Cargando…

Machine Learning Analysis of Alzheimer’s Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions

Advancements in molecular biology have revolutionized our understanding of complex diseases, with Alzheimer’s disease being a prime example. Single-cell sequencing, currently the most suitable technology, facilitates profoundly detailed disease analysis at the cellular level. Prior research has esta...

Descripción completa

Detalles Bibliográficos
Autores principales: Krokidis, Marios G., Vrahatis, Aristidis G., Lazaros, Konstantinos, Skolariki, Konstantina, Exarchos, Themis P., Vlamos, Panagiotis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670182/
https://www.ncbi.nlm.nih.gov/pubmed/37998721
http://dx.doi.org/10.3390/cimb45110544
_version_ 1785139863617536000
author Krokidis, Marios G.
Vrahatis, Aristidis G.
Lazaros, Konstantinos
Skolariki, Konstantina
Exarchos, Themis P.
Vlamos, Panagiotis
author_facet Krokidis, Marios G.
Vrahatis, Aristidis G.
Lazaros, Konstantinos
Skolariki, Konstantina
Exarchos, Themis P.
Vlamos, Panagiotis
author_sort Krokidis, Marios G.
collection PubMed
description Advancements in molecular biology have revolutionized our understanding of complex diseases, with Alzheimer’s disease being a prime example. Single-cell sequencing, currently the most suitable technology, facilitates profoundly detailed disease analysis at the cellular level. Prior research has established that the pathology of Alzheimer’s disease varies across different brain regions and cell types. In parallel, only machine learning has the capacity to address the myriad challenges presented by such studies, where the integration of large-scale data and numerous experiments is required to extract meaningful knowledge. Our methodology utilizes single-cell RNA sequencing data from healthy and Alzheimer’s disease (AD) samples, focused on the cortex and hippocampus regions in mice. We designed three distinct case studies and implemented an ensemble feature selection approach through machine learning, also performing an analysis of distinct age-related datasets to unravel age-specific effects, showing differential gene expression patterns within each condition. Important evidence was reported, such as enrichment in central nervous system development and regulation of oligodendrocyte differentiation between the hippocampus and cortex of 6-month-old AD mice as well as regulation of epinephrine secretion and dendritic spine morphogenesis in 15-month-old AD mice. Our outcomes from all three of our case studies illustrate the capacity of machine learning strategies when applied to single-cell data, revealing critical insights into Alzheimer’s disease.
format Online
Article
Text
id pubmed-10670182
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-106701822023-10-28 Machine Learning Analysis of Alzheimer’s Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions Krokidis, Marios G. Vrahatis, Aristidis G. Lazaros, Konstantinos Skolariki, Konstantina Exarchos, Themis P. Vlamos, Panagiotis Curr Issues Mol Biol Article Advancements in molecular biology have revolutionized our understanding of complex diseases, with Alzheimer’s disease being a prime example. Single-cell sequencing, currently the most suitable technology, facilitates profoundly detailed disease analysis at the cellular level. Prior research has established that the pathology of Alzheimer’s disease varies across different brain regions and cell types. In parallel, only machine learning has the capacity to address the myriad challenges presented by such studies, where the integration of large-scale data and numerous experiments is required to extract meaningful knowledge. Our methodology utilizes single-cell RNA sequencing data from healthy and Alzheimer’s disease (AD) samples, focused on the cortex and hippocampus regions in mice. We designed three distinct case studies and implemented an ensemble feature selection approach through machine learning, also performing an analysis of distinct age-related datasets to unravel age-specific effects, showing differential gene expression patterns within each condition. Important evidence was reported, such as enrichment in central nervous system development and regulation of oligodendrocyte differentiation between the hippocampus and cortex of 6-month-old AD mice as well as regulation of epinephrine secretion and dendritic spine morphogenesis in 15-month-old AD mice. Our outcomes from all three of our case studies illustrate the capacity of machine learning strategies when applied to single-cell data, revealing critical insights into Alzheimer’s disease. MDPI 2023-10-28 /pmc/articles/PMC10670182/ /pubmed/37998721 http://dx.doi.org/10.3390/cimb45110544 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Krokidis, Marios G.
Vrahatis, Aristidis G.
Lazaros, Konstantinos
Skolariki, Konstantina
Exarchos, Themis P.
Vlamos, Panagiotis
Machine Learning Analysis of Alzheimer’s Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions
title Machine Learning Analysis of Alzheimer’s Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions
title_full Machine Learning Analysis of Alzheimer’s Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions
title_fullStr Machine Learning Analysis of Alzheimer’s Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions
title_full_unstemmed Machine Learning Analysis of Alzheimer’s Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions
title_short Machine Learning Analysis of Alzheimer’s Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions
title_sort machine learning analysis of alzheimer’s disease single-cell rna-sequencing data across cortex and hippocampus regions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670182/
https://www.ncbi.nlm.nih.gov/pubmed/37998721
http://dx.doi.org/10.3390/cimb45110544
work_keys_str_mv AT krokidismariosg machinelearninganalysisofalzheimersdiseasesinglecellrnasequencingdataacrosscortexandhippocampusregions
AT vrahatisaristidisg machinelearninganalysisofalzheimersdiseasesinglecellrnasequencingdataacrosscortexandhippocampusregions
AT lazaroskonstantinos machinelearninganalysisofalzheimersdiseasesinglecellrnasequencingdataacrosscortexandhippocampusregions
AT skolarikikonstantina machinelearninganalysisofalzheimersdiseasesinglecellrnasequencingdataacrosscortexandhippocampusregions
AT exarchosthemisp machinelearninganalysisofalzheimersdiseasesinglecellrnasequencingdataacrosscortexandhippocampusregions
AT vlamospanagiotis machinelearninganalysisofalzheimersdiseasesinglecellrnasequencingdataacrosscortexandhippocampusregions