We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve decomposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between cell type proportions and measured phenotypes across abundant and rare cell types. We further propose an additional mode of operation that merely requires a set of known marker genes.

Increased adiposity is a hallmark of obesity and overweight, which affect 2.2 billion people world-wide. Understanding the genetic and molecular mechanisms that underlie obesity-related phenotypes can help to improve treatment options and drug development. Here we perform promoter Capture Hi–C in human adipocytes to investigate interactions between gene promoters and distal elements as a transcription-regulating mechanism contributing to these phenotypes. We find that promoter-interacting elements in human adipocytes are enriched for adipose-related transcription factor motifs, such as PPARG and CEBPB, and contribute to heritability of cis-regulated gene expression. We further intersect these data with published genome-wide association studies for BMI and BMI-related metabolic traits to identify the genes that are under genetic cis regulation in human adipocytes via chromosomal interactions. This integrative genomics approach identifies four cis-eQTL-eGene relationships associated with BMI or obesity-related traits, including rs4776984 and MAP2K5, which we further confirm by EMSA, and highlights 38 additional candidate genes.

Embryonic heart formation requires the production of an appropriate number of cardiomyocytes; likewise, cardiac regeneration following injury relies upon the recovery of lost cardiomyocytes. The basic helix-loop-helix (bHLH) transcription factor Hand2 has been implicated in promoting cardiomyocyte formation. It is unclear, however, whether Hand2 plays an instructive or permissive role during this process. Here, we find that overexpression of hand2 in the early zebrafish embryo is able to enhance cardiomyocyte production, resulting in an enlarged heart with a striking increase in the size of the outflow tract. Our evidence indicates that these increases are dependent on the interactions of Hand2 in multimeric complexes and are independent of direct DNA binding by Hand2. Proliferation assays reveal that hand2 can impact cardiomyocyte production by promoting division of late-differentiating cardiac progenitors within the second heart field. Additionally, our data suggest that hand2 can influence cardiomyocyte production by altering the patterning of the anterior lateral plate mesoderm, potentially favoring formation of the first heart field at the expense of hematopoietic and vascular lineages. The potency of hand2 during embryonic cardiogenesis suggested that hand2 could also impact cardiac regeneration in adult zebrafish; indeed, we find that overexpression of hand2 can augment the regenerative proliferation of cardiomyocytes in response to injury. Together, our studies demonstrate that hand2 can drive cardiomyocyte production in multiple contexts and through multiple mechanisms. These results contribute to our understanding of the potential origins of congenital heart disease and inform future strategies in regenerative medicine.

Single-nucleus RnA sequencing (snRnA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. We observe that snRNA-seq is commonly subject to contamination by high amounts of ambient RNA, which can lead to biased downstream analyses, such as identification of spurious cell types if overlooked. We present a novel approach to quantify contamination and filter droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: (1) human differentiating preadipocytes in vitro, (2) fresh mouse brain tissue, and (3) human frozen adipose tissue (AT) from six individuals. All three data sets showed evidence of extranuclear RNA contamination, and we observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq, our clustering strategy also successfully filtered single-cell RNA-seq data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https ://githu b.com/marca lva/ diem. Single-cell RNA sequencing (scRNA-seq) has grown considerably in use over the previous decade and permitted a transcriptomic view into the composition of heterogeneous mixtures of cells 1,2. Recent advances in droplet-based microfluidics have created a high-throughput opportunity to assay single cells by scaling up previous well-based technologies to tens to hundreds of thousands of cells 3. Single-nucleus RNA sequencing (snRNA-seq), where nuclei are used instead of cells, has allowed the critical extension of single-cell based technologies to solid tissues where isolation and suspension of individual cells is difficult or impossible 4. For example, snRNA-seq has been

1Although recent studies provide evidence for a common genetic basis between complex traits 2 and Mendelian disorders, a thorough quantification of their overlap in a phenotype-specific 3 manner remains elusive. Here, we quantify the overlap of genes identified through large-scale 4 genome-wide association studies (GWAS) for 62 complex traits and diseases with genes known 5 to cause 20 broad categories of Mendelian disorders. We identify a significant enrichment of 6 phenotypically-matched Mendelian disorder genes in GWAS gene sets. Further, we observe 7 elevated GWAS effect sizes near phenotypically-matched Mendelian disorder genes. Finally, we 8 report examples of GWAS variants localized at the transcription start site or physically 9 interacting with the promoters of phenotypically-matched Mendelian disorder genes. Our results 1 0 are consistent with the hypothesis that genes that are disrupted in Mendelian disorders are 1 1 dysregulated by noncoding variants in complex traits, and demonstrate how leveraging findings 1 2 from related Mendelian disorders and functional genomic datasets can prioritize genes that are 1 3 putatively dysregulated by local and distal non-coding GWAS variants.