Genomic and epigenomic insights into purkinje and granule neurons in Alzheimer’s disease and related dementia using single-nucleus multiome analysis

Genomic and epigenomic insights into purkinje and granule neurons in Alzheimer’s disease and related dementia using single-nucleus multiome analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genomic and epigenomic insights into purkinje and granule neurons in Alzheimer’s disease and related dementia using single-nucleus multiome analysis Feixiong Cheng, Yayan Feng, Xiaoyu Yang, Margaret Flanagan, Xin Chen, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6264481/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Although the human cerebellum is known to be neuropathologically impaired in Alzheimer’s disease (AD) and AD-related dementias (ADRD), the cell type-specific transcriptional and epigenomic changes that contribute to this pathology are not well understood. Here, we report single-nucleus multiome (snRNA-seq and snATAC-seq) analysis of 103,861 nuclei isolated from both cerebellum and frontal cortex of AD/ADRD patients and normal controls. Using peak-to-gene linkage analysis, we identified 431,834 significant linkages between gene expression and cell subtype-specific chromatin accessibility regions enriched for candidate cis-regulatory elements (cCREs). These cCREs were associated with AD/ADRD-specific transcriptomic changes and disease-related gene regulatory networks, especially for RAR Related Orphan Receptor A (RORA) and E74 Like ETS Transcription Factor 1 (ELF1) in cerebellar Purkinje cells and granule cells, respectively. Trajectory analysis of granule cell populations further identified disease-relevant transcription factors, such as RORA, and their regulatory targets. Finally, we pinpointed two likely causal genes, Seizure Related 6 Homolog Like 2 (SEZ6L2) in Purkinje cells and KAT8 Regulatory NSL Complex Subunit 1 (KANSL1) in granule cells, through integrative analysis of cCREs derived from snATAC-seq, genome-wide AD/ADRD loci, and three-dimensional (3D) genome data. Via CRISPRi experiments, we found that perturbation of rs4788201 and rs62056801 significantly inhibited the expression of their target genes, SEZ6L2 and KANSL1, in human iPSC-derived neurons. This cell subtype-specific regulatory landscape in the human cerebellum identified here offers novel genomic and epigenomic insights into the neuropathology and pathobiology of AD/ADRD and other neurological disorders if broadly applied. Biological sciences/Biological techniques/Genomic analysis/Gene expression profiling Biological sciences/Computational biology and bioinformatics/Data integration Health sciences/Neurology/Neurological disorders/Dementia/Alzheimer's disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text Additional Declarations Yes there is potential Competing Interest. Dr. Cummings has provided consultation to AB Science, Acadia, Alkahest, AlphaCognition, ALZPathFinder, Annovis, AriBio, Artery, Avanir, Biogen, Biosplice, Cassava, Cerevel, Clinilabs, Cortexyme, Diadem, EIP Pharma, Eisai, GatehouseBio, GemVax, Genentech, Green Valley, Grifols, Janssen, Karuna, Lexeo, Lilly, Lundbeck, LSP, Merck, NervGen, Novo Nordisk, Oligomerix, Ono, Otsuka, PharmacotrophiX, PRODEO, Prothena, ReMYND, Renew, Resverlogix, Roche, Signant Health, Suven, Unlearn AI, Vaxxinity, VigilNeuro pharmaceutical, assessment, and investment companies. Dr. Leverenz has received consulting fees from consulting fees from Vaxxinity, grant support from GE Healthcare and serves on a Data Safety Monitoring Board for Eisai. The other authors have declared no competing interests. Supplementary Files SupplementaryFigureS1S11Cheng.pdf Supplementary Figure S1-S11 SupplementaryTablesS1S14Cheng.zip Supplementary_Tables_S1-S14 Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6264481","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":436452888,"identity":"491e05ca-f5ff-4daf-afae-f35dd4622f6d","order_by":0,"name":"Feixiong 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Clinic","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Hu","suffix":""},{"id":436452907,"identity":"6d554a27-8e62-4ef4-928e-b3d4d41f5b1c","order_by":19,"name":"Yang Li","email":"","orcid":"https://orcid.org/0000-0001-6997-6018","institution":"Washington University in St. Louis","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""},{"id":436452908,"identity":"2a005d18-cf59-4518-821e-70f64692812b","order_by":20,"name":"Andrew Pieper","email":"","orcid":"","institution":"Case Western Reserve University","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Pieper","suffix":""},{"id":436452909,"identity":"358991b4-9381-42bd-9cda-2a823d65eba8","order_by":21,"name":"Jeffrey Cummings","email":"","orcid":"https://orcid.org/0000-0001-8944-4158","institution":"University of Nevada Las Vegas","correspondingAuthor":false,"prefix":"","firstName":"Jeffrey","middleName":"","lastName":"Cummings","suffix":""}],"badges":[],"createdAt":"2025-03-19 21:05:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6264481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6264481/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79674221,"identity":"2b628729-2fd1-45d5-a14c-865834b60058","added_by":"auto","created_at":"2025-04-01 11:53:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1224683,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCellular diversity in the diseased brain revealed by single nuclei multiomics. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Schematic of the samples and sequencing experiments used in this study, created with \u003ca href=\"http://biorender.com/\"\u003eBioRender.com\u003c/a\u003e. (\u003cstrong\u003eB\u003c/strong\u003e) Immunostaining of amyloid-beta using 4G8 antibody in DLBD cerebellum and dentate nucleus using AT8 antibody in PSP cerebellum. (\u003cstrong\u003eC\u003c/strong\u003e) Uniform manifold approximation and projection (UMAP) visualization of the 103,861 brain nuclei profiled with snATAC-seq (left), snRNA-seq (middle), and jointly snATAC-seq and snRNA-seq (right), colored by the annotated clusters. OLs, oligodendrocytes; Gran, granule cells; Astro, astrocytes; Micro, microglia; Glu, glutamate neurons; Excit, excitatory neuron; OPCs, oligodendrocyte progenitor cell. (\u003cstrong\u003eD\u003c/strong\u003e) Raw-normalized gene expression of selected marker genes for each snRNA cluster. Color indicates scaled mean expression across all clusters and dot size indicates fraction of expressing cells in that cluster. (\u003cstrong\u003eE\u003c/strong\u003e) Raw-normalized gene activity score of selected marker genes shown in \u003cstrong\u003eD \u003c/strong\u003efor each snRNA cluster. (\u003cstrong\u003eF\u003c/strong\u003e) Proportion of cells from each sample comprising each cluster in the context of brain region. Measures of cellular composition changes for each cluster between diseased and control cerebellum were analyzed using Wilcoxon test. * \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"MainFigures17Cheng1.png","url":"https://assets-eu.researchsquare.com/files/rs-6264481/v1/d4cf0a1a9c016ae79aa43599.png"},{"id":79674222,"identity":"7d72c453-3e07-417e-a324-98190fc6e663","added_by":"auto","created_at":"2025-04-01 11:53:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1587246,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of candidate \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ecis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e-regulatory elements. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Tn5 bias-subtracted TF footprinting analysis for \u003cem\u003eRFX3 \u003c/em\u003eand \u003cem\u003eATF1 \u003c/em\u003eby cell clusters of snATAC. The upper panel shown TF binding motif logo. (\u003cstrong\u003eB\u003c/strong\u003e) Schematic of peak-to-gene linkages analysis using the full snATAC and snRNA datasets and the five granule cells subclustered datasets. Linkages were analyzed separately and then merged to generate the full set of peak-to-gene linkage sets. (\u003cstrong\u003eC\u003c/strong\u003e) Heatmap of raw-normalized chromatin accessibility and gene expression for the 431,834 peak-to-gene linkages, which were clustered based on \u003cem\u003ek\u003c/em\u003e-means clustering analysis. Genes highlighted were well-known GWAS genes from GWAS catalog and key transcription factors involved in granule cell differentiation. (\u003cstrong\u003eD\u003c/strong\u003e) Genes ranked by the number of significant peak-to-gene associations identified for each gene. The inflection point was set to 150 peak-to-gene linkages and 1,821 genes had \u0026gt;150 peak-to-gene linkages. (\u003cstrong\u003eE\u003c/strong\u003e) Genomic tracks for chromatin accessibility around 250 Kb flanking regions of \u003cem\u003eBIN1 \u003c/em\u003elocus in cerebellum (top) and frontal cortex (bottom). (\u003cstrong\u003eF\u003c/strong\u003e) Genomic tracks for chromatin accessibility around 250 Kb flanking regions of \u003cem\u003eIL33 \u003c/em\u003elocus in cerebellum (top) and frontal cortex (bottom). Peak-to-gene linkages were shown as loops below those genomic tracks and colored by linked correlation value.\u003c/p\u003e","description":"","filename":"MainFigures17Cheng2.png","url":"https://assets-eu.researchsquare.com/files/rs-6264481/v1/af0f3261aa739f877a25c82b.png"},{"id":79675091,"identity":"4c97673c-507f-467a-9068-a1a3771175e8","added_by":"auto","created_at":"2025-04-01 12:01:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":475430,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell type-specific transcriptomic changes in diseased cerebellum. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Raw-normalized log2FC of all up- and downregulated genes in diseased cerebellum. (\u003cstrong\u003eB\u003c/strong\u003e) Upset plot showing the size of overlaps between the sets of up- (top) and downregulated (bottom) genes identified in each cell type. Bar plot on the top shows the number of overlapping genes between multiple cell types or a unique cell type. (\u003cstrong\u003eC\u003c/strong\u003e) Venn diagrams showing overlaps between cCREs-linked genes, genes differentially expressed in a specific cell type (cell-type DEGs) and genes differentially expressed in diseased cerebellum (diagnosis DEGs). One-sided Fisher’s exact test was used for gene-set overlap significance (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05). (\u003cstrong\u003eD\u003c/strong\u003e) Two-sided bar plot showing number of up- (right) and downregulated (left) genes for each cell type in the cerebellum of disease context, including AD/ADRD, AD, DLBD and PSP. (\u003cstrong\u003eE\u003c/strong\u003e) Dot plot showing log-transformed enrichR combined scores for GO terms for differentially expressed gene sets in the cerebellum of disease context, including AD/ADRD, AD, DLBD and PSP. Upset plot on the right showing the size of overlaps between different disease context identified in each enriched term. (\u003cstrong\u003eF\u003c/strong\u003e) Genomic tracks for chromatin accessibility around the \u003cem\u003eCALM1 \u003c/em\u003e(left), \u003cem\u003eTMEM160 \u003c/em\u003e(middle) and \u003cem\u003eARHGDIG \u003c/em\u003e(right) locus in AD/ADRD cerebellum. Violin plot on the right showing expression level of gene under consideration for specific cell type. Peak-to-gene linkages were shown as loops below those genomic tracks and colored by linked correlation value.\u003c/p\u003e","description":"","filename":"MainFigures17Cheng3.png","url":"https://assets-eu.researchsquare.com/files/rs-6264481/v1/f3d10ec7dcde768f93f1847a.png"},{"id":79674226,"identity":"c851fe67-ce52-4800-a9a0-d3baf29f6122","added_by":"auto","created_at":"2025-04-01 11:53:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":950560,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of enhancer-associated gene-regulatory networks in diseased cerebellum. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Schematic of defining peak-gene-TF trios. The accessibility of a linked peak harboring a specific TF motif must be correlated with the mRNA level of that TF and the expression of that TF must be correlated with the linked gene for that peak. (\u003cstrong\u003eB\u003c/strong\u003e) Heatmap/dot-plot showing TF expression of the top eGRNs. Color indicates normalized TF expression and dot size indicates cell-type specificity (RSS) score. (\u003cstrong\u003eC\u003c/strong\u003e) Left: snMultiome UMAP colored by \u003cem\u003eRORA \u003c/em\u003emotif variability (top) and its target gene score (bottom). Right: Tn5 bias-subtracted TF footprinting analysis for \u003cem\u003eRORA \u003c/em\u003eby snATAC granule and Purkinje cell clusters (top) and by disease status (bottom). TF binding motif shown as motif logo above. (\u003cstrong\u003eD\u003c/strong\u003e) Left: snMultiome UMAP colored by \u003cem\u003eELF1 \u003c/em\u003emotif variability (top) and its target gene score (bottom). Right: Tn5 bias-subtracted TF footprinting analysis for \u003cem\u003eELF1 \u003c/em\u003eby snATAC granule and Purkinje cell clusters (top) and by disease status (bottom). TF binding motif shown as motif logo above. (\u003cstrong\u003eE\u003c/strong\u003e) Visualization of \u003cem\u003eRORA\u003c/em\u003e-gene regulatory networks in AD/ADRD Purkinje cells. (\u003cstrong\u003eF\u003c/strong\u003e) Visualization of TF-gene regulatory networks formed by \u003cem\u003eELF1 \u003c/em\u003eand \u003cem\u003eCHD2 \u003c/em\u003ein AD/ADRD mature granule cells.\u003c/p\u003e","description":"","filename":"MainFigures17Cheng4.png","url":"https://assets-eu.researchsquare.com/files/rs-6264481/v1/80649aa21ab4c2e5191b2c5c.png"},{"id":79674229,"identity":"ca22b1eb-9769-4bfc-a86b-20a9b504c3c9","added_by":"auto","created_at":"2025-04-01 11:53:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":969642,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003esnMultiome granule cell trajectory analyses. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Differentiation trajectory starting from granule cell progenitor to mature granule cells using snMultiome data. (\u003cstrong\u003eB\u003c/strong\u003e) RNA velocity revealed differentiation trajectory starting from granule cell progenitor to mature granule cells using snRNA data. (\u003cstrong\u003eC\u003c/strong\u003e) Dot plot showing gene expression of \u003cem\u003eTCF12 \u003c/em\u003eand \u003cem\u003eRORA \u003c/em\u003ecolored by pseudo-time. (\u003cstrong\u003eD\u003c/strong\u003e) Paired heatmap showing gene regulators whose chromatin accessibility (left) and matched gene expression (right) are positively correlated across granule cell pseudo-time trajectory. (\u003cstrong\u003eE \u003c/strong\u003eand \u003cstrong\u003eG\u003c/strong\u003e) Dot plot showing gene expression of \u003cem\u003ePDE4B \u003c/em\u003eand \u003cem\u003eRNF152 \u003c/em\u003ecolored by pseudo-time. (\u003cstrong\u003eF \u003c/strong\u003eand \u003cstrong\u003eH\u003c/strong\u003e) Genomic tracks for chromatin accessibility around the \u003cem\u003ePDE4B \u003c/em\u003e(\u003cstrong\u003eF\u003c/strong\u003e) and \u003cem\u003eRNF152 \u003c/em\u003e(\u003cstrong\u003eH\u003c/strong\u003e) locus in granule cells of AD/ADRD cerebellum. Violin plot on the right showing expression level of gene under consideration. Peak-to-gene linkages were shown as loops below those genomic tracks and colored by linked correlation value.\u003c/p\u003e","description":"","filename":"MainFigures17Cheng5.png","url":"https://assets-eu.researchsquare.com/files/rs-6264481/v1/f763fb63a074cdf60ae3fb30.png"},{"id":79674230,"identity":"5040166f-6105-4fb5-a6c2-02f5eae33674","added_by":"auto","created_at":"2025-04-01 11:53:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":200355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of cell types and genes associated with disease risk loci. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Heatmap showing Linkage Disequilibrium Score Regression (LDSC) enrichment score for various neurodegenerative conditions in peak regions of snATAC clusters. FDR-corrected \u003cem\u003eP \u003c/em\u003evalues are overlaid on the heatmap (*\u003cem\u003eq \u0026lt; \u003c/em\u003e0.05 and **\u003cem\u003eq \u0026lt; \u003c/em\u003e0.005). (\u003cstrong\u003eB\u003c/strong\u003e) One-sided Fisher’s exact test enrichment of fine-mapped, disease-related GWAS SNPs in cell type-specific differentially accessible peaks in diseased cerebellum. Color and dot size indicate FDR-corrected -log10\u003cem\u003eP \u003c/em\u003evalue. GWAS traits are grouped as in \u003cstrong\u003eA\u003c/strong\u003e. (\u003cstrong\u003eC\u003c/strong\u003e) Identification of likely causal GWAS SNPs and linked genes in diseased cerebellum. Left: Manhattan plot showing the -log10(\u003cem\u003eP \u003c/em\u003evalue) distribution of GWAS loci across different neurodegenerative conditions. Diseased-associated SNPs identified by colocalization analysis and fine-mapping are colored by green. All the SNPs shown in this study are annotated with assembly GRCh38. Middle: heatmap showing raw-normalized log2FC of GWAS-linked genes in AD/ADRD, AD and ADRD cerebellum. Right: barplot showing number of linked peaks, number of linked causal SNPs and the mean of fine-mapped posterior probability for linked causal SNPs per gene.\u003c/p\u003e","description":"","filename":"MainFigures17Cheng6.png","url":"https://assets-eu.researchsquare.com/files/rs-6264481/v1/d247f50e041aa6f4bca440d4.png"},{"id":79675092,"identity":"667afb9a-905a-4d78-9eca-d5ab1bc8e663","added_by":"auto","created_at":"2025-04-01 12:01:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":626152,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinking causal variants to target genes through Hi-C chromatin looping and CRISPRi assays in human iPSC-derived neurons. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Normalized chromatin accessibility landscape for cell type-specific pseudobulk tracks around the \u003cem\u003eSEZ6L2 \u003c/em\u003e(Seizure 6-like protein 2) locus. Top: Interaction maps between promoter of \u003cem\u003eSEZ6L2 \u003c/em\u003eand differentially accessible peak containing GWAS SNP rs4788201. Middle: Genomic tracks for chromatin accessibility around the \u003cem\u003eSEZ6L2 \u003c/em\u003elocus in AD/ADRD cerebellum. Violin plot on the right showing expression level of \u003cem\u003eSEZ6L2 \u003c/em\u003efor all cell clusters in cerebellum. Peak-to-gene linkages were shown as loops below those genomic tracks and colored by linked correlation value. Bottom: LocusCompare plots for high-probability genome-wide colocalized loci. The colocalized SNPs are labeled with variant identifiers and annotated as diamonds. Plots are colored based on linkage disequilibrium (LD) bins relative to the lead SNPs (red, ≥ 0.8; orange, 0.6-0.8; green, 0.4-0.6; light blue, 0.2-0.4; and dark blue, \u0026lt; 0.2). The SNP pairwise LD data were calculated based on the 1000 Genomes Phase 3 (ALL) reference panel. (\u003cstrong\u003eB\u003c/strong\u003e) Normalized chromatin accessibility landscape for cell type-specific pseudobulk tracks around \u003cem\u003eKANSL1 \u003c/em\u003e(KAT8 Regulatory NSL Complex Subunit 1) locus. (\u003cstrong\u003eC\u003c/strong\u003e) A diagram illustrating CRISPRi experiments to test the regulatory relationship between a locus and its target gene. (\u003cstrong\u003eD \u0026amp; E\u003c/strong\u003e) Box plot shows relative expression (Quantified by RT-PCR) for \u003cem\u003eSEZ6L2 \u003c/em\u003e(\u003cstrong\u003eD\u003c/strong\u003e) and \u003cem\u003eKANSL1 \u003c/em\u003e(\u003cstrong\u003eE\u003c/strong\u003e) between control (gray) or locus (rs4788201 [\u003cstrong\u003eD\u003c/strong\u003e] or rs62056801 [\u003cstrong\u003eE\u003c/strong\u003e]) sgRNA (light blue) targeted human iPSC-derived neurons. P-value as computed by Two tailed T test.\u003c/p\u003e","description":"","filename":"MainFigures17Cheng7.png","url":"https://assets-eu.researchsquare.com/files/rs-6264481/v1/c533157f00895d810444e4e7.png"},{"id":79676285,"identity":"9daed1fd-9fef-4dc3-b563-d811f4983e84","added_by":"auto","created_at":"2025-04-01 12:09:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3054885,"visible":true,"origin":"","legend":"Article File","description":"","filename":"MainTextChengMarch2025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6264481/v1_covered_79eaf261-9bad-4e2e-b151-efa2ca2e5715.pdf"},{"id":79675093,"identity":"fc453468-bda1-4d60-ae89-336541442645","added_by":"auto","created_at":"2025-04-01 12:01:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9401087,"visible":true,"origin":"","legend":"Supplementary Figure S1-S11","description":"","filename":"SupplementaryFigureS1S11Cheng.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6264481/v1/8c336850b067aaeff72c4db0.pdf"},{"id":79674231,"identity":"41e0e251-803b-4962-a5b7-dc165199bca1","added_by":"auto","created_at":"2025-04-01 11:53:02","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10465626,"visible":true,"origin":"","legend":"Supplementary_Tables_S1-S14","description":"","filename":"SupplementaryTablesS1S14Cheng.zip","url":"https://assets-eu.researchsquare.com/files/rs-6264481/v1/315eebe45536087e6283504c.zip"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nDr. Cummings has provided consultation to AB Science, Acadia, Alkahest, AlphaCognition, ALZPathFinder, Annovis, AriBio, Artery, Avanir, Biogen, Biosplice, Cassava, Cerevel, Clinilabs, Cortexyme, Diadem, EIP Pharma, Eisai, GatehouseBio, GemVax, Genentech, Green Valley, Grifols, Janssen, Karuna, Lexeo, Lilly, Lundbeck, LSP, Merck, NervGen, Novo Nordisk, Oligomerix, Ono, Otsuka, PharmacotrophiX, PRODEO, Prothena, ReMYND, Renew, Resverlogix, Roche, Signant Health, Suven, Unlearn AI, Vaxxinity, VigilNeuro pharmaceutical, assessment, and investment companies. Dr. Leverenz has received consulting fees from consulting fees from Vaxxinity, grant support from GE Healthcare and serves on a Data Safety Monitoring Board for Eisai. The other authors have declared no competing interests.","formattedTitle":"Genomic and epigenomic insights into purkinje and granule neurons in Alzheimer’s disease and related dementia using single-nucleus multiome analysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6264481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6264481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Although the human cerebellum is known to be neuropathologically impaired in Alzheimer’s disease (AD) and AD-related dementias (ADRD), the cell type-specific transcriptional and epigenomic changes that contribute to this pathology are not well understood. Here, we report single-nucleus multiome (snRNA-seq and snATAC-seq) analysis of 103,861 nuclei isolated from both cerebellum and frontal cortex of AD/ADRD patients and normal controls. Using peak-to-gene linkage analysis, we identified 431,834 significant linkages between gene expression and cell subtype-specific chromatin accessibility regions enriched for candidate cis-regulatory elements (cCREs). These cCREs were associated with AD/ADRD-specific transcriptomic changes and disease-related gene regulatory networks, especially for RAR Related Orphan Receptor A (RORA) and E74 Like ETS Transcription Factor 1 (ELF1) in cerebellar Purkinje cells and granule cells, respectively. Trajectory analysis of granule cell populations further identified disease-relevant transcription factors, such as RORA, and their regulatory targets. Finally, we pinpointed two likely causal genes, Seizure Related 6 Homolog Like 2 (SEZ6L2) in Purkinje cells and KAT8 Regulatory NSL Complex Subunit 1 (KANSL1) in granule cells, through integrative analysis of cCREs derived from snATAC-seq, genome-wide AD/ADRD loci, and three-dimensional (3D) genome data. Via CRISPRi experiments, we found that perturbation of rs4788201 and rs62056801 significantly inhibited the expression of their target genes, SEZ6L2 and KANSL1, in human iPSC-derived neurons. This cell subtype-specific regulatory landscape in the human cerebellum identified here offers novel genomic and epigenomic insights into the neuropathology and pathobiology of AD/ADRD and other neurological disorders if broadly applied.","manuscriptTitle":"Genomic and epigenomic insights into purkinje and granule neurons in Alzheimer’s disease and related dementia using single-nucleus multiome analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 11:52:57","doi":"10.21203/rs.3.rs-6264481/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ad3f5c77-38bb-4bc9-b568-2566c61f1bf3","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":46471452,"name":"Biological sciences/Biological techniques/Genomic analysis/Gene expression profiling"},{"id":46471453,"name":"Biological sciences/Computational biology and bioinformatics/Data integration"},{"id":46471454,"name":"Health sciences/Neurology/Neurological disorders/Dementia/Alzheimer's disease"}],"tags":[],"updatedAt":"2025-05-12T10:16:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-01 11:52:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6264481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6264481","identity":"rs-6264481","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}