AUTHOR CONTRIBUTIONS R.C.W. designed and performed all scRNAseq experiments, analyzed the scRNAseq data, performed the RNAscope in-situ hybridization assays, performed and analyzed the CITE-seq and FACS experiments, analyzed the immunofluorescence data, performed the eQTL analyses, assisted with mouse colony breeding, drafted the manuscript, and led the study. D.W. assisted with the design of the scRNAseq experiments and performed scRNAseq capture and library preparation for all samples. D.T.P. performed scRNAseq capture and helped obtain human coronary samples. J.C. assisted with the scRNAseq capture, library preparation and sequencing. T.N. performed qPCR experiments, analyzed the qPCR data and performed TCF21 ChIPseq. M.P., C.L.M., B.L. and S.B.M. performed the eQTL analyses. R.K. performed the immunohistochemistry experiments and bred the mouse colonies. M.N. performed and analyzed immunohistochemistry experiments. K.Z., M.A. and R.C. assisted with network analysis. T.K.K., R.F. and Y.J.W. prepared the human tissue samples. M.D.T. and J.C.W. provided critical expert guidance on the manuscript. J.B.K. helped plan the mouse in situ histology studies, managed the mouse colonies, performed the TCF21 over-expression experiment and performed the quantitative immunohistochemistry analysis of lesion characteristics. T.Q. conceived and supervised the study. All authors discussed the results and contributed critical review to the manuscript.

Introduction The Alzheimer’s Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer’s disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance. Methods Fasting serum samples from the Alzheimer’s Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted. Results Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1–42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease. Discussion Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.

SUMMARY Variability in induced pluripotent stem cell (iPSC) lines remains a concern for disease modeling and regenerative medicine. We have used RNA sequencing analysis and linear mixed models to examine the sources of gene expression variability in 317 human iPSC lines from 101 individuals. We found that ~50% of genome-wide expression variability is explained by variation across individuals and identified a set of expression quantitative trait loci that contribute to this variation. These analyses coupled with allele specific expression show that iPSCs retain a donor specific gene expression pattern. Network, pathway and key driver analyses showed that Polycomb targets contribute significantly to the non-genetic variability seen within and across individuals, highlighting this chromatin regulator as a likely source of reprogramming-based variability. Our findings therefore shed light on variation between iPSC lines and illustrate the potential for our dataset and other similar large-scale analyses to identify underlying drivers relevant to iPSC applications.