Introduction

Inflammation is a pervasive feature of aging with distinct innate and adaptive immune cell types playing instructive roles in tissue dysfunction. In the brain, microglia are multi-functional, resident immune cells, and throughout life, monocytes, macrophages, dendritic cells, neutrophils, B cells, and T cells, populate parenchyma and border regions1. Their function is to maintain homeostasis by responding to damage and stress; however, shifts in immune cell state and abundance may underlie age-dependent changes in brain health and function2,3,4.

In the aged and neurodegenerative brain, disease-associated microglia (DAM) and white matter-associated microglia (WAM) display features of exhaustion and activation, including downregulation of homeostatic genes (Cx3cr1, P2ry12, P2ry13, Tmem119) and upregulation of Itgax/Cd11c, major histocompatibility complex (MHC) components, neurodegenerative risk factors (Apoe, B2m, Fth1, Lpl, Tyrobp), chemokines and cytokines (Ccl2, Ccl3, Ccl4, Spp1), and lysosomal stress factors (lipofuscin [LF] granules, Lgals3)1,3,5,6,7,8,9,10. Despite a speculated role in mediating damage resolution, DAM/WAM may chemoattract peripheral immune cells, collectively resulting in inflammatory activation and maladaptive disruption of microenvironments in the aged brain. New strategies to deplete or rejuvenate DAM and WAM populations that may reinforce sterile brain inflammation could have important therapeutic potential.

Senescence is a cell fate assumed in response to stress and a conserved mechanism of tissue aging. It heterogeneously manifests in many cell types and contexts, characterized by macromolecular and organelle damage (including lysosomal changes, DNA damage, proteome instability), cell cycle arrest mediated by cyclin-dependent kinase inhibitors (including p16Ink4a, p21Cip1), activation of anti-apoptotic pathways (including BCL-2, serpines), and production of a senescence-associated secretory phenotype (SASP) (including chemoattractants, remodeling factors)11,12,13,14. The use of transgenic p16-InkAttac mice, which enable systemic elimination of p16-positive senescent cells, and senolytic drugs, which enable senescent cell killing by targeting anti-apoptotic pathways, have demonstrated that senescent cell clearance improves several domains of age-related tissue decline15,16,17,18,19, including central nervous system dysfunction20,21,22,23,24,25,26,27,28.

We and others hypothesized that a p16-related senescent mechanism may regulate age-related brain dysfunction. Using a tau-dependent neurodegeneration and p16-InkAttac-coupled model, Bussian et al. identified glial population positive for the lysosomal stress marker senescence-associated β-galactosidase (SA-β-gal), p16, and SASP factors and demonstrated that systemic targeting of p16-positive senescent cells reduces neuropathology and cognitive decline22. Also using p16-InkAttac mice, Ogrodnik et al. found that p16- and SASP-positive microglia accumulate in the aged brain and are associated with markers of brain inflammation27. Importantly, the molecular identity of p16-positive senescent microglia within the emerging brain immune landscape and the mechanistic role of systemic cell senescence in regulating steady-state brain inflammation have not been reported to our knowledge. Furthermore, although senescent cells and their SASP are accepted as drivers of age-related tissue inflammation12,29, whether and how their targeting may remodel immune cell composition in age-affected tissues, including the brain, is unknown.

Here, we leveraged single-cell map**, region-specific transcriptional profiling, fluorescent imaging, and behavioral testing to study age- and sex-dependent steady-state brain cell senescence and inflammation in the context of late-life cognitive changes. Our findings reveal remarkable age-related diversity in brain inflammatory activation profiles at the cellular and transcriptional levels across the sexes. Through in vitro and in vivo experiments, we demonstrate that senescent brain myeloid cells are characterized by a SASP that drives peripheral immune cell chemotaxis. Importantly, we show that systemic targeting of p16-positive senescent cells partially rejuvenates brain immune cell composition, which is associated with the preservation of cognitive function.

Results

Senescence and inflammatory gene signatures are differentially upregulated in the aged mouse brain across sexes and brain regions

To explore the influence of age on steady-state inflammation and senescence, we analyzed the expression of 800 genes using the NanoString nCounter mouse neuroinflammatory codeset and a custom senescence and aging codeset in the hippocampus, subventricular zone enriched-striatum, and cerebellum of 6-month-old (young) versus 24-month-old (old) female and male mice. Among females, we identified 274 age-influenced differentially expressed genes (q < 0.05), with 188 in the subventricular zone, 178 in the cerebellum, and 106 in the hippocampus (Fig. 1a). Among males, we identified 141 age-influenced genes, with 35 in the subventricular zone, 75 in the cerebellum, and 93 in the hippocampus (Fig. 1a). Fifteen conserved genes increased with age across regions and sexes (Fig. 1b), and 13 of 15 (B2m, C1qa, C1qb, Ctss, Fcer1g, Grn, Lag3, Ly9, Man2b1, Mpeg1, Slamf9, Tyrobp, and Vav1) were linked to brain myeloid cell activation, including DAM/WAM profiles5,8,9,30,31,32. Next, we studied the age- and region-specific expression patterns that were conserved and distinct between sexes and identified genes exclusively differentially expressed in a single brain region and sex (Fig.1a, c–e). Age-dependent expression profiles in the hippocampus appeared the most distinct between sexes (Fig. 1c). For the subventricular zone and cerebellum, males shared the majority of differentially expressed genes with females, with a greater proportion of female age-altered genes not identified in males (Fig. 1d, e).

Fig. 1: Gene expression signatures of senescence and inflammation are differentially regulated in the aged mouse brain across sexes and brain regions.
figure 1

a NanoString nCounter transcriptional profiling was used to assess age-, sex, and brain region-dependent differences in gene expression for inflammation- and senescence-related genes (n = 5 for young female and male groups, n = 6 for old female and male groups, with the same brains studied per hippocampus [HIP], subventricular zone-enriched striatum [SVZ], and cerebellum [CERE] sample). Black numbers indicate the total number of young versus old differentially expressed genes per region and sex. Red numbers indicate the number of genes that were distinctly expressed as a function of age in a single brain region and sex (linear regression model, false discovery rate (FDR) q < 0.05). b 15 genes were significantly different in young versus old comparisons across all brain regions and both sexes (β-value, indicative of the degree of change in outcome variable for every 1-unit of change in predictor variable; q < 0.05). Comparison of genes significantly up- or down-regulated as a function of age revealed transcripts that were commonly altered across more than one region or sex comparison (black) or distinctly altered in a single age, sex, and region comparison (red) in the c hippocampus, d subventricular zone, or e cerebellum. f Comparative Ingenuity pathway analysis (IPA) analysis of total probed inflammation- and senescence-related genes revealed z-scores for canonical pathways predicted to be up- (yellow) or down- (blue) regulated in the old versus young brain per region and sex. Source data are provided as a Source Data file.

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Since distinct aging signatures defined each sex and brain region, we next considered all young versus old gene comparisons for ingenuity pathway analysis (IPA) (Fig. 1f). We observed consistent age-dependent increases in inflammatory pathways and decreases in synaptic-transmission pathways, with specific pathways distinctly regulated across brain regions and sexes. In the old female hippocampus, we observed pronounced activation of senescence-associated G2/M DNA damage checkpoint regulation (Fig. 1f), and related to this, Cdkn2a/p16Ink4a, a master regulator of the senescence program, was exclusively upregulated in the old female hippocampus at q < 0.05 (Fig. 1c). NF-kB signaling, which mediates the SASP, was also upregulated in the old female hippocampus. Autophagy and netrin signaling, pathways regulating cellular homeostasis and synaptic plasticity, respectively, were downregulated in the old female hippocampus. Dendritic and natural killer (NK) cell crosstalk and TREM1 signaling were robustly activated in the old male hippocampus. In the old female subventricular zone, TREM1 signaling was also strongly activated, and calcium signaling and synaptic long-term potentiation were downregulated. In the old male subventricular zone, HMGB1 signaling, and pattern recognition receptor function were upregulated. In the cerebellum, conserved pathways, including NF-kB signaling, dendritic and NK cell crosstalk, and p38 MAPK signaling, were upregulated (Fig. 1f).

Across sex- and region-specific aging expression profiles, we observed age-dependent upregulation of additional genes involved in brain myeloid cell activation and DAM/WAM states, including Apoe, Cd274/Pd-l1, Fcgr2b, Ifitm3, Ly86, Ptprc/Cd45, and Trem2 (Fig. 1c–e). We also observed upregulation of genes associated with senescence and the SASP, many of which overlapped with myeloid cell activation, including Axl, Bcl2a1a, Ccl2, Ccl3, Ccl4, Ccl5, Cdkn2a, Cenpb, Csf1, Il1b, Itgax, Mmp12, Spp1, Tgfb1, Tmem173/Sting, and Vim (Fig. 1c–e). Upregulated senescence and SASP genes were enriched in the old female datasets (Fig. 1c–e). Downregulated expression profiles were distinct across datasets and included genes implicated in epigenetic regulation, mitochondrial function, apoptosis, and neurotransmission (Fig. 1c–f). Among key mediators of neurotransmission, we identified age-dependent downregulation of Gria1 and Grin2b, particularly in the hippocampus, which is consistent with our prior findings33. We confirmed age-dependent reductions in GRIA1 and GRIN2B receptor abundance using immunofluorescence (IF) imaging and interestingly, observed hippocampal subregion-specific decreases in both old female and male mice (Supplementary Fig. 1). Thus, age-associated upregulation of senescent and inflammatory gene signatures distinctly characterize the aged brain across sexes and brain regions and coincides with the downregulation of key mediators of synaptic transmission.

p16-positive senescent brain myeloid cells exhibit markers of altered chemoattractant SASP, MHC, lysosomal, and homeostatic function

We next searched for senescent cell identities using single-cell RNA-sequencing (scRNA-seq) in young versus old mouse brains, focusing on females due to their more pronounced senescence-related transcriptional signatures (Fig. 1). Using the 10X droplet-based method, we recovered 6385 cells from two young and four old brain single-cell suspensions. Using the Seurat analysis pipeline, we identified six distinct cell clusters (Fig. 2a) defined by T-distributed stochastic neighbor embedding (tSNE) map** of established transcriptional identities34,35, including astrocytes (Aldoc), neurons (Snap25), oligodendrocytes (Mbp), myeloid cells (Cx3cr1), endothelial cells (Pecam1), and ependymocytes (Calml4) (Fig. 2b, Supplementary Fig. 2a, b). An interactive website of the scRNA-seq data can be found at https://mayoxz.shinyapps.io/Brain/.

Fig. 2: Single-cell RNA-sequencing reveals an age-related Cdkn2a/p16Ink4a-positive senescent myeloid brain cell population exhibiting a transcriptional profile of altered chemoattractant, MHC, lysosomal, and homeostatic function.
figure 2

a T-distributed stochastic neighbor embedding (tSNE) plot of cell types identified by scRNA-seq in mouse brain (n = 2 young and n = 4 old samples). b Violin plot of cell type marker expression within the six cell types. c tSNE plot showing Cdkn2a/p16Ink4a expression in the six cell types. Each dot represents an individual cell. The color scale bar represents the Cdkn2a expression level. d tSNE plot of the three clusters of myeloid cells identified by sequential, unbiased subclustering with each cluster indicated by a distinct color. e Scatterplot depicting Cdkn2a expression in subclustered myeloid cells. Each dot represents an individual cell, and the clusters match panel (d). The color scale bar represents the Cdkn2a expression level. f Cell abundance of myeloid cells subclusters between 6- versus 24-month-old females (n = 2 for young, n = 4 for old; mean ± SEM; two-tailed unpaired t-tests). g Heatmap of expression levels of senescence, SASP, and disease-associated (DAM) genes in myeloid subclusters 0, 1, and 2. h Pathways enriched in cluster two identified by GSEA with the normalized enrichment score (NES) and FDR q-values indicated in each panel. Source data are provided as a Source Data file.

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Based on our bulk gene expression observation that the senescence-effector Cdkn2a/p16Ink4a was upregulated in the old female brain, we searched for the Cdkn2a transcript among defined populations. Myeloid cells contained the highest frequency of Cdkn2a-positive cells, with astrocytes, oligodendrocytes, and endothelial cell populations containing fewer Cdkn2a-positive cells (Fig. 2c). Given the pronounced abundance of Cdkn2a signal, we conducted unbiased sequential clustering of the myeloid compartment and identified a single-cell population that was enriched for Cdkn2a-positive cells, denoted ‘cluster two’ (Fig. 2d, e, Supplementary Fig. 2c–e), which trended to increase with age (Fig. 2f). Interestingly, Cdkn2a-positive cells in cluster two mapped with microglial (Tmem119) and monocyte (Ly6c2) markers (Supplementary Fig. 2c–e), suggesting that both resident and infiltrating cells contribute to a Cdkn2a-enriched brain immune population.

The transcriptional signature of cluster two was defined by the upregulation of both canonical senescence genes and overlap** gene signatures implicated in myeloid cell activation. This included upregulation of cyclin-dependent kinase inhibitors (Cdkn2a, Cdkn1a), senescence-related antiapoptotic factors (Bcl2), chemoattractant SASP factors (Ccl2-5, Spp1), MHC genes (Cd74, H2-Aa, H2-Ab1, H2-Eb1, H2-D1, H2-K1, H2-Q7), and lysosomal stress markers (Lgals3, Lgals3bp). Additional DAM/WAM genes were upregulated (Apoe, B2m, Cst7, Fth1, Ifitm3, Itgax, Lpl) and downregulated (Cx3cr1, Hexb, Marcks, P2ry12, P2ry12, Selplg, Tmem119) in cluster two (Fig. 2g). This signature also overlapped with age-related genes in the bulk regional NanoString transcriptional profiles (Fig. 1). Functional assessment based on gene set enrichment analysis (GSEA) revealed downregulation of lysosomal function and upregulation of chemokine signaling, ribosomal function, and Alzheimer’s disease (Fig. 2g, h).

To validate scRNA-seq results and specifically interrogate the contribution of microglia to the senescent myeloid cell compartment in an aged brain in a larger dataset, we analyzed scRNA-seq data previously published by ** of established transcriptional identities34,35 (Supplementary Fig. 3a). We identified a Tmem119-positive microglial population (Supplementary Fig. 3a, b), which we applied to unbiased subclustering (Supplementary Fig. 3c). Of the five subclusters, ** gene signatures of senescence and DAM/WAM activation.

p16-positive senescent myeloid cells accumulate in the aged, female dentate gyrus

We next explored the identity and abundance of p16-positive myeloid cells in the young and old brains of p16-InkAttac mice treated with vehicle (V) or AP20187 (AP) to systemically eliminate p16-positive cells15. We used RNA in situ hybridization (RNAish) to probe Cdkn2a/p16Ink4a and IF imaging to probe IBA1, a marker of resident and infiltrating brain myeloid cells. Based on the transcriptional signature of lysosomal alterations identified in ‘cluster two’ cells by scRNA-seq, we also tracked LF granules as a marker of senescence-related lysosomal stress. We focused on the hippocampal dentate gyrus, an adult neurogenic niche susceptible to deleterious inflammatory remodeling in the aged brain4. In old female mice, we frequently identified p16Ink4a+IBA1+ cells, which trended to be 3.3-fold more abundant than in young females (Supplementary Fig. 4a, b). The majority of p16Ink4a+IBA1+ cells were also positive for LF granules (Supplementary Fig. 4a, c). Importantly, we discovered the necessity for assessing (or quenching) LF signal to accurately quantify p16Ink4a punctae, due to signal overlap resulting from the broad autofluorescence inherent to LF granules (Supplementary Fig. 4a). Among females, no age- or treatment-dependent differences in IBA1-p16Ink4a+ cell abundance were identified (Supplementary Fig. 4d).

Senescent brain myeloid cell SASP chemoattracts peripheral immune cells in vitro

Based on our observation that p16-positive senescent myeloid cells in the aged brain upregulate chemoattractant SASP factors (Fig. 2), we devised an in vitro model to test whether the SASP of senescent brain myeloid cells drives peripheral immune cell chemotaxis. Exposure of primary mouse brain myeloid cells to 10 Gy X-ray irradiation (IR)-induced senescence over two weeks (Fig. 3a, b; Supplementary Fig. 5). Specifically, 71% of IR-exposed brain myeloid cells were positive for the lysosomal stress marker SA-β-gal (Fig. 3a). Among IR-exposed cultures, RT-PCR gene expression profiling revealed significant increases in the senescence effector p16Ink4a and SASP factors Ccl4 (Mip1b) and Mmp9, as well as trends for increases in Ccl3 (Mip1a), Tnfa, and MHC II-associated invariant chain Cd74 (Supplementary Fig. 5). Analysis of secreted proteins revealed significant increases in established and/or chemoattractant SASP factors, including GDF15 (211% increase), CCL2/MCP1 (56% increase), CCL3/MIP1a (74% increase), and TNFRSF1A (58% increase) in the media of senescent IR-exposed cultures, relative to sham control media (Fig. 3b). Comparison of gene expression and secreted protein data revealed chemoattractant factor CCL4 was upregulated at both the gene expression and secreted protein levels, while GDF15 was robustly upregulated in the secreted protein pool but not transcriptionally, suggesting that the SASP is dynamically regulated.

Fig. 3: Senescent brain myeloid cells secrete a SASP that promotes peripheral immune cell recruitment in vitro.
figure 3

IR exposure to primary mouse brain myeloid cells induced senescence, which was confirmed by a SA-β-gal staining (scale bar: 0.1 mm) and b analysis of SASP proteins in conditioned media (mean ± SEM; two-tailed unpaired t-tests). c Schematic representation of the microfluidic migration chamber. The splenocyte entry chamber is connected via 10-micron grooves to the central imaging field. Conditioned medium from senescent or control brain myeloid cells was added to the indicated side chambers. d Migration index indicating average frame-to-frame migration toward SASP or control media from images collected every three min for 12 h (mean ± SEM; two-tailed one-sample t-test). See Supplementary Fig. 6 for a representative video of splenocyte migration to senescent cell media containing the SASP. (Data were generated from two separate experiments of n = 3.) Source data are provided as a Source Data file.

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Comparison of in vitro senescent brain myeloid cultures with scRNA-seq results (Fig. 2) demonstrated common upregulation of p16Ink4a, MHC II components, CCL2, CCL3, and CCL4, suggesting the in vitro model recapitulates critical chemoattractant features of in vivo senescent brain myeloid cluster two. We also noted model differences, including upregulation of Spp1 in cluster two in vivo and downregulation of OPN (encoded by Spp1) in senescent myeloid culture media (Fig. 3b). We next assayed whether the senescent brain myeloid SASP chemoattracts peripheral immune cells using a microfluidic migration chamber system (Fig. 3c). GFP-positive splenocytes, consisting of T and B lymphocytes, dendritic cells, natural killer cells, neutrophils, and monocytes37, preferentially migrated toward senescent media containing SASP proteins (Fig. 3d, Supplementary Fig. 6 Video File). Thus, the SASP of senescent brain myeloid cells drives peripheral immune cell chemotaxis.

Activated resident and infiltrating immune cells accumulate in the aged brain and are reduced by systemic targeting of p16-positive senescent cells

We next implemented high-dimensional mass cytometry to study age-related changes in brain immune cell composition and test whether systemic targeting of p16-positive senescent cells reduces the abundance of infiltrating immune cells and microglia exhibiting DAM features. Twenty immune markers were used to study changes in the frequency of cell types and states within the total CD45+ brain cell compartment in young versus old female and male p16-InkAttac mice treated with V or AP (Fig. 4; Supplementary Fig. 7). The total number of CD45+ cells identified in brain single-cell suspensions from young and old mice treated with V or AP did not differ in females or males (Supplementary Fig. 8). Uniform manifold approximation and projection (UMAP) dimensionality reduction and FlowSOM-guided clustering revealed remarkable immune cell diversity, including populations defined by both distinct and gradient marker expression, which differed by age, treatment, and sex. Seven broad cell type classifications were identified based on canonical marker abundance and cluster proximity, including microglia (CD45+CD11b+Ly6C−Ly6G-CD3−), border macrophages (BAMs: CD45+CD11b+CD206+CD38+), dendritic cells (DCs: CD45hiCD11c+CD103+), B cells (B: CD45hiCD19+), neutrophils (N: CD45hiLy6C+Ly6G+), monocytes (CD45hiLy6C+), and T cells (CD45hiCD3+). The seven clusters were further segregated based on the type and state markers for a total of 19 distinct populations (Fig. 4a).

Fig. 4: Mass cytometry demonstrates the increased frequency of activated resident and infiltrating immune cells in the aged brain, which are reduced by systemically targeting p16-positive senescent cells in females.
figure 4

a Uniform manifold approximation and projection (UMAP) representation of total CD45+ brain cell populations identified by mass cytometry with the colors and numbers corresponding to cell types delineated by FlowSOM-guided clustering, including distinct microglial, border macrophage (BAMs), B cell (B), dendritic cell (DCs), monocyte, neutrophil (N), and T cell populations defined by discrete marker combinations and proximity. b–t Frequency distributions of FlowSOM-delineated populations with numbers and color coding corresponding to panel (a) (n = 4; Y-F-V = 6 months, female, vehicle; O-F-V = 24 months, female, vehicle; O-F-AP = 24 months, female, AP20187; Y-M-V = 6 months, male, vehicle; O-M-V = 24 months, male, vehicle; O-M-AP = 24 months, male, AP20187; mean ± SEM; generalized linear mixed model with multiple comparison testing; female and male samples analyzed separately). Source data are provided as a Source Data file.

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Eleven CD45+ populations differed between old versus young female brains (Y-F-V versus O-F-V), and six CD45+ populations differed between old versus young male brains (Y-M-V versus O-M-V). Two populations that increased in frequency in old females were significantly reduced following AP treatment (O-F-V versus O-F-AP). In old females, we observed a significant decrease in the frequency of CD45+CD11b+CX3CR1hi ‘homeostatic’ microglia, which was the most abundant CD45+ population (population 10; Fig. 4b). The age-related reduction in homeostatic microglia was accompanied by an expansion of activated resident microglia and infiltrating immune cells. Among both males and females, we observed age-dependent increases in frequency of two activated microglial populations, which were CD45+CD11b+CX3CR1hiNES+CD11c+CD103+ (population 5; Fig. 4c) and CD45+CD11b+CX3CR1hiMHCII+NES+CD11chiCD103lo (population 13; Fig. 4d). An additional microglial population increased in frequency in aged females, which was CD45 + CD11b + CX3CR1loMHCIIhiCD206loCD11c+CD103lo (population 15; Fig. 4e). Microglia population 15 exhibited a marker expression pattern overlap** with scRNA-seq senescent myeloid cluster two (Fig. 2g) and ** molecular signature of senescence and DAM/WAM activation.

We identified three putative BAM populations co-expressing CD206 and CD38 (populations 16–18; Fig. 4f–h). Of the BAM clusters, a CD45+CD11b+CX3CR1loMHCIIhiCD206+CD38+CD11c+CD103+ population decreased in frequency in the aged male brain (population 16; Fig. 4f), and a CD45+CD11b+CX3CR1loCD206hiCD38+CD11c+CD103+CD44+CD4lo population decreased in frequency in the aged female brain (population 17; Fig. 4g), which may reflect sex differences in the age-dependent activation state of BAMs. CD45hiCD11b+CX3CR1loMHCIIhiCD206loCD11chiCD103+CD44+CD4lo DCs did not statistically differ with age in either sex (population 14; Fig. 4i). A CD45hiCD11b+CX3CR1+Ly6C+MHCII+CD206loCD11c+CD103+CD44+CD4lo putative plasmacytoid DC population increased in frequency in the aged female brain (population 4; Fig. 4j). Three Ly6C+ monocyte populations (populations 8, 2, 9; Fig. 4k–m) and one Ly6G+Ly6C+ neutrophil population (population 3; Fig. 4n) were identified, which displayed variability in females. CD45hiCD11b+CX3CR1+Ly6ChiCD44+ brain monocytes increased in aged males treated with V (population 2; Fig. 4l). CENPB and p21 are markers that have been previously used to identify senescent cells and here, identified a distinct CD45hiCD11b+Ly6C+Ly6GloNES+p21+Ki67+CENPB+HMGB2+CD44+ monocyte population (population 9; Fig. 4m). A CD45hiCD38+ population was also identified; since the population was CD45hi, these cells are likely other infiltrating immune cells that were not clearly delineated with the markers included on our panel (population 12; Fig. 4o). CD45hiCD19+MHCII+CD38+ B cells increased in frequency in the aged female brain, and AP treatment reduced their abundance, demonstrating an infiltrating immune population that was restored to youthful levels in the aged female brain following senescent cell clearance (population 11; Fig. 4p).

Four CD45hiCD3+ T cell populations were identified, all of which were positive for CD38. All T cell populations increased in the aged female brain, and two increased in the aged male brain (populations 0, 7, 1, 6; Fig. 4q–t). CD45hiCD3+CD4+CD38+ cell abundance increased in the brains of aged females treated with V (population 0; Fig. 4q). The age-dependent increase in CD45hiCD3+CD8+CD38+ cells trended towards a reduction in females treated with AP (population 7; Fig. 4r). CD45hiCD3+CD8+Ly6C+CD38+HMGB2lo cells increased in frequency in the aged brain in both sexes (population 1; Fig. 4s). The least abundant T cell population identified was CD45hiCD3+CD8hiCD11b+CX3CR1loCD38+CD11c+CD103+HMGB2lo. Despite the implementation of an analysis pipeline to eliminate doublets or aggregates, coexpression of canonical T cell markers with other myeloid markers may indicate that this population was comprised of interacting cells, but interestingly, this population increased in frequency in both the aged female and male brain (population 6; Fig. 4t).

Our mass cytometry results demonstrate dynamic remodeling of the inflammatory cell landscape in the old versus young brain, with more pronounced expansion of activated resident microglia and infiltrating immune cell compartments in aged females, relative to males. AP treatment to systemically target p16-positive senescent cells significantly reduced the abundance of a microglia population coexpressing MHCII and CD11c (population 15; Fig. 4e), which demonstrated remarkable marker similarity to the senescent myeloid cluster two and ** of central nervous system immune cells reveals distinct myeloid subsets in health, aging, and disease. Immunity 48, 380–395 e386 (2018)." href="/article/10.1038/s41467-022-33226-8#ref-CR1" id="ref-link-section-d37693864e2257">1,3,5,6,7,8,9,10. Pathway analyses revealed alterations in cytokine, lysosomal, and ribosomal function, as well as a predicted link to Alzheimer’s disease. We corroborated scRNA-seq findings using an independent dataset generated from young and old mice of similar ages and experimental approaches

Methods

Mouse models

Mouse experiments were performed under protocols approved by Mayo Clinic Institutional Animal Care and Use Committee (protocol #A00003875-18). Mice were cared for in compliance with the guidelines in the National Institutes of Health Guide for Care and Use of Laboratory Animals. Wild-type and p16-InkAttac mice (both C57BL/6 background) were used for in vivo studies. Mice were group-housed in ventilated cages with a constant temperature of 25 °C, 30–70% humidity, a 12-h light/dark cycle, and provided standard chow. As indicated in the results, we took advantage of the ability to systemically eliminate p16-positive cells through the administration of AP20187 (AP) to induce Caspase 8-mediated cell death15. For treatment studies, 18-month-old p16-InkAttac mice were randomized (based on body weight and body composition measured by echo-MRI) to receive V or AP (2 mg/kg) with a treatment strategy of 3 consecutive daily intraperitoneal injections per week, every other week (six treatments per month), until 24 months of age. Three-month-old p16-InkAttac young control mice received the same V dosing strategy from 3 to 6 months of age. Thus, all aged mice were treated with V or AP for six months, and young controls were treated with V for 3 months. We recognize a shorter V treatment strategy in young controls is a technical limitation, but we opted to prioritize initiation of treatment when young mice were developmentally mature and endpoint in young mice at 6 months of age since we view this as an early middle age timepoint. All mice were behaviorally phenotyped by investigators blinded to experimental groups in the weeks prior to necropsy, and treatments were discontinued at least one week prior to necropsy. At the time of necropsy, all mice were examined for gross pathology and tumor prevalence, with the exclusion of mice harboring pronounced splenomegaly. For in vitro chemotactic assays described below, splenocytes were harvested from 1 to 2 months old C57BL/6-Tg(CAG-EGFP)131Osb/LeySopJ mice (JAX stock #006567).

Tissue collection and processing

Mice were euthanized with a lethal dose of pentobarbital and were transcardially perfused with ice-cold PBS. Brains were immediately removed and processed for downstream applications. Hemisected brain hemispheres allocated to NanoString nCounter analyses were immediately dissected under a microscope; the hippocampus, subventricular zone-enriched striatum, and cerebellum were stored at −80 °C. Brain hemispheres allocated to RNA in situ hybridization and IF imaging were drop-fixed in 4% paraformaldehyde for 24 h and then subjected to a sucrose gradient (24 h in 12% sucrose-PBS, 24 h in 18% sucrose-PBS, 72 h in 30% sucrose-PBS), followed by storage in cryopreservation media (30% glycerol, 30% ethylene glycol in PBS) at −80 °C. Brains were sectioned at 16 µm using a microtome prior to staining. For scRNA-seq and mass cytometry, single-cell suspensions were generated based on our optimization of a recently published methodSingle-cell RNA-sequencing (scRNA-seq) and data analysis

Single-cell suspensions were counted using the Vi-Cell XR Cell Viability Analyzer (Beckman-Coulter, Brea, CA, USA) and ~5000 cells were loaded into the 10X Genomics Chromium system to generate Gel Beads-In-Emulsion (GEMs). Reverse transcription was performed using Chromium Single Cell 3’ Reagent Kits (v2, 10X Genomics, Pleasanton, CA, USA). Standard Illumina sequencing primers and a unique i7 Sample index were added to each cDNA for library construction. Libraries were sequenced using the Illumina cBot and HiSeq 3000/4000 PE Cluster Kit. The flow cells were sequenced as 100 × 2 paired-end reads on an Illumina HiSeq 4000 using HiSeq 3000/4000 sequencing kit and HCS v3.3.52 collection software. Base-calling was performed using Illumina’s RTA version 2.7.3. Cell Ranger (v3.0) (10X Genomics) was used for alignment to the mouse genome version mm10. R (v3.6.0) and Seurat (v3.1.3)35 were used for further analysis following the standard workflow. For quality control and filtering, cells with detected genes fewer than 200 or >8000, or with mitochondrial gene content >50% were excluded. For ** gene expression and average abundance of the sham condition.

Microfluidic migration chamber assay and splenocyte preparation

Microfluidic chamber (MFC) dimensions are provided in Fig. 3c. Master molds were fabricated by photolithographic patterning of photoresist (SU-8) on silicon wafers. MFCs were replicated in polydimethylsiloxane elastomer (PDMS; Sylgard 184 Elastomer Kit, Dow Corning) by pouring a solution of one part curing reagent per 10 parts PDMS onto a master mold and incubating at 55 °C overnight. Cured PDMS slabs with imprinted fluidic architecture were cut from a master mold. Media reservoirs were created using a 4 mm biopsy punch (Miltex) on a cutting mat (Electron Microscopy Sciences). To sterilize, MFCs were autoclaved in glass Petri dishes and stored until use. Three MFCs were adhered to cover-glass bottom TC treated six-well plates (Cellvis; P06-1.5H-N) using a template for alignment with the IncuCyte camera. To remove air bubbles from interchamber grooves, 100% ethanol was flushed through all chambers. Ethanol was gradually replaced with ddH2O by iteratively transferring 30 µl to the proximal chamber, distal chamber, and left side port for the central chamber and removing fluid from the right port of the central chamber once the volume had equilibrated. In the same fashion, ddH20 was subsequently replaced with complete media for lymphocytes (CML) consisting of RPMI 1640 (Invitrogen), 3% v/v heat-inactivated fetal bovine serum, 1% v/v Na Pyruvate, 1% v/v non-essential amino acids, 1% v/v Penicillin and Streptomycin, and 25 µM beta-mercaptoethanol. The absence of bubbles was confirmed visually and ddH2O was added between wells to limit evaporative loss before proceeding.

Splenocytes were prepared by mechanical dissociation of C57BL/6-Tg(CAG-EGFP)131Osb/LeySopJ mouse spleen in 37 °C CML media with a sterile 7 ml Dounce homogenizer (20 strokes). Cells were centrifuged at 400×g for 4 min at room temperature, followed by red blood cell (RBC) lysis 1 ml ACK (150 mM NH4CL, 10 mM KHCO3, 0.1 mM Na EDTA, pH 7.4) for 90 s at room temperature. Lysis was terminated by adding 9 ml HBSS, and cells were centrifuged for 3 min at 400×g and passed through a 40 µm cell strainer (Falcon; #352340) to obtain a single-cell suspension. Splenocytes were counted, centrifuged at 400×g for 4 min, and resuspended in CML at a final concentration of 2 × 108 cells per ml. Prior to the addition of cells or conditioned media, CML was allowed to equilibrate across all chambers, and equilibrium was thereafter maintained by removing 20 µl media for a given chamber just prior to adding 20 µl cells or conditioned media. GFP + splenocytes (4 × 106 in 20 µl) were added to the proximal chamber of the MFC. Immediately following, 20 µl of conditioned media from senescent (IR) or non-senescent (Sham) microglial cultures were added to the lateral compartments in an identical manner. Finally, 10 µl of additional CML media were added on top of the proximal chamber containing cells to confer a brief fluidic drive to push cells from the proximal chamber into the central chamber and six-well plates were immediately transferred to IncuCyte (Sartorius) for live cell imaging. A representative migration video is provided in Supplementary Fig. 6 Video File. Micrographs (×4 objective) were acquired every 3 min for 12 h using IncuCyte SX5 G/O/NIR Optical Module (300 ms exposure). Images were cropped to include only the central chamber and exported tiff series and were batch imported into time series in Imaris 9.8 (Oxford Instruments) for cell tracking analysis. After background subtraction and alignment, automated spot detection was performed based on pixel area, intensity, and quality score. Inbuilt autoregressive motion algorithms were used to identify cell migration tracks based on the following criteria: gap size <2, interval distance <3 cell lengths. Tracks with displacement <3 cell lengths (e.g., non-motile cells) were excluded. Our experimental design utilized paired comparison of migration toward senescent versus non-senescent conditioned media with randomization of media into chamber A versus B. Migration index was calculated as average cell displacement (in pixels) between frames (3 min) over the 12 h recording. Migration toward a given stimulus is indicated by positive values, whereas toward the alternative stimulus is indicated by negative values.

Mass cytometry and data analysis

Mass cytometry (aka cytometry by time of flight or CyTOF) was used to detect 28 intracellular or extracellular markers (Supplementary Dataset 3). The Maxpar panel designer tool (Fluidigm Sciences Inc., USA) was used to assign metal tags to each antibody in the panel. Antibodies for each marker were purchased directly from Fluidigm or were conjugated with metal tags using the Maxpar labeling kit and were subsequently tested for specificity. Antibodies were titrated to minimize signal overlap into neighboring channels and cell type-specific markers were separated within the panel in a manner that minimized signal interference due to the known predictable overlap from isotopic impurities. Cell pellets were suspended in Maxpar cell staining buffer (CSB) (Fluidigm) and labeled with 0.5 μM cisplatin solution (Fluidigm). After cisplatin labeling, cells were centrifuged at 500×g for 5 min and washed twice with CSB. Cells were resuspended in an antibody cocktail including 19 cell surface antibodies and then incubated with gentle agitation at room temperature for 45 min. Afterward, cells were washed with CSB twice prior to fixation with 2% paraformaldehyde at room temperature with gentle agitation. After fixation, CSB was added, and samples were centrifuged at 800×g for 5 min at room temperature. After removal of the supernatant, cells were resuspended in PERM-S buffer (Fluidigm) and centrifuged at 800×g for 5 min. Cells were then resuspended in a PERM-S antibody cocktail containing nine intracellular antibodies and incubated at 4 °C with gentle agitation. Then cells were washed with CSB and fixed in 2% paraformaldehyde for 30 min with gentle agitation. After washing with CSB, cells were incubated with 62.5 nM solution of DNA intercalator (Cell-ID Intercalator; Fluidigm) diluted in Maxpar Fix/Perm Buffer (Fluidigm) and incubated at 4 °C overnight. On the following day, cells were washed with PBS prior to resuspension in a 1:10 mixture of EQ four-element calibration beads and Cell Acquisition Solution (Fluidigm). Cells were resuspended at a concentration of 500,000 cells per ml. Prior to analysis cells were strained through a 40 µm cell strainer to eliminate excess debris and cell aggregates. Labeled cell suspensions were introduced into the Helios mass cytometer at a rate no greater than 350 events per second to minimize cell doublets. Introduced cells were then nebulized and subjected to an inductively coupled plasma source to ionize the metal labeled cells which were then introduced into a time-of-flight mass spectrometry chamber. Signals corresponding to each marker were captured, integrated, and normalized to spiked-in EQ beads using CyTOF software (v7.0.5189).

Live cells were isolated and exported for downstream analyses by manually gating on beads, residual, center, offset, width, event length, live/dead (195Pt), DNA1 (191Ir), and DNA2 (193Ir)58. Parameters were hyperbolic arcsine (arcsinh) transformed and scaled. FlowSOM clustering59 utilizing 20 cell type markers, including 12 immune cell identity positive selection markers (CD45, CD11b, CD11c, CD103, CD38, CD206, CD19, Ly6C, Ly6G, CD3, CD4, CD8) and 8 negative cell identity selection markers (NEUN, SYP, CD171, SOX2, MPB, MOG, CD31, GFAP), was used to identify and isolate the total CD45+ compartment and exclude aggregates. Following, scales were rechecked and the total CD45+ compartment was dimensionally reduced by uniform manifold approximation and projection (UMAP)60 and FlowSOM clustered using 20 markers, which included both type/identity and state markers (CD45, CD11b, CD11c, CD103, CD38, CD206, CD19, Ly6C, Ly6G, CD3, CD4, CD8, CD44, CENPB, HMGB2, p21, CX3CR1, Ki67, MHC II [I-A/I-E], NES). See Supplementary Dataset 3 for label, scale, and identity information for each marker used in mass cytometry analyses. FlowJo (v10.8.1) with R (v4.1.2) was used for mass cytometry analyses.

Nest building

Nest building was assessed as a measure of executive function, as previously described61,62. Mice were transferred to the testing room for acclimation at least 30 min before the experiment. One cotton nestlet (Ancare) was placed on a clean cage floor prior to placing the mouse in the cage, which contained sanichips free of any additional nesting material and ad libitum access to water and food. Nest quality was scored after 1, 3, 5, 9, and 24 h according to an established scale of 0–5, based on the degree of nestlet shredding, compaction, and organization61.

Stone T-maze navigation

Water-motivated Stone T-maze navigation was assessed as a measure of learning, memory, and decision making, as well as physical function, as previously described39. The paradigm consists of a 2-day protocol, with training and a test day. On each day mice were transferred to the testing room for acclimation at least 30 min before the experiment. A metal reservoir, which was filled with room temperature water to a depth of ~2.2 cm (walkable depth, no swimming required), housed the ‘straight run’, comprised of an opaque entry box with a sliding door leading to an extended, straight clear acrylic alley chamber with a ceiling that ended in a dry, opaque goal box. Mice learn that moving through the straight clear chamber containing aversive water and exposure enables them to escape to the dry enclosed box. The mice were placed in the entry chamber and gently pushed into the straight alley with a plunger in the entry box, and the time required to enter the goal box was scored over six randomized, alternating trials. Successful completion of the straight run required mice to reach the goal box in 15 s or less, in 4 out of 6 trials. Mice unable to reach this criterion were excluded from further testing. The following day, a Stone T-Maze, consisting of an opaque entry box with a sliding door leading to a maze of clear acrylic alleys consisting of T-junctions, dead ends, and sliding guillotine doors (to prevent excessive backtracking), ending in a dry, opaque goal box, was placed in the metal reservoir containing 2.2 cm room temperature water. The mice were placed in the entry chamber and gently pushed into the maze with a plunger in the entry box, and the time required to enter the goal box and the number of errors committed while navigating the maze were scored over six randomized, alternating trials. An error was defined as the entry of the mouse’s entire body into an incorrect path, as delineated by the shoulder down. Successful completion of the maze run required mice to reach the goal box in 135 s or less, in 4 out of 6 trials. Mice unable to reach this criterion were excluded from further testing or analyses. For both the straight and maze runs, the first three randomized, alternating trials were followed by a 15-min break, concluding with the final three alternating trials. Between individual trials, mice were placed in a separate holding cage containing dry bedding material, a heating lamp, and a dry towel, followed by a return to their home cage.

Statistical analyses

For NanoString data, each normalized gene was scaled by subtracting the mean and dividing by the standard deviation, and values were checked graphically for outliers and significant skewedness. The normalized and scaled NanoString data were analyzed using linear regression models and the false discovery rate (q-value) was used to adjust for multiple comparisons. DeepVenn/BioVenn was used to generate proportional Venn diagrams63. Ingenuity pathway analysis (IPA) was used to functionally annotate NanoString gene expression profiles. The Wilcoxon Rank Sum test was used for scRNA-seq differential gene expression analysis using the Seurat package. The Kolmogorov–Smirnov statistic was used for gene set enrichment analysis for the scRNA-seq data using GSEA software. Mass cytometry data were analyzed using generalized linear mixed models (GLMM) with multiple comparison testing. Behavioral data were analyzed using two-way ANOVA. For imaging and in vitro datasets, one- or two-group comparisons were analyzed using one- or two-sample tests, respectively. Associations between mass cytometry and behavioral data were interrogated using Spearman correlations. Graphpad Prism 9 and R were used for statistical analyses and graphing.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.