Ucar, D. — Page 2 | Search bioRxiv

bioRxiv2017

Chromatin accessibility profiling uncovers genetic- and T2D disease state-associated changes in cis-regulatory element use in human islets

Genetic and environmental factors both contribute to islet dysfunction and failure, resulting in type 2 diabetes (T2D). The islet epigenome integrates these cues and can be remodeled by genetic and environmental variation. However, our knowledge of how genetic variants and T2D disease state alter human islet chromatin landscape and cis-regulatory element (RE) use is lacking. To fill this gap, we profiled and analyzed human islet chromatin accessibility maps from 19 genotyped individuals (5 with T2D) using ATAC-seq technology. Chromatin accessibility quantitative trait locus (caQTL) analyses identified 3001 sequence variants (FDR<10%) altering putative cis-RE use/activity. Islet caQTL were significantly and specifically enriched in islet stretch enhancers and islet-specific transcription factor binding motifs, such as FOXA2, NKX6.1, RFX5/6 and PDX1. Importantly, these analyses identified putative functional single nucleotide variants (SNVs) in 13 T2D-associated GWAS loci, including those previously associated with altered ZMIZ1, MTNR1B, RNF6, and ADCY5 islet expression, and linked the risk alleles to increased (n=8) or decreased (n=5) islet chromatin accessibility. Luciferase reporter assays confirmed allelic differences in cis-RE activity for 5/9 caQTL sequences tested, including a T2D-associated SNV in the IL20RA locus. Comparison of T2D and non-diabetic islets revealed 1882 open chromatin sites exhibiting T2D-associated chromatin accessibility changes (FDR<10%). Together, this study provides new insights into genetic variant and T2D disease state effects on islet cis-RE use and serves as an important resource to identify putative functional variants in T2D-and islet dysfunction-associated GWAS loci and link their risk allele to in vivo loss or gain of chromatin accessibility.

genomics↗

bioRxiv2017

A robust statistical framework to detect multiple sources of hidden variation in single-cell transcriptomes

Single cell RNA-sequencing (scRNA-seq) precisely characterize gene expression levels and dissect variation in expression associated with the state (technical or biological) and the type of the cell, which is averaged out in bulk measurements. Multiple and correlated sources contribute to gene expression variation in single cells, which makes their estimation difficult with the existing methods developed for bulk measurements (e.g., surrogate variable analysis (SVA)) that estimate orthogonal transformations of these sources. We developed iteratively adjusted surrogate variable analysis (IA-SVA) that can estimate hidden and correlated sources of variation by identifying a set of genes affected with each hidden factor in an iterative manner. Analysis of scRNA-seq data from human cells showed that IA-SVA could accurately capture hidden variation arising from technical (e.g., stacked doublet cells) or biological sources (e.g., cell type or cell-cycle stage). Furthermore, IA-SVA delivers a set of genes associated with the detected hidden source to be used in downstream data analyses. As a proof of concept, IA-SVA recapitulated known marker genes for islet cell subsets (e.g., alpha, beta), which improved the grouping of subsets into distinct clusters. Taken together, IA-SVA is an effective and novel method to dissect multiple and correlated sources of variation in scRNA-seq data.

bioinformatics↗