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      Unraveling the intercellular communication disruption and key pathways in Alzheimer’s disease: An integrative study of single-nucleus transcriptomes and genetic association

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          Abstract

          Background

          Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer’s disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains.

          Methods

          We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database.

          Results

          We identified 316 dysregulated LR interactions across six major cell types in AD PFC, of which 210 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 60 GO terms significantly linked to AD, highlighting Biological Processes such as ‘amyloid precursor protein processing’ and ‘ion transmembrane transport’, among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs.

          Conclusions

          Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.

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          Most cited references85

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              Inference and analysis of cell-cell communication using CellChat

              Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.
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                Author and article information

                Contributors
                Journal
                Res Sq
                ResearchSquare
                Research Square
                American Journal Experts
                12 September 2023
                : rs.3.rs-3335643
                Affiliations
                Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston
                Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston
                Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston
                Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston
                Author notes

                Authors’ contributions

                Z.Z. and A.L. designed and conceived the study. A.L. and C.C. collected the data and performed the data analyses. A.L., B.S.F., C.C. and Z.Z. interpreted the data analysis, wrote, and revised the manuscript.

                Article
                10.21203/rs.3.rs-3335643
                10.21203/rs.3.rs-3335643/v1
                10543294
                37790454
                d5153122-8c69-4b7f-9360-806ab8a99fa4

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

                History
                Funding
                Funded by: National Institutes of Health (NIH)
                Award ID: R01LM012806
                Award ID: R03AG077191
                Funded by: Cancer Genomics Core funded by the Cancer Prevention and Research Institute of Texas
                Award ID: CPRIT RP180734
                Funded by: Gulf Coast Consortia on Training in Precision Environmental Health Sciences (TPEHS) Training
                Award ID: T32ES027801
                Categories
                Article

                alzheimer’s disease,cell-cell communication,multi-omics,single-nucleus rna sequencing

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