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Identification of senescent, TREM2-expressing microglia in aging and Alzheimer’s disease model mouse brain

Abstract

Alzheimer’s disease (AD) and dementia in general are age-related diseases with multiple contributing factors, including brain inflammation. Microglia, and specifically those expressing the AD risk gene TREM2, are considered important players in AD, but their exact contribution to pathology remains unclear. In this study, using high-throughput mass cytometry in the 5×FAD mouse model of amyloidosis, we identified senescent microglia that express high levels of TREM2 but also exhibit a distinct signature from TREM2-dependent disease-associated microglia (DAM). This senescent microglial protein signature was found in various mouse models that show cognitive decline, including aging, amyloidosis and tauopathy. TREM2-null mice had fewer microglia with a senescent signature. Treating 5×FAD mice with the senolytic BCL2 family inhibitor ABT-737 reduced senescent microglia, but not the DAM population, and this was accompanied by improved cognition and reduced brain inflammation. Our results suggest a dual and opposite involvement of TREM2 in microglial states, which must be considered when contemplating TREM2 as a therapeutic target in AD.

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Fig. 1: Senescent microglia with a distinct signature accumulate in aged mice and in 5×FAD mice.
Fig. 2: High levels of TREM2 are associated with expression of senescence markers in microglia.
Fig. 3: Senescent microglia are found in postmortem brain of patients with AD and in 5×FAD mice.
Fig. 4: Transcriptional signature of senescent microglia.
Fig. 5: Senolytic therapy using ABT-737 reduced the accumulation of senescent microglia, cognitive decline and neuroinflammation in 5×FAD mice.

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Data availability

Data to reproduce the figures are available as of the date of publication at https://github.com/noarachmian/Trem2_senescent_microglia (ref. 77).

Any additional information required to reanalyze the data reported in this paper will be available from the lead contact upon reasonable request.

Code availability

The code is available as of the date of publication at https://github.com/noarachmian/Trem2_senescent_microglia (ref. 77).

Any additional information required to reanalyze the data reported in this paper will be available from the lead contact upon reasonable request.

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Acknowledgements

We sincerely thank A. Tsitsou-Kampeli, H. Ben-Yehuda, S. Suzzi, G. Castellani, M. A. Abellanas, L. Roitman, H. Gal, N. Papismadov, Y. Addadi and I. Sher for technical assistance. We also thank all members of the Krizhanovsky laboratory and the Schwartz laboratory for helpful discussions. This study was supported by a grant from the Advanced European Research Council (no. 741744); Israel Science Foundation (ISF) research grant 991/16; ISF–Legacy Heritage Biomedical Science Partnership research grant 1354/15; grants from the Thompson Foundation and the Adelis Foundation (given to M.S.); and by grants from the European Research Council H2020 program (no. 856487), the Weizmann Centers for Research on Positive Neuroscience and Research on Neurodegeneration, the ISF (no. 1626/20), the Deutsche Forschungsgemeinschaft (CRC 1506), the Israel Ministry of Health, the Belle S. and Irving E. Meller Center for the Biology of Aging and the Sagol Institute for Longevity Research (given to V.K.). V.K. is an incumbent of the Georg F. Duckwitz Professorial Chair and the Shimon and Golde Picker–Weizmann Award.

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Authors

Contributions

N.R., M.S. and V.K. conceptualized the project. N.R., M.S. and V.K. planned the experiments. N.R. designed and performed the CyTOF and flow cytometry experiments and the mouse experiments. S.M. performed and analyzed the NOR task. U.C. and N.R. analyzed flow cytometry. U.C. and N.R. injected mice. H.A. analyzed the snRNA-seq data. N.R. and D.D. analyzed the CyTOF results. H.A., D.D. and N.R. performed data analysis. D.E. and N.R. performed and analyzed RT–PCR. N.R., T.C. and T.M.S. established the CyTOF panel. J.M.P.R. established the flow cytometry panel. S.M. and U.C. contributed to the experiment’s performance and experiment design. L.C. organized the mice colonies and performed genotyping. N.R., M.S. and V.K. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Valery Krizhanovsky or Michal Schwartz.

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Competing interests

V.K. is a co-inventor of patents on senolytics and senolytic approaches and is a consultant for Sentaur Bio. None of these interests influenced the data presented in this manuscript. M.S. is a scientific co-founder of ImmunoBrain Checkpoint, which develops anti-PD-L1 to treat Alzheimerʼs disease. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Association between ApoE and senescence markers.

(a) Pearson’s correlation between the expression of ApoE with TREM2 and with senescence markers, p16, p19 and p21. Each color represents a different experiment from Fig. 1; the median expression was Z-scored within each experimental group to account for batch identity.

Extended Data Fig. 2 Differential protein expression between disease-associated microglia (DAM) and senescent microglia.

Differentially expressed proteins between by senescent microglia compared to disease-associated microglia. Each dot represents one protein. The horizontal line marks the significance threshold p < 0.0016 after Bonferroni correction). The vertical dashed lines represent two fold differences in expression.

Extended Data Fig. 3 Proportion of CD45+ cells following senolytic treatment with ABT-737.

(a-g) 10-11 month-old female mice, vehicle control (WT, vehicle) (n = 4), compared to 10-11-month-old 5xFAD female mice, vehicle control (5xFAD, vehicle) (n = 5), and 10-11-month-old 5xFAD female mice, treated with ABT-737 (5xFAD, ABT) (n = 4). Quantitative analysis showing percentage of (a) resting microglia; (b) activated microglia; (c) disease associated microglia; (d) senescent microglia; (e) border-associated macrophages; (f) monocytes; and (g) CD11b- cells among the CNS CD45+ cells. Data are presented as mean values +/- SEM; *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Fig. 4 Flow cytometry of splenocytes two weeks following ABT-737 treatment.

12.5-13-month-old male mice, vehicle control (WT, vehicle) (n = 7), compared to 12.5-13-month-old 5xFAD male mice, vehicle control (5xFAD, vehicle; n = 7), and 12.5-13-month-old 5xFAD male mice, treated with ABT-737 (5xFAD, ABT; n = 7). (a) Gating strategy. (b-k) Quantitative analysis by flow cytometry; pink and circles represent the WT group, blue and squares represent the 5xFAD vehicle group, and green and triangles represent 5xFAD ABT group (b) Quantitative analysis of B-cell percentage from total CD45+ cells. (c) Quantitative analysis of myeloid cells percentage from total CD45+ cells. (d) Quantitative analysis of monocyte percentage from total CD45+ cells. (e) Quantitative analysis of neutrophil percentage from total CD45+ cells. (f) Quantitative analysis of T-cell percentage from total CD45+ cells. (g) Quantitative analysis of CD4 + T-cell percentage from total CD45+ cells. (h) Quantitative analysis of naïve T-cell percentage from total CD45+ cells. (i) Quantitative analysis of T-effector cell percentage from total CD45+ cells. (j) Quantitative analysis of T-regulatory cell percentage from total CD45+ cells. (k) Quantitative analysis of CD8 + T-cell percentage from total CD45+ cells. Data are presented as mean values +/- SEM; *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Fig. 5 Open Field test.

Two cohorts of mice were combined for the analysis. First cohort of mice: 10-11 month-old female mice, vehicle control (WT, vehicle) (n = 5), compared to 10-11-month-old 5xFAD female mice, vehicle control (5xFAD, vehicle; n = 4), and 10-11-month-old 5xFAD female mice, treated with ABT-737 (5xFAD, ABT; n = 5). Second cohort of mice: 10-11-month-old male mice, vehicle control (WT, vehicle; n = 5), compared to 10-11-month-old 5xFAD male and female, vehicle control (5xFAD, vehicle) (n = 5), and 10-11 month-old 5xFAD male and female mice, treated with ABT-737 (5xFAD, ABT) (n = 7). One-way ANOVA was used for the analyses. Locomotor activity and anxiety were assessed in an open field test and (a) distance moved; (b) mean velocity; (c) cumulative duration in the center zone; (d) frequency entering the center zone; and (e) latency until center entry were recorded, P(WT vs. AD vehicle) = 0.0100, P(WT vs. AD ABT) = 0.8283, P(AD vehicle vs. AD ABT) = 0.0125. Data are presented as mean values +/- SEM; *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Fig. 6 Mass cytometry gating strategy.

In the analysis workflow, i) we initially selected CD11b as a stable signal over time and applied gating. ii) Subsequently, gating was performed based on event length and Gaussian residual parameters. iii) To eliminate bead-related interference, the 140Ce bead channel was utilized for gating. iv) Next, live single cells were identified using cisplatin in the 195Pt channel, and iridium DNA labeling in the 193Ir channel, and v) the single-cell population was further refined using two iridium channels. vi) Finally, gating was applied to isolate CD45-positive cells.

Extended Data Table 1 CyTOF panel
Extended Data Table 2 CyTOF small panel

Supplementary information

Reporting Summary

Supplementary Table

Supplementary Table 1: differentially expressed genes of each cluster.

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Rachmian, N., Medina, S., Cherqui, U. et al. Identification of senescent, TREM2-expressing microglia in aging and Alzheimer’s disease model mouse brain. Nat Neurosci 27, 1116–1124 (2024). https://doi.org/10.1038/s41593-024-01620-8

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