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Sex-dependent APOE4 neutrophil–microglia interactions drive cognitive impairment in Alzheimer’s disease

Nature Medicine volume 30, pages 2990–3003 (2024)Cite this article

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Abstract

APOE4 is the strongest genetic risk factor for Alzheimer’s disease (AD), with increased odds ratios in female carriers. Targeting amyloid plaques shows modest improvement in male non-APOE4 carriers. Leveraging single-cell transcriptomics across APOE variants in both sexes, multiplex flow cytometry and validation in two independent cohorts of APOE4 female carriers with AD, we identify a new subset of neutrophils interacting with microglia associated with cognitive impairment. This phenotype is defined by increased interleukin (IL)-17 and IL-1 coexpressed gene modules in blood neutrophils and in microglia of cognitively impaired female APOE ε4 carriers, showing increased infiltration to the AD brain. APOE4 female IL-17+ neutrophils upregulated the immunosuppressive cytokines IL-10 and TGFβ and immune checkpoints, including LAG3 and PD-1, associated with accelerated immune aging. Deletion of APOE4 in neutrophils reduced this immunosuppressive phenotype and restored the microglial response to neurodegeneration, limiting plaque pathology in AD mice. Mechanistically, IL-17F upregulated in APOE4 neutrophils interacts with microglial IL-17RA to suppress the induction of the neurodegenerative phenotype, and blocking this axis supported cognitive improvement in AD mice. These findings provide a translational basis to target IL-17F in APOE ε4 female carriers with cognitive impairment.

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Fig. 1: Sex-dependent neutrophil degranulation signaling in APOE4 human microglia.
Fig. 2: Sex-dependent neutrophil gene patterns during cognitive impairment.
Fig. 3: Immunosuppressive IL-17 neutrophils infiltrate the APOE4 female AD brain.
Fig. 4: Deletion of APOE4 in neutrophils reduces their suppressive phenotype in AD mice.
Fig. 5: IL-17F license neutrophil brain entry and impairs MGnD response.
Fig. 6: IL-17F–IL-17RA blockade restores MGnD response and cognition.

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Data availability

Our snRNA-seq data have been deposited into the Gene Expression Omnibus (GEO) GSE260461. Our Smartseq2 RNA-seq data that support the findings of this study have been deposited into GEO under SuperSeries GSE239603, GSE243750 and GSE262632. The raw data from Olah et al.39 are available through the AD Knowledge Portal (https://www.synapse.org/#!Synapse:syn21438358). Whole-blood RNA-seq data of MCSA are available through the AD Knowledge Portal (https://www.synapse.org/#!Synapse:syn22024536). Pre-processed scRNA-seq data from Schulte-Schrepping et al.22 were downloaded from https://beta.fastgenomics.org/datasets/detail-dataset-1ad2967be372494a9fdba621610ad3f3#Files. The MGnD signature is available in Supplementary Table 6.

Code availability

The code for analysis and generating the figures is available on GitHub at https://github.com/The-Butovsky-Lab/Neutrophils_manuscript_code_availability.

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Acknowledgements

We thank the NeuroTechnology Studio at Brigham and Women’s Hospital for providing Zeiss LSM710 confocal microscope and Leica DMi8 microscope access and L. Ding for consultation on data acquisition and data analysis; B. Hyman and MADRC for providing AD brain samples; The Broad Institute of MIT and Harvard for Smartseq2 RNA-seq; and M. Jucker for providing APP/PS1 mice; and M. Gunzer and P. Nigrovic for providing Ly6g-CreTdt mice. This study was supported by the Cure Alzheimer’s Fund (O.B. and M.B.J.); BrightFocus Foundation 2020A016806 (O.B.); National Institutes of Health (NIH) National Institute on Aging (NIA) R01AG051812 (O.B.), R01AG054672 (O.B.), R01AG075509 (O.B.), R21AG076982 (O.B.), R01AG080992 (O.B.); NIH National Eye Institute R01EY027921 (O.B.); NIH National Institute of Neurological Disorders and Stroke R01NS088137 (O.B.), R21NS104609 (O.B.), R21NS101673 (O.B.); NIH National Institute of General Medical Sciences R01GM132668 (O.B.); Nancy Davis Foundation innovative Award (O.B.). Alzheimer’s Association research fellowship AARF-21-846786 (N.R.); National Multiple Sclerosis Society FG-2108-38372 (W.B.); Department of Defense MS210257 (W.B.); NIH NIA K23AG062750 (H.-S.Y.); NIH NIA P30 AG066519 (M.B.J.); U19 AG06970101 (M.B.J.); NIH NIA P01AG026694 (R.A.S.). The Davis Memory and Aging Cohort is supported by the Shelby Cullom Davis Charitable Fund through their generous philanthropic gift to R.A.S.

Author information

Author notes

  1. These authors contributed equally: Neta Rosenzweig, Kilian L. Kleemann, Thomas Rust.

Authors and Affiliations

  1. Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Neta Rosenzweig, Kilian L. Kleemann, Madison Carpenter, Madeline Grucci, Michael Aronchik, Isabel Valenbreder, Joya Cooper-Hohn, Malvika Iyer, Rajesh K. Krishnan, Kisha N. Sivanathan, Wesley Brandão, Taha Yahya, Ana Durao, Zhuoran Yin, Roni Nowarski, Howard L. Weiner & Oleg Butovsky

  2. Institute for Clinical Chemistry and Clinical Pharmacology, University Hospital Bonn, Bonn, Germany

    Kilian L. Kleemann

  3. Department of Biomedical Sciences, Section Molecular Neurobiology, University Medical Center Groningen, Groningen, The Netherlands

    Thomas Rust, Nieske Brouwer & Bart J. L. Eggen

  4. Gene Lay Institute of Immunology and Inflammation, Brigham and Women’s Hospital, Mass General Hospital and Harvard Medical School, Boston, MA, USA

    Kisha N. Sivanathan, Roni Nowarski, Howard L. Weiner & Oleg Butovsky

  5. Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA

    Kisha N. Sivanathan & Roni Nowarski

  6. Department of Neurobiology & Behavior, University of California, Irvine, Irvine, CA, USA

    Jean Paul Chadarevian, Hayk Davtyan & Mathew Blurton-Jones

  7. Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, Irvine, CA, USA

    Jean Paul Chadarevian, Hayk Davtyan & Mathew Blurton-Jones

  8. Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, USA

    Jean Paul Chadarevian, Hayk Davtyan & Mathew Blurton-Jones

  9. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Michael J. Properzi, Hyun-Sik Yang & Reisa A. Sperling

  10. Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Howard L. Weiner, Hyun-Sik Yang & Reisa A. Sperling

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  1. Neta Rosenzweig
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Contributions

N.R. and K.L.K. designed the study, analyzed and interpreted the data under the supervision of O.B. K.L.K., T.R., M.C. and I.V. performed RNA-seq data analysis under the supervision of N.R., O.B. and B.J.L.E. N.R., M.G., M.A., A.D. and J.C.H. performed neutrophil isolation from human blood samples. N.B. and T.R. performed the isolation and sequencing of human microglia nuclei under the supervision of B.J.L.E. N.R., M.G., M.A. and T.Y. performed mouse neutrophil and microglia isolation. R.K.K. performed FACS sorting; N.R., M.I., M.G. and J.C.H. performed immunohistochemistry and imaging. M.I., M.G. and N.R. performed image analysis. T.Y., supervised by H.L.W., provided expertise and assisted M.A. to perform stereotactic injections to mouse brain. R.N. and K.N.S. provided expertise in RNAscope, performed by K.N.S. and N.R. J.P.C. and H.D. generated the G795A knock-in iPS cell line, differentiated microglia and generated 5x-hCSF1 mice under the supervision of M.B.J. J.C.H. performed mouse i.p. injections and behavioral testing. N.R. and W.B. performed Cytek Aurora flow cytometry. Z.Y. assisted with the generation of APP/PS1:IL17raMGKO mice. Recruitment of human patients was the responsibility of R.S., H.-S.Y. and M.P. The manuscript was conceptualized and written by N.R. and K.L.K. under the supervision of O.B.

Corresponding author

Correspondence to Oleg Butovsky.

Ethics declarations

Competing interests

O.B., N.R. and K.L.K. are inventors of a patent licensed by Brigham and Women’s Hospital for the use of IL-17F inhibitors for the treatment of neurodegenerative diseases. O.B. declares collaboration with GSK and Regulus Therapeutics; research funding from Sanofi and GSK; honoraria for lectures; and consultancy for UCB, Camp4, Ono Pharma USA and General Biophysics. All other authors declare no competing interests.

Peer review

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Nature Medicine thanks Shahram Oveisgharan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jerome Staal, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Sex is the strongest contributor to microglial variance.

a, FACS gating strategy for the enrichment of Olig2–NeuN– nuclei sorted for single-cell RNA-seq. b, UMAP showing 13 myeloid clusters, identifying cluster 13 as CAMs. c, Density plot showing enrichment of the CAM genes, CD163 and MRC1, in cluster 13, whereas the microglial specific gene P2RY12 was enriched in clusters 1–12. d, Dot plot showing the average expression of key CAM specific genes, MS4A7, LYVE1, CD163 and MRC1 and P2RY12 in clusters 1–13. e, PCA plotting sample variance according to sex, cognitive status, and APOE ε3/3 and APOE ε3/4 genotype. f, Heatmap of DEGs characterizing each microglial cluster. g, Dot plot showing expression of selected genes characterizing MG1, MG2, MG6, MG8, MG7 and MG9.

Extended Data Fig. 2 Correlation of MGnD response with tauopathy.

a, Bar graphs showing the proportions of each microglial cluster in male and female samples according to cognitive status and APOE genotype. b, Dot plot and bar graph showing the proportions and fold change of the homeostatic clusters MG1+MG2, the intermediate clusters MG6+MG8 and the MGnD clusters MG7+MG9+MG11, in female AD APOE ε3/3 and APOE ε3/4 samples. Representative images of p-tau staining (c) and quantification of tau score (d,e) in samples used for snRNA-seq analysis, including female control APOE ε3/3 (n = 5) and APOE ε3/4 (n = 6); female AD APOE ε3/3 (n = 5) and APOE ε3/4 (n = 3); male control APOE ε3/3 (n = 6) and APOE ε3/4 (n = 4); and male AD APOE ε3/3 (n = 5) and APOE ε3/4 (n = 5). Correlation of clusters MG7, MG8 and MG9 with tau score in male samples (f-h). Representative images of Aβ staining (i) and quantification of Aβ score (j,k) in samples used for snRNA-seq analysis, including female control APOE ε3/3 (n = 4) and APOE ε3/4 (n = 6); female AD APOE ε3/3 (n = 5) and APOE ε3/4 (n = 5/group); male control APOE ε3/3 (n = 5) and APOE ε3/4 (n = 4); and male AD APOE ε3/3 (n = 5) and APOE ε3/4 (n = 5). Correlation of clusters MG7, MG8 and MG9 with Aβ score in female samples (l-n). Correlation of clusters MG7, MG8 and MG9 with Aβ score in male samples (o-q). Data were analyzed by one-way ANOVA and are presented as mean ± s.e.m. Fisher’s LSD test for multiple comparisons (d,e,j,k). Two-sided Pearson correlation with BH corrections (d-h,l-q).

Extended Data Fig. 3 Sex-dependent neutrophil gene patterns during cognitive impairment.

a, Correlation of explanatory variables in dataset (Kendals Tau correlation) in subjects described in Fig. 2a,b (n = 94). b, Boxplot showing distribution of samples according to age for HC (n = 47), MCI (n = 38) and AD (n = 9). Boxplots showing MMSE (c) and QRDS (d) scores per patient according to clinical diagnosis HC (n = 47), MCI (n = 38) and AD (n = 9). One-way ANOVA. Data are shown using box-whisker plots (box, median and 25th to 75th percentile; whiskers; minimum to maximum). e, Volcano plot showing DEGs for comparisons between MCI (n = 39) and HC (n = 47) (top half), and AD (n = 9) vs MCI (n = 38) (bottom half). Likelihood Ratio Test with BH correction. (P < 0.05) f, Enriched pathways in APOE ε3/4 vs APOE ε3/3 carriers according to clinical diagnosis and sex (HC:F:33, n = 12; HC:F:34 n = 13, HC:M:33, n = 13; HC:M:34 n = 14. fGSEA permutation-based test. (*P < 0.05) g, DEGs heatmap of male HC and MCI patients carrying APOE ε3/3 and APOE ε3/4 genotypes. Male HC APOE ε3/3 (n = 12), Male HC APOE ε3/4 (n = 12), Male MCI APOE ε3/3 (n = 9), Male MCI APOE ε3/4 (n = 8). h, Volcano plots of DEGs comparing male MCI vs HC neutrophils in APOE ε3/4 carriers. Likelihood Ratio Test with BH correction. i, IPA comparison analysis of MCI vs HC male samples in APOE ε3/3 and APOE ε3/4 carriers (P < 0.05). One-sided Fisher′s Exact test with BH corrections (h,i). j, Upstream regulators identified using IPA, enriched in APOE ε3/4 male carriers according to HC and MCI diagnosis. k, Custom pathway enrichment score for DEGs comparing CI vs HC patients, described in Fig. 2a, b, according to mature neutrophil marker gene list from public snRNA-seq dataset by Schulte-Schrepping et al.22. HC (n = 47), CI (n = 47), Padj < 0.05. Permutation-based test with BH correction. l, PCA of whole blood RNA-seq data from MCSA dataset and cell type prediction. m, Deconvolution of MCSA samples according to immune cell type (n = 422). n, Pathway enrichment analysis of DEGs in HC females APOE ε3/4 vs APOE ε3/3 (n = 72) and males (n = 78), and comparison of MCI vs HC samples in APOE ε3/3 males (n = 54) and females (n = 61).

Extended Data Fig. 4 APOE4 accelerates immune aging in female neutrophils.

FACS gating strategy of blood CD15+CD66b+ (a) and MPO+CitH3+ (b) neutrophils. c, FACS quantification of MPO+CitH3+ neutrophils in MCI APOE ε3/4 (n = 6) vs MCI APOE ε3/3 (n = 5) female carriers. Two-tailed Student’s t-test. Data were shown as mean ± s.e.m. d, Upstream regulators identified using IPA, enriched MCI vs HC APOE ε3/4 female carriers. One-sided Fisher’s exact test with BH correction. e, FACS quantification of TGFβ+CD66b+ and IL-10+CD66b+ neutrophils in female MCI APOE ε3/3 (n = 10) and ε3/4 carriers (n = 11). Two-tailed Student’s t-test. Data were shown as mean ± s.e.m. f, Allocation of HC female samples according to three age groups (n = 8/Old and Older groups; n = 7/Oldest). Data were analyzed by one-way ANOVA and are presented as mean ± s.e.m. Fisher’s LSD test for multiple comparisons. g, Heatmap of DEGs in HC female samples according to three age groups. Normalized counts of TGFBR1 (h), IGF1 (i) and CD274 (j) in HC female neutrophil samples according to three age groups (n = 8/Old and Older groups; n = 7/Oldest). Data were analyzed by one-way ANOVA and are presented as mean ± s.e.m. Fisher’s LSD test for multiple comparisons. k, Two-sided Pearson correlation of CD274 normalized counts and age of female HC neutrophil samples. 95% confidence interval, BH correction. l, CD38 normalized counts in HC female neutrophil samples according to three age groups (n = 8/Old and Older groups; n = 7/Oldest). Data were analyzed by one-way ANOVA and are presented as mean ± s.e.m. Fisher’s LSD test for multiple comparisons. m, Heatmap of aging associated genes, according to pathway analysis described in Extended Data Fig. 3f, in HC female APOE ε3/3 and APOE e3/4 neutrophils (n = 11-12/group; P < 0.05). n, Allocation of HC APOE ε3/3 and APOE e3/4 female neutrophil samples according to two age groups: Old ε3/3 (n = 5), Older ε3/3 (n = 6), Old ε3/4 (n = 7), and ε3/4 (n = 5). o, Heatmap of DEGs in HC female samples according to two age groups comparing Old ε3/3 (n = 5), Older ε3/3 (n = 6), Old ε3/4 (n = 7), and ε3/4 (n = 5). Normalized counts of BACE1 (p) and TYROBP (q) in HC female neutrophil samples according to two age groups comparing Old ε3/3 (n = 5), Older ε3/3 (n = 6), Old ε3/4 (n = 7), and ε3/4 (n = 5). Two-sided Pearson correlation of TGFBR2 (r) and IGF1 (s) normalized counts and age of female HC neutrophil samples plotted for APOE ε3/3 and APOE ε3/4 carriers separately. BH corrections, 95% confidence interval. t, Heatmap of DEGs in HC female neutrophils comparing APOE ε3/3 (n = 8), APOE ε2/3 and APOE e3/4 (n = 12/group). u, Volcano plot of DEGs comparing APOE ε3/4 vs APOE ε2/3 HC female neutrophils (n = 12/group). v, Enriched pathways in APOE ε3/4 vs APOE ε2/3 HC female neutrophils, determined by IPA.

Extended Data Fig. 5 Machine learning of neutrophil transcriptome identified IL18R signaling as a biomarker of cognitive impairment.

a, Top-15 selected genes for prediction using selectKbest analysis of DEGs comparing CI vs HC samples. b, AUC for Random-Forest Predictor method predicting MCI and AD diagnosis based on all samples (AUC = 0.92). c, Confusion matrix illustrating distribution of predictive clinical assignment and true clinical classification (n = 24). d, Linear regression analysis for normalized counts of IL18R1 across female HC (n = 23) and MCI (n = 21) neutrophils samples (R = 0.51, P = 0.00042). 95% confidence interval. e, Heatmap of DEGs in MCI female neutrophils comparing APOE ε3/3 (n = 2), APOE ε2/3 (n = 9) and APOE ε3/4 (n = 5). f, Volcano plot of DEGs comparing APOE ε3/4 vs APOE ε2/3 MCI female neutrophils (n = 5–9/group). Likelihood Ratio Test with BH correction. g, Enriched pathways in APOE ε3/4 vs APOE ε2/3 MCI female neutrophils, determined by IPA. h, IPA scatter-plot comparing female APOE ε2/3 HC female neutrophils diagnosed with hypertension (n = 3) vs no-hypertension (n = 9). i, IPA scatter-plot comparing female APOE ε3/3 HC female neutrophils diagnosed with hypertension (n = 3) vs no-hypertension (n = 5). j, IPA scatter-plot comparing female APOE ε3/4 HC female neutrophils diagnosed with hypertension (n = 3) vs no-hypertension (n = 8).

Extended Data Fig. 6 Gating strategy for multicolor flow cytometry.

a, Plots of MFI for IL17, IL10 and TGFβ1 measured in HC APOE ε3/3 (n = 5), MCI APOE ε3/3 (n = 6), HC APOE ε3/4 (n = 6), and MCI APOE ε3/4 (n = 7) female samples. b, Gating strategy used for multicolor flow cytometry.

Extended Data Fig. 7 Immunosuppressive IL-17+IL-18R1+ neutrophils are enriched in APOE4 female MCI carriers.

a, UMAP of female neutrophils determined by multicolor flow cytometry analysis of MCI patients and neutrophil cluster distribution across APOE ε3/3 and ε3/4 genotypes (n = 4–8/group). b, Donut plots show the percentage of each cluster within each condition. c, Heatmap of normalized Mean Fluorescence Intensity (MFI) for each protein marker in annotated neutrophil clusters. d, UMAP of major identified subclusters of neutrophils from dataset by Schulte-Schrepping et al.22. e, GSEA pathway enrichment according to cluster marker genes (P < 0.05). Permutation-based test. f, Marker genes annotated per neutrophil cluster (Padj < 0.05). g, Circos plot demonstrating predicted top regulatory interactions between neutrophil ligands (this study) and microglia receptors39 upregulated in female CI APOE ε3/4 patients. h, Confocal imaging of degranulating H3Cit+ neutrophils (green) coexpressing IL-17 (red) at sites of Ab plaques in the brains of female AD APOE ε3/4 patients, replicated in two independent cohorts. Scale bar, 20μm. i, Confocal images of RNAscope for IL17F (red) and CEACAM8 (CD66b; Cyan) and immunostaining for Aβ-plaques (green) in AD brain sections from 3 different female APOE ε3/4 carriers. Scale bar, 50μm. j, High magnification confocal images of RNAscope for IL17F (red) and CEACAM8 (Cyan) and DAPI (blue). Scale bar, 5μm. Staining was confirmed in three independent experiments. k, Experimental design depicting microglial repopulation in 5xFAD-hCSF1 pups with human APOE ε3/3 iMGs, and brain injection of human neutrophils at 3.5 months of age. Brains were analyzed the next day using immunohistochemistry. Figure created using Biorender.com. Confocal images of LGALS3+IBA+ iMGs in 5xFAD-hCSF1 brains following the injection with MCI female APOE ε3/4 and APOE ε3/3 neutrophils (l) and quantification of Lgals3 immunoreactivity (n = 6/group) (m). Two-tailed student’s t-test. Data were shown as mean ± s.e.m.

Extended Data Fig. 8 APOE3 restrains neutrophil hyperactivation related aging signature in mice.

a, FACS gating strategy of CD11b+Ly6cintLy6g+ neutrophils in APOE3 and APOE4 mice, and of CD11b+Ly6cintLy6g+/Tdt neutrophils in APOE3NTKO and APOE4NTKO mice; and qPCR validation of neutrophils specific deletion of APOE3 or APOE4 in mice (n = 5/group). b, Volcano plot of DEGs of APOE4 vs APOE3 splenic neutrophils on APP/PS1 background (n = 4/group). c, Volcano plot of DEGs of APOE3NTKO vs APOE3 splenic neutrophils on APP/PS1 background (n = 4/group). d, Enriched pathways in APOE3NTKO vs APOE3 splenic neutrophils on APP/PS1 background (n = 4/group). e, Heatmap of aging genes, identified in pathway analysis described in (d), comparing APOE3, APOE3NTKO, APOE4 and APOE4NTKO mice on APP/PS1 background (n = 4/group). f, IPA pathway enrichment comparing APOE3NTKO vs APOE3, APOE4 vs APOE3 and APOE4NTKO vs APOE4 neutrophils isolated from 12-month-old female mice on APP/PS1 background. g, Transcription factor enrichment analysis comparing APOE3NTKO vs APOE3 neutrophils isolated from 12-month-old female mice on APP/PS1 background, illustrating IL-17F downstream signaling pathway. gating strategy (h) and quantification (i) of Foxp3+IL17F+ and Foxp3+TGFβ1+ neutrophils in APOE3NTKO (n = 6) vs APOE3 (n = 5) mice, and APOE4NTKO (n = 5) vs APOE4 (n = 6). j, Heatmap of DEGs in blood neutrophils isolated from APOE4 vs APOE3 12-month-old female mice on APP/PS1 background (n = 3–4/group), and genes related to NETs and senescence pathways. k, Scatter-plot of IPA comparing blood neutrophils isolated from APOE4 vs APOE3 12-month-old female mice on APP/PS1 background. l, Scatter-plot of IPA comparing blood neutrophils isolated from APOE4NTKO vs APOE4 12-month-old female mice on APP/PS1 background (n = 4/group). m, Volcano plot of DEGs in blood neutrophils isolated from APOE3NTKO vs APOE3 12-month-old female mice on APP/PS1 background (n = 3–5/group). n, Scatter-plot of IPA comparing blood neutrophils isolated from APOE3NTKO vs APOE3 12-month-old female mice on APP/PS1 background (n = 3–5/group). FACS gating strategy of MPO+CitH3+ neutrophils (o) and quantification (p) in APOE3 (n = 5), APOE4 (n = 6) and APOE4NTKO (n = 5) 12-month-old female mice on APP/PS1 background. One-way ANOVA. Data were presented as mean ± s.e.m.

Extended Data Fig. 9 APOE4 neutrophils and IL-17 impair MGnD response in AD mice.

a, Schematics of experimental design describing mouse strains used to sort brain microglia from female mice at 12 months of age. Figure created using Biorender.com. b, Heatmap of DEGs from 12-month-old female APOE3, APOE3NTKO, APOE4 and APOE4NTKO mice on APP/PS1 background (n = 4/group, P < 0.05). c, Volcano plot of DEGs comparing microglia isolated from APOE3NTKO and APOE3 mice on APP/PS1 background (n = 4/group; P < 0.05). d, Volcano plot of DEGs comparing microglia isolated from APOE4NTKO and APOE4 mice on APP/PS1 background (n = 4/group; P < 0.05). e, Confocal images of plaque-associated IBA1 microglia coexpressing the MGnD marker Clec7a in 12-month-old female APOE3, APOE3NTKO, APOE4 and APOE4NTKO mice on APP/PS1 background. Scale bar, 50μm. f, Quantification of Ab-plaque (HJ3.4B) area per ROI in APOE3 (n = 7), APOE3NTKO (n = 6), APOE4 (n = 7) and APOE4NTKO (n = 7) mice. g, Quantification of Clec7a immunoreactivity per plaque in APOE3 (n = 7), APOE3NTKO (n = 6), APOE4 (n = 7) and APOE4NTKO (n = 7) mice. h, Y-maze results for spontaneous alternations in APOE4 (n = 5), 5xFAD:APOE4 (n = 6) and 5xFAD:APOE4NTKO (n = 6) 9-month-old female mice. Data were analyzed by one-way ANOVA and are presented as mean ± s.e.m. Fisher’s LSD test for multiple comparisons. i, Il17ra normalized count in Clec7a– and Clec7a+ microglia sorted from APP/PS1 mice and non-transgene littermates3. one-way ANOVA and are presented as mean ± s.e.m. j, FACS gating strategy for neutrophils infiltrating the brains of female Ragγc mice following the injection of IL17F recombinant protein vs PBS. k, Microglial overlapping DEGs comparing APP/PS1:APOE3 vs APP/PS1:APOE3NTKO; APP/PS1:APOE4NTKO vs APP/PS1:APOE4; and APP/PS1:Il17raMGKO vs APP/PS1:Il17raWT mice. Heatmap represents overlapping DEGs. l, MGnD enrichment signature score in APP/PS1:Il17raMGKO microglia based on dataset by Kraseman et. al.3 (n = 6/group). Permutation-based test with BH correction. m, Scatter-plot comparing microglial DEGs in APP/PS1:APOE4NTKO vs APP/PS1:APOE4, and APP/PS1:Il17raMGKO vs APP/PS1:Il17raWT mice. Likelihood Ratio Test with BH corrections (b,c,d,m).

Extended Data Fig. 10 Gating strategy and effect of IL17F blockade in 5xFAD:APOE4 mice.

FACS gating strategy of splenic T cells (a) and quantification (b) following IL17 blockade (n = 5/group). c, FACS gating strategy of choroid plexus neutrophils and quantification following IL17 blockade (n = 5/group). d, FACS gating strategy of meningeal neutrophils and quantification following IL17 blockade (n = 5/group). e, FACS gating strategy of brain neutrophils following IL17 blockade.

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Rosenzweig, N., Kleemann, K.L., Rust, T. et al. Sex-dependent APOE4 neutrophil–microglia interactions drive cognitive impairment in Alzheimer’s disease. Nat Med 30, 2990–3003 (2024). https://doi.org/10.1038/s41591-024-03122-3

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