{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:21:30Z","timestamp":1760145690380,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T00:00:00Z","timestamp":1724716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union-Next Generation EU","award":["TAEDR-0535850"],"award-info":[{"award-number":["TAEDR-0535850"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is the most common cause of neurodegenerative dementia in the elderly, which is characterized by progressive cognitive impairment. Herein, we undertake a sophisticated computational analysis by integrating single-cell RNA sequencing (scRNA-seq) data from multiple brain regions significantly affected by the disease, including the entorhinal cortex, prefrontal cortex, superior frontal gyrus, and superior parietal lobe. Our pipeline combines datasets derived from the aforementioned tissues into a unified analysis framework, facilitating cross-regional comparisons to provide a holistic view of the impact of the disease on the cellular and molecular landscape of the brain. We employed advanced computational techniques such as batch effect correction, normalization, dimensionality reduction, clustering, and visualization to explore cellular heterogeneity and gene expression patterns across these regions. Our findings suggest that enabling the integration of data from multiple batches can significantly enhance our understanding of AD complexity, thereby identifying key molecular targets for potential therapeutic intervention. This study established a precedent for future research by demonstrating how existing data can be reanalysed in a coherent manner to elucidate the systemic nature of the disease and inform the development of more effective diagnostic tools and targeted therapies.<\/jats:p>","DOI":"10.3390\/info15090523","type":"journal-article","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T11:58:46Z","timestamp":1724759926000},"page":"523","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Computational Analysis of Marker Genes in Alzheimer\u2019s Disease across Multiple Brain Regions"],"prefix":"10.3390","volume":"15","author":[{"given":"Panagiotis","family":"Karanikolaos","sequence":"first","affiliation":[{"name":"Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marios G.","family":"Krokidis","sequence":"additional","affiliation":[{"name":"Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Themis P.","family":"Exarchos","sequence":"additional","affiliation":[{"name":"Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0053-7847","authenticated-orcid":false,"given":"Panagiotis","family":"Vlamos","sequence":"additional","affiliation":[{"name":"Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fan, L., Mao, C., Hu, X., Zhang, S., Yang, Z., Hu, Z., Sun, H., Fan, Y., Dong, Y., and Yang, J. (2020). New insights into the pathogenesis of Alzheimer\u2019s disease. Front. Neurol., 10.","DOI":"10.3389\/fneur.2019.01312"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13024-020-00391-7","article-title":"Molecular and cellular mechanisms underlying the pathogenesis of Alzheimer\u2019s disease","volume":"15","author":"Guo","year":"2020","journal-title":"Mol. Neurodegener."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Breijyeh, Z., and Karaman, R. (2020). Comprehensive review on Alzheimer\u2019s disease: Causes and treatment. Molecules, 25.","DOI":"10.3390\/molecules25245789"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chen, G., Ning, B., and Shi, T. (2019). Single-cell RNA-seq technologies and related computational data analysis. Front. Genet., 10.","DOI":"10.3389\/fgene.2019.00317"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.coisb.2017.07.004","article-title":"Single cells make big data: New challenges and opportunities in transcriptomics","volume":"4","author":"Angerer","year":"2017","journal-title":"Curr. Opin. Syst. Biol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e694","DOI":"10.1002\/ctm2.694","article-title":"Single-cell RNA sequencing technologies and applications: A brief overview","volume":"12","author":"Jovic","year":"2022","journal-title":"Clin. Transl. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1186\/s13073-022-01136-5","article-title":"Cell type-specific changes identified by single-cell transcriptomics in Alzheimer\u2019s disease","volume":"14","author":"Luquez","year":"2022","journal-title":"Genome Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.semcdb.2022.05.007","article-title":"April. Revealing cell vulnerability in Alzheimer\u2019s disease by single-cell transcriptomics","volume":"139","author":"Saura","year":"2023","journal-title":"Semin. Cell Dev. Biol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lampinen, R., Fazaludeen, M.F., Avesani, S., \u00d6rd, T., Penttil\u00e4, E., Lehtola, J.M., Saari, T., Hannonen, S., Saveleva, L., and Kaartinen, E. (2022). Single-cell RNA-Seq analysis of olfactory mucosal cells of Alzheimer\u2019s disease patients. Cells, 11.","DOI":"10.3390\/cells11040676"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Soreq, L., Bird, H., Mohamed, W., and Hardy, J. (2023). Single-cell RNA sequencing analysis of human Alzheimer\u2019s disease brain samples reveals neuronal and glial specific cells differential expression. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0277630"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Aslanis, I., Krokidis, M.G., Dimitrakopoulos, G.N., and Vrahatis, A.G. (2022). Identifying Network Biomarkers for Alzheimer\u2019s Disease Using Single-Cell RNA Sequencing Data. Worldwide Congress on \u201cGenetics, Geriatrics and Neurodegenerative Diseases Research\", Springer International Publishing.","DOI":"10.1007\/978-3-031-31978-5_19"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Krokidis, M.G., Vrahatis, A.G., Lazaros, K., and Vlamos, P. (2023). Exploring Promising Biomarkers for Alzheimer\u2019s Disease through the Computational Analysis of Peripheral Blood Single-Cell RNA Sequencing Data. Appl. Sci., 13.","DOI":"10.3390\/app13095553"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pushparaj, P.N., Kalamegam, G., Wali Sait, K.H., and Rasool, M. (2022). Decoding the role of astrocytes in the entorhinal cortex in Alzheimer\u2019s disease using high-dimensional single-nucleus RNA sequencing data and next-generation knowledge discovery methodologies: Focus on drugs and natural product remedies for dementia. Front. Pharmacol., 12.","DOI":"10.3389\/fphar.2021.720170"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Adewale, Q., Khan, A.F., Bennett, D.A., and Iturria-Medina, Y. (2024). Single-nucleus RNA velocity reveals critical synaptic and cell-cycle dysregulations in neuropathologically confirmed Alzheimer\u2019s disease. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-57918-x"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guennewig, B., Lim, J., Marshall, L., McCorkindale, A.N., Paasila, P.J., Patrick, E., Kril, J.J., Halliday, G.M., Cooper, A.A., and Sutherland, G.T. (2021). Defining early changes in Alzheimer\u2019s disease from RNA sequencing of brain regions differentially affected by pathology. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-83872-z"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"101769","DOI":"10.1016\/j.isci.2020.101769","article-title":"scREAD: A single-cell RNA-Seq database for Alzheimer\u2019s disease","volume":"23","author":"Jiang","year":"2020","journal-title":"iScience"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8652","DOI":"10.3390\/cimb45110544","article-title":"Machine Learning Analysis of Alzheimer\u2019s Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions","volume":"45","author":"Krokidis","year":"2023","journal-title":"Curr. Issues Mol. Biol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ibanez, L., Cruchaga, C., and Fern\u00e1ndez, M.V. (2021). Advances in genetic and molecular understanding of Alzheimer\u2019s disease. Genes, 12.","DOI":"10.3390\/genes12081247"},{"key":"ref_19","first-page":"313","article-title":"The epidemiology of Alzheimer\u2019s disease modifiable risk factors and prevention","volume":"8","author":"Zhang","year":"2021","journal-title":"J. Prev. Alzheimer\u2019s Dis."},{"key":"ref_20","first-page":"189","article-title":"Dissecting cellular heterogeneity using single-cell RNA sequencing","volume":"42","author":"Choi","year":"2019","journal-title":"Mol. Cells"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1038\/s41582-023-00809-y","article-title":"Single-cell and spatial transcriptomics: Deciphering brain complexity in health and disease","volume":"19","author":"Piwecka","year":"2023","journal-title":"Nat. Rev. Neurol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1038\/s41586-019-1195-2","article-title":"Single-cell transcriptomic analysis of Alzheimer\u2019s disease","volume":"570","author":"Mathys","year":"2019","journal-title":"Nature"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Krix, S., Wilczynski, E., Falg\u00e0s, N., S\u00e1nchez-Valle, R., Yoles, E., Nevo, U., Baruch, K., and Fr\u00f6hlich, H. (2024). Towards early diagnosis of Alzheimer\u2019s disease: Advances in immune-related blood biomarkers and computational approaches. Front. Immunol., 15.","DOI":"10.3389\/fimmu.2024.1343900"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"66","DOI":"10.2174\/1570163815666180417120833","article-title":"Methodologies Related to Computational Models in View of Developing Anti-Alzheimer Drugs: An Overview","volume":"16","author":"Baheti","year":"2019","journal-title":"Curr. Drug Discov. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Johnson, T.S., Xiang, S., Helm, B.R., Abrams, Z.B., Neidecker, P., Machiraju, R., Zhang, Y., Huang, K., and Zhang, J. (2020). Spatial cell type composition in normal and Alzheimer\u2019s human brains is revealed using integrated mouse and human single cell RNA sequencing. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-74917-w"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kim, D., Tran, A., Kim, H.J., Lin, Y., Yang, J.Y.H., and Yang, P. (2023). Gene regulatory network reconstruction: Harnessing the power of single-cell multi-omic data. NPJ Syst. Biol. 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