Johnson, Travis S.Xiang, ShunianDong, TianhanHuang, ZhiCheng, MichaelWang, TianfuYang, KaiNi, DongHuang, KunZhang, Jie2022-04-292022-04-292021-01-11Johnson TS, Xiang S, Dong T, et al. Combinatorial analyses reveal cellular composition changes have different impacts on transcriptomic changes of cell type specific genes in Alzheimer's Disease. Sci Rep. 2021;11(1):353. Published 2021 Jan 11. doi:10.1038/s41598-020-79740-xhttps://hdl.handle.net/1805/28805Alzheimer’s disease (AD) brains are characterized by progressive neuron loss and gliosis. Previous studies of gene expression using bulk tissue samples often fail to consider changes in cell-type composition when comparing AD versus control, which can lead to differences in expression levels that are not due to transcriptional regulation. We mined five large transcriptomic AD datasets for conserved gene co-expression module, then analyzed differential expression and differential co-expression within the modules between AD samples and controls. We performed cell-type deconvolution analysis to determine whether the observed differential expression was due to changes in cell-type proportions in the samples or to transcriptional regulation. Our findings were validated using four additional datasets. We discovered that the increased expression of microglia modules in the AD samples can be explained by increased microglia proportions in the AD samples. In contrast, decreased expression and perturbed co-expression within neuron modules in the AD samples was likely due in part to altered regulation of neuronal pathways. Several transcription factors that are differentially expressed in AD might account for such altered gene regulation. Similarly, changes in gene expression and co-expression within astrocyte modules could be attributed to combined effects of astrogliosis and astrocyte gene activation. Gene expression in the astrocyte modules was also strongly correlated with clinicopathological biomarkers. Through this work, we demonstrated that combinatorial analysis can delineate the origins of transcriptomic changes in bulk tissue data and shed light on key genes and pathways involved in AD.en-USAttribution 4.0 InternationalNeurological disordersComputational biology and bioinformaticsData miningFunctional clusteringMolecular neuroscienceArticle