Processing...

Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes

Video Abstract (AI generated) Paper Preprint

Recently developed single cell technologies allow researchers to characterize cell states at ever greater resolution and scale. C. elegans is a particularly tractable system for studying development, and recent single cell RNA-seq studies characterized the gene expression patterns for nearly every cell type in the embryo and at the second larval stage (L2). Gene expression patterns are useful for learning about gene function and give insight into the biochemical state of different cell types; however, in order to understand these cell types, we must also determine how these gene expression levels are regulated. We present the first single cell ATAC-seq study in C. elegans . We collected data in L2 larvae to match the available single cell RNA-seq data set, and we identify tissue-specific chromatin accessibility patterns that align well with existing data, including the L2 single cell RNA-seq results. Using a novel implementation of the latent Dirichlet allocation algorithm, we leverage the single-cell resolution of the sci-ATAC-seq data to identify accessible loci at the level of individual cell types, providing new maps of putative cell type-specific gene regulatory sites, with promise for better understanding of cellular differentiation and gene regulation in the worm.

Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies

Scientific Reports, 2017-12-05

Video Abstract (AI generated) Paper Preprint Scientific Reports

A comprehensive characterization of tumor genetic heterogeneity is critical for understanding how cancers evolve and escape treatment. Although many algorithms have been developed for capturing tumor heterogeneity, they are designed for analyzing either a single type of genomic aberration or individual biopsies. Here we present THEMIS (Tumor Heterogeneity Extensible Modeling via an Integrative System), which allows for the joint analysis of different types of genomic aberrations from multiple biopsies taken from the same patient, using a dynamic graphical model. Simulation experiments demonstrate higher accuracy of THEMIS over its ancestor, TITAN. The heterogeneity analysis results from THEMIS are validated with single cell DNA sequencing from a clinical tumor biopsy. When THEMIS is used to analyze tumor heterogeneity among multiple biopsies from the same patient, it helps to reveal the mutation accumulation history, track cancer progression, and identify the mutations related to treatment resistance. We implement our model via an extensible modeling platform, which makes our approach open, reproducible, and easy for others to extend.

Accurate functional classification of thousands of BRCA1 variants with saturation genome editing

Nature, 2018-09-12

Video Abstract (AI generated) Paper Preprint Nature

Variants of uncertain significance (VUS) fundamentally limit the utility of genetic information in a clinical setting. The challenge of VUS is epitomized by BRCA1, a tumor suppressor gene integral to DNA repair and genomic stability. Germline BRCA1 loss-of-function (LOF) variants predispose women to early-onset breast and ovarian cancers. Although BRCA1 has been sequenced in millions of women, the risk associated with most newly observed variants cannot be definitively assigned. Data sharing attenuates this problem but it is unlikely to solve it, as most newly observed variants are exceedingly rare. In lieu of genetic evidence, experimental approaches can be used to functionally characterize VUS. However, to date, functional studies of BRCA1 VUS have been conducted in a post hoc, piecemeal fashion. Here we employ saturation genome editing to assay 96.5% of all possible single nucleotide variants (SNVs) in 13 exons that encode functionally critical domains of BRCA1. Our assay measures cellular fitness in a haploid human cell line whose survival is dependent on intact BRCA1 function. The resulting function scores for nearly 4,000 SNVs are bimodally distributed and almost perfectly concordant with established assessments of pathogenicity. Sequence-function maps enhanced by parallel measurements of variant effects on mRNA levels reveal mechanisms by which loss-of-function SNVs arise. Hundreds of missense SNVs critical for protein function are identified, as well as dozens of exonic and intronic SNVs that compromise BRCA1 function by disrupting splicing or transcript stability. We predict that these function scores will be directly useful for the clinical interpretation of cancer risk based on BRCA1 sequencing. Furthermore, we propose that this paradigm can be extended to overcome the challenge of VUS in other genes in which genetic variation is clinically actionable.

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

Molecular Metabolism, 2019-12-20

Video Abstract (AI generated) Paper Preprint Molecular Metabolism

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 the 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, and 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 the 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, we find that T2D GWAS SNPs are significantly enriched in beta cell-specific and cross 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.

Precise genomic deletions using paired prime editing

Video Abstract (AI generated) Paper Preprint Supplementary Figures Supplementary sequences

Technologies that precisely delete genomic sequences in a programmed fashion can be used to study function as well as potentially for gene therapy. The leading contemporary method for programmed deletion uses CRISPR/Cas9 and pairs of guide RNAs (gRNAs) to generate two nearby double-strand breaks, which is often followed by deletion of the intervening sequence during DNA repair. However, this approach can be inefficient and imprecise, with errors including small indels at the two target sites as well as unintended large deletions and more complex rearrangements. Here we describe a prime editing-based method that we term PRIME-Del, which induces a deletion using a pair of prime editing gRNAs (pegRNAs) that target opposite DNA strands, effectively programming not only the sites that are nicked but also the outcome of the repair. We demonstrate that PRIME-Del achieves markedly higher precision in programming deletions than CRISPR/Cas9 and gRNA pairs. We also show that PRIME-Del can be used to couple genomic deletions with short insertions, enabling deletions whose junctions do not fall at protospacer-adjacent motif (PAM) sites. Finally, we demonstrate that lengthening the time window of expression of prime editing components can substantially enhance efficiency without compromising precision. We anticipate that PRIME-Del will be broadly useful in enabling precise, flexible programming of genomic deletions, including in-frame deletions, as well as for epitope tagging and potentially for programming rearrangements.

TransMPRA: A framework for assaying the role of many trans-acting factors at many enhancers

Video Abstract (AI generated) Paper Preprint Supplementary Table 1 Supplementary Data 1 Supplementary Data 2 Data/Code

Gene regulation occurs through trans -acting factors ( e.g. transcription factors) acting on cis -regulatory elements ( e.g. enhancers). Massively parallel reporter assays (MPRAs) functionally survey large numbers of cis -regulatory elements for regulatory potential, but do not identify the trans -acting factors that mediate any observed effects. Here we describe trans MPRA — a reporter assay that efficiently combines multiplex CRISPR-mediated perturbation and MPRAs to identify trans- acting factors that modulate the regulatory activity of specific enhancers. ### Competing Interest Statement The authors have declared no competing interest.