value for a particular gene set. A gene set with FDR <0.25, ∣NES∣>1, and nominal (NOM) p value<0.05 was recognized as statistically significant.

Protein-protein interaction analysis

Interactions between brain infiltrated Treg cells and Treg cell-activated microglia were calculated based on STRING database (v11.0) (Szklarczyk et al., 2019). Briefly, we extracted all extracelluar factors from the functional term “activation of phagocytes”, which was significantly up-regulated in brain infiltrated Treg cells compared to blood Treg cells in IPA analysis. We also extracted all significantly up-regulated receptors in Treg cell-activated microglia. Protein-protein interactions were calculated by using STRING database.

Supplementary Material

NIHMS1697821-supplement-1.pdf^{(2.5MB, pdf)}

Table S1. Signature genes that define immune cell clusters within CD45^high cells derived from the ischemic brain 14 days after tMCAO. Related to Figure 1.

NIHMS1697821-supplement-2.xlsx^{(345.3KB, xlsx)}

Table S2. Signature genes that distinguish the subclusters of αβ T cells derived from the ischemic brain 14 days after tMCAO. Related to Figure 1.

NIHMS1697821-supplement-3.xlsx^{(102.8KB, xlsx)}

Table S3. Differentially expressed genes (FDR<0.05, ∣fold change∣ > 4) in Treg cells from the ischemic brain 14 days after tMCAO vs. Treg cells from the blood of sham or stroke mice. Related to Figure 3, Figure 5 and Figure S4.

NIHMS1697821-supplement-4.xlsx^{(158.5KB, xlsx)}

Table S4. Differentially expressed genes (FDR<0.05, ∣fold change∣ > 2) in Treg cell-stimulated microglia vs. unstimulated microglia. Related to Figure 4 and Figure 5.

NIHMS1697821-supplement-5.xlsx^{(99KB, xlsx)}

Table S5. Summary of statistical analyses. Related to STAR Methods.

NIHMS1697821-supplement-6.xlsx^{(29.5KB, xlsx)}

Highlights:

Brain-infiltrating Treg cells are essential for behavioral recovery and brain repair
Interactions between Treg cells and microglia enhance oligodendrogenesis after stroke
Treg cells secrete osteopontin to promote tissue-reparative microglial reactions
Boosting Treg numbers improves long-term outcomes after stroke

Acknowledgements:

We thank Rania Elbakri of the Health Sciences Sequencing Core at UPMC Children’s Hospital of Pittsburgh for her help with RNA sequencing. We thank Mara Lisa Grove Sullivan of Center for Biologic Imaging in University of Pittsburgh for technical support with TEM. This work was supported by a grant from the NINDS (NS094573 to Xiaoming Hu) and by the fund from University of Pittsburgh. Xiaoming Hu is supported by a VA grant (I01 BX003651). Jun Chen is a recipient of the VA Senior Research Career Scientist Award. Rehana Leak is supported by Duquesne University and NIH grants.

Footnotes

Declaration of Interests: The authors declare no competing interests.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

NIHMS1697821-supplement-1.pdf^{(2.5MB, pdf)}

Table S1. Signature genes that define immune cell clusters within CD45^high cells derived from the ischemic brain 14 days after tMCAO. Related to Figure 1.

NIHMS1697821-supplement-2.xlsx^{(345.3KB, xlsx)}

Table S2. Signature genes that distinguish the subclusters of αβ T cells derived from the ischemic brain 14 days after tMCAO. Related to Figure 1.

NIHMS1697821-supplement-3.xlsx^{(102.8KB, xlsx)}

NIHMS1697821-supplement-4.xlsx^{(158.5KB, xlsx)}

Table S4. Differentially expressed genes (FDR<0.05, ∣fold change∣ > 2) in Treg cell-stimulated microglia vs. unstimulated microglia. Related to Figure 4 and Figure 5.

NIHMS1697821-supplement-5.xlsx^{(99KB, xlsx)}

Table S5. Summary of statistical analyses. Related to STAR Methods.

NIHMS1697821-supplement-6.xlsx^{(29.5KB, xlsx)}

Data Availability Statement

Key Resources Table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
Anti-APC Antibody	Millipore Sigma	Cat#OP80; RRID: AB_2057371
Anti-BrdU antibody	Abcam	Cat#ab6326; RRID: AB_305426
Anti-CD28 Antibody	Thermo Fisher Scientific	Cat#14-0281; RRID: AB_467191
Anti-CD29 Antibody	Thermo Fisher Scientific	Cat# 14-0291-82; RRID: AB_657727
Anti-CD3e Antibody	Thermo Fisher Scientific	Cat#14-0031; RRID: AB_467050
Anti-GFP Antibody	Thermo Fisher Scientific	Cat#G10362; RRID: AB_2536526
Anti-Iba1Antibody	Abcam	Cat# ab5076; RRID: AB_2224402
Anti-IL-2 Antibody	Thermo Fisher Scientific	Cat#16-7022-85; RRID: AB_469207
Anti-MAP2 Antibody	Santa Cruz	Cat# sc-20172; RRID: AB_2250101
Anti-MBP Antibody	Abcam	Cat# ab40390; RRID: AB_1141521
Anti-NF-H Antibody (SMI32)	BioLegend	Cat# 801701; RRID: AB_2564642
Anti-NG2 Antibody	Millipore Sigma	Cat#AB5320; RRID: AB_11213678
Anti-Osteopontin Antibody	Abcam	Cat#ab8448; RRID: AB_306566
Anti-TGM2 Antibody	R&D Systems	Cat# AF4376; RRID: AB_10890213
Anti-VEGF-A Antibody	Biolegend	Cat# 512809; RRID: AB_2814439
Anti-Arg1 Antibody, APC	R&D Systems	Cat# IC5868A; RRID: AB_2810265
Anti-CD11b Antibody, APC	BioLegend	Cat# 101211; RRID: AB_312794
Anti-CD25 Antibody, Alexa Fluor® 700	BioLegend	Cat# 102024; RRID: AB_493709
Anti-CD25 Antibody, APC	BioLegend	Cat# 102012; RRID: AB_312861
Anti-CD25 Antibody, PE	Thermo Fisher Scientific	Cat# 12-0251; RRID: AB_465607
Anti-CD29 (Integrin beta 1) Antibody, PE	Thermo Fisher Scientific	Cat#12-0291-81; RRID: AB_657733
Anti-CD3 Antibody, APC-Cy7	BioLegend	Cat# 100221; RRID: AB_2057374
Anti-CD3 Antibody, FITC	Thermo Fisher Scientific	Cat# 11-0032-80; RRID: AB_2572430
Anti-CD4 Antibody, BUV395	BD Biosciences	Cat#563790; RRID: AB_2738426
Anti-CD4 Antibody, BV510	BioLegend	Cat # 100449; RRID: AB_2564587
Anti-CD4 Antibody, ef450	Thermo Fisher Scientific	Cat# 48-0042; RRID: AB_1272194
Anti-CD45 Antibody, PE-Cyanine5	Thermo Fisher Scientific	Cat#15-0451-81; RRID: AB_468751
Anti-CD45 Antibody, PE-CF594	BD Biosciences	Cat#562420; RRID: AB_11154401
Anti-CTLA4 Antibody, BV605	Biolegend	Cat# 106323; RRID: AB_2566467
Anti-Foxp3 Antibody, APC	Thermo Fisher Scientific	Cat#17-5773-82; RRID: AB_469457
Anti-Foxp3 Antibody, PE-Cy7	Thermo Fisher Scientific	Cat# 25-5773-82; RRID: AB_891552
Anti-GLAST Antibody, APC	Miltenyi Biotec	Cat# 130-123-555; RRID: AB_2811532
Anti-Helios Antibody, FITC	Biolegend	Cat# 137214; RRID: AB_10662745
Anti-Human and Sheep IgG Fc Antibody, APC-Cy7	Biolegend	Cat# 409313; RRID: AB_2561858
Anti-IL-10 Antibody, APC-Cy7	Biolegend	Cat# 505035; RRID: AB_2566330
Anti-O4 Antibody, APC	Miltenyi Biotec	Cat#130-119-155; RRID: AB_2751644
Anti-Osteopontin Antibody, PE	R&D Systems	Cat# IC808P; RRID: AB_10643832
Anti-Rat IgG Antibody, BV421	Biolegend	Cat# 405414; RRID: AB_10900808
IgG2a kappa Isotype antibody	Thermo Fisher Scientific	Cat#: 16-4321-82; AB_470156
Chemicals, Peptides, and Recombinant Proteins
Diphtheria Toxin	Sigma-Aldrich	Cat#D0564-1MG
IL-2, Recombinant Protein	Thermo Fisher Scientific	Cat#34-8021-85
Luxol® Fast Blue MBSN	Sigma-Aldrich	Cat#S3382-25G
Osteopontin Protein	R&D Systems	Cat#441OP050CF
Percoll	GE Healthcare	Cat#17-0891-01
PLX5622	Chemgood	Cat#C-1521
(Z)-4-Hydroxytamoxifen	Sigma-Aldrich	Cat#H7904
Critical Commercial Assays
CD4⁺CD25⁺ Regulatory T Cell Isolation Kit, mouse	Miltenyi Biotec	Cat# 130-091-041
Mouse on Mouse (M.O.M.) Basic Kit	Vector laboratories	Cat#BMK-2202
Neural Tissue Dissociation Kit (T)	Miltenyi Biotec	Cat#130-093-231
Nextera XT DNA Library Prep Kit	Illumina	Cat#15031942
RNeasy Plus Micro Kit	Qiagen	Cat#74034
SMART-Seq HT Kit	Takara Bio	Cat#634456
SMART-Seq Ultralow Input RNA Kit	Takara Bio	Cat#634890
UltraComp eBeads Compensation Beads	Thermo Fisher Scientific	Cat#01-2222-42
Deposited Data
Raw data files for RNA-seq	This paper	GEO: GSE171171
Experimental Models: Organisms/Strains
Mouse: C57BL/6J (WT)	The Jackson Laboratory	Stock No: 000664
Mouse: Cx3cr^cre/ER	The Jackson Laboratory	Stock No: 021160
Mouse: Foxp3^DTR/GFP	The Jackson Laboratory	Stock No: 016958
Mouse: Itgb1^flox/flox	The Jackson Laboratory	Stock No: 004605
Mouse: Rag1^−/−	The Jackson Laboratory	Stock No: 002216
Mouse: Spp1^−/−	The Jackson Laboratory	Stock No: 004936
Oligonucleotides
qPCR primer Mbp:	Thermo Fisher Scientific	F:CACAGAAGAGACCCTCACAGCGACA R:CCGCTAAAGAAGCGCCCGATGGA
qPCR primer Spp1:	Thermo Fisher Scientific	F:GAATCTCCTTGCGCCACAGAATG R:CATCTGTGGCATCAGGATACTGTTC
qPCR primer Gapdh:	Thermo Fisher Scientific	F: TGCTGGTGCTGAGTATGTCGTG R: CGGAGATGATGACCCTTTTGG
Software and Algorithms
Cell Ranger 3.0.1	10xGenomics	https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger
Chipster 3.15	CSC-IT Center for Science	https://chipster.csc.fi/
DESeq2	Bioconductor	http://bioconductor.org/packages/DESeq2/
DSI studio	(Yeh et al., 2013)	http://dsi-studio.labsolver.org/
edgeR 3.26.8	Bioconductor	http://bioinf.wehi.edu.au/edgeR
FastQC	Babraham Bioinformatics	http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
FlowJo 10.5.3	TreeStar FlowJo LLC.	https://www.flowjo.com/
GraphPad Prism 8.4.3	GraphPad Software	https://www.graphpad.com
GSEA 4.0.1	UC San Diego and Broad Institute	http://gsea-msigdb.org/gsea/index.jsp
HTSeq	(Anders et al., 2015)	http://htseq.readthedocs.io/
ImageJ	NIH	https://imagej.nih.gov/ij/
Imaris 9.3	Bitplane	https://imaris.oxinst.com/
Ingenuity Pathway Analysis	Qiagen	http://qiagen.force.com/KnowledgeBase/KnowledgeIPAPage
pClamp 10 software	Molecular Devices, LLC	https://www.moleculardevices.com/products/axon-patch-clamp-system
R 3.5	R	https://www.r-project.org/
Seurat 2.3.4	(Stuart et al., 2019)	https://cran.r-project.org/web/packages/Seurat
STAR 2.7.8	GitHub	https://github.com/alexdobin/STAR
STRING 11.0	STRING	https://string-db.org/

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PERMALINK

Treg cell-derived osteopontin promotes microglia-mediated white matter repair after ischemic stroke

Ligen Shi

Zeyu Sun

Wei Su

Fei Xu

Di Xie

Qingxiu Zhang

Xuejiao Dai

Kartik Iyer

T Kevin Hitchens

Lesley M Foley

Sicheng Li

Donna B Stolz

Kong Chen

Ying Ding

Angus W Thomson

Rehana K Leak

Jun Chen

Xiaoming Hu

Summary:

Graphical Abstract

In Brief

Introduction

Results:

Treg cells accumulate in the injured brain over a long time frame after ischemic stroke

Figure 1. Treg cells accumulate in the ischemic brain and play pivotal roles in neurological recovery.

Treg cells underlie long-term functional recovery and enhance white matter integrity after stroke

Figure 2. Treg cells are essential for long-term white matter integrity after stroke.

Treg cells promote oligodendrogenesis contingent upon microglia

Figure 3. Treg cells promote oligodendrogenesis contingent upon microglia.

Treg cells promote tissue-reparative microglia responses to ischemic brain injury

Figure 4. Treg cells promote tissue reparative microglial responses to ischemic brain injury.

OPN-mediated Treg cell-microglia interaction is essential for white matter restoration after stroke

Figure 5. Treg cell-microglia crosstalk via OPN is essential for Treg cell-afforded long-term protection of WM after stroke.

Boosting the numbers of endogenous Treg cells post-tMCAO improves long-term stroke outcomes

Figure 6. Boosting Treg cells by post-treatment with IL-2:IL-2Ab complexes rescues long-term outcomes after tMCAO.

Discussion

Limitations of Study

STAR* METHODS:

RESOURCE AVAILABILITY

Lead Contact

Materials Availability

Data and Code Availability

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Experimental animals

Primary microglia, OPC and oligodendrocyte cultures

METHOD DETAILS

Murine models of transient cerebral ischemia

Cell Depletion

Treg cell isolation and adoptive transfer

In vitro Treg cell cultures and cocultures with microglia

Intraperitoneal injection of IL-2:IL-2 antibody complex

BrdU Injections

Behavioral tests

Measurement of tissue loss

Measurement of myelin loss

Immunohistochemistry and image analysis

Image reconstruction and morphology study

Flow Cytometry

Compound action potential measurements

Transmission electron microscopy

DTI

Real-time PCR

Single cell RNA sequencing

RNA sequencing

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical analyses

Single cell RNA sequencing data analysis

RNAseq data analysis

Differential expression analysis

Ingenuity Pathway Analysis (IPA)

Gene Set Enrichment Analysis (GSEA)

Protein-protein interaction analysis

Supplementary Material

Highlights:

Acknowledgements:

Footnotes

References:

Associated Data