Abstract
Exercise’s protective effects in Alzheimer’s disease (AD) are well recognized, but cell-specific contributions to this phenomenon remain unclear. Here we used single-nucleus RNA sequencing (snRNA-seq) to dissect the response to exercise (free-wheel running) in the neurogenic stem-cell niche of the hippocampal dentate gyrus in male APP/PS1 transgenic AD model mice. Transcriptomic responses to exercise were distinct between wild-type and AD mice, and most prominent in immature neurons. Exercise restored the transcriptional profiles of a proportion of AD-dysregulated genes in a cell type-specific manner. We identified a neurovascular-associated astrocyte subpopulation, the abundance of which was reduced in AD, whereas its gene expression signature was induced with exercise. Exercise also enhanced the gene expression profile of disease-associated microglia. Oligodendrocyte progenitor cells were the cell type with the highest proportion of dysregulated genes recovered by exercise. Last, we validated our key findings in a human AD snRNA-seq dataset. Together, these data present a comprehensive resource for understanding the molecular mediators of neuroprotection by exercise in AD.
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Data availability
snRNA-seq data are available at the Sequence Read Archive with accession no. SUB13470069 and BioProject accession no. PRJNA976296 and the Broad Institute Single Cell Portal with accession no. SCP2229 and PIN 3JBO34XYYU. The human single-nucleus data from the Knight Alzheimer Disease Research Center accessed in the present study are found in the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site with accession no. NG00108. All snRNA-seq analysis results are provided as Supplementary Data. Source data are provided with this paper.
Code availability
The relevant code can be found in Supplementary Code 1.
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Acknowledgements
This work was supported by the NIH (grant nos. NS117694, AG062904, AG064580 and AG072054 to C.D.W., HL140187 to N.R.T., AG066171 to K.V.K., AG057777 and AG072464 to O.H. and NS118146 and NS127211 to B.A.B.); the Cure Alzheimer’s Fund, an Alzheimer Association Research Grant, a SPARC Award from the McCance Center for Brain Health, the Hassenfeld Clinical Scholar Award and the Claflin Distinguished Scholar Award to C.D.W.; the BIDMC 2023 Translational Research Hub Spark Grant Award to B.A.B.; and the MGH Fund for Medical Discovery (grant no. 2024A022508) to J.F.d.R. C.D.W. is an ADDF-Harrington Scholar. O.H. is an Archer Foundation Research Scientist. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank all members of C.D.W. and N.R.T.’s labs for helpful discussions. We thank the Neurobiology Imaging Facility at Harvard Medical School, Boston, MA, for their support with the RNAscope, and the Center of Excellence for Molecular Imaging at Mass General Brigham for access to the Nikon Ti2 Microscope with Yokogawa W1 and SoRa Module. We thank T. Kafri, Director of the UNC Lenti-shRNA Core Facility, for helpful advice.
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R.L. and P.S. contributed equally. M.L.L., R.L., P.S., R.S.G., P.K., J.F.d.R. and N.R.T. performed the bioinformatic analyses. J.F.d.R., L.M., M.A.I., S.V., M.R.I. and C.D.W. performed and analyzed the in vivo experiments. J.F.d.R., M.A.I, S.A., K.V.K. and C.D.W. performed and analyzed the in vitro experiments. L.M., J.F.d.R., M.A.I., P.S., R.L., G.M.G., N.B.H., K.V.M.-F., E.B.H., K.H., S.M., S.S. and P.K. performed tissue analysis. S.V. and R.D.P. performed the nuclei isolation. R.D.P. prepared the libraries for sequencing. L.B., J.F.d.R., P.K., P.S., O.H. and B.A.B. performed the human data analysis. J.F.d.R., L.M., M.A.I., S.V. and R.L. contributed to the experimental design. N.R.T. supervised the bioinformatic analysis. C.D.W. directed the research. J.F.d.R., R.L., N.R.T. and C.D.W. co-wrote the paper with assistance from all other authors.
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C.D.W. is an academic co-founder and consultant for Aevum Therapeutics and has a financial interest in Aevum Therapeutics, a company developing drugs that harness the protective molecular mechanisms of exercise to treat neurodegenerative and neuromuscular disorders. Her interests were reviewed and are managed by the MGH and Mass General Brigham in accordance with their conflict-of-interest policies. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Neurogenic niche response to exercise and AD at the single-nuclei level.
a, MWM latency to reach the escape platform in acquisition (Three-way ANOVA, Exercise n.s. P = 0.0964, Genotype ****P < 0.0001, Exercise x genotype n.s. P = 0.2664, Exercise x genotype x time *P = 0.0263). b, Acquisition 24 h probe trial (Two-way ANOVA, Exercise **P = 0.0074, Genotype n.s. P = 0.2919, Exercise x genotype n.s. P = 0.8698), and c, 24 h probe trial in reversal (Two-way ANOVA, Exercise **P = 0.0067, Genotype n.s. P = 0.3812, Exercise x genotype n.s. P = 0.904). d, Daily running activity (Two-way repeated measures ANOVA, Time ****P < 0.0001, Genotype n.s. P = 0.2706, Time x genotype n.s. P = 0.3457). e, Open field (OPF) (Two-way ANOVA, Exercise n.s. P = 0.8558, Genotype n.s. P = 0.2011, Exercise x genotype n.s. P = 0.6738), f, Spontaneous alternation behavior (SAB) (Two-way ANOVA, Exercise n.s. P = 0.1004, Genotype n.s. P = 0.1187, Exercise x genotype n.s. P = 0.2082), and g, Contextual fear conditioning (CFC) test in all mice (Two-way repeated measures ANOVA, Context ****P < 0.0001, Group n.s. P = 0.438, Context x group n.s. P = 0.4465, followed by Sidak’s multiple comparisons WT-Sed AvsB ***P = 0.0002, WT-Run AvsB ***P = 0.0004, APP/PS1-Sed AvsB n.s. P = 0.0519, APP/PS1-Run AvsB n.s. P = 0.1497). h, Number of cells per cell cluster within each group. i, PCA analysis of pseudobulk data from all samples. j, UMAP representation of marker genes expression in different clusters. Color represents expression level according to the scale bar on the right. k, Percentage of cells per cell cluster within each group. l, The scatter plot shows regulator genes based on GeneWalk analysis observed in WSvsAS. Each dot represents a regulator gene, and the color represents the cell cluster. For all behavior experiments WT-Sed n = 12, WT-Run n = 12, APP/PS1-Sed n = 9, APP/PS1-Run n = 9 (a-g), for snRNAseq WT-Sed, WT-Run, APP/PS1-Run n = 5, APP/PS1-Sed n = 4 (h and k). Data represent the mean ± s.e.m. of biologically independent samples.
Extended Data Fig. 2 Representative enriched pathways across different cell types.
GSEA pre-ranked on gene ontology results for all cell types between the ASvsAR and WSvsAS in neuronal cells (a) and glia cells (b). The representative enriched pathways were selected based on FDR < 0.25.
Extended Data Fig. 3 Remodeling of adult hippocampal neurogenesis in exercise and AD.
a, Quantification of BrdU+NeuN+ adult-born neurons in the dorsal and ventral DG and representative higher magnification confocal images with anti-BrdU (green) and NeuN (red) from WT and APP/PS1, sedentary or running, mice. Scale bar, 50 µm. n = 6 per group. Two-way ANOVA, Dorsal DG: Exercise ****P < 0.0001, Genotype *P = 0.0269, Exercise x genotype n.s. P = 0.058, Ventral DG: Exercise *P < 0.0101, Genotype n.s. P = 0.1629, Exercise x genotype n.s. P = 0.305. b, Quantification of DCX+ cells in the dorsal and ventral DG from WT and APP/PS1, sedentary or running, mice. n = 6 per group. Two-way ANOVA, Dorsal DG: Exercise n.s. P = 0.0914, Genotype **P = 0.0013, Exercise x genotype n.s. P = 0.1552, Ventral DG: Exercise **P = 0.0069, Genotype ****P < 0.0001, Exercise x genotype n.s. P = 0.1426. c, Heatmap shows the normalized mean expression (z-score) of neurogenesis-related genes reported by Hochgerner et al. in our dataset. d, UMAP representations of early neuronal marker genes expression. Color represents expression level according to the scale bar on the right. e, f, Scatter plots showing the correlation between AD and exercise effects in Neuroblast I (e) and II (f). Each dot represents a statistically significant DEG in AD (WSvsAS). Dots with black borders represent statistically significant DEGs with exercise in AD mice (ASvsAR). The color gradient illustrates the recovery score (|logFC ASvsAR | ). The dot size represents the fraction of non-zero count nuclei in the AR group. g, Dot plots showing Immature Neurons’ recDEGs for all neurogenic cell types. In each, the hue and size of the dot represent the mean expression and fraction of non-zero count nuclei, respectively. Data represent the mean ± s.e.m. (a and b) of biologically independent samples.
Extended Data Fig. 4 Immature Neurons recDEG Atpif1 knock-down disrupts neuronal proliferation and differentiation in vitro.
a-d, Primary cortical neurons were transduced with LV-shRNA for five days. PrestoBlue HS normalized cell viability (a, one-way ANOVA followed by Dunnett’s against shCtrl: shSlc25a4 n.s. P = 0.9967, shAtp6v0c n.s. P = 0.0573, shAtpif1 *P = 0.013), confirmation of the gene knock-down (KD) by qPCR (b, two-way ANOVA, KD ****P < 0.0001, KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl ****P < 0.0001), and gene expression of neuronal markers in response to Atp6v0c (c, two-way ANOVA, KD ****P < 0.0001, KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl: Neurod1 ****P < 0.0001, Dcx **P = 0.0017, Tubb3 n.s. P = 0.8694, Map2 ***P = 0.0003, Dlg4 **P = 0.0071, Syn1 **P = 0.0037, Bax *P = 0.028) and Atpif1 knock-down (d, two-way ANOVA, KD ****P < 0.0001, KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl: Neurod1, Dcx, Map2, Dlg4, and Syn1 ****P < 0.0001, Tubb3 **P = 0.0046, Bax **P = 0.0022). shAtp6v0c and shSlc25a4 n = 6, shAtpif1 n = 4 (a), shAtp6v0c and shAtpif1 n = 6, shSlc25a4 n = 5 (b-d). e, Representative confocal images of EdU (red) and Nestin (green) staining of embryonic neural stem and progenitor cells transduced with LV-shRNA and maintained in proliferating media for 5 days. Scale bar, 50 µm. f-h, Neurospheres were transduced with LV-shRNA and maintained in differentiation media for five days. Confirmation of the gene knock-down by qPCR (f, two-way ANOVA, KD ****P < 0.0001, KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl ****P < 0.0001), and gene expression of neuronal markers in response to Atp6v0c (g, two-way ANOVA, KD n.s. P = 0.0877, KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl: Neurod1 and Tubb3 ****P < 0.0001, Dcx n.s. P = 0.3704, Map2 n.s. P = 0.0826, Dlg4 n.s. P = 0.1893, Syn1 n.s. P = 0.1901) and Atpif1 knock-down (h, two-way ANOVA, KD ****P < 0.0001, KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl: Neurod1, Dcx, Tubb3, and Map2 ****P < 0.0001, Dlg4 **P = 0.0015, Syn1***P = 0.0004). n = 11 per group. i-l, Primary cortical neurons were transduced with LV-shRNA for five days and treated with 20 µM recombinant amyloid-beta 42 for the last 16 h (i and j), or Abeta-enriched Tg2576 conditioned-media for the last 3 h (k and l). Normalized calcein fluorescent signal indicative of live cells after 16 h amyloid-beta 42 treatment (i, Welch’s ANOVA followed by Dunnett’s T3 against shCtrl ****P < 0.0001), normalized EthD1 fluorescent signal indicative of dead cells after 16 h amyloid-beta 42 (j, Welch’s ANOVA followed by Dunnett’s T3 against shCtrl, shAtp6v0c **P = 0.0055, shAtpif1 ***P = 0.0002), normalized calcein fluorescent signal after 3 h Tg2576 conditioned-media (k, Welch’s ANOVA followed by Dunnett’s T3 against shCtrl ****P < 0.0001), and normalized EthD1 fluorescent signal after 3 h Tg2576 conditioned-media (l, one-way ANOVA followed by Dunnett’s against shCtrl, shAtp6v0c n.s. P = 0.0726, shAtpif1 n.s. P = 0.0754). n = 6 per group. m-o, Adult hippocampus derived neurospheres were transduced with LV-shRNA and maintained in differentiation media for three days. Confirmation of the gene knock-down by qPCR (m, two-tailed unpaired t-test ****P < 0.0001), PrestoBlue HS normalized cell viability (n, two-tailed unpaired t-test **P = 0.0065), and gene expression of neuronal markers in response to Atpif1 knock-down (o, two-way ANOVA, KD ****P < 0.0001, KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl: Neurod1, Dcx, and Map2 ****P < 0.0001, Tubb3 n.s. P = 0.681, Dlg4 *P = 0.0152, Syn1 n.s. P = 0.7519, Bax n.s. P = 0.9609). n = 4 (n) and 12 per group (m, o). Data represent the mean ± s.e.m. of biologically independent samples.
Extended Data Fig. 5 Exercise regulates DAM-like microglia in AD mouse models.
a, b, Quantification of IBA-1+ microglia per mm2 in the dorsal DG (a, two-way ANOVA, Exercise **P = 0.0021, Genotype ****P < 0.0001, Exercise x genotype **P = 0.0031, followed by Fisher’s LSD, WT-Sed vs Run ns P = 0.8979, APP/PS1 Sed vs Run ***P = 0.0001) and ventral DG (b, two-way ANOVA, Exercise *P = 0.0495, Genotype ****P < 0.0001, Exercise x genotype n.s. P = 0.1564) (WT-Sed n = 6, WT-Run n = 6, APP/PS1-Sed n = 5, APP/PS1-Run n = 6). c, UMAP representation of marker genes for perivascular macrophages (Mrc1, Cd163, Cd74), monocytes (S100a4), B cells (Cd79b, Rag1), T cells (Trbc2, Cd3g), and natural killer cells (Nkg7). Color represents expression level according to the scale bar on the right. d, Cell composition in percentage for each subcluster shown in Fig. 4g–i. Two-way ANOVA, Exercise n.s. P = 0.9956, Genotype ****P < 0.0001, Exercise x genotype n.s. P = 0.8881. e, Heatmap shows the normalized mean expression (z-score) group of IFN, MHC, and Cyc-M microglia genes reported by Chen et al. in our dataset. f, Dot plots showing microglia subcluster 1 markers. In each, the hue and size of the dot represent the mean expression and fraction of non-zero count nuclei, respectively. g, Violin plots of gene signatures for DAM (Irf8, Trem2, Igf1, Axl, and Csf1) and Homeostatic (P2ry12, Cd33, Tmem119, Csf1r, Cx3cr1) microglia using snRNAseq data of microglia subcluster 1 from AD mice DG. Gene signature = sum of normalized gene expression for all genes of the gene signature per cell (n = 150 and 229 cells for ‘Sed’ and ‘Run’, respectively). Two-tailed Mann Whitney, DAM P = 0.0195 and Homeostatic P = 0.9858. h, Bar plots of gene signatures for DAM (Irf8, Trem2, Igf1, Axl, and Csf1) and Homeostatic microglia (P2ry12, Cd33, Tmem119, Csf1r, Cx3cr1) in isolated CD11b+ cells (microglia) from the cortex and hippocampus of the 5xFAD mouse model using QPCR. Gene signature = sum of normalized gene expression for all genes of the gene signature per animal (n = 8 and 7 for ‘Sed’ and ‘Run’, respectively). Two-tailed unpaired t-test, DAM P = 0.0616 and Homeostatic P = 0.9462. Data represented by the mean ± s.e.m. (a, b, d, h) or by the median (middle bold line) and upper and lower quartiles (lighter dotted lines) (g) of biologically independent samples.
Extended Data Fig. 6 Exercise shifts the transcriptional state of astrocytes.
a, b, Quantification of GFAP+ astrocytes per mm2 in the dorsal DG (a, two-way ANOVA, Exercise n.s. P = 0.9007, Genotype n.s. P = 0.5594, Exercise x genotype n.s. P = 0.3558) and ventral DG (b, two-way ANOVA, Exercise n.s. P = 0.6633, Genotype n.s. P = 0.9575, Exercise x genotype n.s. P = 0.9111) (WT-Sed n = 6, WT-Run n = 6, APP/PS1-Sed n = 5, APP/PS1-Run n = 6). c, Heatmap shows the normalized mean expression (z-score) of the marker genes for each astrocyte subcluster (subcluster 0 in blue and subcluster 1 in orange). d, UMAP representation of the expression of the high-confidence markers for Radial Glia-like cells in astrocytes. Yellow dots are all nuclei in the astrocyte cluster; grey and green dots represent potentially Radial Glia-like cells based on the expression of listed markers. e, Heatmap shows the normalized mean expression (z-score) per group of previously identified disease-associated astrocytes (DAA) markers in our dataset (subcluster 0 in blue and subcluster 1 in orange). f, Heatmap shows the normalized mean expression (z-score) per group of previously identified reactive astrocytes markers in our dataset (subcluster 0 in blue and subcluster 1 in orange). g, Bar chart of the relevant enriched terms for the astrocyte subcluster 1 marker genes from Enrichr. Enriched terms displayed presented an adjusted p-value < 0.05 determined by Fisher exact test with the Benjamini-Hochberg correction for multiple hypotheses. h, CDH4 counts in astrocytes subclusters from human parietal cortex snRNA-seq in Brase et al.42. The subclusters presented are the originally described ones from42. Linear mixed effect model (covariates, sex and cluster; random effect, sample). i, Violin plots of gene signatures in the astrocyte subcluster 1 (Mfge8, Plxna2, Grin2b, Bmper, Dab1, Pde1c, and Cdh4) using snRNAseq data of astrocytes subcluster 1 from AD mice DG. Gene signature = sum of normalized gene expression for all genes of the gene signature per cell (n = 36 and 60 cells for ‘Sed’ and ‘Run’, respectively). Two-tailed Mann Whitney P = 0.0028. j, Bar plots of gene signatures for astrocyte subcluster 1 (Mfge8, Plxna2, Grin2b, Bmper, Dab1, Pde1c, and Cdh4) in isolated ACSA2+ cells (astrocytes) from the cortex and hippocampus of the 5xFAD mouse model using qPCR. Gene signature = sum of normalized gene expression for all genes of the gene signature per animal (n = 7 for ‘Sed’ and ‘Run’). Two-tailed unpaired t-test P = 0.0240. Data represented by the mean ± s.e.m. (a, b, j) or by the median (middle bold line) and upper and lower quartiles (lighter dotted lines) (i) of biologically independent samples.
Extended Data Fig. 7 Astrocytes recDEGs knock-down alters astrocytes states in vitro.
Primary cortical mixed glia cultures were transduced with LV-shRNA for five days in standard growth media (a-d) or treated in the last 24 h with a reactive astrocyte activating cocktail of cytokines (e-h) or amyloid-β 42 peptide (i-l). Confirmation of the gene knock-down by QPCR (a, e, i), and gene expression of reactive astrocyte markers and markers of our subcluster 0 and 1 in response to Nme7 (b, f, j), St7 (c, g, k), and Thra (d, h, l) knock-down. n = 3 for shCtrl in e-l, n = 4 for all other groups. Two-way ANOVA followed by Fisher’s LSD. *p < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, n.s., not significant. Data represent the mean ± s.e.m. of biologically independent samples. a: two-way ANOVA, KD and KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl ****P < 0.0001; b: two-way ANOVA, KD and KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl Gfap, Cdh20, Rorb, and Cdh4 ****P < .0001, C3 n.s. P = 0.3171, Serpina3n ***P = 0.0009, Hspb1 **P = 0.004, Csmd1 **P = 0.0068; c: two-way ANOVA, KD n.s. P = 0.2011, KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl Gfap, C3, Serpina3n, Cdh20, and Cdh4 ****P < 0.0001, Hspb1 *P = 0.0277, Rorb n.s. P = 0.1921, Csmd1 n.s. P = 0.9147; d: two-way ANOVA, KD and KD x gene ****P < .0001, followed by Fisher’s LSD compared to shCtrl Gfap, Serpina3n, Rorb, Cdh4, and Csmd1****P < 0.0001, C3 ***P = 0.0001, Hspb1 n.s. P = 0.283, Cdh20 *P = 0.0174; e: two-way ANOVA, KD and KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl ****P < .0001; f: two-way ANOVA, KD and KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl Gfap, Serpina3n, Cdh20, and Rorb ****P < 0.0001, C3 n.s. P = 0.7271, Hspb1 n.s. P = 0.107, Cdh4 n.s. P = 0.0856, Csmd1 n.s. P = 0.1875; g: two-way ANOVA, KD and KD x gene ****P < .0001, followed by Fisher’s LSD compared to shCtrl Gfap **P = 0.0092, C3, Cdh20, Cdh4, and Csmd1 ****P < .000, Serpina3n **P = 0.0012, Hspb1 ***P = 0.001, Rorb n.s. P = 0.159; h: two-way ANOVA, KD and KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl Gfap, Serpina3n, Cdh20, Rorb, and Cdh4****P < 0.0001, C3 n.s. P = 0.8352, Hspb1 n.s. P = 0.9755, Csmd1 n.s. P = 0.1847; i: two-way ANOVA, KD and KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl ****P < 0.0001; j: two-way ANOVA, KD and KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl Gfap ***P = 0.0002, C3 n.s. P = 0.2851, Serpina3n **P = 0.003, Hspb1 n.s. P = 0.5116, Cdh20, Rorb, and Cdh4 ****P < 0.0001, Csmd1 **P = 0.0093; k: two-way ANOVA, KD n.s. P = 0.7698 and KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl Gfap, ***P = 0.0001, C3, Cdh20, and Cdh4 ****P < 0.0001, Serpina3n n.s. P = 0.137, Hspb1 n.s. P = 0.0896, Rorb n.s. P = 0.6144, Csmd1 n.s. P = 0.6805. l: two-way ANOVA, KD n.s. P = 0.4382 and KD x gene ****P < 0.0001, followed by Fisher’s LSD compared to shCtrl Gfap, Serpina3n, Hspb1, Cdh20, Rorb, and Cdh4 ****P < 0.0001, C3 n.s. P = 0.9321, Csmd1 * P = 0.0236.
Extended Data Fig. 8 Exercise remodels AD-dysregulated pathways in mGCs.
a, Average size of amyloid-beta plaques (3D6 staining) in ventral DG sections (APP/PS1-Sed n = 5, APP/PS1-Run n = 6, three section per animal; Two-tailed unpaired t-test P = 0.6679). Data is represented by the median (middle bold line) and upper and lower quartiles (lighter dotted lines). b, Schematic representation of amyloid precursor protein (APP) processing and amyloid beta degradation pathways. Adapted from the KEGG pathway database. c, Expression level of chimeric mouse/human APPswe and the human PS1-dE9 transgene in the APP/PS1 mice, and the fraction of cells expressing the gene. d, Expression levels of alpha-secretase (Adam10), beta-secretase (Bace1), and gamma-secretase (Psenen, Ncstn, Aph1a) coding- genes in all neuronal clusters and the fraction of cells expressing the gene. e, Expression levels of the Aβ-degrading enzyme coding-gene Ide and Mme in all cell clusters and the fraction of cells expressing the gene. f, Immediate early gene expression in the different neuronal cell types by group. In each dotplot, the hue and size of the dot represent the mean expression and fraction of non-zero count nuclei, respectively. Data represented by biologically independent samples. Panel b created using BioRender.com.
Extended Data Fig. 9 Exercise and AD responses in interneurons and vascular cells.
a, Scd2 average expression of all groups in oligodendrocyte progenitor cells (OPCs) and oligodendrocytes. b, Scd2 expression in oligodendrocytes in different groups. c, Cell compositional analysis for the OPCs subclusters. Two-way ANOVA, Exercise n.s. P = 0.1966, Genotype n.s. P = 0.3081, Exercise x genotype n.s. P = 0.2483. d, Comparison of our exercise effects in DG oligodendrocytes and OPCs with reported exercise effects in the subventricular zone in Buckley et al.20. Genes common to both projects, and those we found differentially expressed in exercise vs. sedentary conditions with an FDR-adjusted p value < 0.05 are displayed. Genes with > 0.5 Log2FC change in both projects are labeled. e-g, Interneuron subcluster UMAP representation (e), mean proportion (f), and marker genes for each subcluster (g). h, Scatter plot showing the correlation between AD and exercise effects in Interneurons. Each dot represents a statistically significant DEG in AD (WSvsAS). Dots with black borders represent statistically significant DEGs with exercise in AD mice (ASvsAR). The color gradient illustrates the recovery score (|log(FC ASvsAR) | ). The dot size represents the fraction of non-zero count nuclei in the AR group. i, Dot plots showing recDEGs in Interneurons. j, Cell compositional analysis for the Vascular cell subclusters. Two-way ANOVA, subcluster 0: Exercise *P = 0.0266, Genotype n.s. P = 0.9986, Exercise x genotype n.s. P = 0.2262, subcluster 1: Exercise *P = 0.0113, Genotype n.s. P = 0.378, Exercise x genotype n.s. P = 0.5948, subcluster 2: Exercise n.s. P = 0.9024, Genotype n.s. P = 0.492, Exercise x genotype n.s. P = 0.4417. k, Sema3c was a significant recDEGs shared by different cell types. l, Body weights at the start and end of the experiment. Two-way repeated measures ANOVA, Group n.s. P = 0.0603, Time *P = 0.0232, Group x time **P = 0.0018. WT-Sed n = 5, WT-Run n = 5, APP/PS1-Sed n = 4, APP/PS1-Run n = 5 (c and j). WT-Sed n = 12, WT-Run n = 12, APP/PS1-Sed n = 9, APP/PS1-Run n = 9 (l). In each dotplot, the hue and size of the dot represent the mean expression and fraction of non-zero count nuclei, respectively. Data represent the mean ± s.e.m. of biologically independent samples (c, j, l).
Supplementary information
Supplementary Code 1
Jupyter Notebook with relevant code.
Supplementary Data 1
snRNA-seq quality control metrics.
Supplementary Data 2
Marker genes for the global cell clusters.
Supplementary Data 3
DEGs for each cell type.
Supplementary Data 4
GeneWalk analysis.
Supplementary Data 5
GSEA analysis of AD and exercise effects.
Supplementary Data 6
CellChat analysis.
Supplementary Data 7
Marker genes for the neurogenic cell types.
Supplementary Data 8
Genes dysregulated in AD and recovered with exercise (recDEGs).
Supplementary Data 9
Comparison with human AD snRNA-seq from ref. 42.
Supplementary Data 10
Marker genes for cellular subclusters.
Supplementary Data 11
Nuclei counts for subclusters.
Supplementary Data 12
Functional enrichment analysis for astrocyte subcluster 1.
Supplementary Table 1
Antibodies, primers and demographics of human samples used in the present study.
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da Rocha, J.F., Lance, M.L., Luo, R. et al. Protective exercise responses in the dentate gyrus of Alzheimer’s disease mouse model revealed with single-nucleus RNA-sequencing. Nat Neurosci (2025). https://doi.org/10.1038/s41593-025-01971-w
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DOI: https://doi.org/10.1038/s41593-025-01971-w