Meta-analysis of Alzheimer’s disease scRNA data for identification of cell-types and eigengenes at 3 stages

Abstract

Abstract (Max. 300 words) Background and Objective: Alzheimer’s disease is a tough condition which takes its toll on the individuals as well as society and whose statistics exacerbate each year, especially in the western world. The work to combat the disease proceeds on all levels, including profiling of gene expression of individual cells. The separate data of different research groups accumulates, while integration of this data might help turn quantity into augmented quality. The objective of this study is to integrate materials from different scRNA experiments to obtain extra value, not yielded separately. Methodology: NCBI dataset and ADPortal dataset were integrated using R package for scRNA analysis Seurat, and the data was split by the criterion of age (3 groups: Healthy Controls, Mild Cognitive Impairment, and Alzheimer’s disese). Eigengenes were calculated, cell clusters extracted, and the cell types inferred using Human Protein Atlas judging the by protein expression profile. Results: The analysis demonstrated successful identification of genes, conserved between three age groups. Significantly expressed genes were verified against the existing literature on the subject of AD-associated genes, proving credibility. In addition, eigengenes not reported before (including database original papers) achieved significance levels, while the study identified their likely cell groups. Conclusions: This work is a part of a major project of the lab for evaluation of apoptosis markers by AD and their profiling through disease progress. The results of the data will, on one hand, help further laboratory work aimed at marker refinement, and, on the dry lab aspect, pave way to further integration of separate datasets with the idea of methodology improvement with the purpose of building on statistical significance with accumulated data volume.