MICROGLIA AGING IN THE HIPPOCAMPUS ADVANCES THROUGH INTERMEDIATE STATES THAT DRIVE INFLAMMATORY ACTIVATION AND COGNITIVE DECLINE

SUMMARY During aging, microglia – the resident macrophages of the brain – exhibit dystrophic phenotypes and contribute to age-related neuroinflammation. While numerous hallmarks of age-related microglia dystrophy have been elucidated, the progression from homeostasis to dysfunction during the aging process remains unresolved. To bridge this gap in knowledge, we undertook complementary cellular and molecular analyses of microglia in the mouse hippocampus across the adult lifespan and in the experimental aging model of heterochronic parabiosis. Single-cell RNA-Seq and pseudotime analysis revealed age-related transcriptional heterogeneity in hippocampal microglia and identified intermediate states of microglial aging that also emerge following heterochronic parabiosis. We tested the functionality of intermediate stress response states via TGFβ1 and translational states using pharmacological approaches in vitro to reveal their modulation of the progression to an inflammatory state. Furthermore, we utilized single-cell RNA-Seq in conjunction with an in vivo adult microglia-specific Tgfb1 conditional genetic knockout mouse model, to demonstrate that microglia advancement through intermediate aging states drives inflammatory activation and associated hippocampal-dependent cognitive decline.


INTRODUCTION
Microglia are one of few immune cells with long term residence in the brain parenchyma 1 .
Microglia support neural development by synaptic pruning and neurogenic function in the developing brain, followed by homeostatic maintenance in the adult brain 2 .Alternatively, in the aged brain microglia exhibit dystrophic phenotypes, aggravating perceived insults rather than responding productively to environmental signals 3 .While microglia have been identified as principal mediators of multiple age-related neurodegenerative pathologies 4 , mechanisms leading to microglia dysfunction during aging remain obscure.
Numerous studies have indicated that aged microglia are inflamed 5 , have reduced phagocytic capacity 6,7 , and have decreased motility 8 .Microglia exhibit several hallmarks of aging that potentially contribute to their age-related dysfunction, such as shortened telomeres 9 , altered intercellular communication 10 , molecular alterations 11 , and a loss of proteostasis 12 .Furthermore, many recent studies have started to reveal the molecular changes that define microglial aging.
Single cell RNA-Seq (scRNA-Seq) analyses indicate that microglia isolated from the entire brain lose homeostasis and activate inflammatory transcriptional profiles with age 13,14 .Data rich studies have also revealed partial overlaps between aging microglia and those from disease models, including Alzheimer's disease [15][16][17][18][19] .Interestingly, the microglial response to aging shows regional variation, as white matter-rich regions induce an interferon signature in aged microglia 20 .Studies using aged plasma administration and heterochronic blood exchange demonstrate that microglia aging is in part driven by the aged systemic environment 21,22 .However, the genesis of age-related dystrophic microglial phenotypes has not been extensively investigated.So, we set out to characterize the progression of age-related hippocampal microglial dysfunction, aiming to uncover intermediate states that could be intrinsic to the aging process.To do so, we undertook complementary cellular and molecular analyses of microglia across the adult lifespan and in heterochronic parabiosis -an experimental model of aging in which the circulatory systems of young adult and aged animals are joined 23 .
In this study, we report that microglia in the adult mouse hippocampus, a brain region responsible for learning and memory and susceptible to age-related cognitive decline, advance through intermediate states that drive inflammatory activation during aging.We utilize scRNA-Seq across the adult lifespan to identify intermediate transcriptional states of microglial aging that emerge following exposure to an aged systemic environment.Functionally, we tested the role of these intermediate states using in vitro microglia approaches and an in vivo temporally controlled adult microglia-specific Tgfb1 conditional genetic knockout mouse model to demonstrate that .CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made The copyright holder for this preprint (which this version posted April 9, 2024.; https://doi.org/10.1101/2024.04.09.588665 doi: bioRxiv preprint intermediates represent checkpoints in the progression of microglia from homeostasis to inflammatory activation, with functional implications for hippocampal-dependent cognitive decline.

Single-cell transcriptional analysis reveals the dynamics of hippocampal microglia aging
Previously, single-cell transcriptional profiling of microglia uncovered a diversity of responses during development and in response to pathology aggregated over multiple brain regions 13,14,16,19,24,25 .Microglia have region-specific transcriptional states, so we decided to focus our analyses on the hippocampus 11 , a region with well-reported functional differences during aging 26 .Correspondingly, we investigated age-related transcriptional heterogeneity by performing scRNA-Seq on hippocampal CD11b-positive cells isolated from mice at 6, 12, 18, and 24 months of age using a commonly employed pooling strategy 18,27,28 (n = 1 pool of 5 biological replicates for each age) to reveal the dynamics of microglia aging.82% of cells isolated with CD11b were identified as microglia using canonical markers with a small subset being proliferative (Figure S1A-C).Dimensionality reduction revealed that non-proliferative microglia were grouped into interconnected clusters that contained homeostatic, transition, and activated states, along with a semi-distinct cluster with interferon activation (Figure 1a, S1D-G).Overlaying microglia ages onto the clusters revealed progression from a homeostatic state in younger microglia to an activated state in old microglia, suggesting that gradual transcriptional changes occur during microglial aging (Figure 1B,C, S1H).Furthermore, the heterogeneity of microglial transcriptional programs increased with age (Figure S1I).Expression of Cx3cr1 and Itgam exemplified the homeostatic state, while B2m, Apoe, Cd48, and Lyz2 depict the activated state (Figure 1D).Interestingly, several immediate early genes (Jun, Klf2, Fos), as well as the purinergic receptor gene P2ry12, exhibited transient increases in expression at middle-age (Figure 1D).Alternatively, the interferon cluster represents a small proportion of the overall microglia population with little age-related changes in the number of these cells, but augmented expression of interferon genes during aging (Figure 1A,C,D, S1G).Thus, gene expression changes exhibited unique patterns during aging, with certain genes showing age-related decreases (e.g.Tgfbr1) or increases (e.g.Apoe), while a subset of genes (e.g.Tgfb1) have peaks of expression at middle age (Figure 1D,E,G; Table S1).
Several complement genes implicated in age-related synaptic loss 10,29 (e.g.C1q and C3) demonstrated progressive age-related transcriptional increases, and the increased complement activity in microglia was confirmed by immunohistochemistry (Figure 1E,F).
The effects of aging on microglia could be progressive or sudden, so we investigated if we could find evidence of early aging of hippocampal microglia.We observe relatively few .CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made The copyright holder for this preprint (which this version posted April 9, 2024.; https://doi.org/10.1101/2024.04.09.588665 doi: bioRxiv preprint differentially genes between 6-and 12-month (Figure 1G,H, Table S1).The number of differentially expressed genes gradually increased through the 24-month time point (Figure 1G-J, Table S1), suggesting that hippocampal microglia progressively accumulate aged-related expression changes.Furthermore, when investigating the outcome for genes with significant changes for each age, we find that genes that are differentially expressed at 12 months lose their expression change, while genes that change at later ages show progressive expression differences (Figure 1H).GO analysis of the microglia from 6-and 12-month-old mice revealed that activation and translational programs are modestly enriched in genes with increased expression during early aging (Figure 1I).The latter stages of aging display more prominent enrichment of immune activation and translation processes in genes with increased expression, while revealing that genes that regulate transcriptional processes exhibit decreased expression (Figure 1J,K).We corroborated the progressive increase in microglial activation by immunohistochemical analysis for CD68 and p65 (Figure S1J,K).Thus, microglia progressively age with subsets of cells at each age group being forerunners towards inflammatory activation or refractory to aging by remaining homeostatic.This analysis further suggests that microglia could pass through transitory states during aging.

Trajectory analysis indicates that aging microglia pass through intermediate states
To determine the transcriptional progression of microglia aging towards an inflammatory state, we performed pseudotime analysis of the scRNA-Seq data and observed several trajectories (Figure 2A).We find that the pseudotime trajectories progressed along the biological ages of the mice, indicating the suitability of this analysis for modeling aging progression in microglia (Figure 2B).We focus our analysis on one trajectory with two semi-distinct subbranches that terminated in inflammatory activation, since the alternate branch had minimal transcriptional differences between the beginning and end of the trajectory (Figure 2B,C).To gain insight into the transcriptional states of microglial aging along this inflammatory trajectory, we used spatial autocorrelation analysis and identified five expression modules of coregulated genes (Figure 2B,C, Table S2).We named these modules according to the most enriched GO pathways and known roles of prominent genes for microglia.This trajectory proceeds from high expression of homeostatic genes and mitochondrial processes (module 1) to transient expression of stress response genes and Tgfb1 (module 2), of which TGFb signaling is critical for microglia development 30 .A concerted increase in expression of ribosomal genes (module 3) precedes one subbranch of inflammatory activation (module 4), while the other myeloid activation subbranch (module 5), characterized by B2m and C1qc expression, proceeds independently of increased ribosomal gene expression.GO analysis of genes specifically upregulated at 12-months showed an enrichment of stress response pathways (Figure S2A,B), corroborating the findings of the pseudotime analysis that intermediate stages of microglial aging pass through stress response pathways.Thus, pseudotime trajectory analysis identifies a progression of intermediate states of microglial aging with potential involvement in age-related maladaptive inflammation.
The gene level dynamics of microglia aging are further revealed when interrogating the modules at each timepoint.Expression of several genes in the homeostatic cluster, including Cx3cr1 and Tgfbr1, progressively decline throughout the aging timeline (Figure 2D).Stress response gene expression peaks at early timepoints during aging, while those genes in the translation module have coherent upregulation at 24-months of age (Figure 2D).The top ranked genes in the inflammation module gradually increase their expression throughout aging in a consistent manner (Figure 2D).Alternatively, several genes in the myeloid activation module have expression peaks at 12-(Cst3, Selplg) or 18-months (Ctss, Cd9) of age (Figure 2D).When the modules are collapsed into metagenes, we observe that homeostatic module is reduced early on in aging, while the stress response module follows later (Figure 2E-G).The ribosomal and inflammatory modules display augmented expression at latter ages (Figure 2E-G).Alternatively, the myeloid activation consistently has slightly increased expression throughout aging.These results suggest that with advancing age microglia lose homeostatic and stress response functions, while increasing their translational and inflammatory capacity.
Next, we utilized a publicly available scRNA-Seq datasets of pro-aging systemic interventions and age-related neurodegenerative disease models to determine their impact on hippocampal microglia aging trajectories.We used the heterochronic parabiosis model 31 to investigate whether exposure to an aged systemic environment could promote progression of young adult microglia along the identified aging trajectory (Figure 2H).First, using immunohistochemistry for IBA1 and CD68, we reveal that an aged systemic environment activates microglia (Figure S2C).Next, we find that hippocampal microglia from young adult heterochronic parabionts have decreased expression of homeostatic genes (module 1), while having increased expression of ribosomal genes (modules 3), inflammatory activation genes (module 4) and myeloid activation genes (module 5) (Figure 2I, S2D).We used the App NL-G-F transgenic mouse model at 12 months of age to determine the effects of Alzheimer's disease pathology on microglial aging 17 (Figure 2J).We observe exaggerated advancement along the aging trajectory in App NL-G-F mice compared to controls, as expression is shifted towards the ageassociated modules (Figure 2K, S2E,F).These shift in gene expression posit that the aged systemic and diseased environments drive adult microglia advancement along an agingassociated inflammatory trajectory.

Intermediate states of microglia aging act as checkpoints on inflammatory progression
Next, we sought to corroborate microglial age-related molecular changes associated with individual intermediate states in the aging hippocampus by RNAscope and immunohistochemical analysis.Specifically, we assessed changes in the intermediate stress response (module 2) and translation (module 3) modules by examining expression of Tgfb1 (Figure 3A,B) and the ribosomal protein S6 (Figure 3G,H), respectively.The vast majority (>95%) of Tgfb1 signal localized to microglia at every age (Figure S3A), indicating microglia are the predominant source for hippocampal TGFB1.Consistent with scRNA-Seq data of aging microglia (Figure 1E), we observed highest Tgfb1 expression in microglia by middle-age (Figure 3B).TGFBR1 activation was reduced during aging in hippocampal microglia (Figure S3B), and genes involved in the TGFb signaling pathway exhibited age-related expression changes with several having peak expression at 12 months of age (Figure S3C).Additionally, we observed an increase in translational components found during the scRNA-Seq analysis by immunohistochemistry for the ribosomal protein S6 (Figure 3H) 32 .
To complement hippocampal aging analysis, we assessed the impact of the aging systemic milieu on both the TGFb pathway and translational components in the young adult hippocampus following heterochronic parabiosis.We observed a decrease in microglia Tgfb1 and TGFb pathway expression in young adult heterochronic parabionts compared to age-matched young adult isochronic controls 31 (Figure 3C, S3D).Additionally, expression of the translation module in microglia increases following exposure to an aged systemic environment in the heterochronic parabiosis model (Figure 3I).
To investigate the role of the intermediate stress response (module 2) and translation (module 3) modules in mediating advancement through microglial activation states, we used an in vitro approach.Primary microglia were treated with LPS, which induced gene expression changes with a significant overlap to aging, as well as expression changes in aging modules genes (Figure S3E,F; Table S3).We administered TGFB1 to activate TGFb signaling (module 2) (Figure 3A) and CX-5461 to inhibit RNA Pol I synthesis and interfere with translation (module 3) (Figure 3G) in the context of microglial activation.TGFB1 treatment modified the transcriptional states of LPS-treated microglia (Figure S3G) and caused decreased expression of inflammatory immune processes after LPS stimulation (Figure 3D).TGFB1 treatment reduced expression of later aging modules (modules 2, 3 and 4) and restored expression of the homeostatic gene Cx3cr1  3E,F), suggesting that TGFBb signaling acts as an aging checkpoint during microglial stress response.Targeting translation (module 3) with CX-5461 attenuated expression of immune processes after LPS stimulation (Figure 3J; Table S3), as well as decreased expression of genes in modules 2, 3, and 4, while restoring expression of homeostatic genes in module 1 (Figure 3K,L).Interestingly, CX-5461 treatment had no effect on genes in module 5, which is located on an independent myeloid activation trajectory from altered translational gene expression (Figure 3K,L).These in vitro perturbation data identify active roles for intermediate states in mediating advancement along aging-associated inflammatory trajectories.

Mimicking age-related changes in microglia-derived TGFB1 promotes microglial advancement along an aging-associated inflammatory trajectory in vivo
Having uncovered roles for intermediate states in advancing microglia along aging-associated inflammatory trajectories in vitro, we next investigated the role of individual aging modules in adult microglia in vivo.We elected to disrupt the transition from homeostasis to inflammatory activation by targeting microglia-derived TGFB1, a key marker of the stress response intermediate state (module 2).While TGFB1 is critical for microglia development 33 , the role of microglia-derived TGFB1 in regulating microglia homeostasis during aging remains to be defined.
We generated Tgfb1 flox/flox , Tgfb1 flox/wt , and Tgfb1 wt/wt mice carrying an inducible Cx3cr1-Cre-ER gene, in which Tgfb1 is excised specifically in adult microglia upon tamoxifen administration (Tgfb1 cKO, Tgfb1 Het, and WT, respectively) (Figure 4A).To disrupt the agerelated increase in microglia Tgfb1 observed between mature and middle-age (Figure 1E and Figure 3B), mature adult mice were administered tamoxifen and molecular changes were assessed two months later (Figure 4A).We find that microglia activation, as measured by CD68/IBA1 immunohistochemistry, exhibited genotype-dependent effects (Figure S4A).We performed scRNA-Seq on microglia isolated from WT, Tgfb1 Het and Tgfb1 cKO mature mice (n = 2 pools of 3 animals per genotype), and detected genotype-dependent changes in Tgfb1expression levels, as well as effectors of TGFB1 signaling (Figure S4B).Dimensionality reduction and clustering of cells from scRNA-Seq revealed readily identifiable clusters based on marker expression (Figure 4B).Composition of these clusters were dependent on the microglia genotype, with WT microglia representing the largest fraction of the homeostatic cluster and Tgfb1 cKO microglia representing the largest fraction of the activated cluster (Figure 4C, S4C).Next, we assessed whether deletion of Tgfb1 in mature adult microglia impacted advancement along aging-associated inflammatory trajectories.Interestingly, when we analyze the effects of Tgfb1 dosage on the aging modules, we find that the loss of a single Tgfb1 allele reduces expression of .CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made The copyright holder for this preprint (which this version posted April 9, 2024.; https://doi.org/10.1101/2024.04.09.588665 doi: bioRxiv preprint homeostatic genes (module 1), while increasing expression of translation-related genes (module 3) (Figure 4D,E).In addition to a decrease in expression of homeostatic genes (module 1), loss of both Tgfb1 alleles caused a greater increase in expression of translation-related genes (module 3), and led to a profound increase in inflammatory activation genes (module 4) (Figure 4D,F).
Results were corroborated in an independent cohort of mature Tgfb1 cKO mice, in which we detected altered levels of immune-and age-related cell surface markers (Figure S4D,E), transcriptional alterations that significantly overlapped with aging (Figure S4F-H; Table S4), and progression along the aging-associated inflammatory trajectories (Figure S4I,J).These data indicate that microglia-derived TGFB1 is both necessary for expression of youth-associated homeostatic genes and prevents aberrant microglia inflammatory activation, further suggesting that TGFBb signaling acts as a checkpoint in mediating advancement along aging-associated inflammatory trajectories.

Mimicking age-related changes in microglia-derived TGFB1 impairs hippocampaldependent cognitive function
It is becoming increasingly apparent that microglia interact with other cell types in the brain to influence cognitive function 2,34 .Given loss of microglia-derived TGFB1 promoted microglia progression along aging-associated inflammatory trajectories, we next investigated its functional consequence on cognition.We assessed hippocampal-dependent learning and memory in WT, Tgfb1 Het and Tgfb1 cKO mature mice using novel object recognition and contextual fear conditioning -behavioral paradigms that are sensitive to age-related impairments 35,36 .During novel object recognition testing, WT mice were biased toward a novel object relative to a familiar object while neither Tgfb1 Het or Tgfb1 cKO mice showed any preference (Figure 4G).During contextual fear conditioning testing, Tgfb1 Het and Tgfb1 cKO mice exhibited less freezing compared to WT controls (Figure 4H).Alternatively, we assessed amygdala-dependent fear memory by cued fear conditioning (Figure S4K) 37 and short-term spatial reference memory by Y maze (Figure S4L) 38 , and observed no differences across genotypes.As a control, we also profiled general health using an open field paradigm and observed no differences in total distance traveled or time spent in the center of the open field, indicative of normal motor and anxiety functions (Figure S4M).These behavioral data indicate that loss of microglia-derived TGFB1 not only impacts aging-associated inflammatory trajectories but also promote hippocampaldependent cognitive impairments characteristic of age-related functional decline.

DISCUSSION
. CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made The copyright holder for this preprint (which this version posted April 9, 2024.; https://doi.org/10.1101/2024.04.09.588665 doi: bioRxiv preprint Cumulatively, we demonstrate that microglia aging in the hippocampus advances through intermediate states that precede inflammatory activation, with functional implications for agerelated cognitive decline.These transient states, such as activation of the stress response and TGFb signaling, act as checkpoints on further age-related activation.Furthermore, microglia asynchronously progress through this inflammatory aging trajectory that is in turn influenced by the systemic environment and neurodegenerative disease states. Analysis of our scRNA-Seq data elucidate cryptic, but necessary, intermediate states of microglia progression to age-related inflammatory activation.These states are present in middleaged mice and constitute stress response pathways likely responsive to internal and external cellular insults 39,40 .Pseudotime analysis suggests a highly plausible sequence of molecular alterations that lead to age-related inflammatory activation of hippocampal microglia.First, mitochondrial dysfunction triggers a stress response, and products of this stress response (e.g.TGFB1) attempt to return microglia to homeostasis 41 .Here, chronic activation of the stress response leads to subsequent increased translational capacity that promotes inflammatory activation 42 .Alternatively, another branch of microglial activation advances independently of increased translational capacity, evidenced by translational inhibition having little effect on its activation following LPS stimulation.Thus, we find that increases in translational capacity predicate age-related inflammatory phenotypes, identifying potential targets to counter microglial aging.Analysis of scRNA-Seq data further identify intermediate states of microglial aging in young adult heterochronic parabionts, suggesting a pivotal role for the aged systemic environment in driving advancement towards age-related inflammatory activation.
While we find evidence of the beginnings of inflammatory activation in the hippocampus by middle age (12 months), consistent with the timing recently reported in the whole brain 14 , it should be noted that scRNA-Seq analysis of the whole brain did not observe intermediate stages in microglial aging 14 .These disparate observations could likely be the result of analyzing multiple regions with highly differentiated transcriptional programs 11 that could obfuscate regional aging trajectories.This further underscores the need for both temporal and regional specificity in aging studies, given the level of cellular heterogeneity observed across ages and brain regions.
Moreover, studying microglia -or other cell types -at early stages of aging in a region-specific manner could also represent useful models to investigate the ontogeny of aging without the confounding effects of accumulated functional deficits.
Of note, we find that an inflammatory trajectory of microglia aging can be mitigated in vitro by environmental TGFb signaling, while loss of microglia-derived TGFB1 promoted progression along aging-associated inflammatory trajectories in vivo.Together, these data indicate that TGFb .CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made The copyright holder for this preprint (which this version posted April 9, 2024.; https://doi.org/10.1101/2024.04.09.588665 doi: bioRxiv preprint signaling is necessary for maintaining youth-associated homeostasis and represents a checkpoint in mediating advancement along aging-associated inflammatory trajectories.Functionally, loss of microglia-derived TGFB1 resulted in hippocampal-dependent cognitive impairments, suggesting that advancement through aging-associated inflammatory trajectories may facilitate age-related cognitive decline.TGFb likely acts in an autocrine fashion to return microglia to a homeostatic state after encountering stressful stimuli.Given that a transient increase in Tgfb1 expression is observed in middle-aged microglia, it is possible that maintaining stress response-mediated signaling into older ages may also provide a means to delay age-related microglial dysfunction and maintain cognitive function.Alternatively, TGFb signaling impairs microglial responses to Alzheimer's disease pathology 43 , suggesting that the dynamic regulation of TGFb signaling in response to aging-associated pathologies is necessary for tuning microglial responses.
While microglia have been demonstrated to mediate the establishment of cognitive function during development 2 and play prominent roles in neurodegeneration 44 , little is known about how age-related changes in microglia affect cognitive function.The specificity of the cognitive deficits that we observe in Tgfb1 Het and Tgfb1 cKO mice suggests that age-related changes in microglia-derived TGFB1 predominantly impacts hippocampal-dependent cognitive decline.Furthermore, the deficits observed in Tgfb1 heterozygous mice point to the importance of maintaining youthful levels of microglia-derived TGFB1 to counteract stressors that accrue

Mice
All animal procedures were performed in accordance with protocols approved by the UCSF IACUC.Animals were housed in SPF barrier facilities and provided continuous food and water along with environmental enrichment.Aging characterizations were performed on an inhouse C57BL/6J mouse colony where 2-month-old mice were purchased from Jackson Laboratories (Stock 000664) and aged in the UCSF Parnassus barrier facility.Timed pregnant females were obtained for isolation of primary microglia from newborn pups.Male mice were used in all experiments.B6.129P2(Cg)-Cx3cr1 tm2.1(cre/ERT2)Litt /WganJ (Cx3cr1Cre ER ) (Stock 021160) and C57BL/6J-Tgfb1 em2Lutzy /Mmjax (Tgfb1-flox) (Stock 65809-JAX) mice were obtained from Jackson Laboratories.Mice were bred to obtain Cx3cr1Cre ER +/wt with either Tgfb1 wt/wt, Tgfb1 fl/fl.As Cx3cr1Cre ER is a knock-in/knock-out allele for the microglia homeostatic gene Cx3cr1, all mice tested were Cx3cr1Cre ER +/wt.To induce recombination and deletion of Tgfb1 by Cre ER , we injected tamoxifen for 5 days at 90mg/kg daily, and mice were analyzed after 60 days.

Parabiosis
Parabiosis surgery followed previously described procedures 45 .Mirror-image skin incisions at the left and right flanks were made through the skin, and shorter incisions were made through the peritoneum.The peritoneal openings of the adjacent parabionts were sutured together with chromic gut suture (MYCO Medical, GC635-BRC).Apposing elbow and knee joints from each parabiont were sutured together (Coated VICRYL Suture, Ethicon, J386) and the skin of each mouse was stapled (9-mm Autoclip, Clay Adams, 427631) to the skin of the adjacent parabiont.
Each mouse was injected subcutaneously with Carpofen, Enrofloxacin, and Buprenex as directed for pain and monitored during recovery.For overall health and maintenance behavior, several recovery characteristics were analyzed at various times after surgery, including pair weights and grooming behavior.
RNA was transformed into RNA-Seq libraries using an adapted version of the Smart-Seq2 protocol 46 .Briefly, 5ng of total RNA was reverse transcribed with SuperScript II (Thermo Fisher, 18064014) supplemented with betaine and MgCl2 using an oligo-dT RT primer (AAGCAGTGGTATCAACGCAGAGTACT(30)VN) with a PCR binding site and a template switching oligonucleotide (5′ AAGCAGTGGTATCAACGCAGAGTACATrGrG+G) with a homotypic PCR binding site.Betaine and MgCl2 were added to enhance the reaction.After reverse transcription, whole transcriptome amplification by PCR was performed for 12 cycles using KAPA HiFi HotStart ReadyMix (Roche, 7958935001) with a PCR primer (AAGCAGTGGTATCAACGCAGAGT). The PCR reaction was cleaned up with Ampure XP beads (Beckman Coulter, A63881).In addition, after the whole transcriptome PCR amplification step, qPCR was performed to determine the presence of microglia-specific transcripts (Cx3cr1) and housekeeping genes (Gapdh).The amplified DNA was diluted to a concentration of 0.5ng/ul, and subjected to tagmentation with the Illumina Nextera XT kit (Illumina, FC-131-1096).Each sample was PCR amplified with a unique set of Nextera indices.Bulk microglia RNA-Seq libraries were sequenced on an Illumina HiSeq, while in vitro microglia RNA-Seq libraries were sequenced on an Illumina Nova-Seq.

RNA-Seq Analysis
FASTQ reads were pseudo aligned to the mouse transcriptome (GRCm39 cDNA from Ensembl) using kallisto 47 with default parameters.Next, transcript abundance estimates were imported into DeSeq2 48 .Differential expression analysis was performed using DeSeq2 using the Wald significance test.Gene ontology analysis was performed with Panther [49][50][51] .Principal component analysis plots were generated with the plotPCA function included with DeSeq2 and graphed with ggplot2.Heatmaps were generated with the pheatmap package in R.
The overlap between gene expression differences in aging and LPS treatment were determined using genes significantly changed between the 6-and 24-month-old timepoints in the scRNA-Seq data and the control and LPS treated primary microglia.c 2 test was used to determine the significance of the overlap between these two datasets.The Venn diagram was generated using the VennDiagram package in R.
10x Genomics Single Cell RNA-Sequencing 10x Single-Cell RNA-Seq libraries were generated from Cd11b+ cells isolated from the hippocampi of an aging cohort of mice consisting of 6-, 12-, 18-, and 24-month-old C57BL/6J mice that were all collected and processed on the same day in an interspersed order.Mice were sedated with ketamine followed by perfusion with ice-cold PBS.Subsequently, the entire brain was removed from the mouse followed by sub-dissection of the hippocampus.For each age, hippocampi from five mice were pooled during the dissection step in HBSS at 4°C.Single-cell suspensions were generated by enzyme-mediated (papain) and mechanical dissociation using Miltenyi Neural Dissociation Kits (P).The papain dissociation was done at 37°C for 10 minutes with 3 trituration steps.All other processing steps were performed at 4°C.Myelin was depleted from the suspensions using Myelin Removal Beads (Miltenyi).Microglia were enriched using Cd11b magnetic beads (Miltenyi, 130-126-725).Cell viability was determined to be over 95%.
Single-cell RNA-Seq 3' libraries were generated from the cell suspension using 10x Genomics Chromium Single-Cell 3' Solution.Libraries were sequenced on the Illumina Nova-Seq.Cell Ranger demultiplexed and mapped (using bcl2fastq) reads, followed by alignment (with STAR), and generation of single-cell expression matrices 52 .
The isolation protocol for the Tgfb1 genetic mouse model was modified.We added transcriptional and translational inhibitors (Actinomycin D (Sigma-Aldrich, A1410), Anisomycin (Sigma-Aldrich, A9789), and Triptolide (Sigma-Aldrich, T3652) to prevent transcriptional changes due to ex vivo activation 53 .We utilized the concentrations presented in Marsh et al. 2022 53 .

Single-cell RNA-Seq Analysis
Seurat: The single-cell gene expression matrices generated by Cell Ranger were loaded into Seurat 54 .
Aging microglia dataset.Cells were filtered according to the following parameters: Genes > 1000 & RNA count > 10000 & mitochondrial percentage < 5.The count data was log normalized.The 2,500 most variable genes were identified using the "vst" method.The expression data was scaled and centered.Principal component analysis was performed, and the first 25 PCAs were used to identify nearest neighbors, and interconnected clusters were identified with a resolution of 0.4.
UMAP was used to visualize the results.Preliminary dimensionality reduction and visualization with UMAP using 25 PCAs and 25 nearest neighbors in Seurat identified a large microglia cluster, plus sparse clusters consisting of astrocytes, vascular cells, neutrophils, and macrophages.Hexb, P2ry12, Tmem119, and Slc2a5 are used as markers to identify microglia in the preliminary clustering, while Nrxn1 (neurons), Tm4sf1 (vasculature), Dclk1 (astrocytes), Sdpr (vasculature smooth muscle), Abcb1a (pericyte/vasculature), and Foxq1 (macrophages) identified other cell types.The data was then subsetted on the non-proliferating microglial clusters (to avoid confounds associated with the cell cycle).The top 1,500 most variable genes in the subsetted microglia were identified using the "vst" method.The data was scaled and centered.Principal component analysis was performed, and the first 8 PCAs were used to identify nearest neighbors, and interconnected clusters were identified with a resolution of 0.25.UMAP was used to visualize the results using 8 PCAs and 100 nearest neighbors.Gene enrichment and differential expression analysis was performed on the filtered dataset to determine differences between ages.
Tgfb1 cKO microglia dataset.Cells were prefiltered according to the following parameters: Hexb Monocle 3: Monocle 3 was used to generate the pseudotime analysis [55][56][57][58] .Data was imported from Seurat analysis and a Monocle dataset was generated.The dataset was preprocessed with normalization of the data and PCA generation.Next, the preprocessed data underwent further non-linear dimensionality reduction using UMAP.Cells were clustered, and trajectories were determined for cells within a cluster.For determining "pseudotime" aging of microglia, the root node of the pseudotime trajectory was manually placed in the cluster of 6-month cells.The graph segments of the pseudotime trajectories resulting in inflammatory activation were chosen for further analysis.Graph autocorrelation analysis was performed on the inflammatory trajectory using Moran's I, which determines if genes are expressed in focal regions of graph space.A cutoff q-value of 0.005 was set as significant for focal expression.Subsequently, coregulated modules were determined using Louvain community analysis at an optimized resolution of 0.0078.The modules were then overlaid onto the UMAP plot to determine the region of the trajectory with focal expression of the module.The ten most significant genes of each module, as determined by qvalue were used to construct the heatmaps in Figures 3F and 3I.The significant genes of each module were used to determine the effects of manipulations in the presence of LPS in Figure 3J.

Immunohistochemistry
Mice were sedated with ketamine followed by perfusion with 30mL of ice-cold PBS.Brains were collected from PBS perfused mice and fixed overnight at 4°C in 4% paraformaldehyde in PBS.
Images were analyzed with FIJI.Maximum intensity projections of Z-stacked images were constructed.Images were converted to 8-bit images and consistent adjustments were made for each channel within an experiment.The Iba1 channel was used to quantify microglia using the particle counter function in FIJI and the counts were validated by manual visual counting.The presence of CD68, NFKB(p65), C3, or C1q in microglia was determined by creating ROIs within the microglia in a field using the create selection function on the Iba1 images.The ROIs of the hippocampal subregions and the microglia were combined, and CD68 puncta meeting a minimum particle size threshold (100 pixels, equating to 31.2µM) within microglia determined if the microglia were activated (CD68+IBA1+), while the measure function determined the intensity of NFKB(p65), C3, or C1q.To determine percent activation of microglia with IBA1/CD68 staining, we divided the number of microglia that were activated (CD68+IBA1+) by the total number of microglia (IBA1+) in the analyzed regions and multiplied by 100.

In vitro treatment of microglia
Mixed glial cultures were generated from p1 male pups.Cortices and hippocampi were isolated from the pups, and meninges were removed.The tissue was broken up by repeatedly pipetting up and down, followed by dissociation with Trypsin.Single cell suspensions were plated on poly-L-lysine coated flasks in DMEM (Thermo Fisher, 11965-118) supplemented with 10% FBS (Gemini Bio-Products, 900-208) and antibiotic mix (Penicillin/Streptomycin)(Genesee Scientific, 25-512) and incubated at 37°C in 5% CO2.Media was changed after 4 days to remove debris.
Microglia were isolated after 14 days by agitating the plates to dislodge microglia, then transferring the supernatant of each sample evenly amongst 4 wells of a 24 well plate.Microglia were .CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made The copyright holder for this preprint (which this version posted April 9, 2024.; https://doi.org/10.1101/2024.04.09.588665 doi: bioRxiv preprint incubated for 16 hours followed by replacement with serum free macrophage SFM media (Thermo Fisher, 12065074) supplemented with M-CSF (10ng/m) (Peprotech, 315-02) and antibiotic mix.

RNAscope
RNAscope was performed with the RNAscope® Multiplex Fluorescent Reagent Kit v2 (ACD, 323110).40µM coronal sections were washed with TBST, then incubated with hydrogen peroxide for 45 minutes at 25°C.After washing with TBST, the sections were incubated in Target Retrieval Buffer at 95°C for 10 minutes.The sections were mounted on slides and dried overnight.The sections were treated with RNAscope Protease III for 10 minutes at 40°C.Sections were washed in water four times.Sections were incubated with Tgfb1 probe (ACD, 443571-C2) for 2 hours at 40°C.The sections were washed twice with RNAscope wash buffer.The sections were incubated with AMP1 for 30 minutes, AMP2 for 30 minutes, AMP3 for 15 minutes, HRP-C2 for 15 minutes, TSA Plus Cy3 (1:1000, Perkin Elmer, SKU NEL744001KT) for 30 minutes, and HRP-Blocker for 15 minutes; all at 40°C with washes with RNAscope wash buffer between every incubation.RNA-Protein Co-Detection Ancillary Kit (ACD, 323180) buffer was used to block the sections before incubating with rabbit anti-IBA1 (1:500, Wako, Cat# 019-19741, RRID:AB_839504) overnight at 4°C.Sections were washed three times in TBST.Sections were incubated with secondary antibody solution with goat anti-rabbit AlexaFluor 488 (1:1000, Life Technologies, Cat# A-11008, RRID:AB_143165) for two hours at room temperature.The sections were washed three times with TBST, with the first wash containing Hoescht 33342 (Invitrogen, H3570) at a concentration of 1µg/mL Coverslips were mounted with Prolong Gold Antifade (Thermo Fisher, P10144) reagent after the sections were dried.Slides were imaged at 40x magnification on a Zeiss LSM 900. 10 z-planes were imaged at 1µM intervals.
Images were analyzed with FIJI.Maximum intensity projections of Z-stacked images were constructed.Images were converted to 8-bit images and the Tgfb1 was thresholded.For each .CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made The copyright holder for this preprint (which this version posted April 9, 2024.; https://doi.org/10.1101/2024.04.09.588665 doi: bioRxiv preprint section, the intensity of 5-7 individual Tgfb1 puncta were averaged to get the average puncta intensity of that section.The intensity of all Tgfb1 signal in clearly identifiable individual IBA1 cells was measured for 20-25 cells in the molecular layer of the hippocampus.The mean Tgfb1 count per microglia for each sample was calculated by dividing the average Tgfb1 intensity in IBA1 cells by the average puncta intensity.

Contextual fear conditioning
In this task, mice learned to associate the environmental context (fear conditioning chamber) with an aversive stimulus (mild foot shock; unconditioned stimulus, US) during the training phase enabling testing for hippocampal-dependent contextual fear conditioning.To also assess amygdala-dependent cued fear conditioning, the mild foot shock was paired with a light and tone cue (conditioned stimulus, CS) during the training phase.Conditioned fear was displayed as freezing behavior.Specific training parameters are as follows: tone duration is 30 seconds; level is 70 dB, 2 kHz; shock duration is 2 seconds; intensity is 0.6 mA.This intensity is not painful and can easily be tolerated but will generate an unpleasant feeling.More specifically, on day 1 each mouse was placed in a fear-conditioning chamber and allowed to explore for 2 min before delivery of a 30-second tone (70 dB) and light ending with a 2-second foot shock (0.6 mA).Two minutes later, a second CS-US pair was delivered.On day 2, each mouse was first placed in the fearconditioning chamber containing the same exact context, but with no CS or foot shock.Freezing was analyzed for 2 minutes and represented as the percentage of time that the mouse froze over the 2 minutes.One hour later, the mice were placed in a new context containing a different odor, floor texture, chamber walls and shape.Animals were allowed to explore for 2 minutes before being re-exposed to the CS.Freezing was analyzed for 30 seconds following the CS and represented as the percentage of time that the mouse froze over those 30 seconds.Determination of freezing behavior was performed using FreezeScan video tracking system and software (Cleversys, Inc).Single outliers were removed using the extreme studentized deviate method (Grubbs' test) with an alpha of 0.05.

Novel object recognition
The novel object recognition task was adapted from a previously described protocol 59 .Specifically, during the habituation phase (day 1), mice could freely explore an empty open field arena for 10 minutes (motor activity and anxiety (time in center) were measured during this phase).During the training phase (day 2), two identical objects were placed in the habituated arena, and mice could explore the objects for 5 minutes.For the testing phase (day 3), one object .CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made The copyright holder for this preprint (which this version posted April 9, 2024.; https://doi.org/10.1101/2024.04.09.588665 doi: bioRxiv preprint was replaced with a novel object, and mice could explore the objects for 5 minutes.Time spent exploring each object was quantified using the Smart Video Tracking Software (Panlab; Harvard Apparatus).Two different sets of objects are used.To control for any inherent object preference, half of the mice are exposed to object A as their novel object and half to object B. To control for any potential object-independent location preference, the location of the novel object relative to the trained object is also varied.The objects were chosen based on their ability to capture the animal's interest, independent of genetic background or age.To determine percent time with novel object, we calculate (Time with novel object)/(Time with Trained Object + Time with Novel Object) * 100.In this preference index, 100% indicates full preference for the novel object, and 0% indicates full preference for the trained object.A mouse with a value of 50% would have spent equal time exploring both objects.Mice that did not explore both objects for 5 seconds during the training phase or testing phase were excluded from analysis.Single outliers were removed using the extreme studentized deviate method (Grubbs' test) with an alpha of 0.05.

Y maze
The Y Maze task was conducted using an established forced alternation protocol 60 .During the training phase, mice were placed in the start arm facing the wall and allowed to explore the start and trained arm for 5 minutes, while entry to the 3rd arm (novel arm) was blocked.The maze was cleaned between each mouse to remove odor cues, and the trained arm was alternated between mice.The mouse was then removed to its home cage.After 30 minutes, the block was removed, and the mouse was returned to the start arm and allowed to explore all 3 arms for 5 minutes.Time spent in each arm was quantified using the Smart Video Tracking Software (Panlab; Harvard Apparatus).The percent time in the novel arm was defined as time in the novel arm divided by time spent in the novel and trained arms during the task.

Datasets
All RNA-Seq and scRNA-Seq data have been deposited in the Gene Expression Omnibus and are publicly available as of the date of publication.The following datasets were generated for this manuscript and deposited in Gene Expression Omnibus: aging single-cell RNA-Seq (GSE179358), in vitro treated primary microglia (GSE179611), and Tgfb1 cKO microglia RNA-Seq (GSE190007).

Table S1 | Gene markers for each age group and differential expression between the 6-and
12-, 18-, or 24-month-old microglia in single-cell RNA-Seq of aging hippocampal microglia.
The tables contain genes selectively increased or decreased in each age group when compared to every other age group combined and genes differentially expressed between 6-and 12-, 18-, or 24-month microglia.The tables contain the average log fold change, the percentage of cells in each group with expression, and the adjusted p-value.
Table S2 | Pseudotime trajectories of aging microglia.The pseudotime analysis contains the spatial autocorrelation analysis.Moran's I was used to detect focal expression of genes that were consequently constructed into co-regulated modules using Louvain community analysis.The table contains the adjusted p-value (q-value) and module information.S3 | RNA-Seq analysis of primary microglia treated with LPS and TGFB1 or CX-5461.
The tables contain the RNA-Seq differential expression analysis of control and LPS activated primary microglia, DMSO and CX-5461 treated primary microglia activated by LPS, and DMSO and TGFB1 treated primary microglia activated by LPS, respectively.The tables contain expression values of genes for the samples that were compared along with mean expression values for all samples combined, log fold change between sample groups, and adjusted p-values.in the RNA-Seq differential expression analysis of control and LPS activated primary microglia, DMSO and CX-5461 treated primary microglia activated by LPS, and DMSO and TGFB1 treated primary microglia activated by LPS, respectively.

Table S4 | Differential expression analysis of control and
during aging.Altogether, our results highlight the need to investigate aging across the lifespan in a region-specific manner to reveal necessary cellular intermediates in the aging process.While the present study focuses on age-related changes in microglia, our findings have broad implications for other studies in determining the extent to which aging sequelae are conserved across different cell types in the hippocampus.Furthermore, therapeutic interventions targeting intermediate states in the aging process hold far reaching potential to counteract the development of agerelated pathologies, including those promoting cognitive dysfunction.

Fig. 1 |
Fig. 1 | Hippocampal microglia exhibit progressive age-related transcriptional inflammatory activation.A, UMAP plot of microglia separated into transcriptional clusters.(n = 1 pool of 5 animals for each age) B, Superimposition of ages onto the UMAP plot with dashed lines signifying relative cluster demarcation.C, Percent composition of each cluster by age.D, Dot plot of expression of cluster markers sorted by age.Percent of cells expressing the gene and average normalized expression are represented.E, Violin plots of genes with dynamic age-related expression patterns.F, Representative images and quantification of C1q (yellow), C3 (red), and Iba1 (cyan) staining in the hilus and molecular layer (ML) of 6-and 24-month-old mice.(n=4-5 mice per group; mixed effects analysis followed by Dunnett's multiple comparisons; *P<0.05,**P<0.01,****P<0.0001).G, Number of differentially expressed genes from the 6-month timepoint at each age.Bars above the intersect represent increased expression and those below represent decreased expression.H, The average expression change at all ages for genes differentially regulated genes at individual ages represented by the color scheme in B. I, J, K, Volcano plots of differentially expressed genes in microglia between 6-and 12-months (I), 6-and 18-months (J), and 6-and 24-months (K) and corresponding gene ontology analysis of genes with significantly increased (brown) or decreased (green) expression for each comparison.Data are shown as mean±s.e.m.

Fig. 2 |
Fig. 2 | Hippocampal microglia aging advances through intermediate states that respond to systemic interventions or disease states.A, Pseudotime trajectories of microglia from an anchor point located in 6-month microglia presented in a UMAP plot.(n = 1 pool of 5 animals for each age) B, Microglia ages superimposed over pseudotime trajectories.C, Gene expression modules representing sections of the inflammatory aging trajectory over the right half of the UMAP plot (left).Modules were discovered using Moran's I autocorrelation test.Top Gene Ontology terms and representative genes in each module (right).D, Dotplot of pseudotime modules sorted by age.Percent of cells expressing the gene and average normalized expression are represented.E, F, G, Average gene expression changes for each aging module represented as log2 fold change of 12-months (E), 18-months (F), or 24-months (G) over 6-months.H, Diagram of the heterochronic parabiosis model with the comparisons made in scRNA-Seq.I, Average gene expression changes for each aging module represented as log2 fold change of heterochronic young (HY) over isochronic young (IY) adult parabionts.Data from 31 .J, Diagram of microglia surrounding an Ab plaque.K, Average gene expression changes for each aging module represented as log2 fold change of the App NL-G-F genotype (AD) over wildtype (WT).Data from 17 .(one sample T-test with the expected value of 0 (no change); *P<0.05,**P<0.01,***P<0.001,****P<0.0001).

Fig. 3 |
Fig. 3 | Intermediate states of microglia aging act as checkpoints on inflammatory progression.A, Representation of the microglia aging trajectory over the UMAP plot highlighting the region of peak Tgfb1 expression.B, Representative RNAscope images and quantification of Tgfb1 (red) expression in IBA1 (cyan) cells across ages.(n=5 per group; one-way ANOVA with Dunnett's post-hoc test; *P<0.05,**P<0.01).C, Schematic of the heterochronic parabiosis model and quantification of hippocampal microglia expression of Tgfb1 from isochronic young (IY) and heterochronic young (HY) adult parabionts.Data derived from Pálovics et al 31 .D, Top gene ontology terms of genes with significantly decreased expression in bulk microglia RNA-Seq following TGFB1 treatment compared to control (DMSO) in LPS treated microglia.(n=5 per group) E, Heatmap of top 10 genes in each aging module following TGFB1 compared to DMSO in LPS treated microglia.F, Average gene expression changes for each aging module represented as log2 fold change of TGFB1 treatment over DMSO.(one sample T-test with the expected value of 0 (no change); *P<0.05,***P<0.001,****P<0.0001).G, Representation of the microglia aging trajectory over the UMAP plot highlighting the stage where CX-5461 modulates the trajectory.H, Representative images of S6 (magenta) and Iba1 (cyan) staining in the hippocampus of 6-and 24-month-old mice and quantification across aging.(n=3 mice per group; one-way ANOVA with Tukey's post hoc test; *P<0.05,**P<0.005,****P<0.0001).I, Schematic of the heterochronic parabiosis model and quantification of hippocampal microglia expression of translation module from isochronic young (IY) and heterochronic young (HY) adult parabionts.Data derived from Pálovics et al 31 .(one sample T-test with the expected value of 0 (no change); **P<0.01).J, Top gene ontology terms of genes with significantly decreased expression in bulk microglia RNA-Seq following CX-5461 treatment compared to control (DMSO) in LPS treated microglia.(n=3 per group) K, Heatmap of top 10 genes in each aging module following CX-5461 compared to DMSO in LPS treated microglia.L, Average gene expression changes for each aging module represented as log2 fold change of CX-5461 treatment over DMSO.(one sample T-test with the expected value of 0 (no change); *P<0.05,**P<0.01,****P<0.0001).

Fig. 4 |
Fig. 4 | Targeting age-related changes in microglia-derived TGFB1 promotes microglia advancement along inflammatory trajectories in the hippocampus and impairs cognition.A, Schematic of in vivo manipulation schema of Module 2. Adult (7-8 months) littermate Cx3cr1-Cre-ER +/wt ; Tgfb1 flox/flox (cKO), Cx3cr1-Cre-ER +/wt ; Tgfb1 flow/wt (Het), and Cx3cr1-Cre-ER +/wt ; Tgfb1 wt/wt (WT) mice were administered tamoxifen and subject to hippocampal microglia scRNA-Seq and behavior analysis two months later.B, UMAP plot of microglia separated into transcriptional clusters.(n = 2 pools of 3 animals per genotype) C, Stacked bar plot of the normalized relative percentage of cells of each genotype in the identified clusters.D, Dot plot of expression of aging module markers sorted by genotype.Percent of cells expressing the gene and average normalized expression are represented.E,F, Average gene expression changes for each aging module represented as log2 fold change of either Het over WT (E) or KO over WT (F).(one sample T-test with the expected value of 0 (no change); *P<0.05,****P<0.0001).G, Novel object recognition task.(Top) Diagram of the training and testing phases of the novel object recognition paradigm.(Bottom) Quantification of the NOR testing phase represented as percentage of time spent with the novel object (over the total time spent interacting with the objects).(n=9-13 per genotype; one sample T-test with the expected value of 50 (equal time spent with each object); ***P<0.001)(differencesbetween groups determined by one-way ANOVA; **P<0.01).H, Contextual fear conditioning.(Top) Diagram of the fear conditioning paradigm.(Bottom) Quantification of the percentage of time mice froze in the contextual fear conditioning testing phase.(n=14-16 per genotype; one-way ANOVA; *P<0.05).Data are shown as mean±s.e.m.

> 4 &
Tmem119 > 1 & Cx3cr1 > 4 & Top2a < 1 & Mki67 < 1 nFeature_RNA > 1000 & nFeature_RNA < 6000 & nCount_RNA > 2000 & nCount_RNA < 40000 & percent.mt<10.Data was integrated using IntegrateData and scaled.Principal component analysis was performed, and the first 50 PCAs were used to identify nearest neighbors, and interconnected clusters were identified with a resolution of 0.35.UMAP was used to visualize the results.Preliminary dimensionality reduction and visualization with UMAP using 50 PCAs identified a large microglia cluster, plus clusters of other cell types.The non-proliferative microglia clusters were subsetted for further analysis (the macrophage cluster was differentiated from the microglia cluster based on increased expression of Mcp1, Pf4, and Ms4a7).The data was scaled and centered.Principal component analysis was performed, and the first 10 PCAs were used for UMAP dimensionality reduction and to identify nearest neighbors, and interconnected clusters were identified with a resolution of 0.20.

.
CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made The copyright holder for this preprint (which this version posted April 9, 2024.; https://doi.org/10.1101/2024.04.09.588665 doi: bioRxiv preprintResearchers were blinded throughout histological assessments with groups being un-blinded at the end of each experiment upon statistical analysis.Data are expressed as mean ± s.e.m with individual sample values being shown.Statistical analysis was performed with Prism 8.0 (GraphPad), R, DESeq2, Seurat, or Monocle 3. Comparisons of means in histology experiments were analyzed with multiple T-tests followed by Holm-Sidak correction or mixed effects analysis followed by Dunnett's multiple comparisons (Prism).Changes in expression for aggregated gene sets were determined with one sample T-tests with the expected value of 0 in log2 space (null hypothesis of no change).Differential expression analysis in DESeq2 was based on Wald's significance test of the negative binomial distribution.Differential expression analysis of singlecell RNA-Seq data in Seurat was performed using non-parametric Wilcoxon rank sum tests.Autocorrelation analysis to determine focal expression Monocle 3 was done using Moran's I.The significance of gene set overlap was determined using the c 2 test with R. All data generated or analyzed in this study are included in this article.

Figure S2 |
Figure S2 | Hippocampal microglia aging advances through intermediate states that respond to systemic interventions or disease states.A, Volcano plot of differential gene expression of 12-month microglia versus all other ages with significant genes in teal.B, Gene ontology analysis of biological processes enriched in those genes with increased expression at 12-months of age.C, Representative images and quantification of CD68 (red) and IBA1 (cyan) staining in the hippocampus of isochronic young (IY) and heterochronic young (HY) along with a diagram of the comparisons.(n=5 mice per group; unpaired Student's T-test; ***P<0.001).Data are shown as mean±s.e.m.D, Dotplot of pseudotime modules in young (Y), isochronic young (IY), and heterochronic young (HY) parabiont microglia.Data is from Palovics et al 61 .Percent of cells expressing the gene and average normalized expression are represented.E, Volcano plots of differential gene expression of App NL-G-F genotype (AD) over wildtype (WT) microglia at 6-(left) and 12-months of age.Significant genes are in teal.Data is from Frigerio 62 .F, Dotplot of pseudotime modules across ages (3-, 6-, 12-, and 21-months old) and genotypes (App NL-G-F genotype (AD) over C57Bl/6 (WT)).Data is from Frigerio 17 .Percent of cells expressing the gene and average normalized expression are represented.

Fig. S3 |
Fig. S3 | Intermediate states of microglia aging act as checkpoints on inflammatory progression.A, Quantification of percentage of Tgfb1 RNAscope signal within IBA1 cells.B, Quantification of IHC of pTGFBR1 signal in IBA1 cells across ages.(n=4-5 mice per group; oneway ANOVA; *P<0.05)C, Dotplot of the expression values of TGFB1 signaling components from scRNA-Seq of aging hippocampal microglia (6-, 12-, 18-, and 24-month-old).Percent of cells expressing the gene and average normalized expression are represented.D, Dotplot of the expression values of TGFB1 signaling components in young (Y), aged (A), isochronic young (IY), and heterochronic young (HY) parabiont microglia.Data is from Palovics et al 61 .Percent of cells expressing the gene and average normalized expression are represented.UMAP plot of Tgfb1 scRNA-Seq colored by genotype.E, Heatmap of gene expression changes in the top 10 genes in each aging module induced by LPS.F, Overlap between microglia gene expression changes induced by LPS and aging.(c 2 <2.2e-16).G, PCA plot of pharmacological manipulations with or without LPS treatment.

Fig. S4 |
Fig. S4 | Targeting age-related changes in microglia-derived TGFB1 promotes microglia advancement along inflammatory trajectories in the hippocampus and impairs cognition.A, Representative images and quantification of IBA1 (cyan)/CD68 (red)-positive microglia in wildtype, Tgfb1 cHet, and Tgfb1 cKO hippocampi.(n=3-5; one way ANOVA with Tukey post hoc test; *P<0.05)B, Dotplot of the expression values of TGFB1 signaling components from scRNA-Seq of Tgfb1 WT, cHet, and cKO microglia.(n = 2 pools of 3 animals per genotype) C, UMAP plot of Tgfb1 scRNA-Seq colored by genotype.D, FACS plots of gating strategy for microglia isolation for RNA-Seq in cHet and cKO hippocampi.Quantification of the flow cytometry analysis of CD11b and CD45.(n=3-5; mixed effects analysis; *P<0.05,****P<0.0001).E, Example histogram and quantification of CD48 in control and Tgfb1 cKO FACS-sorted microglia.(n=3-5 per group; T-test; ****P<0.0001).F, Top gene ontology terms associated with genes significantly increased in microglia bulk RNA-Seq in Tgfb1 cKO samples (n=3-5 samples per genotype).G, Top gene ontology terms associated with genes significantly decreased in microglia bulk RNA-Seq in Tgfb1 cKO samples.H, Overlap between concordant microglia gene expression changes induced by Tgfb1 knockout and aging.(c 2 <2.2e-16).I, Heatmap of gene expression of the top 10 genes in each aging module in control and Tgfb1 cKO microglia.J, Average gene expression changes for each aging module represented as log2 fold change of Tgfb1 cKO over control.(one sample Ttest with the expected value of 0 (no change); ****P<0.0001).K, Freezing during the cued fear conditioning phase of the fear conditioning test.The percent freezing was calculated for the last 30 seconds of the test following the conditioned stimulus.(n=13-16 per genotype) L, Diagram and results of Y-maze test.Results are presented as preference for the novel arm versus the trained armed (time in novel arm/ (time in novel arm + trained arm) x 100).(n=14-16 per genotype) M, Percent time in the center of the open field and distance traveled during the open field test.(n=14-16 per genotype) Data are shown as mean±s.e.m. Figure1

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Table
by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made