Radiation‑Induced Microglial Turnover Elicits a cGAS‑Mediated Interferon Response

Radiation‑Induced Microglial Turnover Elicits a cGAS‑Mediated Interferon Response | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Radiation‑Induced Microglial Turnover Elicits a cGAS‑Mediated Interferon Response Klas Blomgren, Efthalia Preka, Alejandro Lastra Romero, Maria Querol Canut, and 29 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8775672/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cranial radiotherapy is associated with progressive neurocognitive decline in cancer survivors, yet the mechanisms governing delayed neuroinflammatory responses remain insufficiently defined. Through integrated transcriptomic, computational, proteomic, and histological analyses, we identify a distinct microglial response emerging two weeks after irradiation, characterized by pronounced activation of interferon (IFN) signaling. Irradiation induces microglial loss, followed by compensatory proliferation, including cells harboring irradiation‑induced DNA damage, which in turn activates the cGAS–STING pathway. In silico and in vivo perturbation of pathway components establishes cGAS as the principal regulator of this response. Notably, pharmacological suppression of cGAS—but not STING or TBK1—using antisense oligonucleotides selectively attenuates the IFN program. These findings delineate a previously unrecognized, cGAS‑dependent IFN response arising during a subacute phase after cranial irradiation, providing mechanistic insight into how microglial turnover and innate immune activation may contribute to neurocognitive impairment in cancer survivors. Biological sciences/Neuroscience/Neuroimmunology Biological sciences/Cancer/CNS cancer Radiotherapy neuroinflammation cognition senescence hippocampus cGAS-STING Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cranial irradiation (IR) is standard of care in the treatment of high-grade primary and metastatic brain tumors. However, it causes long-term neurocognitive complications in 50–90% of patients, especially in children 1 , 2 . Impaired cognitive domains include learning, processing speed, memory, executive function, and attention 3 . Attempts to omit upfront craniospinal IR in brain cancers with good prognosis, aiming to reduce IR-associated neurotoxicity, by replacing it with focal IR, have failed 4 . Debilitating IR-induced cognitive deficits are, thus, an unavoidable clinical problem that demands careful attention, particularly since the overall survival rates and remaining life expectancy of children with brain tumors by far exceed those of adults, and are expected to improve even further with current advances in cancer therapies 5 . The molecular underpinnings of IR-induced cognitive deficits remain largely unknown. For the past two decades, a dominant hypothesis was the depletion of postnatal hippocampal neurogenesis - the process by which new neurons are continuously generated from dividing neural stem and progenitor cells (NSPCs) in the subgranular zone (SGZ) - thereby impairing learning and memory throughout life 6 – 9 . IR-induced depletion of neurogenesis has been linked to an increase in the number and activation status of microglia within the neurogenic zone, creating a hostile microenvironment that hinders NSPC proliferation and neuronal differentiation 10 , 11 . Despite compelling evidence linking reduced neurogenesis to neuroinflammation 12 , the adverse effects of inflammation on brain function and its consequent cognitive deficits are unlikely to be limited to hippocampal neurogenesis, as evidenced by other models of central nervous system (CNS) diseases 13 . Microglia, the resident immune cells and phagocytes in the brain, play key roles in neuroinflammation 14 . Microglia respond rapidly to IR and undergo a series of molecular events, resulting in the production of cytokines and chemokines and the engulfment of dying NSPCs 15 – 17 . There is a fundamental lack of defined mechanisms by which the IR-induced microglial responses contribute to cognitive deficits. Current knowledge is based on in vitro or in vivo studies using only one or a few time points close to the time of IR, utilizing conventional histological analyses and measurements of pre-selected inflammatory mediators in the brain 18 . Available data on post-IR inflammatory responses from single-cell transcriptomic analyses are, as of yet, limited to microglia and acquired at time points within days post-IR 16,19–21 , based on the selection of the microglial population, leaving a need for high-resolution, unbiased data that simultaneously address delayed molecular events in microglia and other cell types. Given the dynamic nature of microglia 22 , 23 and the fact that post-IR cognitive deficits appear in phases 1 , it is imperative to investigate trajectories at the cellular and tissue levels over a prolonged period. We performed unbiased, longitudinal in vivo investigations of the post-IR inflammatory response in the hippocampus, a brain structure central to cognition 24 , spanning both acute and subchronic phases. Using transcriptomics, computational, and genetic approaches, combined with histological and protein analyses, we uncovered a delayed microglial response characterized by activation of the interferon (IFN) signaling pathway. We demonstrate that IR causes microglial loss, triggering compensatory microglial proliferation, even in cells with damaged DNA, thereby activating the cytosolic DNA sensor cGAS and downstream IFN signaling. Results Irradiation causes a biphasic inflammatory response in the hippocampus We have previously shown that hippocampal microglia in the juvenile brain are activated as early as 2 h post-IR and return to baseline within 1 week (wk) 16 . However, data from adult rodent models suggest that IR induces persistent microglial activation and chronic neuroinflammation 12 , 25 , 26 . Thus, we asked whether long-term inflammation occurs also in the juvenile brain and, if so, what roles microglia play. To this end, we performed a longitudinal, unbiased bulk RNA sequencing (RNA-seq) analysis of hippocampal tissue from 6 h to 6 wk post-IR. Juvenile mice were subjected to whole-brain IR with a single dose of 8 Gy. This IR dose is equivalent to a total radiation dose of 18 Gy when delivered in repeated 2-Gy fractions, as in a clinical setting, estimated using the linear-quadratic (LQ) model and an α/β ratio of three for late effects in normal brain tissue 27 . Hippocampi were collected 6 h (acute phase), 1 day (acute phase), 1 wk (early subacutephase), 2 wk (delayed subacutephase), and 6 wk (subchronicsubchronicphase) post-IR and compared to age-matched sham controls (SH) (Fig. 1 A). At all time points, principal component analysis (PCA) showed that IR hippocampi formed distinct clusters that were clearly separate from their respective SH samples (Fig. 1 B), indicating long-term transcriptomic alterations post-IR. Numerous differentially expressed genes (DEGs; q < 0.05) between the SH and IR animals were detected as early as 6 h post-IR (Fig. 1 C). Downregulated DEGs peaked 1day post-IR and then decreased over time (Fig. 1 C; Supplementary list 1). The dynamics of upregulated DEGs were different. The number of upregulated DEGs peaked at 6 h post-IR, decreased by 1 day and 1 wk, then increased again at 2 wk, and decreased again at 6 wk post-IR (Fig. 1 C; Supplementary list 1), suggesting a second, delayed response 2 wk post-IR. While the earlier alterations have been extensively studied 11 , 15 – 17 , 28 , we focused on the later response 2 wk post-IR. Gene set enrichment analysis (GSEA) revealed activation ( p < 0.05) of pathways associated with inflammation and response to viruses (Supplementary Fig. 1A). Out of the 58 upregulated DEGs, 27 genes were related to inflammation, the majority of which were involved in interferon (IFN) signaling pathways (Supplementary Figs. 1B and 1C), indicative of a second inflammatory wave mediated by IFN signaling. Targeted expression analysis of genes related to cytokines and chemokines displaying > 3-fold expression changes at any time point revealed that chemokines were the most upregulated inflammatory mediators (Supplementary Fig. 1D). We found a significant induction of a set of chemokines within the first 24 h post-IR. At 1 wk, Ccl12 was the only chemokine that remained significantly increased. At 2 wk, however, we detected a significant increase in the expression of Cxcl10 , Ccl5 (both belonging to interferon signaling pathways), Ccl2 , and Ccl12. The increased expression of Ccl2 and Ccl12 remained significant 6 wk post-IR (Supplementary Fig. 1D). This biphasic inflammatory response was validated in independent hippocampal tissue at both RNA and protein levels (Figs. 1 D- 1 I; Supplementary Fig. 1E). Hence, cranial IR triggers a biphasic inflammatory response in the hippocampus: the first response occurs acutely within the first 24 h post-IR, and the second, delayed response, occurs in the subacute phase, mediated by IFN signaling pathways. Multiple microglial states orchestrate the delayed response to irradiation Next, we asked whether the delayed inflammatory response was driven exclusively by microglia or whether other cell types were also involved. To gain better cellular and molecular resolutions, we dissected the hippocampi of IR mice and age-matched SH controls 2 wk post-IR. We refined our previous cell isolation method, which favors microglia and vascular cells 29 , by incorporating two percoll gradients, thereby improving myelin removal and enabling the generation of a viable, neuron-containing single-cell suspension suitable for droplet-based single-cell RNA sequencing (scRNA-seq) (Fig. 2 A). After quality control of the sequenced cells (detailed in the Method section), we acquired 51,872 cells (SH: 25,969; IR: 25,903). Our cell isolation protocol captured all major cell types, including microglia and macrophages, glial cells (both astrocytes and oligodendrocytes), neurons (both mature and immature), vascular cells (endothelial cells, pericytes, vascular smooth muscle cells, and myofibroblasts), ependymal cells, immune cells, and meningeal fibroblasts, from both SH and IR animals (Fig. 2 B; Supplementary Figs. 2A − 2C). Microglia displayed the most DEGs (Fig. 2 C), with increased expression of inflammatory mediators 2 wk post-IR ( e.g. Cxcl10 and Ccl12 ) (Fig. 2 D). To further explore the contribution of microglia to the observed delayed IFN response, we depleted microglia using the selective CSF1R inhibitor PLX5622 30,31 . Mice were fed either a control diet or a diet containing PLX5622 (1200 ppm) starting 24 hours post-IR, a time point by which the majority of IR-induced apoptotic cells in the hippocampus had been cleared 16 , and brains were collected 13 days later (2 wk post-IR) (Fig. 2 E). We confirmed the microglial depletion using immunofluorescence (Fig. 2 F). Consistent with our previous experiments, we detected increased expression of the IFN-related genes Ifit1 , Ifit3 , and Cxcl10 , and the chemokine Ccl12 , in mice fed a control diet, but this response was absent in mice fed PLX5622 (Fig. 2 G). These results clearly indicate that microglia mediate the delayed inflammatory response observed post-IR. Thus, we focused on in-depth analyses of microglial cells. Microglia from SH (8,029 cells) and IR (7,873 cells) were distinctly clustered (Fig. 3 A). Sub-clustering analysis, considering only the clusters (subpopulations) with significantly enriched proportions ( p < 0.05) due to the experimental conditions (SH or IR), revealed 11 clusters, of which clusters 1, 3, 4, 6, 7, 9, and 10 were induced post-IR (Figs. 3 B and 3 C; Supplementary list 2). GSEA of signature genes of the IR-induced clusters revealed activation of pathways related to response to viruses ( i.e. IFN signaling) in clusters 1, 3, 4, 6, and 10, most abundant in cluster 6; cell cycle progression (clusters 7, 9, and 10), oxidative phosphorylation (clusters 3, 4, and 9), P53 signaling (clusters 1, 6, 9, and 10), cellular senescence (clusters 1, 6, and 10), and endocytosis and phagocytosis (clusters 1 and 6) (Fig. 3 D; Supplementary Fig. 3A). In addition to the IFN response, microglial proliferation emerged as a major feature in our scRNA-seq data at this time point. We quantified Ki67-expressing cells (Ki67 + ) in two distinct hippocampal regions, the molecular layer (ML) and the cornu ammonis 1 (CA1) region, intentionally excluding the granule cell layer and SGZ, where NSPCs and their progenies often proliferate and microglia actively eliminate apoptotic cells 32 . The following time points post-IR were covered: 6 h (when the first wave of the inflammation occurred), 1 wk (by which time the first inflammatory wave was resolved), 2 wk (the second inflammatory wave), and 6 wk (after the second inflammatory wave). We found that Ki67 + cells in these regions were either oligodendrocyte progenitors (OLIG2 + cells) or microglia (IBA1 + cells) (Supplementary Fig. 3B). While the total number of Ki67 + cells decreased 6 h and 6 wk post-IR (Supplementary Fig. 3C), phenotyping of Ki67 + cells revealed that IR caused a striking shift in the identity of cycling cells in the hippocampus. In the intact hippocampus (SH), OLIG2 + cells were the most abundant cycling cells (OLIG2 + /Ki67 + ), whereas post-IR, cycling cells were predominantly IBA1 + cells in both examined areas at 2 and 6 wk (Supplementary Figs. 3D and 3E). The number of proliferating microglia (IBA1 + /Ki67 + cells) was significantly increased 2 wk post-IR (Fig. 3 E), while the number of OLIG2 + /Ki67 + cells decreased over time post-IR (Supplementary Fig. 3F). Interestingly, despite the microglial proliferation at this time point, we found that the microglial numbers in the IR animals were significantly lower (~ 40%) compared to SH animals, as judged by the expression of IBA1 (pan microglia/macrophage marker) and TMEM119 (parenchymal microglia marker) (Fig. 3 F). This led us to hypothesize that IR resulted in microglial loss, triggering proliferation of the remaining microglia, and that repopulation via self-renewal failed. To test this, we labeled proliferating cells between days 10 and 14 post-IR with 5-bromo-2’-deoxyuridine (BrdU), a thymidine analog incorporated during S-phase 33 , as these days fall within the proliferative response shown above. Animals were sacrificed either 2 h after the last BrdU injection ( i.e. , 2 wk post-IR) to assess microglia proliferation, or 4 wk later (i.e., 6 wk post-IR) to assess survival of the newborn cells (Fig. 3 G). As expected, IR significantly increased the number of TMEM119 + /BrdU + cells compared to SH at both time points, but the number of TMEM119 + /BrdU + cells was reduced by nearly 70% between 2 and 6 wk post-IR (Fig. 2 G; Supplementary Fig. 3G), indicating that most of the newborn microglia post-IR did not survive. Collectively, these results show that microglia undergo reactivation 2 wk post-IR, characterized by compensatory proliferation, driven by microglial loss, and activation of IFN signaling programs. Microglial mitotic progression after irradiation activates the cytosolic nucleic sensing system and induces the IFN response Both genomic instability and mitotic progression in the presence of damaged DNA have been shown to result in nucleic acid leakage into the cytosol, generation of micronuclei, and activation of the cyclic GMP-AMP synthase (cGAS) and stimulator of interferon genes (STING) pathway, which triggers type I IFN signaling 34 – 36 . We hypothesized that the cGAS-STING pathway may regulate the observed IFN response in microglia due to IR-induced DNA damage and/or mitotic progression with damaged DNA. Cell cycle analysis of the proliferating microglial population (expressing the pan-proliferation marker Mki67 ) in our scRNA-seq data 2 wk post-IR revealed that cells were distributed across different cell‑cycle stages (Figs. 4 A and 4 B). Cells in cluster 7 were in S-phase, and cells in clusters 9 and 10 had progressed to G2/M phase (expressing the M phase gene Cdk1 ), while upregulating Cdkn1a , a DNA damage response gene, and subsets of these cells, especially in cluster 10, were expressing the IFN signaling-related gene Cxcl10 (Fig. 4 B). In a proof-of-concept experiment, we leveraged microglial in vitro systems with actively proliferating cells to test this. We irradiated mouse primary hippocampal microglia and mouse microglial BV2 cells with a single dose of 8 Gy, cultured the cells for 24 h, and processed them for immunofluorescence staining and protein analysis (Fig. 4 C; Supplementary Fig. 4A). The IR response was confirmed by expression of the DNA damage response marker γH2AX 1 h post-IR (Supplementary Fig. 4B). We found that IR significantly increased the fraction of cells with micronuclei and induced cGAS expression, the upstream component of the pathway (Fig. 4 D; Supplementary Fig. 4C). Next, we used the BV2 to perform downstream analyses, since the hippocampal microglial primary cell cultures yield a limited number of cells and have lower growth potentials. We found that IR significantly increased the fraction of cells expressing phosphorylated STING (pSTING) (Fig. 4 E). Immunoblotting further revealed increased levels of phosphorylated TANK-binding kinase 1 (pTBK1), a canonical downstream component of this pathway (Fig. 4 F). Finally, we found that IR increased the expression of the IFN-related genes Ifit1 , Ifit3 , and Cxcl10 , and that inhibition of mitotic progression using the CDK1 inhibitor RO-3306 abolished this response (Fig. 4 G). These results indicate that microglial mitotic progression in the presence of damaged DNA activates the cytosolic DNA-sensing cGAS-STING pathway, contributing to the observed delayed IFN response post-IR. In silico and genetic perturbation of key components of the cGAS-STING pathway identify cGAS as the mediator of the delayed IFN response post-irradiation We inferred from our in vitro experiments that the cGAS-STING pathway is involved in the delayed IFN response post-IR. Given that in vitro IR of cells induces considerable cell death 37 , 38 , which could confound our findings, we sought to validate these in vitro results using our mouse model by assessing the expression of the central components of the cGAS-STING pathway, Mb21d1 (encoding cGAS), Tmem173 (encoding STING), and Tbk1 (encoding TBK1) in our scRNA-seq dataset. All three genes showed increased average expression in microglia from IR animals compared to SH controls (Fig. 5 A). While Tmem173 and Tbk1 expressions were broadly observed across multiple microglia clusters in both SH and IR animals, pronounced expression of Mb21d1 was found specifically in proliferating IR microglia belonging to clusters 10 (progressed into the G2/M phase) and 7 (progressed into S phase) (Fig. 5 B). To investigate the dynamic relationships among these states of IR microglia, we leveraged Cell2fate, a state-of-the-art Bayesian framework that builds on RNA splicing kinetics and differential equation modeling to infer fate biases and lineage commitment 39 . This analysis revealed trajectories spanning multiple clusters, with a prominent trajectory connecting cells in clusters 6 (featuring IFN signalling) and the proliferation clusters 7, 9, and 10 (Fig. 5 C), suggesting a link between the IFN response and microglial proliferation. Cell2fate trajectory decomposition identified modules (Supplementary Fig. 5A; Supplementary list 3) associated with microglial proliferation or IFN response, supporting the hypothesis that the cGAS-STING pathway plays a central role in this process. To identify critical regulators of the cGAS-STING pathway, we used Cell2fate-derived RNA velocities to reconstruct a continuous vector field in Dynamo 66 , enabling computation of the Jacobian matrix and in silico genetic perturbations along this trajectory. We evaluated the effects of single and combined knockouts of Mb21d1 , Tmem173 , or Tbk1 . Simultaneous knockout of all three genes substantially perturbed velocity vectors and reversed multiple trajectories, including the transition between clusters 6, 7, 9, and 10. (Fig. 5 D). To disentangle the individual contributions of each gene, we performed single-gene knockouts. Knockout of Tmem173 or Tbk1 only partially reversed the trajectory between clusters 6, 7, 9, and 10, despite their broader expression patterns ( Tmem173 was globally expressed; Tbk1 was enriched in clusters 1, 2, 3, 6, 7, and 10) (Figs. 5 B and 5 E). In contrast, knockout of Mb21d1 , which was preferentially expressed in clusters 1, 6, 7, 9, and 10, and was most highly expressed in proliferating microglia, completely reversed this trajectory (Figs. 5 B and 5 E). As a control, we knocked out the proliferation marker Mki67 and observed that this knockout fully reversed the proliferation trajectory (Supplementary Fig. 5B), validating our perturbation framework. Together, these results indicate that cGAS ( Mb21d1 ) is the principal pathway regulator linking IFN signaling to microglial proliferation after IR. To validate these in silico predictions and investigate whether cGAS is crucial for the IFN response 2 wk post-IR in vivo , we used STING mutant (STING mut/mut ) 40 and cGAS KO (cGAS KO ) 41 mice. Notably, neither genetic model should affect IR-induced DNA damage but still enable assessment of how each target protein contributes to the IFN response 42 . Disruption of STING did not prevent the IR-induced increase of Ifit1 , Ifit3 , or Cxcl10 (Fig. 5 F; Supplementary Fig. 5C); however, the absence of cGAS completely abolished this increase (Fig. 5 G; Supplementary Fig. 5D). Importantly, the post-IR microglial proliferation was not compromised, neither in STING mut/mut nor in cGAS KO mice (Supplementary Figs. 5E and 5F). Collectively, these results indicate that cGAS is crucial for the delayed microglial IFN response post-IR and that its absence prevents this response, even when irradiated microglia enter the cell cycle. Pharmacological inhibition of cGAS suppresses the delayed IFN response in the irradiated brain. We finally sought to test the feasibility of pharmacologically targeting the delayed IFN response through inhibition of Mb21d1 (encoding cGAS), Tmem173 (encoding STING), or Tbk1 , using second-generation mouse-specific antisense oligonucleotides (ASOs). Target or control ASOs were inoculated into the lateral ventricle one week post-IR, a time point when the first inflammatory response has subsided 16 , and brains were collected one week later ( i.e. , 2 wk post-IR) (Fig. 6 A). The knockdown (KD) efficiency was > 90% for Mb21d1 and > 80% for Tmem173 and Tbk1 by 1 wk after inoculation, and IR did not affect the KD levels for any target (Supplementary Figs. 6A − 6C). Consistent with the computational and genetic models, we found that KD of the downstream effectors Tmem173 or Tbk1 did not prevent the IR-induced delayed IFN response (Figs. 6 B and 6 C). However, KD of Mb21d1 suppressed this response (Fig. 6 D). The results demonstrate that pharmacological targeting of cGAS expression with ASO biologics abolishes the delayed IFN response observed post-IR. Discussion Microglia shape the physio-anatomical properties of neurons through synaptic pruning, somatic interaction, and secretion of trophic factors to maintain proper neuronal function 14 , 43 – 46 . Perturbed microglial-neuronal interaction due to microglial hyperreactivity or dysfunction may cause neuronal dysfunction, reflected in impaired cognition, as documented in numerous CNS disease models 14 , 47 . Impaired cognition is a hallmark complication in CNS cancer survivors treated with cranial radiotherapy 1 . Microglia play pivotal roles in IR-induced neurotoxicity through secretion of inflammatory molecules, generation of reactive oxygen species, and excessive synaptic elimination 18 . Despite this consensus in the field, no successful treatments targeting microglial responses to prevent or reverse IR-induced cognitive deficits have been developed. Here, we uncover that cranial IR induces a biphasic inflammatory response in the hippocampus: an acute response occurring within hours post-IR and a delayed response after 2 wk, mediated by microglia and characterized by IFN signaling. IFN signaling has been implicated in the development of cognitive deficits in various CNS disease models, including neurodegeneration and aging 48 – 50 , suggesting that the observed delayed IFN response may contribute to the neurocognitive dysfunction observed in cancer survivors treated with cranial radiotherapy. Radiation is genotoxic, causing DNA damage and genomic instability, and cells that fail to repair their DNA may undergo cell death or cellular senescence 51 , 52 . Senescent cells often resist cell death, do not replicate, and secrete soluble factors known as the senescence-associated secretory phenotype (SASP), which affect the functions of neighboring cells 53 . Microglia express the DNA damage response and cell cycle arrest genes Cdkn1a and Ccng1 early post-IR 16 , yet previous work 12 and this study demonstrated increased microglial proliferation. Mitotic progression with damaged DNA or chromosomal instability activates the cGAS-STING pathway, leading to the induction of type-I IFN signaling 34 , 54 , resulting in cellular senescence in neighboring cells via SASP production 42 , 55 . In this study, we uncovered subsets of microglia characterized by mitotic progression and IFN signaling, concurrent with damaged DNA. Our in silico , in vitro , and in vivo experiments showed that microglial mitotic progression post-IR activated the cGAS-STING pathway. Despite numerous studies linking canonical cGAS-STING activation to stress, tissue damage, infection, and inflammation, including the IFN response 36 , 56 , other studies have demonstrated that cGAS and STING can, independent of each other, regulate cellular programs, such as senescence, metabolism, and response to viral infection 57 – 59 . Our in vivo dissection of the roles of three key effector proteins in this pathway, cGAS, STING, and TBK1, showed that cGAS is crucial for the delayed IFN response 2 wk post-IR, whereas targeting STING or TBK1 was insufficient to dampen it. Considering the causative links between the cGAS-mediated IFN signaling, cellular senescence 42 , 57 , neurodegeneration, and aging 50 , it is tempting to speculate that the uncovered delayed IFN response after cranial irradiation may trigger neurodegeneration, consequently leading to premature brain aging, and that this could be linked to the neurocognitive dysfunction observed in cancer survivors treated with cranial radiotherapy 1 . Overall, we demonstrate that microglia drive a delayed inflammatory response post-IR. Microglia depletion before IR did not affect hippocampal neurogenesis 60 , a process necessary for memory and learning, but elimination of microglia post-IR has been shown to improve cognitive performance in animal models when assessed shortly after repopulation 26 , 61 . Despite this promising preclinical finding, microglial depletion in a clinical setting remains challenging 62 . ASOs offer a promising therapeutic strategy in which the expression of specific mRNA transcripts can be reduced 63 . Although they have not been evaluated in pediatric neuro-oncology, radiotherapy, or neuroinflammation, ASOs have been proven safe in children with spinal muscular atrophy (nusinersen). ASOs against cGAS could potentially be used to treat radiation-induced chronic neuroinflammation, thereby preventing cognitive dysfunction in childhood brain tumor survivors treated with cranial radiotherapy, paving the way for future clinical trials. Materials and Methods Experimental model and subject details Animals Female mice were used in all studies, as irradiation (IR)-induced cognitive deficits are more severe in females, both in animal models and patients 2,64 . Twenty-one- or twenty-five-day-old mice of the following strains were used C57BL/6J (Charles River, Sulzfeld, Germany, stock #000664), Sting1gt/J (Jackson laboratory; stock #017537), and cGAS KO (Jackson laboratory; stock #026554). Animals were housed in equal light/dark cycles (12/12 h) and were fed ad libitum . All experimental procedures were carried out in accordance with the European and Swedish animal welfare regulations, approved by the Northern Stockholm ethical committee (application numbers: N248/13, N141-16, 13676-2020, and 12653-2025). Irradiation procedure Mice were initially anesthetized with 5% isoflurane in an induction chamber in a mixture of air and oxygen (1:1), then transferred to the irradiation machine and placed in a prone position. The anesthesia was maintained with 1.5% isoflurane during the irradiation procedure. The following irradiation machines were used: X-RAD 320 (PXi Precision X-ray, North Branford, CT, USA) or CIX3 cabinet X-ray irradiator (Xstrahl, Surrey, England). For the X-RAD 320 irradiator, the animal head was distanced approximately 50 cm from the radiation source, and an irradiation field of 2 ´ 2 cm was used to cover the entire head. Animals received a single dose of 8 Gy delivered at a rate of 0.73 Gy/min. For the Xstrahl irradiator, the animal head was irradiated with a circular field of 1.5 cm in diameter. The collimator was an Xstrahl Perspex tip applicator. A single dose of 8 Gy was delivered with a dose rate of 1.349 Gy/min (dosimetry uncertainty ~ 2%) at 300 kV and 10 mA. The focus skin distance (FSD) to the animal’s head was 32 cm. External filtration giving a half-value layer (HVL) of 4.0738 mm Cu was applied by adding a Thoraeus filter (1.0 mm Sn, 0.25 mm Cu, 1.50 mm Al). Litter-mates sham controls (SH) were subjected to the same duration of anesthesia in the absence of IR. Animals were allowed to recover from the anesthesia and returned to their cages. Microglia depletion For microglial depletion, mice were fed either PLX5622 (1200 p.p.m.; MedChemExpress formulated in AIN-76A standard chow by Research Diets Inc.) or control AIN-76A standard chow. The experimental diet was supplemented starting 1 day after irradiation, provided ad libitum for 13 days. 5-bromo-2’-deoxyuridine (BrdU) administration To assess the survival of proliferating microglia, dividing cells were labeled between day 10 and 13 post-IR with the thymidine analog BrdU (SigmaAldrich #B5002). BrdU was injected i.p. at a dose of 50 mg/kg, two injections per day (6 h apart). Animals were sacrificed either 2 h or 4 weeks after the last BrdU injection to assess the proliferation and survival of the newborn cells, respectively. Stereotaxic injections One week after cranial irradiation, mice were anesthetized with isoflurane (5% for induction and 2% for maintenance) using a mouse mask coupled to a stereotaxic apparatus. Mice were also injected subcutaneously with 5 mg/kg rimadyl (Carprofen) and 0.1 mg/kg buprenorphine (Temgesic) 15 min prior to surgery for systemic analgesia. The fur on top of the head was shaved, and the skin was disinfected with 70% ethanol. The scalp was infiltrated with lidocaine administration (4 mg/kg), and a 2 mm incision was then made. Mouse-specific antisense oligonucleotides (ASO) targeting Mb21d1 (GCACTAGTTTTTATAACACA), Tmem173 (ACGCATTATGACCTCCTTTC), Tbk1 (CAGTACCTTTATTCACCGCA), non-target control ASO (TCGCCGAATACCAACATGTT) or PBS were injected intracerebroventricularly (i.c.v.) using the following stereotaxic coordinates: 0.3 mm anterior to bregma (AP), 1.0 mm lateral to bregma (ML), and 3 mm deep (measured from when the orifice of the needle has passed the skull) (DV) using a Hamilton 10 μl syringe (#80030 1701 RN) and a (22s/51/2)S needle (#7758-03 RN) without drilling the skull. The injection volume (10 μl) and injection speed (0.1 μl/sec) were controlled by an automatic microinjection pump. The needle remained at the injection site for 3 min to reduce backflow, then was slowly retracted. After the surgery, 2-3 stitches (Ethilon monofilament 5.0) were applied to suture the wound. Eye gel drops (Viscotears 2 mg /ml, # 541760) were used to hydrate the eyes throughout the surgery. Mice were allowed to recover in a separate cage on a heating pad (35.5ºC) and monitored until they were fully awake and active. Mice received post-operative analgesia (Carprofen 5 mg/kg) 24 hours later. Tissue collection Animals were sacrificed at 6 hours, 1 day, 1 week, 2 weeks, 6 weeks, 6 months, or 1 year post-IR. Animals were deeply anesthetized with sodium pentobarbital (100 mg/kg, ABCUR AB #444362, Sweden) and transcardially perfused with 1´ phosphate-buffered saline (PBS; ThermoFisher Scientific #10010023). Brains were collected and the hemispheres were separated. The left hemispheres were placed into 4% paraformaldehyde (PFA; Histolab Products # HL96753.1000, Sweden) and stored at 4ºC for 48 h. The brains were then cryoprotected in 30% sucrose solution (Sigma-Aldrich #S7903) made in 0.1 M phosphate buffer pH 7.4 and stored at 4ºC until slicing. Right hemisphere hippocampi were dissected and kept in a -80 ºC freezer until further processing for RNA or protein extraction. Generation of single-cell suspension for RNA sequencing Single-cell suspensions were prepared from three SH controls and three IR mice per time point. Animals were deeply anesthetized with sodium pentobarbital (100 mg/kg, ABCUR AB, Sweden) and transcardially perfused with ice-cold 1´ PBS without Ca 2+ and Mg 2+ (PBS; pH 7.4; Gibco/Life Technologies #10010056). Brains were collected and the hippocampi from the two hemispheres were dissected and put into 1.5 mL microtubes containing 1´ PBS placed on ice. The tissue was chopped into small pieces using scalpel and transferred into conical tubes containing an enzymatic mixture of Dispase II (0.01%; Sigma-Aldrich #D4693 ), papain (0.1%; Roche #000000010108014001), and DNaseI (0.05%; Roche # 000000010104159001) in 1´ Hank’s buffered salt solution (HBSS) without Ca 2+ and Mg 2+ (Gibco/Life Technologies #14175095), later supplemented with 12.4 mM magnesium sulfate (Sigma-Aldrich #M7506). Tubes were incubated at 37ºC for 10 min, after which the enzymatic activities were stopped with 20% ice-cold heat-inactivated fetal bovine serum (FBS; Gibco/Life Technologies #10500064). The cell suspension was then filtered through a 70 μm cell strainer and centrifuged for 5 min at 500 ´ g at 4ºC. After washing with 1´ HBSS, cells were resuspended in 20% percoll solution (percoll plus, GE Healthcare, #GE17-0891-02); 10´ phenol red HBSS (Gibco/Life Technologies #14060040) and 1´ HBSS, and overlaid with an equal volume of 1´ HBSS. Tubes were spun at 1,000 ´ g at 4ºC for 30 min with no break to remove myelin. The cell pellet was resuspended in 1 ml flow cytometry buffer (R&D Systems #FC001). In protocols intended to collect neurons, an additional centrifugation step in a higher percoll concentration was applied. The supernatant from the first percoll step (20%) was collected in 50 ml conical tubes and mixed with an equal volume of 50% percoll solution to obtain a concentration of 30 % percoll, and spun at 1,000 ´ g at 4ºC for 30 min with a break. The supernatant was removed, and the pellet was resuspended in 1 ml flow cytometry buffer. Both cellular fractions collected from the two-step percoll were mixed and spun at 500 ´ g at 4ºC for 5 min. The pellet was resuspended in 0.5 ml flow cytometry buffer. Cells processed for single-cell RNA-seq were pooled from three SH and three IR animals before adding the flow cytometry buffer and processed for 10´ single-cell RNA sequencing. For each time point, cells were isolated from SH and IR animals at the same time and processed in the same manner (as one batch). Cell culture For the primary hippocampal microglia culture, fourteen-day-old mouse pups were deeply anesthetized with sodium pentobarbital (100 mg/kg, ABCUR AB, Sweden) and transcardially perfused with ice-cold 1´ PBS without Ca 2+ and Mg 2+ (PBS; pH 7.4; Gibco/Life Technologies #10010056). Brains were collected, and the hippocampi from the two hemispheres were dissected and put into 1.5 ml microtubes containing 1´ PBS placed on ice. The tissue was chopped into small pieces using a scalpel and transferred into conical tubes containing an enzymatic mixture of Dispase II (0.01%; Sigma-Aldrich #D4693), papain (0.1%; Roche #000000010108014001), and DNaseI (0.05%; Roche # 000000010104159001) in 1´ HBSS without Ca 2+ and Mg 2+ (Gibco/Life Technologies #14175095), later supplemented with 12.4 mM magnesium sulfate (Sigma-Aldrich #M7506). Tubes were incubated at 37ºC for 10 min, after which the enzymatic activities were stopped with 20% ice-cold heat-inactivated fetal bovine serum (FBS; Gibco/Life Technologies #10500064). The cell suspension was then filtered through a 70 μm cell strainer and centrifuged for 5 min at 500 ´ g at 4ºC. After washing with 1´ HBSS, cells were resuspended in 20% percoll solution (percoll plus, GE Healthcare, #GE17-0891-02); 10´ phenol red HBSS (Gibco/Life Technologies #14060040) and 1´ HBSS, and overlaid with an equal volume of 1´ HBSS. Tubes were spun at 1,000 ´ g at 4ºC for 30 min with no break to remove myelin. Cells were seeded in T25 cell culture flasks in DMEM/F12 with Glutamax culture medium (Gibco/Life Technologies #31331028) containing 10% heat-inactivated FBS and supplemented with 10 ng/ml recombinant mouse M-CSF (R&D systems 416-ML-010) and grown at 37 o C in 5% CO 2 . The culture medium was changed every second day. After reaching approximately 90% confluence, cells were washed with 1´ PBS and incubated with 0.05% trypsin-EDTA (Gibco/Life Technologies) for 5 min at 37 o C. The enzyme activity was stopped with FBS, and cells were washed with 1´ PBS. Cells were resuspended in flow cytometry buffer (R&D Systems #FC001) and processed for magnetic separation using CD11b micro-beads (Miltenyi Biotec #130-093-636) following the manufacturer’s recommendations. Cells were washed and seeded in 12-well plates at a density of approximately 2 ´ 10 4 cultures in the above-mentioned cell culture medium, however, without M-CSF, and left to settle for 48 h prior to irradiation. For the microglial cell lines, the murine microglia BV2 cell lines were used. BV2 cells were cultured in DMEM GlutaMAX (GIBCO/Life Technologies #10564011). Cells were supplemented with 10% FBS and 5% penicillin/streptomycin, grown at 37°C in 5% CO 2 , split and passaged every 2 - 3 days. For irradiation, BV2 cells were seeded in 6-well plates at a density of 1.5 ´ 10 5 and allowed to settle overnight. Cells were irradiated in a CIX2 X-ray irradiator cabinet (Xstrahl, Surrey, England) with a single dose of 8 Gy delivered at a rate of 1.35 Gy/min at 195 kV and 10 mA. Focus skin distance (FSD) to the flask /plate was 40 cm. External filtration giving an HVL of 9.0436 mm was applied by adding a 3.0 mm Al filter. A rotating platform was used to ensure homogeneous dose delivery. Control cells were placed on the rotating platform for a time equivalent to that required to deliver 8 Gy without IR. Irradiated cells and their respective sham controls were grown for 1 h or 24 h post-IR. The culture media were collected, and the cells were washed with 1´ PBS. The following buffers were added depending on the intended downstream analysis: protein extraction buffer (for western blot), RLT buffer containing 1% β-mercaptoethanol (for RNA expression), or ice-cold 4% PFA (for immunostaining), and the samples were processed for the intended downstream analysis. RNA isolation and quantitative real-time polymerase chain reaction (qPCR) RNA was extracted using the following kits: RNeasy Plus Micro Kit (Qiagen, #74034) and RNeasy Plus Mini Kit (Qiagen, #74136). cDNA was generated using the QuantiTect® Reverse Transcription Kit (Qiagen, #205311) or Iscript™ cDNA Synthesis Kit (BioRAD #1708891) using the AB Simpliamp Thermal PCR machine (Applied Biosystems). The qPCR was performed using the QuantiTect SYBR Green PCR Kit (Qiagen, #204143) or Ssoadvanced™ Universal SYBR (BioRAD #1725274). qPCRTarget mRNA expression was assessed using the QuantiTect primer assay (Qiagen). The following primers were used: Mm_ Gapdh _3_SG (#QT01658692); Mm_ Ifit1 _1_SG (#QT01161286); Mm_ Ifit3 _1_SG (#QT00292159); Mm_ Cxcl10 _1_SG (#QT00093436); Mm_ Ccl12 _1_SG (#QT00244391); Tbp (Sigma-Aldrich; Forward # SY200704801-089: 5’-CTCAGTTACAGGTGGCAGGA-3’; reverse # SY200704801-090: 5’-CAGCACAGAGCAAGCAACTC-3’). qPCR was performed using either the StepOnePlus™ Real-Time PCR System (Applied Biosystems #4376600) or the CFX384 Touch Real-Time PCR Detection System (Bio-Rad #1855484). Relative mRNA expression was determined using the ΔΔ CT method. Gapdh and Tbp were used as housekeeping genes. Bulk RNA sequencing Isolated RNA was sequenced at BGI Genomics in China. For data analysis, unsupervised hierarchical clustering and principal component analyses (PCA) were performed in Qlucore Omics Explorer 3.2 (Qlucore, Lund, Sweden). Differentially expressed genes were determined by comparing irradiated- and sham-control animals using heteroscedastic two-tailed t -tests. Multiple testing correction was performed using the Benjamini-Hochberg algorithm with a false discovery rate (FDR) of 5%.For targeted expression analysis of cytokine- and chemokine-related genes, genes displaying >3-fold expression changes at any time point were included. For gene set enrichment analysis, ranked gene lists were prepared based on signal-to-noise ((Mean irradiated - mean sham control)/sum of standard deviations). Human gene names were used to input ranked gene lists into the GSEA application https://www.gsea-msigdb.org/gsea/ index.jsp, Broad Institute, Inc.), using the GSEAPreranked tool. Single-cell RNA sequencing Isolated cells were processed for single-cell RNA sequencing at the eukaryotic single-cell genomics facility (ESCG) at Karolinska Institutet, Sweden, using the 10´ Genomics Chromium method (version 3) and sequenced using Illumina NovaSeq 6000 with an S1flow cell. The Cellranger 6.0.1 pipeline was used to align the raw sequencing reads to the mouse reference genome, Mus musculus version mm10, and generate the unique molecular identifier (UMI) count matrix. The generated count matrix was analyzed using the Seurat R package (version 3.0.0). Each condition included 3 technical replicates, and each replicate was processed independently before integration. Low-quality cells were excluded based on the number of genes expressed in the cells and the percentage of mitochondrial counts. Cells expressing a minimum of 250 genes and a maximum of 6,000 genes were considered for downstream analysis, given that each gene was expressed in at least three cells. The hippocampal cellular components are heterogeneous, and each cell type has a different energy demand, with neurons having the highest energy consumption 65 . To ensure inclusion of all cell types, no threshold for the mitochondrial counts was included during the first cleanup. The filtered count matrices were normalized by dividing the feature counts for each cell by the total number of counts from all cells, then multiplied by 10,000 and log-transformed. Next, we identified highly variable features for each dataset independently. We used the Canonical Correlation Analysis (CCA) - default Seurat method for the integration of datasets and removal of the batch effect. To ensure optimal results, we separately integrated our datasets using Harmony integration. Both methods yielded similar results, so we proceeded with CCA. In brief, we used canonical correlation analysis and identified “anchors” (conserved cell groups) between datasets, which were used for batch correction and the comparison of the differentially expressed genes between experimental conditions. The data were scaled using Pearson residuals,, regressing out the variation by the mitochondrial genes. PCA was then performed on the scaled data for the dimensionality reduction, using 30 components. The output was used to produce a Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) with a resolution of 0.5 for identification of the clusters. Identification of each cluster was based on signature genes of each cell type according to the existing literature as well as through the automatic cell type recognition package SingleR. Signature genes for microglia included Sall1 , Cx3cr1 , P2ry12, Tmem119 ; for macrophages Mrc1 and Ms4a7 ;for oligodendrocytes, Pdgfra , Mbp, Olig2, and Mog ; for astrocytes Gja1, Gfap, and Aqp4 ; for neurons Dcx , Rbfox3, Neurod1, and Syt1 ; for endothelial cells Cldn5 ; for pericytes Vtn and Pdgfrb ; for fibroblasts/myofibroblasts Col1a1, Acta2, and Lama1 ; for B cells Cd79a, Cd19 and Igkc ; for T- and natural killer cells Cd7, Cd3g, and Itgb7 . The proliferating cells were identified using markers such as Mki67 and Top2a . At this stage, we revisited mitochondrial levels and customized them for each cell type. Specifically, microglia threshold was set to 10 %, astrocytes/radial glia to 40%, and oligodendrocytes, ependymal, pericytes, endothelial, VSMCs/fibroblasts, B/T/NK cells, and meningeal fibroblasts to 20%. After the second quality control, a total of 51,872 single cells were analyzed, SH: 25,969; IR: 25903. As we focused on microglia, the 15902 microglial cells, SH: 8,029; IR: 7,873 were analyzed in-depth. The Speckle package was used to analyze differences in cell type proportions across experimental conditions and time points. Using the propeller function, we looked for significantly different clusters between the SH and IR groups, based on ANOVA statistical testing. The ClusterProfiler package was used to perform gene set enrichment analysis of the upregulated, statistically significantly differentially expressed genes (DEGs) in the microglial clusters at each time point. Using the compareCluster function, we directly compared the enriched functional profiles of each cluster using the KEGG database. DEGs were considered when expressed by at least 30% of cells in the cluster, the log2 fold-change was higher than 0,8, and the p -adjusted value was lower than 0.05. Scoring of the cell cycle phases of each cell was calculated using the CellCycleScoring function in the Seurat pipeline, based on a set of genes expressed during the S and G2/M phases. The assigned scores for the S and G2/M phases were saved on the metadata of the Seurat object. In silico perturbation For the estimation of the RNA velocity, the post-alignment bam files were used to run velocyto command v0.17, run for any technique. To mask potential repeats of expressed genes, we retrieved the mouse repeat annotation file from the UCSC Genome Browser (mm10_rmsk.gtf). The mouse genome annotation reference was acquired from CellRanger, and the resulting loom files contained the spliced and unspliced counts. The scVelo tool was used to estimate the generalized RNA velocity model. We extracted the coordinates, count matrix, and gene names of the processed Seurat object for microglia from R and created an anndata object. The obtained loom file and the anndata objects were merged, and the generalized dynamical model was used for velocity estimation and plotting. We used Cell2Fate for RNA velocity analysis, which linearizes the differential equations describing RNA velocity and solves them in a fully Bayesian manner, enabling better interpretability of microglial transcriptional dynamics after IR. We trained the Cell2fate dynamical model using 500 epochs. For in silico perturbations to identify critical regulators of the cGAS–STING pathway, we extracted the Cell2Fate-derived 39 velocity transition matrix and used it as input to reconstruct a continuous vector field in Dynamo, using 1000 basis functions (control points) 66 . We then computed the analytical Jacobian matrix from this vector field and performed in silico genetic knockouts using the dyn.pd.perturbation function to predict the effects of gene perturbations on cell-fate trajectories. Immunohistochemistry and immunofluorescence For mouse brain tissues, the left hemispheres were cut sagittally into 25-μm-thick free-floating sections made in 1:12 series intervals using a sliding microtome (Leica SM2010R) and stored in 2 ml microtubes containing a cryoprotectant solution (25% glycerol, 25% ethylene glycol in 0.1M phosphate buffer) and kept at +4 o C.After several washes with 1´ Tris-buffered saline (TBS), samples were incubated in a 10 mM sodium citrate solution (pH 6.0) or 1´ citrate buffer solution (pH 6.0; Sigma-Aldrich #C9999) for 30 min at 80 o C for antigen retrieval. For BrdU staining, the double-stranded DNA was denatured by incubating the sections in 2 N HCl at 37°C for 30 min followed by a neutralization step performed by incubating the sections in a 0.1 M borate buffer for 10 min at room temperature. Sections were washed with 1´ TBS. Non-specific binding was blocked by incubating the sections in a solution of 3% or 5% normal donkey serum (Jackson ImmunoResearch Laboratories; #017000121), 0.1% Triton X-100 (made in 1´ TBS) for 1 h at room temperature. Sections were then incubated with primary antibodies at 4ºC for 24 - 72 h, depending on the antibody. The following primary antibodies were used: goat anti-IBA1 (Abcam# ab5076; 1:500); rabbit anti-IBA1 (Wako Chemicals #1919741; 1:1,000); rabbit anti-TMEM119 (Abcam #ab209064; 1:500); rat anti-Ki67 (ThermoFisherScientific #14-5698-82; 1:500); goat anti-OLIG2 (R&D Systems #AF2418; 1:500); rat anti-BrdU (Abcam #6326; 1:500). Sections were incubated for 2 h at room temperature with appropriate fluorescent secondary antibodies. The following secondary antibodies were used: AlexaFlour-488 donkey anti-goat IgG (Molecular probes/Life Technologies #A11055; 1:1,000); AlexaFlour-488 donkey anti-mouse IgG (Molecular probes/Life Technologies #A21202; 1:1,000); AlexaFlour-555 donkey anti-rabbit IgG (Molecular probes/Life Technologies #A31572; 1:1,000); AlexaFlour-555 donkey anti-rat IgG (Abcam #ab150154; 1:1,000); CF-633 donkey anti-goat IgG (Biotium #20127; 1:1,000). Sections were mounted into slides and coverslipped using ProLong Gold anti-fade reagent (Molecular probes/Life Technologies; #P36930). For cultured cells, after fixation (explained above) and several washes with 1´ TBS, non-specific binding was blocked by incubating the sections in a solution of 5% normal donkey serum (Jackson ImmunoResearch Laboratories), 0.1% Triton X-100 (made in 1´ TBS) for 1 h at room temperature. Cells were incubated overnight with the following primary antibodies: mouse anti- γ H2AX (Phospho S139; Abcam #Ab26350; 1:500); rabbit anti-cGAS (D3O8O; Cell signaling technology #31659; 1:250); rabbit anti-phospho-STING (Ser365; Cell signaling technology #62912; 1:250). Cells were incubated with the appropriate secondary antibody for 1 h at room temperature. The following antibodies were used: AlexaFluor-488 donkey anti-rabbit IgG (Molecular probes/Life Technologies #A-21206; 1:1,000); AlexaFluor-555 donkey anti-rabbit IgG (Molecular probes/Life Technologies #A31572; 1:1,000); CF-555 donkey anti-mouse IgG (Biotium #20037; 1:1,000). Hoechst 33342 (Molecular Probes/Life Technologies #H3570) was used as a nuclear counterstain. Slides were coverslipped using ProLong Gold anti-fade reagent (Molecular probes/Life Technologies; #P36930). Microscopy and cell quantification All histological analyses were performed in the molecular layer (ML) and the Cornu Ammonis 1 (CA1) region of the hippocampus in sections containing the dorsal hippocampus spaced 300 μm apart ( i.e. every 1:12 series). Analyses of total Ki67 positive (Ki67 + ) cells, total microglia (IBA1 + and Tmem119 + ), proliferating microglia (IBA1 + /Ki67 + or Tmem119 + /BrdU + ) or Oligodendrocyte progenitors (OLIG2 + /Ki67 + ), were performed using the LSM 700 or LSM900 Zeiss confocal scanning microscopy (Carl Zeiss, Germany), equipped with the Zen software (Black or Blue editions Carl Zeiss). Z-stack images were acquired in sequential scans performed at 1 μm section intervals using a 20× objective lens and 1 airy pinhole setting and analyzed using Zen Blue Lite software (Carl Zeiss; Germany). In all quantifications, the total number of cells was the sum of all counted cells in all sections per animal multiplied by the series interval ( i.e. 1:12). The cell density was determined by dividing the total number of quantified cells by counting volume. For analysis of micronuclei and cGAS expression in SH or IR cultured microglial cells, images were acquired using the LSM 700 laser scanning confocal microscope (Carl Zeiss, Germany) equipped with the Zen software (Black edition 2012, Carl Zeiss) for BV2, and the LSM 900 (Carl Zeiss, Germany) for primary culture. For analysis of phospho-STING, images were acquired using the ZEISS Axio Scan.Z1 slide scanner (Carl Zeiss, Germany). For each SH or IR condition, three coverslips were analyzed. For each coverslip, at least 7 random fields were imaged, covering the borders and the center of the coverslip. Image analysis was performed using the Zen Blue Lite software (Carl Zeiss, Germany). To analyze the percentage of cells with concomitant micronuclei and cGAS expression, at least 2,300 and 1,100 cells (Hoechst + ) from SH and IR cells, respectively, were analyzed. A micronucleus is defined as a discrete Hoechst + DNA aggregate apart from the primary nucleus of the cell. For analysis of phospho-STING expression, at least 1,200 and 700 cells from SH and IR cells, respectively, were analyzed. Protein extraction, ELISA, and immunoblotting For protein extraction from the hippocampal tissues, ice-cold protein extraction buffer (50 mM Tris-HCl; Sigma-Aldrich #T1503, 100 mM NaCl; Sigma-Aldrich, #S7653; 5 mM EDTA, Sigma-Aldrich #E5134 and 1 mM EGTA, Sigma-Aldrich #E3889) supplemented with protease inhibitor cocktail (Roche #11836170001) and phosphatase inhibitors (Roche #04906837001) were added to the frozen tissue and homogenized with a sonicator. Samples were then centrifuged for 10 min at 4 o C at 10,000 ´ g, and the supernatants were transferred into 0.5 ml tubes and stored at -80 o C. The total protein concentration was determined using the Pierce BCA protein assay kit (ThermoFischer Scientific #23225), and absorbance was measured using the FLUOstar Omega (BMG LABTECH, Germany) plate reader. The levels of the chemokines in the hippocampal homogenates were measured using the following ELISA kits: mouse IP-10 (CXCL10) ELISA kit (Abcam #ab214563), mouse MCP5 (CCL12) ELISA kit (Abcam #ab100723), and mouse/rat CCL2/JE/MCP-1 Quantikine ELISA kit (R&D Systems #MJE00). The assays were performed following the manufacturer’s instructions. For BV2 cells, after media aspiration and gentle washing with 1´ PBS, a 2.5´ loading buffer (Tris HCl 62 mM; 2% sodium dodecyl sulfate; 10% glycerol; 5% β-mercaptoethanol; 0.02 % Bromphenol Blue) was added, and cells were collected. Cells were then sonicated, and the resulting protein extracts were processed for Western blot analysis. Proteins were detected using the following antibodies: rabbit anti-phospho-TBK1/NAK (Ser172) (D52C2); Cell Signaling #5483; 1:1,000); mouse anti-β-actin (Sigma-Aldrich #A2228; 1:2,000). Membranes were visualized using the Odyssey CLx LI-COR system with ImageStudio software. Membranes were visualized with Odyssey CLx LI-COR (software: Image Studio). Protein band densitometry was done in ImageJ software. Quantification of protein expression used the ratio of target to housekeeping protein (β-actin); sham controls were set to 1. Statistical analysis Statistical analyses were performed using GraphPad Prism (GraphPad, Inc., San Diego, CA, USA). Data were presented as mean ± SEM. The unpaired Student’s t -test was used to compare SH and IR animals at each time point, aiming to eliminate the effect of animal age. Comparisons of multiple variants per time point were performed using a two-way ANOVA with Bonferroni’s post hoc testfor multiple comparisons. Significance was considered when p < 0.05. The statistical analyses, number of animals, and in vitro experiments applied were noted in each figure legend. For bulk RNA-seq data analysis, Qlucore Omics Explorer 3.2 (Qlucore, Lund, Sweden) was used. The GSEAP reranking tool was used for the GSEA analysis, while for single-cell RNA-seq analyses, both R (versions 4.3.0 and 4.3.3) and the Seurat package (version 4.0.0) were used. Declarations Acknowledgments We thank Annika Andersson, senior lab manager in the Blomgren group, for technical assistance; Dr. Na Sun and Stuart Fass from Kellis’ lab for input on the computational analyses. The authors acknowledge support from the National Genomics Infrastructure in Stockholm, funded by Science for Life Laboratory, the Knut and Alice Wallenberg Foundation, and the Swedish Research Council. The authors also thank SNIC/Uppsala Multidisciplinary Center for Advanced Computational Science for assistance with massively parallel sequencing and access to the UPPMAX computational infrastructure, the KI X-ray Irradiation Core Facility, and the Eukaryotic Single Cell Genomics Facility (ESCG) for running the scRNA-seq. Illustrations were partly created using BioRender.com. KB was supported by the Swedish Childhood Cancer Fund (Barncancerfonden), the Swedish Cancer Foundation (Cancerfonden), the Swedish Research Council (Vetenskapsrådet), grants provided by the Stockholm Region (ALF projects), the Swedish Brain Foundation (Hjärnfonden), Radiumhemmets Forskningsfonder, the Karolinska Institute Doctoral (KID) funding, the KI Foundation for Research, the Märta and Gunnar V. Philipson Foundation, the Frimurare barnhusfonden, and the Irstadska Foundation. AMO was supported by Erik Rönnbergs Stipendium, the ARMEC Foundation, and the Åke Wiberg Foundation. AF was supported by Stiftelsen Samariten, KI Foundation for Research, Mary Béves Stiftelse för Barncancerforskning, and ìShizu Matsumuraî’s Donation. EP was supported by Stiftelsen Barnforskningen Astrid Lindgrens Barnsjukhus, and the Boehringer Ingelheim Fonds. VML was supported by the ERC Consolidator Grant 3DMASH [101170408], the Swedish Research Council [2021-02801, 2023-03015 and 2024-03401], the Novo Nordisk Foundation [NNF23OC0085944 and NNF23OC0084420], Cancerfonden [24-3735Pj], the Ming Wai Lau Center for Reparative Medicine, the SciLifeLab and Wallenberg National Program for Data-Driven Life Science [WASPDDLS22:006] and the Robert Bosch Foundation (Stuttgart, Germany). CB was supported by Karolinska Institutet, and the Center for Innovative Medicine (CIMED). Author contributions A.M.O., A.F., and K.B. conceived, designed, and supervised the study. E.P., A.L.R., A.F., A.M.O., and K.B. interpreted the results and wrote the manuscript. E.P., A.L.R., M.Q.C., K.T., Y.O.V., S.S., K.Z., G.A.Z., C.F.D.R., A.F., and A.M.O. performed the animal experiments. A.L.R., G.P., G.A.Z., M.S., E.W., and A.M.O. performed the histological analyses. A.L.R., E.P., and A.M.O. performed the isolation of microglia and all-brain cells for transcriptomic analyses. E.P., A.L.R., M.Q.C., K.T., Y.O.V., S.S., T.S., Y.R., and A.F. performed the qPCR. K.Z. Y.X., and C.Z. contributed to bulk RNA-seq data. N.O-V. and V.M.L. analyzed the bulk RNA-seq data. E.P. performed single-cell RNA-seq and computational analyses. Y.S. and C.B. contributed to the single-cell RNA-seq analysis. A.L.R., T.S., M.Q.C., Y.R., L.F., and B.J. contributed to the BV2, primary microglia culture, and related immunoblotting experiments. L.H., Z.L., and M.K. contributed to the machine learning analysis and interpretation. J.D., C.H., and F.K. developed the ASOs used in this study. F.K. contributed to the planning and interpretation of the ASO experiments. All authors discussed the results, commented on, or edited the manuscript. Disclosure of Potential Conflicts of Interest VML is the CEO and shareholder of HepaPredict AB, as well as a co-founder and shareholder of Shanghai Hepo Biotechnology Ltd. J.D., C.H., and FK are shareholders of Ionis Pharmaceuticals. The other authors declare no competing interests. Resource availability Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact: [email protected] Materials Availability This study did not generate new unique reagents. Data and code availability The datasets included in this study are available from the lead contact author upon request. The RNA sequencing data supporting the current study have been deposited in the public repository Gene Expression Omnibus (GEO) under the accession numbers… (for bulk RNA-seq) and … (for single-cell RNA-seq). [To be added]. 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Rodrigues","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"F.D.","lastName":"Rodrigues","suffix":""},{"id":589023442,"identity":"86c2b5b6-7aeb-4fba-8050-b3c6b97eaf5c","order_by":26,"name":"Manolis Kellis","email":"","orcid":"https://orcid.org/0000-0001-7113-9630","institution":"MIT","correspondingAuthor":false,"prefix":"","firstName":"Manolis","middleName":"","lastName":"Kellis","suffix":""},{"id":589023443,"identity":"ce3485e0-e3b1-4c4c-a1b5-e0aed3700610","order_by":27,"name":"Bertrand Joseph","email":"","orcid":"https://orcid.org/0000-0001-5655-9979","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Bertrand","middleName":"","lastName":"Joseph","suffix":""},{"id":589023444,"identity":"26ce5f48-4fd6-40dd-a126-3602e248dc26","order_by":28,"name":"Volker Lauschke","email":"","orcid":"https://orcid.org/0000-0002-1140-6204","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Volker","middleName":"","lastName":"Lauschke","suffix":""},{"id":589023445,"identity":"06d1a405-1cc5-47f4-bda0-3629af006f4d","order_by":29,"name":"Fredrik Kamme","email":"","orcid":"","institution":"Ionis Pharmaceuticals","correspondingAuthor":false,"prefix":"","firstName":"Fredrik","middleName":"","lastName":"Kamme","suffix":""},{"id":589023446,"identity":"18b4ec15-c3b5-467c-8a1c-61dc666210b7","order_by":30,"name":"Christer Betsholtz","email":"","orcid":"https://orcid.org/0000-0002-8494-971X","institution":"Karolinska Institute","correspondingAuthor":false,"prefix":"","firstName":"Christer","middleName":"","lastName":"Betsholtz","suffix":""},{"id":589023447,"identity":"0055504e-30d9-4d6e-bd50-54c5edb9e988","order_by":31,"name":"Adamantia Fragopoulou","email":"","orcid":"https://orcid.org/0000-0001-7715-652X","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Adamantia","middleName":"","lastName":"Fragopoulou","suffix":""},{"id":589023448,"identity":"c6a3fddf-fdfc-4804-b4f4-979a8206777d","order_by":32,"name":"Ahmed Osman","email":"","orcid":"https://orcid.org/0000-0002-5255-2136","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Osman","suffix":""}],"badges":[],"createdAt":"2026-02-03 12:16:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8775672/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8775672/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102395590,"identity":"ed73eb62-daf1-4e56-81d8-2cce6c0f1b71","added_by":"auto","created_at":"2026-02-11 09:37:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3637560,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIrradiation causes a biphasic inflammatory response in the hippocampus.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlso see\u003cstrong\u003e \u003c/strong\u003eSupplementary figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Experimental design. Sham (SH), n = 6; Irradiation (IR), n = 6, per time point. h = hour, d = day, wk = week.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e Principal component analysis (PCA) plot showing distinct clustering of SH and IR samples from all studied time points.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C)\u003c/strong\u003e Bar plot showing the number of differentially expressed genes (DEGs) between SH and IR animals (\u003cem\u003eq\u003c/em\u003e \u0026lt; 0.05). Positive y-axis indicates upregulated DEGs; negative y-axis indicates downregulated DEGs.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e - \u003cstrong\u003eG\u003c/strong\u003e) Line graphs showing qPCR analyses of \u003cem\u003eIfit1\u003c/em\u003e, \u003cem\u003eIfit3\u003c/em\u003e, \u003cem\u003eCxcl10,\u003c/em\u003eand \u003cem\u003eCcl12\u003c/em\u003e in the hippocampus across the studied time points. SH, n = 3-4; IR, n = 3-4. Mean ± SEM, unpaired \u003cem\u003et\u003c/em\u003e-test per time per time point to eliminate the animal age factor. \u003cem\u003e*p\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003e**p\u003c/em\u003e \u0026lt; 0.01, \u003cem\u003e***p \u003c/em\u003e\u0026lt; 0.001, \u003cem\u003e****p\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eH\u003c/strong\u003e and \u003cstrong\u003eI\u003c/strong\u003e) Line graphs showing ELISA measurements of CXCL10 and CCL12, respectively, in the hippocampus across the time post-IR. SH, n = 3-5; IR, n = 4-5. Mean ± SEM, unpaired \u003cem\u003et\u003c/em\u003e-test per time point. \u003cem\u003e*p\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003e**p\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/2d284fc77fa09ea8a6522679.png"},{"id":102395594,"identity":"5982e15a-e0b5-49e9-af34-689d93ee2e38","added_by":"auto","created_at":"2026-02-11 09:37:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":821648,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Microglia mediate the delayed interferon response post-irradiation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlso see\u003cstrong\u003e \u003c/strong\u003eSupplementary figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eExperimental design. SH, n = 3; IR, n = 3.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Uniform manifold approximation and projection (UMAP) showing clustering of the cell types captured using our cell isolation protocol from SH and IR hippocampi 2 wk post-IR. n = 3 per group. VSMCs = Vascular smooth muscle cells. B/T/NK cells = B cells, T cells, and natural killer cells, respectively.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Bar plot showing the number of DEGs in all captured cell types.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Dot plots show the average expression\u003cem\u003e \u003c/em\u003eof \u003cem\u003eCxcl10\u003c/em\u003e (left) and \u003cem\u003eCcl12\u003c/em\u003e (right) across captured cell types post-IR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(E) \u003c/strong\u003eExperimental design of microglial depletion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(F) \u003c/strong\u003eRepresentative confocal image visualizing microglia (IBA1\u003csup\u003e+\u003c/sup\u003e, green) in the mouse hippocampus 2 wk after feeding the mice with either a control or a PLX5622 diet. Hoechst (blue), nuclear counterstain.\u0026nbsp; Scale bar = 400 mm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(G) \u003c/strong\u003eBar plot showing qPCR analyses of \u003cem\u003eIfit1\u003c/em\u003e, \u003cem\u003eIfit3\u003c/em\u003e, \u003cem\u003eCxcl10,\u003c/em\u003e and \u003cem\u003eCcl12\u003c/em\u003e in the hippocampus 2 wk post-IR of mice fed with Control or PLX5622 diet. n = 4-5. Mean ± SEM, two-way ANOVA with Bonferroni’s \u003cem\u003eposthoc \u003c/em\u003efor multiple comparisons. \u003cem\u003e**p\u003c/em\u003e \u0026lt; 0.01, \u003cem\u003e***p\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003e****p\u003c/em\u003e \u0026lt; 0.0001. ns = not significant.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/f4a05f5237245c55eace3660.png"},{"id":102398019,"identity":"89b8102a-0bdf-4b6e-b6c1-d51564bce69f","added_by":"auto","created_at":"2026-02-11 10:20:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5572130,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe microglial delayed response constitutes multiple states.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlso see\u003cstrong\u003e \u003c/strong\u003eSupplementary figures 3 and 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eUMAP showing distinct clustering of hippocampal microglia from SH (blue) and IR (red) 2 wk post-IR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) \u003c/strong\u003eMicroglial sub-clustering 2 wk post-IR revealed 11 subpopulations. Cells in clusters 1, 3, 4, 6, 7, 9, and 10 were uniquely enriched post-IR.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC)\u003c/strong\u003e Stack plot showing the proportions of microglial clusters 2 wk post-IR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(D) \u003c/strong\u003eDot plot showing\u003cstrong\u003e \u003c/strong\u003ethe\u003cstrong\u003e \u003c/strong\u003eenriched pathways in irradiation-enriched microglia clusters (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05) 2 wk post-IR as revealed by GSEA.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE\u003c/strong\u003e) Left: Representative confocal image displaying co-labeling of microglia (IBA1\u003csup\u003e+\u003c/sup\u003e, white) with Ki67 (red) in the \u003cem\u003ecornu ammonis\u003c/em\u003e 1 (CA1) region of the hippocampus 2 wk post-IR. Hoechst (blue), nuclear counterstain. Scale bar = 10 mm. Middle and right panels: Quantification of IBA1\u003csup\u003e+\u003c/sup\u003e/Ki67\u003csup\u003e+\u003c/sup\u003e cells in the molecular layer (ML) and CA1, respectively, across the studied time points. SH, n = 3-4; IR, n = 3-4. Mean ± SEM, unpaired \u003cem\u003et\u003c/em\u003e-test per time point. \u003cem\u003e*p\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003e****p\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(F) \u003c/strong\u003eLeft:\u003cstrong\u003e \u003c/strong\u003eBar plot showing quantification of IBA1\u003csup\u003e+\u003c/sup\u003e cells in the CA1, 2 wk post-IR. SH, n = 6; IR, n = 6. Mean ± SEM, unpaired \u003cem\u003et\u003c/em\u003e-test per time point. \u003cem\u003e****p\u003c/em\u003e \u0026lt; 0.0001. Right:\u003cstrong\u003e \u003c/strong\u003eBar plot showing quantification of TMEM119\u003csup\u003e+\u003c/sup\u003e cells in the CA1, 2 wk post-IR. SH, n = 6; IR, n = 6. Mean ± SEM, unpaired \u003cem\u003et\u003c/em\u003e-test per time point. \u003cem\u003e****p\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eG) \u003c/strong\u003eTop: Experimental design for assessing microglial survival. Bottom left: Representative confocal image displaying co-labeling of microglia (TMEM119\u003csup\u003e+\u003c/sup\u003e, green) with BrdU (red) in the CA1 2 wk post-IR. Hoechst (blue), nuclear counterstain.\u0026nbsp; Scale bar = 10 mm. Bottom right: Bar plot showing quantification of TMEM119\u003csup\u003e+\u003c/sup\u003e/BrdU\u003csup\u003e+\u003c/sup\u003e cells 2 wk and 6 wk post-IR in the CA1. 2 wk, n = 4-5; 6 wk, n = 5. Mean ± SEM, two-way ANOVA with Bonferroni’s \u003cem\u003eposthoc \u003c/em\u003efor multiple comparisons. \u003cem\u003e**p\u003c/em\u003e \u0026lt; 0.01, \u003cem\u003e****p\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/3e6719b81251c84cf49d933f.png"},{"id":102398301,"identity":"3b687c6e-1dc8-47fa-b7a4-8f25d294b886","added_by":"auto","created_at":"2026-02-11 10:22:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6623636,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe role of the cytoplasmic DNA sensing on the IFN response post-irradiation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlso see\u003cstrong\u003e \u003c/strong\u003eSupplementary figure 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eCell cycle analysis of proliferating microglia 2 wk post-IR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) \u003c/strong\u003eUMAPs showing expression of genes related to pan cell cycle (\u003cem\u003eMki67\u003c/em\u003e), G2/M phase (\u003cem\u003eCdk1\u003c/em\u003e), DNA damage response (\u003cem\u003eCdkn1a\u003c/em\u003e), and IFN response (\u003cem\u003eCxcl10\u003c/em\u003e) in proliferating microglia 2 wk post-IR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C) \u003c/strong\u003e\u003cem\u003eIn vitro\u003c/em\u003e experimental design. IF = immunofluorescence.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Left: Representative confocal image displaying the micronuclei and expression of cGAS (green; indicated by yellow arrows) in BV2 cells. Hoechst (blue), nuclear counterstain. Scale bar = 20 mm. Right: Bar plots showing the percentage of BV2 cells with micronuclei and cGAS expression 24 h post-IR. Three independent experiments. Mean ± SEM, unpaired \u003cem\u003et\u003c/em\u003e-test. \u003cem\u003e***p\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003e****p\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(E) \u003c/strong\u003eLeft: Representative confocal image displaying expression of phosphorylated STING (pSTING; red; indicated by yellow arrows). Hoechst (blue), nuclear counterstain. Scale bar = 10 mm. Right: Bar plots showing the percentage of BV2 cells expressing pSTING 24 h post-IR. Three independent experiments. Mean ± SEM, unpaired \u003cem\u003et\u003c/em\u003e-test. \u003cem\u003e**p\u003c/em\u003e\u0026lt; 0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(F) \u003c/strong\u003eLeft: Immunoblots showing the expression of phosphorylated TBK1 (pTBK1) and b-actin expression in BV2 cells in three technical replicates of SH and IR 24 h post-IR from one experimental set. Right: quantification of pTBK1 24 h post-IR from three independent experiments. Mean ± SEM, unpaired \u003cem\u003et\u003c/em\u003e-test. \u003cem\u003e*p\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(G) \u003c/strong\u003eBar plots showing qPCR analyses of IFN-related genes \u003cem\u003eIfit1\u003c/em\u003e,\u003cem\u003eIfit3\u003c/em\u003e,\u003cem\u003e \u003c/em\u003eand\u003cem\u003e Cxcl10 \u003c/em\u003ein BV2 microglial cells 24 h post-IR after treatment with either DMSO or the CDK1 inhibitor (CDK1i) RO3306. Three independent experiments. Mean ± SEM, unpaired \u003cem\u003et\u003c/em\u003e-test. \u003cem\u003e*p\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003e**p\u003c/em\u003e \u0026lt; 0.01, \u003cem\u003e***p\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003e****p\u003c/em\u003e \u0026lt; 0.0001. ns = not significant.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/a5a7a3274945c3b35a14539d.png"},{"id":102398349,"identity":"cbca14e7-7f59-4525-bff2-e66a6bde12c4","added_by":"auto","created_at":"2026-02-11 10:22:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":575276,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIn silico\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vivo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e modulation of key genes in the cGAS-STING pathway.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlso see\u003cstrong\u003e \u003c/strong\u003eSupplementary figure 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eDot plot shows the \u003cem\u003ein vivo\u003c/em\u003e average expression of \u003cem\u003eMb21d1\u003c/em\u003e, \u003cem\u003eTmem173\u003c/em\u003e, and \u003cem\u003eTbk1\u003c/em\u003e in microglia per treatment group (\u003cem\u003ei.e., \u003c/em\u003eSH vs IR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) \u003c/strong\u003eDot plot showing average expression of \u003cem\u003eMb21d1\u003c/em\u003e, \u003cem\u003eTmem173\u003c/em\u003e, and \u003cem\u003eTbk1\u003c/em\u003e across all microglial clusters from SH and IR animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C) \u003c/strong\u003eCell2fate velocity graph embedding showing trajectories associating clusters 6, 7, 9, and 10. Arrow lengths indicate the speed of transcriptional activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(D) \u003c/strong\u003eCombined\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003ein silico\u003c/em\u003e perturbation of \u003cem\u003eMb21d1\u003c/em\u003e, \u003cem\u003eTmem173\u003c/em\u003e, and \u003cem\u003eTbk1 \u003c/em\u003eexpression\u003cem\u003e \u003c/em\u003ereversed multiple trajectories observed in (\u003cstrong\u003eC\u003c/strong\u003e), with notable disruption of the trajectory between clusters 6, 7, and 10.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(E) \u003c/strong\u003eSingle\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003ein silico\u003c/em\u003e perturbations of \u003cem\u003eMb21d1\u003c/em\u003e, \u003cem\u003eTmem173\u003c/em\u003e, or \u003cem\u003eTbk1 \u003c/em\u003eexpressions differently affected the cell trajectories, with a robust effect of \u003cem\u003eMb21d1\u003c/em\u003e in reversing the trajectory between clusters 6, 7, and 10. Top: UMAP displaying cell trajectories across the clusters. Bottom: UMAP displaying the expression of each target gene.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(F) \u003c/strong\u003eLeft:\u003cstrong\u003e \u003c/strong\u003eExperimental design using STING mutant mice (STING\u003csup\u003emut/mut\u003c/sup\u003e). Right: Bar plots showing qPCR analyses of IFN-related genes \u003cem\u003eIfit1\u003c/em\u003e, \u003cem\u003eIfit3, \u003c/em\u003eand\u003cem\u003e Cxcl10 \u003c/em\u003ein wild type (WT) and STING\u003csup\u003emut/mut\u003c/sup\u003e mice 2 wk post-IR. Expression levels in response to IR were normalized within each genotype, as the strains had different baseline expression levels for these genes (see supplementary figure 5B). n = 5-8 per group. Mean ± SEM, unpaired \u003cem\u003et\u003c/em\u003e-test. \u003cem\u003e*p\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003e**p\u003c/em\u003e \u0026lt; 0.01, \u003cem\u003e***p\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(G) \u003c/strong\u003eLeft:\u003cstrong\u003e \u003c/strong\u003eExperimental design using cGAS knockout (KO) mice (cGAS\u003csup\u003eKO\u003c/sup\u003e). Right: Bar plots showing qPCR analyses of IFN-related genes \u003cem\u003eIfit1\u003c/em\u003e, \u003cem\u003eIfit3, \u003c/em\u003eand\u003cem\u003e Cxcl10 \u003c/em\u003ein wild type (WT) and cGAS\u003csup\u003eKO\u003c/sup\u003e mice 2 wk post-IR. Expression levels were normalized to SH WT, as the strains showed no difference in the baseline expression levels for these genes (see supplementary figure 5B). n = 6-7 per group. Mean ± SEM, two-way ANOVA with Bonferroni’s \u003cem\u003epost hoc \u003c/em\u003efor multiple comparisons. \u003cem\u003e*p\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003e**p\u003c/em\u003e \u0026lt; 0.01, \u003cem\u003e***p\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/0904d486f3da461931bb396f.png"},{"id":102395592,"identity":"9dd4c073-32df-47a7-9063-271b56e85951","added_by":"auto","created_at":"2026-02-11 09:37:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1756155,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIn vivo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e knockdown (KD) of key genes in the cGAS-STING pathway using antisense oligonucleotides (ASO).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlso see\u003cstrong\u003e \u003c/strong\u003eSupplementary figure 6.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eExperimental design. i.c.v. = intracerebroventricular.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) \u003c/strong\u003eBar plots showing qPCR analyses of IFN-related genes \u003cem\u003eIfit1\u003c/em\u003e, \u003cem\u003eIfit3, \u003c/em\u003eand\u003cem\u003e Cxcl10 \u003c/em\u003e2 wk post-IR in animals that were inoculated i.c.v. with either a mouse-specific \u003cem\u003eTmem173\u003c/em\u003e ASO or control ASO. n = 9-11 per group. Mean ± SEM, two-way ANOVA with Bonferroni’s \u003cem\u003eposthoc \u003c/em\u003efor multiple comparisons. \u003cem\u003e***p\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003e****p\u003c/em\u003e \u0026lt; 0.0001. ns = not significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C) \u003c/strong\u003eBar plots showing qPCR analyses of IFN-related genes \u003cem\u003eIfit1\u003c/em\u003e, \u003cem\u003eIfit3, \u003c/em\u003eand\u003cem\u003e Cxcl10 \u003c/em\u003e2 wk post-IR in animals that were i.c.v. inoculated with either a mouse-specific \u003cem\u003eTbk1\u003c/em\u003eASO or control ASO. n = 5-8 per group. Mean ± SEM, two-way ANOVA with Bonferroni’s \u003cem\u003eposthoc \u003c/em\u003efor multiple comparisons. \u003cem\u003e*p\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003e***p\u003c/em\u003e \u0026lt; 0.001. ns = not significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(D) \u003c/strong\u003eBar plots showing qPCR analyses of IFN-related genes \u003cem\u003eIfit1\u003c/em\u003e, \u003cem\u003eIfit3, \u003c/em\u003eand\u003cem\u003e Cxcl10 \u003c/em\u003e2 wk post-IR in animals that were i.c.v. inoculated with either a mouse-specific \u003cem\u003eMb21d1\u003c/em\u003e ASO or control ASO. n = 8-9 per group. Mean ± SEM, two-way ANOVA with Bonferroni’s \u003cem\u003eposthoc \u003c/em\u003efor multiple comparisons. \u003cem\u003e*p\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003e**p\u003c/em\u003e \u0026lt; 0.01, \u003cem\u003e***p\u003c/em\u003e\u0026lt; 0.001, \u003cem\u003e****p\u003c/em\u003e \u0026lt; 0.0001. ns = not significant.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/13f023214075a23310e89b43.png"},{"id":104781192,"identity":"feb5fb59-43d9-4745-944a-1234f5a3bf0d","added_by":"auto","created_at":"2026-03-17 07:55:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19605398,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/a5be7be9-aa23-42be-80bb-2ca75342102e.pdf"},{"id":102395593,"identity":"c7c283ac-1c8e-4259-9ee2-88eefec0a109","added_by":"auto","created_at":"2026-02-11 09:37:14","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":164265,"visible":true,"origin":"","legend":"Supplementary list 3","description":"","filename":"SupplementaryList3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/d0184edee5b0d14c1d112a05.xlsx"},{"id":102395600,"identity":"97374eb8-95ef-4096-bc8b-c68e98dcd3fb","added_by":"auto","created_at":"2026-02-11 09:37:15","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3143657,"visible":true,"origin":"","legend":"Supplementary list 2","description":"","filename":"SupplementaryList2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/43505a254a350581268d0851.xlsx"},{"id":102395602,"identity":"c939ad77-5e74-4043-8381-df5ad9b7d4ba","added_by":"auto","created_at":"2026-02-11 09:37:15","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":291476,"visible":true,"origin":"","legend":"Supplementary list 1","description":"","filename":"Supplementarylist1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/47f7ea872a4d5d47283764e4.xlsx"},{"id":102395599,"identity":"8fa774b2-d258-4448-ba85-7484ecd85c29","added_by":"auto","created_at":"2026-02-11 09:37:15","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":344454,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.fig1Relatedtofig1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/36dbe104c713fc07aa01147b.tif"},{"id":102395598,"identity":"8b939752-41b7-44e1-a381-21537d72c995","added_by":"auto","created_at":"2026-02-11 09:37:14","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1230903,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.fig2Relatedtofig2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/8c95efc28e07d0ede1b813fb.tif"},{"id":102397950,"identity":"a8615a21-bed3-4f8d-9627-ec49d2069fc6","added_by":"auto","created_at":"2026-02-11 10:20:15","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1122318,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.fig3relatedtofig3.tif","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/4e87e605cf87c7ccaa623436.tif"},{"id":102398302,"identity":"ce0d01bd-479b-4abe-aaac-c79075760888","added_by":"auto","created_at":"2026-02-11 10:22:04","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":713757,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.fig4relatedtofigure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/9dd237bbf90d6be0f0aa6310.tif"},{"id":102398259,"identity":"7e632c28-15ba-4bd0-9a54-f3c10ef16504","added_by":"auto","created_at":"2026-02-11 10:21:57","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1641410,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.fig5Relatedtofigure5.tif","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/752177a23b630e90eb007c9a.tif"},{"id":102395601,"identity":"d8fca40d-fd43-4aaa-9ea8-42d083bc80a1","added_by":"auto","created_at":"2026-02-11 09:37:15","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":149503,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.fig6Relatedtofigure6.tif","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/08c6489fc31eb59b52dc0e3d.tif"},{"id":102398246,"identity":"a977c9b8-ceac-41e6-9492-80d5c355af9f","added_by":"auto","created_at":"2026-02-11 10:21:54","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":18832,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-8775672/v1/2c358cf6794b26296d84fade.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nVML is the CEO and shareholder of HepaPredict AB, as well as a co-founder and shareholder of Shanghai Hepo Biotechnology Ltd. J.D., C.H., and FK are shareholders of Ionis Pharmaceuticals. The other authors declare no competing interests.","formattedTitle":"Radiation‑Induced Microglial Turnover Elicits a cGAS‑Mediated Interferon Response","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCranial irradiation (IR) is standard of care in the treatment of high-grade primary and metastatic brain tumors. However, it causes long-term neurocognitive complications in 50\u0026ndash;90% of patients, especially in children \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Impaired cognitive domains include learning, processing speed, memory, executive function, and attention \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Attempts to omit upfront craniospinal IR in brain cancers with good prognosis, aiming to reduce IR-associated neurotoxicity, by replacing it with focal IR, have failed \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Debilitating IR-induced cognitive deficits are, thus, an unavoidable clinical problem that demands careful attention, particularly since the overall survival rates and remaining life expectancy of children with brain tumors by far exceed those of adults, and are expected to improve even further with current advances in cancer therapies \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe molecular underpinnings of IR-induced cognitive deficits remain largely unknown. For the past two decades, a dominant hypothesis was the depletion of postnatal hippocampal neurogenesis - the process by which new neurons are continuously generated from dividing neural stem and progenitor cells (NSPCs) in the subgranular zone (SGZ) - thereby impairing learning and memory throughout life \u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. IR-induced depletion of neurogenesis has been linked to an increase in the number and activation status of microglia within the neurogenic zone, creating a hostile microenvironment that hinders NSPC proliferation and neuronal differentiation \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Despite compelling evidence linking reduced neurogenesis to neuroinflammation\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, the adverse effects of inflammation on brain function and its consequent cognitive deficits are unlikely to be limited to hippocampal neurogenesis, as evidenced by other models of central nervous system (CNS) diseases \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMicroglia, the resident immune cells and phagocytes in the brain, play key roles in neuroinflammation \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Microglia respond rapidly to IR and undergo a series of molecular events, resulting in the production of cytokines and chemokines and the engulfment of dying NSPCs \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. There is a fundamental lack of defined mechanisms by which the IR-induced microglial responses contribute to cognitive deficits. Current knowledge is based on \u003cem\u003ein vitro\u003c/em\u003e or \u003cem\u003ein vivo\u003c/em\u003e studies using only one or a few time points close to the time of IR, utilizing conventional histological analyses and measurements of pre-selected inflammatory mediators in the brain \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Available data on post-IR inflammatory responses from single-cell transcriptomic analyses are, as of yet, limited to microglia and acquired at time points within days post-IR\u003csup\u003e16,19\u0026ndash;21\u003c/sup\u003e, based on the selection of the microglial population, leaving a need for high-resolution, unbiased data that simultaneously address delayed molecular events in microglia and other cell types. Given the dynamic nature of microglia \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and the fact that post-IR cognitive deficits appear in phases \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, it is imperative to investigate trajectories at the cellular and tissue levels over a prolonged period.\u003c/p\u003e \u003cp\u003eWe performed unbiased, longitudinal \u003cem\u003ein vivo\u003c/em\u003e investigations of the post-IR inflammatory response in the hippocampus, a brain structure central to cognition \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, spanning both acute and subchronic phases. Using transcriptomics, computational, and genetic approaches, combined with histological and protein analyses, we uncovered a delayed microglial response characterized by activation of the interferon (IFN) signaling pathway. We demonstrate that IR causes microglial loss, triggering compensatory microglial proliferation, even in cells with damaged DNA, thereby activating the cytosolic DNA sensor cGAS and downstream IFN signaling.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eIrradiation causes a biphasic inflammatory response in the hippocampus\u003c/h2\u003e \u003cp\u003eWe have previously shown that hippocampal microglia in the juvenile brain are activated as early as 2 h post-IR and return to baseline within 1 week (wk) \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, data from adult rodent models suggest that IR induces persistent microglial activation and chronic neuroinflammation \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Thus, we asked whether long-term inflammation occurs also in the juvenile brain and, if so, what roles microglia play. To this end, we performed a longitudinal, unbiased bulk RNA sequencing (RNA-seq) analysis of hippocampal tissue from 6 h to 6 wk post-IR. Juvenile mice were subjected to whole-brain IR with a single dose of 8 Gy. This IR dose is equivalent to a total radiation dose of 18 Gy when delivered in repeated 2-Gy fractions, as in a clinical setting, estimated using the linear-quadratic (LQ) model and an α/β ratio of three for late effects in normal brain tissue \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHippocampi were collected 6 h (acute phase), 1 day (acute phase), 1 wk (early subacutephase), 2 wk (delayed subacutephase), and 6 wk (subchronicsubchronicphase) post-IR and compared to age-matched sham controls (SH) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). At all time points, principal component analysis (PCA) showed that IR hippocampi formed distinct clusters that were clearly separate from their respective SH samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), indicating long-term transcriptomic alterations post-IR. Numerous differentially expressed genes (DEGs; \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the SH and IR animals were detected as early as 6 h post-IR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Downregulated DEGs peaked 1day post-IR and then decreased over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC; Supplementary list 1). The dynamics of upregulated DEGs were different. The number of upregulated DEGs peaked at 6 h post-IR, decreased by 1 day and 1 wk, then increased again at 2 wk, and decreased again at 6 wk post-IR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC; Supplementary list 1), suggesting a second, delayed response 2 wk post-IR. While the earlier alterations have been extensively studied \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, we focused on the later response 2 wk post-IR.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGene set enrichment analysis (GSEA) revealed activation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of pathways associated with inflammation and response to viruses (Supplementary Fig.\u0026nbsp;1A). Out of the 58 upregulated DEGs, 27 genes were related to inflammation, the majority of which were involved in interferon (IFN) signaling pathways (Supplementary Figs.\u0026nbsp;1B and 1C), indicative of a second inflammatory wave mediated by IFN signaling. Targeted expression analysis of genes related to cytokines and chemokines displaying\u0026thinsp;\u0026gt;\u0026thinsp;3-fold expression changes at any time point revealed that chemokines were the most upregulated inflammatory mediators (Supplementary Fig.\u0026nbsp;1D). We found a significant induction of a set of chemokines within the first 24 h post-IR. At 1 wk, \u003cem\u003eCcl12\u003c/em\u003e was the only chemokine that remained significantly increased. At 2 wk, however, we detected a significant increase in the expression of \u003cem\u003eCxcl10\u003c/em\u003e, \u003cem\u003eCcl5\u003c/em\u003e (both belonging to interferon signaling pathways), \u003cem\u003eCcl2\u003c/em\u003e, and \u003cem\u003eCcl12.\u003c/em\u003e The increased expression of \u003cem\u003eCcl2\u003c/em\u003e and \u003cem\u003eCcl12\u003c/em\u003e remained significant 6 wk post-IR (Supplementary Fig.\u0026nbsp;1D). This biphasic inflammatory response was validated in independent hippocampal tissue at both RNA and protein levels (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eI; Supplementary Fig.\u0026nbsp;1E). Hence, cranial IR triggers a biphasic inflammatory response in the hippocampus: the first response occurs acutely within the first 24 h post-IR, and the second, delayed response, occurs in the subacute phase, mediated by IFN signaling pathways.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultiple microglial states orchestrate the delayed response to irradiation\u003c/h3\u003e\n\u003cp\u003eNext, we asked whether the delayed inflammatory response was driven exclusively by microglia or whether other cell types were also involved. To gain better cellular and molecular resolutions, we dissected the hippocampi of IR mice and age-matched SH controls 2 wk post-IR. We refined our previous cell isolation method, which favors microglia and vascular cells \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, by incorporating two percoll gradients, thereby improving myelin removal and enabling the generation of a viable, neuron-containing single-cell suspension suitable for droplet-based single-cell RNA sequencing (scRNA-seq) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). After quality control of the sequenced cells (detailed in the Method section), we acquired 51,872 cells (SH: 25,969; IR: 25,903). Our cell isolation protocol captured all major cell types, including microglia and macrophages, glial cells (both astrocytes and oligodendrocytes), neurons (both mature and immature), vascular cells (endothelial cells, pericytes, vascular smooth muscle cells, and myofibroblasts), ependymal cells, immune cells, and meningeal fibroblasts, from both SH and IR animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB; Supplementary Figs.\u0026nbsp;2A \u0026minus;\u0026thinsp;2C). Microglia displayed the most DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), with increased expression of inflammatory mediators 2 wk post-IR (\u003cem\u003ee.g. Cxcl10\u003c/em\u003e and \u003cem\u003eCcl12\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). To further explore the contribution of microglia to the observed delayed IFN response, we depleted microglia using the selective CSF1R inhibitor PLX5622 \u003csup\u003e30,31\u003c/sup\u003e. Mice were fed either a control diet or a diet containing PLX5622 (1200 ppm) starting 24 hours post-IR, a time point by which the majority of IR-induced apoptotic cells in the hippocampus had been cleared \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and brains were collected 13 days later (2 wk post-IR) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). We confirmed the microglial depletion using immunofluorescence (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Consistent with our previous experiments, we detected increased expression of the IFN-related genes \u003cem\u003eIfit1\u003c/em\u003e, \u003cem\u003eIfit3\u003c/em\u003e, and \u003cem\u003eCxcl10\u003c/em\u003e, and the chemokine \u003cem\u003eCcl12\u003c/em\u003e, in mice fed a control diet, but this response was absent in mice fed PLX5622 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). These results clearly indicate that microglia mediate the delayed inflammatory response observed post-IR.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThus, we focused on in-depth analyses of microglial cells. Microglia from SH (8,029 cells) and IR (7,873 cells) were distinctly clustered (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Sub-clustering analysis, considering only the clusters (subpopulations) with significantly enriched proportions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) due to the experimental conditions (SH or IR), revealed 11 clusters, of which clusters 1, 3, 4, 6, 7, 9, and 10 were induced post-IR (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC; Supplementary list 2). GSEA of signature genes of the IR-induced clusters revealed activation of pathways related to response to viruses (\u003cem\u003ei.e.\u003c/em\u003e IFN signaling) in clusters 1, 3, 4, 6, and 10, most abundant in cluster 6; cell cycle progression (clusters 7, 9, and 10), oxidative phosphorylation (clusters 3, 4, and 9), P53 signaling (clusters 1, 6, 9, and 10), cellular senescence (clusters 1, 6, and 10), and endocytosis and phagocytosis (clusters 1 and 6) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD; Supplementary Fig.\u0026nbsp;3A).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition to the IFN response, microglial proliferation emerged as a major feature in our scRNA-seq data at this time point. We quantified Ki67-expressing cells (Ki67\u003csup\u003e+\u003c/sup\u003e) in two distinct hippocampal regions, the molecular layer (ML) and the \u003cem\u003ecornu ammonis\u003c/em\u003e 1 (CA1) region, intentionally excluding the granule cell layer and SGZ, where NSPCs and their progenies often proliferate and microglia actively eliminate apoptotic cells \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The following time points post-IR were covered: 6 h (when the first wave of the inflammation occurred), 1 wk (by which time the first inflammatory wave was resolved), 2 wk (the second inflammatory wave), and 6 wk (after the second inflammatory wave). We found that Ki67\u003csup\u003e+\u003c/sup\u003e cells in these regions were either oligodendrocyte progenitors (OLIG2\u003csup\u003e+\u003c/sup\u003e cells) or microglia (IBA1\u003csup\u003e+\u003c/sup\u003e cells) (Supplementary Fig.\u0026nbsp;3B). While the total number of Ki67\u003csup\u003e+\u003c/sup\u003e cells decreased 6 h and 6 wk post-IR (Supplementary Fig.\u0026nbsp;3C), phenotyping of Ki67\u003csup\u003e+\u003c/sup\u003e cells revealed that IR caused a striking shift in the identity of cycling cells in the hippocampus. In the intact hippocampus (SH), OLIG2\u003csup\u003e+\u003c/sup\u003e cells were the most abundant cycling cells (OLIG2\u003csup\u003e+\u003c/sup\u003e/Ki67\u003csup\u003e+\u003c/sup\u003e), whereas post-IR, cycling cells were predominantly IBA1\u003csup\u003e+\u003c/sup\u003e cells in both examined areas at 2 and 6 wk (Supplementary Figs.\u0026nbsp;3D and 3E). The number of proliferating microglia (IBA1\u003csup\u003e+\u003c/sup\u003e/Ki67\u003csup\u003e+\u003c/sup\u003e cells) was significantly increased 2 wk post-IR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), while the number of OLIG2\u003csup\u003e+\u003c/sup\u003e/Ki67\u003csup\u003e+\u003c/sup\u003e cells decreased over time post-IR (Supplementary Fig.\u0026nbsp;3F).\u003c/p\u003e \u003cp\u003eInterestingly, despite the microglial proliferation at this time point, we found that the microglial numbers in the IR animals were significantly lower (~\u0026thinsp;40%) compared to SH animals, as judged by the expression of IBA1 (pan microglia/macrophage marker) and TMEM119 (parenchymal microglia marker) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). This led us to hypothesize that IR resulted in microglial loss, triggering proliferation of the remaining microglia, and that repopulation via self-renewal failed. To test this, we labeled proliferating cells between days 10 and 14 post-IR with 5-bromo-2\u0026rsquo;-deoxyuridine (BrdU), a thymidine analog incorporated during S-phase \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, as these days fall within the proliferative response shown above. Animals were sacrificed either 2 h after the last BrdU injection (\u003cem\u003ei.e.\u003c/em\u003e, 2 wk post-IR) to assess microglia proliferation, or 4 wk later (i.e., 6 wk post-IR) to assess survival of the newborn cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). As expected, IR significantly increased the number of TMEM119\u003csup\u003e+\u003c/sup\u003e/BrdU\u003csup\u003e+\u003c/sup\u003e cells compared to SH at both time points, but the number of TMEM119\u003csup\u003e+\u003c/sup\u003e/BrdU\u003csup\u003e+\u003c/sup\u003e cells was reduced by nearly 70% between 2 and 6 wk post-IR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG; Supplementary Fig.\u0026nbsp;3G), indicating that most of the newborn microglia post-IR did not survive. Collectively, these results show that microglia undergo reactivation 2 wk post-IR, characterized by compensatory proliferation, driven by microglial loss, and activation of IFN signaling programs.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMicroglial mitotic progression after irradiation activates the cytosolic nucleic sensing system and induces the IFN response\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBoth genomic instability and mitotic progression in the presence of damaged DNA have been shown to result in nucleic acid leakage into the cytosol, generation of micronuclei, and activation of the cyclic GMP-AMP synthase (cGAS) and stimulator of interferon genes (STING) pathway, which triggers type I IFN signaling\u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. We hypothesized that the cGAS-STING pathway may regulate the observed IFN response in microglia due to IR-induced DNA damage and/or mitotic progression with damaged DNA. Cell cycle analysis of the proliferating microglial population (expressing the pan-proliferation marker \u003cem\u003eMki67\u003c/em\u003e) in our scRNA-seq data 2 wk post-IR revealed that cells were distributed across different cell‑cycle stages (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Cells in cluster 7 were in S-phase, and cells in clusters 9 and 10 had progressed to G2/M phase (expressing the M phase gene \u003cem\u003eCdk1\u003c/em\u003e), while upregulating \u003cem\u003eCdkn1a\u003c/em\u003e, a DNA damage response gene, and subsets of these cells, especially in cluster 10, were expressing the IFN signaling-related gene \u003cem\u003eCxcl10\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn a proof-of-concept experiment, we leveraged microglial \u003cem\u003ein vitro\u003c/em\u003e systems with actively proliferating cells to test this. We irradiated mouse primary hippocampal microglia and mouse microglial BV2 cells with a single dose of 8 Gy, cultured the cells for 24 h, and processed them for immunofluorescence staining and protein analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC; Supplementary Fig.\u0026nbsp;4A). The IR response was confirmed by expression of the DNA damage response marker γH2AX 1 h post-IR (Supplementary Fig.\u0026nbsp;4B). We found that IR significantly increased the fraction of cells with micronuclei and induced cGAS expression, the upstream component of the pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD; Supplementary Fig.\u0026nbsp;4C). Next, we used the BV2 to perform downstream analyses, since the hippocampal microglial primary cell cultures yield a limited number of cells and have lower growth potentials. We found that IR significantly increased the fraction of cells expressing phosphorylated STING (pSTING) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Immunoblotting further revealed increased levels of phosphorylated TANK-binding kinase 1 (pTBK1), a canonical downstream component of this pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Finally, we found that IR increased the expression of the IFN-related genes \u003cem\u003eIfit1\u003c/em\u003e, \u003cem\u003eIfit3\u003c/em\u003e, and \u003cem\u003eCxcl10\u003c/em\u003e, and that inhibition of mitotic progression using the CDK1 inhibitor RO-3306 abolished this response (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). These results indicate that microglial mitotic progression in the presence of damaged DNA activates the cytosolic DNA-sensing cGAS-STING pathway, contributing to the observed delayed IFN response post-IR.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn silico\u003c/b\u003e \u003cb\u003eand genetic perturbation of key components of the cGAS-STING pathway identify cGAS as the mediator of the delayed IFN response post-irradiation\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe inferred from our \u003cem\u003ein vitro\u003c/em\u003e experiments that the cGAS-STING pathway is involved in the delayed IFN response post-IR. Given that \u003cem\u003ein vitro\u003c/em\u003e IR of cells induces considerable cell death \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, which could confound our findings, we sought to validate these \u003cem\u003ein vitro\u003c/em\u003e results using our mouse model by assessing the expression of the central components of the cGAS-STING pathway, \u003cem\u003eMb21d1\u003c/em\u003e (encoding cGAS), \u003cem\u003eTmem173\u003c/em\u003e (encoding STING), and \u003cem\u003eTbk1\u003c/em\u003e (encoding TBK1) in our scRNA-seq dataset. All three genes showed increased average expression in microglia from IR animals compared to SH controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). While \u003cem\u003eTmem173\u003c/em\u003e and \u003cem\u003eTbk1\u003c/em\u003e expressions were broadly observed across multiple microglia clusters in both SH and IR animals, pronounced expression of \u003cem\u003eMb21d1\u003c/em\u003e was found specifically in proliferating IR microglia belonging to clusters 10 (progressed into the G2/M phase) and 7 (progressed into S phase) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). To investigate the dynamic relationships among these states of IR microglia, we leveraged Cell2fate, a state-of-the-art Bayesian framework that builds on RNA splicing kinetics and differential equation modeling to infer fate biases and lineage commitment \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. This analysis revealed trajectories spanning multiple clusters, with a prominent trajectory connecting cells in clusters 6 (featuring IFN signalling) and the proliferation clusters 7, 9, and 10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), suggesting a link between the IFN response and microglial proliferation. Cell2fate trajectory decomposition identified modules (Supplementary Fig.\u0026nbsp;5A; Supplementary list 3) associated with microglial proliferation or IFN response, supporting the hypothesis that the cGAS-STING pathway plays a central role in this process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo identify critical regulators of the cGAS-STING pathway, we used Cell2fate-derived RNA velocities to reconstruct a continuous vector field in Dynamo \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, enabling computation of the Jacobian matrix and \u003cem\u003ein silico\u003c/em\u003e genetic perturbations along this trajectory. We evaluated the effects of single and combined knockouts of \u003cem\u003eMb21d1\u003c/em\u003e, \u003cem\u003eTmem173\u003c/em\u003e, or \u003cem\u003eTbk1\u003c/em\u003e. Simultaneous knockout of all three genes substantially perturbed velocity vectors and reversed multiple trajectories, including the transition between clusters 6, 7, 9, and 10. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). To disentangle the individual contributions of each gene, we performed single-gene knockouts. Knockout of \u003cem\u003eTmem173\u003c/em\u003e or \u003cem\u003eTbk1\u003c/em\u003e only partially reversed the trajectory between clusters 6, 7, 9, and 10, despite their broader expression patterns (\u003cem\u003eTmem173\u003c/em\u003e was globally expressed; \u003cem\u003eTbk1\u003c/em\u003e was enriched in clusters 1, 2, 3, 6, 7, and 10) (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). In contrast, knockout of \u003cem\u003eMb21d1\u003c/em\u003e, which was preferentially expressed in clusters 1, 6, 7, 9, and 10, and was most highly expressed in proliferating microglia, completely reversed this trajectory (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). As a control, we knocked out the proliferation marker \u003cem\u003eMki67\u003c/em\u003e and observed that this knockout fully reversed the proliferation trajectory (Supplementary Fig.\u0026nbsp;5B), validating our perturbation framework. Together, these results indicate that cGAS (\u003cem\u003eMb21d1\u003c/em\u003e) is the principal pathway regulator linking IFN signaling to microglial proliferation after IR.\u003c/p\u003e \u003cp\u003eTo validate these \u003cem\u003ein silico\u003c/em\u003e predictions and investigate whether cGAS is crucial for the IFN response 2 wk post-IR \u003cem\u003ein vivo\u003c/em\u003e, we used STING mutant (STING\u003csup\u003emut/mut\u003c/sup\u003e) \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and cGAS KO (cGAS\u003csup\u003eKO\u003c/sup\u003e) \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e mice. Notably, neither genetic model should affect IR-induced DNA damage but still enable assessment of how each target protein contributes to the IFN response \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Disruption of STING did not prevent the IR-induced increase of \u003cem\u003eIfit1\u003c/em\u003e, \u003cem\u003eIfit3\u003c/em\u003e, or \u003cem\u003eCxcl10\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF; Supplementary Fig.\u0026nbsp;5C); however, the absence of cGAS completely abolished this increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG; Supplementary Fig.\u0026nbsp;5D). Importantly, the post-IR microglial proliferation was not compromised, neither in STING\u003csup\u003emut/mut\u003c/sup\u003e nor in cGAS\u003csup\u003eKO\u003c/sup\u003e mice (Supplementary Figs.\u0026nbsp;5E and 5F). Collectively, these results indicate that cGAS is crucial for the delayed microglial IFN response post-IR and that its absence prevents this response, even when irradiated microglia enter the cell cycle.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePharmacological inhibition of cGAS suppresses the delayed IFN response in the irradiated brain.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe finally sought to test the feasibility of pharmacologically targeting the delayed IFN response through inhibition of \u003cem\u003eMb21d1\u003c/em\u003e (encoding cGAS), \u003cem\u003eTmem173\u003c/em\u003e (encoding STING), or \u003cem\u003eTbk1\u003c/em\u003e, using second-generation mouse-specific antisense oligonucleotides (ASOs). Target or control ASOs were inoculated into the lateral ventricle one week post-IR, a time point when the first inflammatory response has subsided \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and brains were collected one week later (\u003cem\u003ei.e.\u003c/em\u003e, 2 wk post-IR) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The knockdown (KD) efficiency was \u0026gt;\u0026thinsp;90% for \u003cem\u003eMb21d1\u003c/em\u003e and \u0026gt;\u0026thinsp;80% for \u003cem\u003eTmem173\u003c/em\u003e and \u003cem\u003eTbk1\u003c/em\u003e by 1 wk after inoculation, and IR did not affect the KD levels for any target (Supplementary Figs.\u0026nbsp;6A \u0026minus;\u0026thinsp;6C). Consistent with the computational and genetic models, we found that KD of the downstream effectors \u003cem\u003eTmem173\u003c/em\u003e or \u003cem\u003eTbk1\u003c/em\u003e did not prevent the IR-induced delayed IFN response (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). However, KD of \u003cem\u003eMb21d1\u003c/em\u003e suppressed this response (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). The results demonstrate that pharmacological targeting of cGAS expression with ASO biologics abolishes the delayed IFN response observed post-IR.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMicroglia shape the physio-anatomical properties of neurons through synaptic pruning, somatic interaction, and secretion of trophic factors to maintain proper neuronal function \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Perturbed microglial-neuronal interaction due to microglial hyperreactivity or dysfunction may cause neuronal dysfunction, reflected in impaired cognition, as documented in numerous CNS disease models \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Impaired cognition is a hallmark complication in CNS cancer survivors treated with cranial radiotherapy \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Microglia play pivotal roles in IR-induced neurotoxicity through secretion of inflammatory molecules, generation of reactive oxygen species, and excessive synaptic elimination \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Despite this consensus in the field, no successful treatments targeting microglial responses to prevent or reverse IR-induced cognitive deficits have been developed. Here, we uncover that cranial IR induces a biphasic inflammatory response in the hippocampus: an acute response occurring within hours post-IR and a delayed response after 2 wk, mediated by microglia and characterized by IFN signaling. IFN signaling has been implicated in the development of cognitive deficits in various CNS disease models, including neurodegeneration and aging \u003csup\u003e\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, suggesting that the observed delayed IFN response may contribute to the neurocognitive dysfunction observed in cancer survivors treated with cranial radiotherapy.\u003c/p\u003e \u003cp\u003eRadiation is genotoxic, causing DNA damage and genomic instability, and cells that fail to repair their DNA may undergo cell death or cellular senescence \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Senescent cells often resist cell death, do not replicate, and secrete soluble factors known as the senescence-associated secretory phenotype (SASP), which affect the functions of neighboring cells \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Microglia express the DNA damage response and cell cycle arrest genes \u003cem\u003eCdkn1a\u003c/em\u003e and \u003cem\u003eCcng1\u003c/em\u003e early post-IR \u003csup\u003e16\u003c/sup\u003e, yet previous work \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and this study demonstrated increased microglial proliferation. Mitotic progression with damaged DNA or chromosomal instability activates the cGAS-STING pathway, leading to the induction of type-I IFN signaling \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, resulting in cellular senescence in neighboring cells via SASP production \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. In this study, we uncovered subsets of microglia characterized by mitotic progression and IFN signaling, concurrent with damaged DNA. Our \u003cem\u003ein silico\u003c/em\u003e, \u003cem\u003ein vitro\u003c/em\u003e, and \u003cem\u003ein vivo\u003c/em\u003e experiments showed that microglial mitotic progression post-IR activated the cGAS-STING pathway. Despite numerous studies linking canonical cGAS-STING activation to stress, tissue damage, infection, and inflammation, including the IFN response \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, other studies have demonstrated that cGAS and STING can, independent of each other, regulate cellular programs, such as senescence, metabolism, and response to viral infection \u003csup\u003e\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Our \u003cem\u003ein vivo\u003c/em\u003e dissection of the roles of three key effector proteins in this pathway, cGAS, STING, and TBK1, showed that cGAS is crucial for the delayed IFN response 2 wk post-IR, whereas targeting STING or TBK1 was insufficient to dampen it. Considering the causative links between the cGAS-mediated IFN signaling, cellular senescence \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, neurodegeneration, and aging \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, it is tempting to speculate that the uncovered delayed IFN response after cranial irradiation may trigger neurodegeneration, consequently leading to premature brain aging, and that this could be linked to the neurocognitive dysfunction observed in cancer survivors treated with cranial radiotherapy \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOverall, we demonstrate that microglia drive a delayed inflammatory response post-IR. Microglia depletion before IR did not affect hippocampal neurogenesis \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, a process necessary for memory and learning, but elimination of microglia post-IR has been shown to improve cognitive performance in animal models when assessed shortly after repopulation \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Despite this promising preclinical finding, microglial depletion in a clinical setting remains challenging \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. ASOs offer a promising therapeutic strategy in which the expression of specific mRNA transcripts can be reduced \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Although they have not been evaluated in pediatric neuro-oncology, radiotherapy, or neuroinflammation, ASOs have been proven safe in children with spinal muscular atrophy (nusinersen). ASOs against cGAS could potentially be used to treat radiation-induced chronic neuroinflammation, thereby preventing cognitive dysfunction in childhood brain tumor survivors treated with cranial radiotherapy, paving the way for future clinical trials.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eExperimental model and subject details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFemale mice were used in all studies, as irradiation (IR)-induced cognitive deficits are more severe in females, both in animal models and patients \u003csup\u003e2,64\u003c/sup\u003e. Twenty-one- or twenty-five-day-old mice of the following strains were used C57BL/6J (Charles River, Sulzfeld, Germany, stock #000664), Sting1gt/J (Jackson laboratory; stock #017537), and cGAS KO (Jackson laboratory; stock #026554). Animals were housed in equal light/dark cycles (12/12 h) and were fed \u003cem\u003ead libitum\u003c/em\u003e. All experimental procedures were carried out in accordance with the European and Swedish animal welfare regulations, approved by the Northern Stockholm ethical committee (application numbers: N248/13, N141-16, 13676-2020, and 12653-2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIrradiation procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMice were initially anesthetized with 5% isoflurane in an induction chamber in a mixture of air and oxygen (1:1), then transferred to the irradiation machine and placed in a prone position. The anesthesia was maintained with 1.5% isoflurane during the irradiation procedure. The following irradiation machines were used: X-RAD 320 (PXi Precision X-ray, North Branford, CT, USA) or CIX3 cabinet X-ray irradiator (Xstrahl, Surrey, England). For the X-RAD 320 irradiator, the animal head was distanced approximately 50 cm from the radiation source, and an irradiation field of 2 \u0026acute; 2 cm was used to cover the entire head. Animals received a single dose of 8 Gy delivered at a rate of 0.73 Gy/min. For the Xstrahl irradiator, the animal head was irradiated with a circular field of 1.5 cm in diameter. The collimator was an Xstrahl Perspex tip applicator. A single dose of 8 Gy was delivered with a dose rate of 1.349 Gy/min (dosimetry uncertainty\u0026nbsp;~ 2%) at 300 kV and 10 mA. The focus skin distance (FSD) to the animal\u0026rsquo;s head was 32 cm. External filtration giving a half-value layer (HVL) of 4.0738 mm Cu was applied by adding a Thoraeus filter (1.0 mm Sn, 0.25 mm Cu, 1.50 mm Al). Litter-mates sham controls (SH) were subjected to the same duration of anesthesia in the absence of IR. Animals were allowed to recover from the anesthesia and returned to their cages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicroglia depletion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor microglial depletion, mice were fed either PLX5622 (1200 p.p.m.; MedChemExpress formulated in AIN-76A standard chow by Research Diets Inc.) or control AIN-76A standard chow. The experimental diet was supplemented starting 1 day after irradiation, provided \u003cem\u003ead libitum\u003c/em\u003e for 13 days.\u003c/p\u003e\n\u003cp\u003e5-bromo-2\u0026rsquo;-deoxyuridine (BrdU) administration\u003c/p\u003e\n\u003cp\u003eTo assess the survival of proliferating microglia, dividing cells were labeled between day 10 and 13 post-IR with the thymidine analog BrdU (SigmaAldrich #B5002). \u0026nbsp;BrdU was injected i.p. at a dose of 50 mg/kg, two injections per day (6 h apart). Animals were sacrificed either 2 h or 4 weeks after the last BrdU injection to assess the proliferation and survival of the newborn cells, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStereotaxic injections\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne week after cranial irradiation, mice were anesthetized with isoflurane (5% for induction and 2% for maintenance) using a mouse mask coupled to a stereotaxic apparatus. Mice were also injected subcutaneously with 5 mg/kg rimadyl (Carprofen) and 0.1 mg/kg buprenorphine (Temgesic) 15 min prior to surgery for systemic analgesia. The fur on top of the head was shaved, and the skin was disinfected with 70% ethanol. The scalp was infiltrated with lidocaine administration (4 mg/kg), and a 2 mm incision was then made. Mouse-specific antisense oligonucleotides (ASO) targeting \u003cem\u003eMb21d1\u003c/em\u003e (GCACTAGTTTTTATAACACA), \u003cem\u003eTmem173\u003c/em\u003e (ACGCATTATGACCTCCTTTC), \u003cem\u003eTbk1\u003c/em\u003e (CAGTACCTTTATTCACCGCA), non-target control ASO (TCGCCGAATACCAACATGTT) or PBS were injected intracerebroventricularly (i.c.v.) using the following stereotaxic coordinates: 0.3 mm anterior to bregma (AP), 1.0 mm lateral to bregma (ML), and 3 mm deep (measured from when the orifice of the needle has passed the skull) (DV) using a Hamilton 10 \u0026mu;l syringe (#80030 1701 RN) and a (22s/51/2)S needle (#7758-03 RN) without drilling the skull. The injection volume (10 \u0026mu;l) and injection speed (0.1 \u0026mu;l/sec) were controlled by an automatic microinjection pump. The needle remained at the injection site for 3 min to reduce backflow, then was slowly retracted. After the surgery, 2-3 stitches (Ethilon monofilament 5.0) were applied to suture the wound. Eye gel drops (Viscotears 2 mg /ml, # 541760) were used to hydrate the eyes throughout the surgery. Mice were allowed to recover in a separate cage on a heating pad (35.5\u0026ordm;C) and monitored until they were fully awake and active. Mice received post-operative analgesia (Carprofen 5 mg/kg) 24 hours later.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnimals were sacrificed at 6 hours, 1 day, 1 week, 2 weeks, 6 weeks, 6 months, or 1 year post-IR. Animals were deeply anesthetized with sodium pentobarbital (100 mg/kg, ABCUR AB #444362, Sweden) and transcardially perfused with 1\u0026acute; phosphate-buffered saline (PBS; ThermoFisher Scientific #10010023). Brains were collected and the hemispheres were separated. The left hemispheres were placed into 4% paraformaldehyde (PFA; Histolab Products # HL96753.1000, Sweden) and stored at 4\u0026ordm;C for 48 h. The brains were then cryoprotected in 30% sucrose solution (Sigma-Aldrich #S7903) made in 0.1 M phosphate buffer pH 7.4 and stored at 4\u0026ordm;C until slicing. Right hemisphere hippocampi were dissected and kept in a -80 \u0026ordm;C freezer until further processing for RNA or protein extraction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneration of single-cell suspension for RNA sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-cell suspensions were prepared from three SH controls and three IR mice per time point. Animals were deeply anesthetized with sodium pentobarbital (100 mg/kg, ABCUR AB, Sweden) and transcardially perfused with ice-cold 1\u0026acute;\u0026nbsp;PBS without Ca\u003csup\u003e2+\u003c/sup\u003eand Mg\u003csup\u003e2+\u003c/sup\u003e(PBS; pH 7.4; Gibco/Life Technologies #10010056). Brains were collected and the hippocampi from the two hemispheres were dissected and put into 1.5 mL microtubes containing 1\u0026acute;\u0026nbsp;PBS placed on ice. The tissue was chopped into small pieces using scalpel and transferred into conical tubes containing an enzymatic mixture of Dispase II (0.01%; Sigma-Aldrich #D4693 ), papain (0.1%; Roche #000000010108014001), and DNaseI (0.05%; Roche # 000000010104159001) in 1\u0026acute;\u0026nbsp;Hank\u0026rsquo;s buffered salt solution (HBSS) without Ca\u003csup\u003e2+\u003c/sup\u003eand Mg\u003csup\u003e2+\u003c/sup\u003e (Gibco/Life Technologies #14175095), later supplemented with 12.4 mM magnesium sulfate (Sigma-Aldrich #M7506). Tubes were incubated at 37\u0026ordm;C for 10 min, after which the enzymatic activities were stopped with 20% ice-cold heat-inactivated fetal bovine serum (FBS; Gibco/Life Technologies #10500064). The cell suspension was then filtered through a 70 \u0026mu;m cell strainer and centrifuged for 5 min at 500\u0026nbsp;\u0026acute;\u0026nbsp;g at 4\u0026ordm;C. After washing with 1\u0026acute;\u0026nbsp;HBSS, cells were resuspended in 20% percoll solution (percoll plus, GE Healthcare, #GE17-0891-02); 10\u0026acute;\u0026nbsp;phenol red HBSS (Gibco/Life Technologies #14060040) and 1\u0026acute;\u0026nbsp;HBSS, and overlaid with an equal volume of 1\u0026acute;\u0026nbsp;HBSS. Tubes were spun at 1,000\u0026nbsp;\u0026acute;\u0026nbsp;g at 4\u0026ordm;C for 30 min with no break to remove myelin. The cell pellet was resuspended in 1 ml flow cytometry buffer (R\u0026amp;D Systems #FC001). In protocols intended to collect neurons, an additional centrifugation step in a higher percoll concentration was applied. The supernatant from the first percoll step (20%) was collected in 50 ml conical tubes and mixed with an equal volume of 50% percoll solution to obtain a concentration of 30 % percoll, and spun at 1,000\u0026nbsp;\u0026acute;\u0026nbsp;g at 4\u0026ordm;C for 30 min with a break. The supernatant was removed, and the pellet was resuspended in 1 ml flow cytometry buffer. Both cellular fractions collected from the two-step percoll were mixed and spun at 500\u0026nbsp;\u0026acute;\u0026nbsp;g at 4\u0026ordm;C for 5 min. The pellet was resuspended in 0.5 ml flow cytometry buffer. Cells processed for single-cell RNA-seq were pooled from three SH and three IR animals before adding the flow cytometry buffer and processed for 10\u0026acute;\u0026nbsp;single-cell RNA sequencing. For each time point, cells were isolated from SH and IR animals at the same time and processed in the same manner (as one batch).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the primary hippocampal microglia culture, fourteen-day-old mouse pups were deeply anesthetized with sodium pentobarbital (100 mg/kg, ABCUR AB, Sweden) and transcardially perfused with ice-cold 1\u0026acute;\u0026nbsp;PBS without Ca\u003csup\u003e2+\u003c/sup\u003eand Mg\u003csup\u003e2+\u003c/sup\u003e(PBS; pH 7.4; Gibco/Life Technologies #10010056). Brains were collected, and the hippocampi from the two hemispheres were dissected and put into 1.5 ml microtubes containing 1\u0026acute;\u0026nbsp;PBS placed on ice. The tissue was chopped into small pieces using a scalpel and transferred into conical tubes containing an enzymatic mixture of Dispase II (0.01%; Sigma-Aldrich #D4693), papain (0.1%; Roche #000000010108014001), and DNaseI (0.05%; Roche # 000000010104159001) in 1\u0026acute;\u0026nbsp;HBSS without Ca\u003csup\u003e2+\u003c/sup\u003eand Mg\u003csup\u003e2+\u003c/sup\u003e (Gibco/Life Technologies #14175095), later supplemented with 12.4 mM magnesium sulfate (Sigma-Aldrich #M7506). Tubes were incubated at 37\u0026ordm;C for 10 min, after which the enzymatic activities were stopped with 20% ice-cold heat-inactivated fetal bovine serum (FBS; Gibco/Life Technologies #10500064). The cell suspension was then filtered through a 70 \u0026mu;m cell strainer and centrifuged for 5 min at 500\u0026nbsp;\u0026acute;\u0026nbsp;g at 4\u0026ordm;C. After washing with 1\u0026acute;\u0026nbsp;HBSS, cells were resuspended in 20% percoll solution (percoll plus, GE Healthcare, #GE17-0891-02); 10\u0026acute;\u0026nbsp;phenol red HBSS (Gibco/Life Technologies #14060040) and 1\u0026acute;\u0026nbsp;HBSS, and overlaid with an equal volume of 1\u0026acute;\u0026nbsp;HBSS. Tubes were spun at 1,000\u0026nbsp;\u0026acute;\u0026nbsp;g at 4\u0026ordm;C for 30 min with no break to remove myelin. Cells were seeded in T25 cell culture flasks in DMEM/F12 with Glutamax culture medium (Gibco/Life Technologies #31331028) containing 10% heat-inactivated FBS and supplemented with 10 ng/ml recombinant mouse M-CSF (R\u0026amp;D systems 416-ML-010) and grown at 37\u003csup\u003eo\u003c/sup\u003eC in 5% CO\u003csub\u003e2\u003c/sub\u003e. The culture medium was changed every second day. After reaching approximately 90% confluence, cells were washed with 1\u0026acute;\u0026nbsp;PBS and incubated with 0.05% trypsin-EDTA\u0026nbsp;(Gibco/Life Technologies) for 5 min at 37\u003csup\u003eo\u003c/sup\u003eC. The enzyme activity was stopped with FBS, and cells were washed with 1\u0026acute;\u0026nbsp;PBS. Cells were resuspended in\u0026nbsp;flow cytometry buffer (R\u0026amp;D Systems #FC001) and processed for magnetic separation using CD11b micro-beads (Miltenyi Biotec #130-093-636) following the manufacturer\u0026rsquo;s recommendations. Cells were washed and seeded in 12-well plates at a density of approximately 2 \u0026acute;\u0026nbsp;10\u003csup\u003e4\u0026nbsp;\u003c/sup\u003ecultures in the above-mentioned cell culture medium, however, without M-CSF, and left to settle for 48 h prior to irradiation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the microglial cell lines, the murine microglia BV2 cell lines were used. BV2 cells were cultured in DMEM GlutaMAX (GIBCO/Life Technologies #10564011). Cells were supplemented with 10% FBS and 5% penicillin/streptomycin, grown at 37\u0026deg;C in 5% CO\u003csub\u003e2\u003c/sub\u003e, split and passaged every 2 - 3 days. For irradiation, BV2 cells were seeded in 6-well plates at a density of 1.5\u0026nbsp;\u0026acute;\u0026nbsp;10\u003csup\u003e5\u003c/sup\u003e and allowed to settle overnight. Cells were irradiated in a CIX2 X-ray irradiator cabinet (Xstrahl, Surrey, England) with a single dose of 8 Gy delivered at a rate of 1.35 Gy/min at 195 kV and 10 mA. Focus skin distance (FSD) to the flask /plate was 40 cm. External filtration giving an HVL of 9.0436 mm was applied by adding a 3.0 mm Al filter. A rotating platform was used to ensure homogeneous dose delivery. Control cells were placed on the rotating platform for a time equivalent to that required to deliver 8 Gy without IR. Irradiated cells and their respective sham controls were grown for 1 h or 24 h post-IR. The culture media were collected, and the cells were washed with 1\u0026acute;\u0026nbsp;PBS. The following buffers were added depending on the intended downstream analysis: protein extraction buffer (for western blot), RLT buffer containing 1% \u0026beta;-mercaptoethanol (for RNA expression), or ice-cold 4% PFA (for immunostaining), and the samples were processed for the intended downstream analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA isolation and quantitative real-time polymerase chain reaction (qPCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA was extracted using the following kits: RNeasy Plus Micro Kit (Qiagen, #74034) and RNeasy Plus Mini Kit (Qiagen, #74136). cDNA was generated using the QuantiTect\u0026reg; Reverse Transcription Kit (Qiagen, #205311) or Iscript\u0026trade; cDNA Synthesis Kit (BioRAD #1708891) using the AB Simpliamp Thermal PCR machine (Applied Biosystems). The qPCR was performed using the QuantiTect SYBR Green PCR Kit (Qiagen, #204143) or Ssoadvanced\u0026trade; Universal SYBR\u0026nbsp;(BioRAD #1725274). qPCRTarget mRNA expression was assessed using the QuantiTect primer assay (Qiagen). The following primers were used: Mm_\u003cem\u003eGapdh\u003c/em\u003e_3_SG (#QT01658692); Mm_\u003cem\u003eIfit1\u003c/em\u003e_1_SG (#QT01161286); Mm_\u003cem\u003eIfit3\u003c/em\u003e_1_SG (#QT00292159); Mm_\u003cem\u003eCxcl10\u003c/em\u003e_1_SG (#QT00093436); Mm_\u003cem\u003eCcl12\u003c/em\u003e_1_SG (#QT00244391); \u003cem\u003eTbp\u003c/em\u003e (Sigma-Aldrich; Forward # SY200704801-089: 5\u0026rsquo;-CTCAGTTACAGGTGGCAGGA-3\u0026rsquo;; reverse # SY200704801-090: 5\u0026rsquo;-CAGCACAGAGCAAGCAACTC-3\u0026rsquo;). qPCR was performed using either the StepOnePlus\u0026trade; Real-Time PCR System (Applied Biosystems #4376600) or the CFX384 Touch Real-Time PCR Detection System (Bio-Rad #1855484). Relative mRNA expression was determined using the\u0026nbsp;\u003csup\u003e\u0026Delta;\u0026Delta;\u003c/sup\u003eCT method. \u003cem\u003eGapdh\u003c/em\u003e and \u003cem\u003eTbp\u003c/em\u003e were used as housekeeping genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBulk RNA sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIsolated RNA was sequenced at BGI Genomics in China. For data analysis, unsupervised hierarchical clustering and principal component analyses (PCA) were performed in Qlucore Omics Explorer 3.2 (Qlucore, Lund, Sweden). Differentially expressed genes were determined by comparing irradiated- and sham-control animals using heteroscedastic two-tailed \u003cem\u003et\u003c/em\u003e-tests. Multiple testing correction was performed using the Benjamini-Hochberg algorithm with a false discovery rate (FDR) of 5%.For targeted expression analysis of cytokine- and chemokine-related genes, genes displaying \u0026gt;3-fold expression changes at any time point were included.\u003c/p\u003e\n\u003cp\u003eFor gene set enrichment analysis, ranked gene lists were prepared based on signal-to-noise ((Mean irradiated - mean sham control)/sum of standard deviations). Human gene names were used to input ranked gene lists into the GSEA application https://www.gsea-msigdb.org/gsea/ index.jsp, Broad Institute, Inc.), using the GSEAPreranked tool.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell RNA sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIsolated cells were processed for single-cell RNA sequencing at the eukaryotic single-cell genomics facility (ESCG) at Karolinska Institutet, Sweden, using the 10\u0026acute;\u0026nbsp;Genomics Chromium method (version 3) and sequenced using Illumina NovaSeq 6000 with an S1flow cell.\u0026nbsp;The Cellranger 6.0.1 pipeline was used to align the raw sequencing reads to the mouse reference genome, \u003cem\u003eMus musculus\u003c/em\u003e version \u003cem\u003emm10,\u003c/em\u003e and generate the unique molecular identifier (UMI) count matrix. The generated count matrix was analyzed using the Seurat R package (version 3.0.0). Each condition included 3 technical replicates, and each replicate was processed independently before integration. \u0026nbsp;Low-quality cells were excluded based on the number of genes expressed in the cells and the percentage of mitochondrial counts. Cells expressing a minimum of 250 genes and a maximum of 6,000 genes were considered for downstream analysis, given that each gene was expressed in at least three cells. The hippocampal cellular components are heterogeneous, and each cell type has a different energy demand, with neurons having the highest energy consumption \u003csup\u003e65\u003c/sup\u003e.\u0026nbsp;To ensure inclusion of all cell types, no threshold for the mitochondrial counts was included during the first cleanup. The filtered count matrices were normalized by dividing the feature counts for each cell by the total number of counts from all cells, then multiplied by 10,000 and log-transformed. Next, we identified highly variable features for each dataset independently. We used the Canonical Correlation Analysis (CCA) - default Seurat method for the integration of datasets and removal of the batch effect. To ensure optimal results, we separately integrated our datasets using Harmony integration. Both methods yielded similar results, so we proceeded with CCA. In brief, we used canonical correlation analysis and identified \u0026ldquo;anchors\u0026rdquo; (conserved cell groups) between datasets, which were used for batch correction and the comparison of the differentially expressed genes between experimental conditions. The data were scaled using Pearson residuals,, regressing out the variation by the mitochondrial genes. PCA was then performed on the scaled data for the dimensionality reduction, using 30 components. The output was used to produce a Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) with a resolution of 0.5 for identification of the clusters. Identification of each cluster was based on signature genes of each cell type according to the existing literature as well as through the automatic cell type recognition package SingleR. Signature genes for microglia included \u003cem\u003eSall1\u003c/em\u003e, \u003cem\u003eCx3cr1\u003c/em\u003e, \u003cem\u003eP2ry12, Tmem119\u003c/em\u003e; for macrophages \u003cem\u003eMrc1\u003c/em\u003e and \u003cem\u003eMs4a7\u003c/em\u003e;for oligodendrocytes, \u003cem\u003ePdgfra\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Mbp, Olig2,\u003c/em\u003e and \u003cem\u003eMog\u003c/em\u003e; for astrocytes \u003cem\u003eGja1, Gfap,\u003c/em\u003e and \u003cem\u003eAqp4\u003c/em\u003e; for neurons \u003cem\u003eDcx\u003c/em\u003e, \u003cem\u003eRbfox3, Neurod1,\u003c/em\u003e and \u003cem\u003eSyt1\u003c/em\u003e; for endothelial cells \u003cem\u003eCldn5\u003c/em\u003e; for pericytes \u003cem\u003eVtn\u003c/em\u003e and \u003cem\u003ePdgfrb\u003c/em\u003e; for fibroblasts/myofibroblasts \u003cem\u003eCol1a1,\u003c/em\u003e \u003cem\u003eActa2, and Lama1\u003c/em\u003e; for B cells \u003cem\u003eCd79a, Cd19 and Igkc\u003c/em\u003e; for T- and natural killer cells \u003cem\u003eCd7, Cd3g,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Itgb7\u003c/em\u003e. The proliferating cells were identified using markers such as \u003cem\u003eMki67\u003c/em\u003e and \u003cem\u003eTop2a\u003c/em\u003e. At this stage, we revisited mitochondrial levels and customized them for each cell type. Specifically, microglia threshold was set to 10 %, astrocytes/radial glia to 40%, and oligodendrocytes, ependymal, pericytes, endothelial, VSMCs/fibroblasts, B/T/NK cells, and meningeal fibroblasts to 20%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter the second quality control, a total of 51,872 single cells were analyzed, SH: 25,969; IR: 25903. As we focused on microglia, the 15902 microglial cells, SH: 8,029; IR: 7,873 \u0026nbsp; were analyzed in-depth.\u003c/p\u003e\n\u003cp\u003eThe Speckle package was used to analyze differences in cell type proportions across experimental conditions and time points. Using the \u003cem\u003epropeller\u003c/em\u003e function, we looked for significantly different clusters between the SH and IR groups, based on ANOVA statistical testing. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe ClusterProfiler package was used to perform gene set enrichment analysis of the upregulated, statistically significantly differentially expressed genes (DEGs) in the microglial clusters at each time point. Using the compareCluster function, we directly compared the enriched functional profiles of each cluster using the KEGG database. \u0026nbsp;DEGs were considered when expressed by at least 30% of cells in the cluster, the log2 fold-change was higher than 0,8, and the \u003cem\u003ep\u003c/em\u003e-adjusted value was lower than 0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eScoring of the cell cycle phases of each cell was calculated using the CellCycleScoring function in the Seurat pipeline, based on a set of genes expressed during the S and G2/M phases. The assigned scores for the S and G2/M phases were saved on the metadata of the Seurat object.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIn silico\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;perturbation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the estimation of the RNA velocity, the post-alignment bam files were used to run velocyto command v0.17, \u003cem\u003erun\u003c/em\u003e for any technique. To mask potential repeats of expressed genes, we retrieved the mouse repeat annotation file from the UCSC Genome Browser (mm10_rmsk.gtf). The mouse genome annotation reference was acquired from CellRanger, and the resulting loom files contained the spliced and unspliced counts.\u0026nbsp;The scVelo tool was used to estimate the generalized RNA velocity model. We extracted the coordinates, count matrix, and gene names of the processed Seurat object for microglia from R and created an anndata object. The obtained loom file and the anndata objects were merged, and the generalized dynamical model was used for velocity estimation and plotting.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used Cell2Fate for RNA velocity analysis, which linearizes the differential equations describing RNA velocity and solves them in a fully Bayesian manner, enabling better interpretability of microglial transcriptional dynamics after IR. We trained the Cell2fate dynamical model using 500 epochs. For \u003cem\u003ein silico\u003c/em\u003e perturbations to identify critical regulators of the cGAS\u0026ndash;STING pathway, we extracted the Cell2Fate-derived \u003csup\u003e39\u003c/sup\u003e velocity transition matrix and used it as input to reconstruct a continuous vector field in Dynamo, using 1000 basis functions (control points) \u003csup\u003e66\u003c/sup\u003e. We then computed the analytical Jacobian matrix from this vector field and performed \u003cem\u003ein silico\u003c/em\u003e genetic knockouts using the dyn.pd.perturbation function to predict the effects of gene perturbations on cell-fate trajectories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemistry and immunofluorescence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor mouse brain tissues, the left hemispheres were cut sagittally into 25-\u0026mu;m-thick free-floating sections made in 1:12 series intervals using a sliding microtome (Leica SM2010R) and stored in 2 ml microtubes containing a cryoprotectant solution (25% glycerol, 25% ethylene glycol in 0.1M phosphate buffer) and kept at\u0026nbsp;+4\u003csup\u003eo\u003c/sup\u003eC.After several washes with 1\u0026acute; Tris-buffered saline (TBS), samples were incubated in a 10 mM sodium citrate solution (pH 6.0) or 1\u0026acute;\u0026nbsp;citrate buffer solution (pH 6.0; Sigma-Aldrich #C9999) for 30 min at 80\u003csup\u003eo\u003c/sup\u003eC for antigen retrieval. For BrdU staining, the double-stranded DNA was denatured by incubating the sections in 2 N HCl at 37\u0026deg;C for 30 min followed by a neutralization step performed by incubating the sections in a 0.1 M borate buffer for 10 min at room temperature. Sections were washed with 1\u0026acute;\u0026nbsp;TBS. Non-specific binding was blocked by incubating the sections in a solution of 3% or 5% normal donkey serum (Jackson ImmunoResearch Laboratories; #017000121), 0.1% Triton X-100 (made in 1\u0026acute;\u0026nbsp;TBS) for 1 h at room temperature. Sections were then incubated with primary antibodies at 4\u0026ordm;C for 24 - 72 h, depending on the antibody. The following primary antibodies were used: goat anti-IBA1 (Abcam# ab5076; 1:500); rabbit anti-IBA1 (Wako Chemicals #1919741; 1:1,000); rabbit anti-TMEM119 (Abcam #ab209064; 1:500); rat anti-Ki67 (ThermoFisherScientific #14-5698-82; 1:500); goat anti-OLIG2 (R\u0026amp;D Systems #AF2418; 1:500); rat anti-BrdU (Abcam #6326; 1:500). Sections were incubated for 2 h at room temperature with appropriate fluorescent secondary antibodies. The following secondary antibodies were used: AlexaFlour-488 donkey anti-goat IgG (Molecular probes/Life Technologies #A11055; 1:1,000); AlexaFlour-488 donkey anti-mouse IgG (Molecular probes/Life Technologies #A21202; 1:1,000); AlexaFlour-555 donkey anti-rabbit IgG (Molecular probes/Life Technologies #A31572; 1:1,000); AlexaFlour-555 donkey anti-rat IgG (Abcam #ab150154; 1:1,000); CF-633 donkey anti-goat IgG (Biotium #20127; 1:1,000). Sections were mounted into slides and coverslipped using ProLong Gold anti-fade reagent (Molecular probes/Life Technologies; #P36930).\u003c/p\u003e\n\u003cp\u003eFor cultured cells, after fixation (explained above) and several washes with 1\u0026acute;\u0026nbsp;TBS, non-specific binding was blocked by incubating the sections in a solution of 5% normal donkey serum (Jackson ImmunoResearch Laboratories), 0.1% Triton X-100 (made in 1\u0026acute;\u0026nbsp;TBS) for 1 h at room temperature. Cells were incubated overnight with the following primary antibodies: mouse anti-\u003cstrong\u003e\u0026gamma;\u003c/strong\u003eH2AX (Phospho S139; Abcam #Ab26350; 1:500); rabbit anti-cGAS (D3O8O; Cell signaling technology #31659; 1:250); rabbit anti-phospho-STING (Ser365; Cell signaling technology #62912; 1:250). Cells were incubated with the appropriate secondary antibody for 1 h at room temperature. The following antibodies were used: AlexaFluor-488 donkey anti-rabbit IgG (Molecular probes/Life Technologies #A-21206; 1:1,000); AlexaFluor-555 donkey anti-rabbit IgG (Molecular probes/Life Technologies #A31572; 1:1,000); CF-555 donkey anti-mouse IgG (Biotium #20037; 1:1,000).\u003c/p\u003e\n\u003cp\u003eHoechst 33342 (Molecular Probes/Life Technologies #H3570) was used as a nuclear counterstain. Slides were coverslipped using ProLong Gold anti-fade reagent (Molecular probes/Life Technologies; #P36930).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicroscopy and cell quantification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll histological analyses were performed in the molecular layer (ML) and the \u003cem\u003eCornu Ammonis\u0026nbsp;\u003c/em\u003e1 (CA1)\u0026nbsp;region of the hippocampus in sections containing the dorsal hippocampus spaced 300 \u0026mu;m apart (\u003cem\u003ei.e.\u003c/em\u003e every 1:12 series). Analyses of total Ki67 positive (Ki67\u003csup\u003e+\u003c/sup\u003e) cells, total microglia (IBA1\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eand Tmem119\u003csup\u003e+\u003c/sup\u003e), proliferating microglia (IBA1\u003csup\u003e+\u003c/sup\u003e/Ki67\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eor Tmem119\u003csup\u003e+\u003c/sup\u003e/BrdU\u003csup\u003e+\u003c/sup\u003e) or Oligodendrocyte progenitors (OLIG2\u003csup\u003e+\u003c/sup\u003e/Ki67\u003csup\u003e+\u003c/sup\u003e), were performed using the LSM 700 or LSM900 Zeiss confocal scanning microscopy (Carl Zeiss, Germany), equipped with the Zen software (Black or Blue editions Carl Zeiss). Z-stack images were acquired in sequential scans performed at 1 \u0026mu;m section intervals using a 20\u0026times; objective lens and 1 airy pinhole setting and analyzed using Zen Blue Lite software (Carl Zeiss; Germany). In all quantifications, the total number of cells was the sum of all counted cells in all sections per animal multiplied by the series interval (\u003cem\u003ei.e.\u003c/em\u003e 1:12). The cell density was determined by dividing the total number of quantified cells by counting volume.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor analysis of micronuclei and cGAS expression in SH or IR cultured microglial cells, images were acquired using the LSM 700 laser scanning confocal microscope (Carl Zeiss, Germany) equipped with the Zen software (Black edition 2012, Carl Zeiss) for BV2, and the LSM 900 (Carl Zeiss, Germany) for primary culture. For analysis of phospho-STING, images were acquired using the ZEISS Axio Scan.Z1 slide scanner (Carl Zeiss, Germany). For each SH or IR condition, three coverslips were analyzed. For each coverslip, at least 7 random fields were imaged, covering the borders and the center of the coverslip. Image analysis was performed using the Zen Blue Lite software (Carl Zeiss, Germany).\u0026nbsp;To analyze the percentage of cells with concomitant micronuclei and cGAS expression,\u0026nbsp;at least 2,300 and 1,100 cells (Hoechst\u003csup\u003e+\u003c/sup\u003e) from SH and IR cells, respectively, were analyzed. A micronucleus is defined as a discrete Hoechst\u003csup\u003e+\u003c/sup\u003e DNA aggregate apart from the primary nucleus of the cell. For analysis of phospho-STING expression, at least 1,200 and 700 cells from SH and IR cells, respectively, were analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein extraction, ELISA, and immunoblotting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;For protein extraction from the hippocampal tissues, ice-cold protein extraction buffer (50 mM Tris-HCl; Sigma-Aldrich #T1503, 100 mM NaCl; Sigma-Aldrich, #S7653; 5 mM EDTA, Sigma-Aldrich #E5134 and 1 mM EGTA, Sigma-Aldrich #E3889) supplemented with protease inhibitor cocktail (Roche #11836170001) and phosphatase inhibitors (Roche #04906837001) were added to the frozen tissue and homogenized with a sonicator. Samples were then centrifuged for 10 min at 4\u003csup\u003eo\u003c/sup\u003eC at 10,000\u0026nbsp;\u0026acute;\u0026nbsp;g, and the supernatants were transferred into 0.5 ml tubes and stored at -80\u003csup\u003eo\u003c/sup\u003eC. The total protein concentration was determined using the Pierce BCA protein assay kit (ThermoFischer Scientific #23225), and absorbance was measured using the FLUOstar Omega (BMG LABTECH, Germany) plate reader.\u003c/p\u003e\n\u003cp\u003eThe levels of the chemokines in the hippocampal homogenates were measured using the following ELISA kits: mouse IP-10 (CXCL10) ELISA kit (Abcam #ab214563), mouse MCP5 (CCL12) ELISA kit (Abcam #ab100723), and mouse/rat CCL2/JE/MCP-1 Quantikine ELISA kit (R\u0026amp;D Systems #MJE00). \u0026nbsp;The assays were performed following the manufacturer\u0026rsquo;s instructions. For BV2 cells, after media aspiration and gentle washing with 1\u0026acute;\u0026nbsp;PBS, a 2.5\u0026acute;\u0026nbsp;loading buffer (Tris HCl 62 mM; 2% sodium dodecyl sulfate; 10% glycerol; 5% \u0026beta;-mercaptoethanol; 0.02 % Bromphenol Blue) was added, and cells were collected. Cells were then sonicated, and the resulting protein extracts were processed for Western blot analysis. Proteins were detected using the following antibodies: rabbit anti-phospho-TBK1/NAK (Ser172) (D52C2); Cell Signaling #5483; 1:1,000); mouse anti-\u0026beta;-actin (Sigma-Aldrich #A2228; 1:2,000). Membranes were visualized using the Odyssey CLx LI-COR system with ImageStudio software. Membranes were visualized with Odyssey CLx LI-COR (software: Image Studio). Protein band densitometry was done in ImageJ software. Quantification of protein expression used the ratio of target to housekeeping protein (\u0026beta;-actin); sham controls were set to 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using GraphPad Prism (GraphPad, Inc., San Diego, CA, USA). Data were presented as mean \u0026plusmn; SEM. The unpaired Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test was used to compare SH and IR animals at each time point, aiming to eliminate the effect of animal age. Comparisons of multiple variants per time point were performed using a two-way ANOVA with Bonferroni\u0026rsquo;s \u003cem\u003epost hoc\u0026nbsp;\u003c/em\u003etestfor multiple comparisons. Significance was considered when \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05. The statistical analyses, number of animals, and \u003cem\u003ein vitro\u003c/em\u003e experiments applied were noted in each figure legend. For bulk RNA-seq data analysis, Qlucore Omics Explorer 3.2 (Qlucore, Lund, Sweden) was used. The GSEAP reranking tool was used for the GSEA analysis, while for single-cell RNA-seq analyses, both R (versions 4.3.0 and 4.3.3) and the Seurat package (version 4.0.0) were used. \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Annika Andersson, senior lab manager in the Blomgren group, for technical assistance; Dr. Na Sun and Stuart Fass from Kellis’ lab for input on the computational analyses.\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge support from the National Genomics Infrastructure in Stockholm, funded by Science for Life Laboratory, the Knut and Alice Wallenberg Foundation, and the Swedish Research Council. The authors also thank SNIC/Uppsala Multidisciplinary Center for Advanced Computational Science for assistance with massively parallel sequencing and access to the UPPMAX computational infrastructure, the KI X-ray Irradiation Core Facility, and the Eukaryotic Single Cell Genomics Facility (ESCG) for running the scRNA-seq. Illustrations were partly created using BioRender.com.\u003c/p\u003e\n\u003cp\u003eKB was supported by the Swedish Childhood Cancer Fund (Barncancerfonden), the Swedish Cancer Foundation (Cancerfonden), the Swedish Research Council (Vetenskapsrådet), grants provided by the Stockholm Region (ALF projects), the Swedish Brain Foundation (Hjärnfonden), Radiumhemmets Forskningsfonder, the Karolinska Institute Doctoral (KID) funding, the KI Foundation for Research, the Märta and Gunnar V. Philipson Foundation, the Frimurare barnhusfonden, and the Irstadska Foundation. AMO was supported by Erik Rönnbergs Stipendium, the ARMEC Foundation, and the Åke Wiberg Foundation.\u0026nbsp;AF was supported by Stiftelsen Samariten, KI Foundation for Research, Mary Béves Stiftelse för Barncancerforskning, and ìShizu Matsumuraî’s Donation. EP was supported by Stiftelsen Barnforskningen Astrid Lindgrens Barnsjukhus, and the Boehringer Ingelheim Fonds.\u0026nbsp;VML was supported by the ERC Consolidator Grant 3DMASH [101170408], the Swedish Research Council [2021-02801, 2023-03015 and 2024-03401], the Novo Nordisk Foundation [NNF23OC0085944 and NNF23OC0084420], Cancerfonden [24-3735Pj], the Ming Wai Lau Center for Reparative Medicine, the SciLifeLab and Wallenberg National Program for Data-Driven Life Science [WASPDDLS22:006] and the Robert Bosch Foundation (Stuttgart, Germany). CB was supported by\u0026nbsp;Karolinska Institutet, and the Center for Innovative Medicine (CIMED).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.M.O., A.F., and K.B. conceived, designed, and supervised the study. E.P., A.L.R., A.F., A.M.O., and K.B. interpreted the results and wrote the manuscript. E.P., A.L.R., M.Q.C., K.T., Y.O.V., S.S., K.Z., G.A.Z., C.F.D.R., A.F., and A.M.O. performed the animal experiments. A.L.R., G.P., G.A.Z., M.S., E.W., and A.M.O. performed the histological analyses. A.L.R., E.P., and A.M.O. performed the isolation of microglia and all-brain cells for transcriptomic analyses. E.P., A.L.R., M.Q.C., K.T., Y.O.V., S.S., T.S., Y.R., and A.F. performed the qPCR. K.Z. Y.X., and C.Z. contributed to bulk RNA-seq data. N.O-V. and V.M.L. analyzed the bulk RNA-seq data. E.P. performed single-cell RNA-seq and computational analyses. Y.S. and C.B. contributed to the single-cell RNA-seq analysis. A.L.R., T.S., M.Q.C., Y.R., L.F., and B.J. contributed to the BV2, primary microglia culture, and related immunoblotting experiments. L.H., Z.L., and M.K. contributed to the machine learning analysis and interpretation. J.D., C.H., and F.K. developed the ASOs used in this study. F.K. contributed to the planning and interpretation of the ASO experiments. \u0026nbsp;All authors discussed the results, commented on, or edited the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of Potential Conflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVML is the CEO and shareholder of HepaPredict AB, as well as a co-founder and shareholder of Shanghai Hepo Biotechnology Ltd. J.D., C.H., and FK are shareholders of Ionis Pharmaceuticals. The other authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResource availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLead contact\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact: [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not generate new unique reagents.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and code availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets included in this study are available from the lead contact author upon request. The RNA sequencing data supporting the current study have been deposited in the public repository Gene Expression Omnibus (GEO) under the accession numbers\u0026hellip; (for bulk RNA-seq) and \u0026hellip; (for single-cell RNA-seq). [To be added].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMakale MT, McDonald CR, Hattangadi-Gluth JA, Kesari S (2017) Mechanisms of radiotherapy-associated cognitive disability in patients with brain tumours. 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Cell 185:690\u0026ndash;711e645. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2021.12.045\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2021.12.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Radiotherapy, neuroinflammation, cognition, senescence, hippocampus, cGAS-STING","lastPublishedDoi":"10.21203/rs.3.rs-8775672/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8775672/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCranial radiotherapy is associated with progressive neurocognitive decline in cancer survivors, yet the mechanisms governing delayed neuroinflammatory responses remain insufficiently defined. Through integrated transcriptomic, computational, proteomic, and histological analyses, we identify a distinct microglial response emerging two weeks after irradiation, characterized by pronounced activation of interferon (IFN) signaling. Irradiation induces microglial loss, followed by compensatory proliferation, including cells harboring irradiation‑induced DNA damage, which in turn activates the cGAS\u0026ndash;STING pathway. \u003cem\u003eIn silico\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e perturbation of pathway components establishes cGAS as the principal regulator of this response. Notably, pharmacological suppression of cGAS\u0026mdash;but not STING or TBK1\u0026mdash;using antisense oligonucleotides selectively attenuates the IFN program. These findings delineate a previously unrecognized, cGAS‑dependent IFN response arising during a subacute phase after cranial irradiation, providing mechanistic insight into how microglial turnover and innate immune activation may contribute to neurocognitive impairment in cancer survivors.\u003c/p\u003e","manuscriptTitle":"Radiation‑Induced Microglial Turnover Elicits a cGAS‑Mediated Interferon Response","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 09:37:09","doi":"10.21203/rs.3.rs-8775672/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22157c57-7bd6-4d66-8c24-05134df8fd5b","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62670855,"name":"Biological sciences/Neuroscience/Neuroimmunology"},{"id":62670856,"name":"Biological sciences/Cancer/CNS cancer"}],"tags":[],"updatedAt":"2026-03-13T13:35:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 09:37:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8775672","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8775672","identity":"rs-8775672","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}