Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures

Objective: Type 2 diabetes (T2D) is a complex disease characterized by pancreatic islet dysfunction, insulin resistance, and disruption of blood glucose levels. Genome-wide association studies (GWAS) have identified > 400 independent signals that encode genetic predisposition. More than 90% of associated single-nucleotide polymorphisms (SNPs) localize to non-coding regions and are enriched in chromatin-defined islet enhancer elements, indicating a strong transcriptional regulatory component to disease susceptibility. Pancreatic islets are a mixture of cell types that express distinct hormonal programs, so each cell type may contribute differentially to the underlying regulatory processes that modulate T2D-associated transcriptional circuits. Existing chromatin profiling methods such as ATAC-seq and DNase-seq, applied to islets in bulk, produce aggregate profiles that mask important cellular and regulatory heterogeneity. Methods: We present genome-wide single-cell chromatin accessibility profiles in >1,600 cells derived from a human pancreatic islet sample using single-cell combinatorial indexing ATAC-seq (sci-ATAC-seq). We also developed a deep learning model based on U-Net architecture to accurately predict open chromatin peak calls in rare cell populations. Results: We show that sci-ATAC-seq profiles allow us to deconvolve alpha, beta, and delta cell populations and identify cell-type-specific regulatory signatures underlying T2D. Particularly, T2D GWAS SNPs are significantly enriched in beta cell-specific and across cell-type shared islet open chromatin, but not in alpha or delta cell-specific open chromatin. We also demonstrate, using less abundant delta cells, that deep learning models can improve signal recovery and feature reconstruction of rarer cell populations. Finally, we use co-accessibility measures to nominate the cell-specific target genes at 104 non-coding T2D GWAS signals. Conclusions: Collectively, we identify the islet cell type of action across genetic signals of T2D predisposition and provide higher-resolution mechanistic insights into genetically encoded risk pathways. Published by Elsevier GmbH.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

The ALMA Survey of 70 μm Dark High-mass Clumps in Early Stages (ASHES). I. Pilot Survey: Clump Fragmentation

\ua9 2019. The American Astronomical Society. All rights reserved. The ALMA Survey of 70 μm dark High-mass clumps in Early Stages (ASHES) is designed to systematically characterize the earliest stages and constrain theories of high-mass star formation. Twelve massive (>500 M⊙ ), cold (≤15 K), 3.6-70 μm dark prestellar clump candidates, embedded in infrared dark clouds, were carefully selected in the pilot survey to be observed with the Atacama Large Millimeter/submillimeter Array (ALMA). We have mosaicked each clump (∼1 arcmin2) in continuum and line emission with the 12 m, 7 m, and Total Power (TP) arrays at 224 GHz (1.34 mm), resulting in ∼1.″2 resolution (∼4800 au, at the average source distance). As the first paper in the series, we concentrate on the continuum emission to reveal clump fragmentation. We detect 294 cores, from which 84 (29%) are categorized as protostellar based on outflow activity or "warm core" line emission. The remaining 210 (71%) are considered prestellar core candidates. The number of detected cores is independent of the mass sensitivity range of the observations and, on average, more massive clumps tend to form more cores. We find a large population of low-mass (30 M⊙) prestellar cores (maximum mass 11 M⊙). From the prestellar core mass function, we derive a power-law index of 1.17 \ub1 0.10, which is slightly shallower than Salpeter. We used the minimum spanning tree (MST) technique to characterize the separation between cores and their spatial distribution, and to derive mass segregation ratios. While there is a range of core masses and separations detected in the sample, the mean separation and mass per clump are well explained by thermal Jeans fragmentation and are inconsistent with turbulent Jeans fragmentation. Core spatial distribution is well described by hierarchical subclustering rather than centrally peaked clustering. There is no conclusive evidence of mass segregation. We test several theoretical conditions and conclude that overall, competitive accretion and global hierarchical collapse scenarios are favored over the turbulent core accretion scenario

Imputation-based meta-analysis of severe malaria in three African populations.

Combining data from genome-wide association studies (GWAS) conducted at different locations, using genotype imputation and fixed-effects meta-analysis, has been a powerful approach for dissecting complex disease genetics in populations of European ancestry. Here we investigate the feasibility of applying the same approach in Africa, where genetic diversity, both within and between populations, is far more extensive. We analyse genome-wide data from approximately 5,000 individuals with severe malaria and 7,000 population controls from three different locations in Africa. Our results show that the standard approach is well powered to detect known malaria susceptibility loci when sample sizes are large, and that modern methods for association analysis can control the potential confounding effects of population structure. We show that pattern of association around the haemoglobin S allele differs substantially across populations due to differences in haplotype structure. Motivated by these observations we consider new approaches to association analysis that might prove valuable for multicentre GWAS in Africa: we relax the assumptions of SNP-based fixed effect analysis; we apply Bayesian approaches to allow for heterogeneity in the effect of an allele on risk across studies; and we introduce a region-based test to allow for heterogeneity in the location of causal alleles