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Characterization of transcriptional and proteomics changes in brain cells derived from isogenic hiPSCs reveals cell type–and genotype‐specific mechanisms modified by APOE
Author(s) -
Ried Janina S.,
Curado Marco Rocha,
Sáez María Eugenia,
Bahnassawy Lamiaa,
Lee Heyne,
Reinhardt Peter,
Mohler Eric G.,
Madrid Laura,
Socorro Alfredo Cabrera,
Grezella Clara,
Nicolas Armel,
Rao Francesco,
Ramaswamy Gayathri,
Bakker Margot H.M.
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.040280
Subject(s) - apolipoprotein e , biology , genotype , transcriptome , phenotype , proteomics , gene , cell type , microglia , computational biology , genetics , gene expression , cell , immunology , disease , medicine , inflammation , pathology
Background While being the most prominent genetic risk factor for AD, the effects of APOE variants on biology and the development of AD is still poorly understood. The IMI ADAPTED consortium strives to illuminate the mechanisms underlying the effect of APOE with several approaches. Complementing the reanalysis of the publicly available human data, hiPSC lines with different APOE genotypes (ε3/ε3, ε4/ε4, ε3/ε4, ε2/ε2), differentiated into distinct brain cell types provide a tool to study cell‐type level effects of the APOE genotype. Further, OMICS data of human APOE modified mouse model (ε2/ε2, ε3/ε3, ε4/ε4) allowed cross species comparison and model validation. Method For multiple brain cell types differentiated from the APOE modified isogenic hiPSC (neurons, astrocytes, microglia and macrophages) and mice transcriptomics and proteomics data were generated. Differential gene expression (DGE) and protein expression (DPE) was calculated, followed by gene set enrichment. Clustering approaches were used to identify shared and differing gene signatures across genotypes. We further applied upstream regulator and network analysis on the individual cell‐type results and integrated these results across cell types. The results were compared with DGE and DPE results from the humanized APOE mouse model. Finally, the identified genes and mechanisms were combined with the results of the integrated analysis of data from postmortem human brain samples of AD cases and controls. Results The observed transcriptional changes confirmed and extended the phenotypic observations and refined the insight of genes identified in the integrated analysis of APOE genotype stratified human OMICS data. Several genes/proteins and pathways were consistently identified on transcriptome and proteome level. Further, shared patterns of expressions of genes across genotypes and potential mechanisms involved in this were detected. Conclusions In depth transcriptomics and proteomics analysis of APOE modified hiPSCs‐derived cells enabled to study cell type and genotype specific effects and contributed with this to the understanding of mechanisms affected by different APOE genotypes. The findings in hiPSC we be compared with human blood and CSF OMICS analysis to be potentially used as biomarker for AD and AD progression.