Biology subjects

Katsevich, E.

Publications and source records attributed to Katsevich, E..

9 recordsLinked to original sources

bioRxiv2026

Enhancer-gene regulatory interactions in colorectal cancer revealed through genome-wide CRISPRi perturbations

BackgroundEstablishing functional relationships between distal enhancers and their target genes is a central challenge in genome biology and cancer genetics. CRISPR/dCas9-mediated perturbations followed by single-cell RNA sequencing (Perturb-seq) has emerged as an efficient means of establishing enhancer-target gene relationships. ResultsWe used genome-wide CRISPRi Perturb-seq with 35,139 guide RNAs targeting 12,117 enhancers to create a functional enhancer-gene map of colorectal cancer (CRC), identifying 238 significant regulatory associations (FDR < 0.1). Integration of chromatin accessibility (ATAC-seq), histone modification profiling (ChIP-seq), and ultra-high resolution chromatin conformation capture (Micro-C) data revealed that enhancer regulation in CRC is constrained by topological domains and often targets the nearest gene, findings corroborated by Activity-by-Contact modelling. ConclusionsWe present here a comprehensive, genome-scale functional atlas of enhancer-gene associations in CRC. This resource should provide a foundation for studying gene regulation in colorectal tumorigenesis and for prioritising candidate non-coding drivers in cancer sequencing studies. PLAIN LANGUAGE SUMMARYO_ST_ABSMapping the hidden DNA switches that control colon cancer genesC_ST_ABSThe total amount of DNA in humans is vast, but only a tiny proportion of it is directly related to genes. While people may know that genes act as blueprints for the body, there are also short stretches of DNA called "enhancers", that fine-tune how strongly genes are activated. When these enhancers get damaged or mutated, it can trigger disease, suggesting that changes to these "control switches" themselves, and not just the genes, can be a driver of cancer. Thus, understanding how these enhancers function is key to uncovering the mechanisms that drive colorectal cancer. In this study, we aimed to map the role of enhancers in colorectal (bowel) cancer development. We used Perturb-seq, a cutting-edge genetic tool that allowed us to systematically turn off more than 12,000 individual enhancers, to identify which ones control which genes. We successfully linked 238 enhancers to the genes they regulate. Combining these results with other biological information, such as the 3D-shape of the DNA, allowed us to build a detailed picture of how enhancers physically interact with their target genes inside cancer cells. This work provides a comprehensive "wiring diagram" of enhancer-gene connections in colorectal cancer, and a resource for researchers seeking to develop new treatment approaches.

genomics↗

bioRxiv2026

PerturbPlan: An analytical framework for designing Perturb-seq experiments

CRISPR screens with single-cell RNA-seq readouts provide a powerful tool for characterizing the functions of noncoding elements and genes. However, designing these experiments to balance statistical power and cost is challenging, given the large number of design parameters. The only available tool for this purpose is a simulation-based power calculator, but it is computationally costly and requires high-performance computing to run. We derive a novel analytical formula for the power to detect perturbation-expression associations, recapitulating power estimates from the simulation-based tool while reducing runtime by up to seven orders of magnitude. This acceleration unlocks the possibility of interactive single-cell CRISPR screen design. Accordingly, we develop PerturbPlan, an interactive web application built on the analytical power formula. PerturbPlan helps users address 11 design questions for two types of single-cell CRISPR screens, Perturb-seq and targeted Perturb-seq (TAP-seq). We apply PerturbPlan to carry out a comparative analysis of three recent Perturb-seq designs, demonstrating how optimal design varies across experiments of different scales. We also use PerturbPlan to quantify the cost savings of a recent TAP-seq study relative to a hypothetical Perturb-seq study assaying the same perturbations, illustrating how the tool can inform decisions about targeted versus whole-transcriptome readouts. In sum, PerturbPlan is the first tool to facilitate flexible and interactive design of well-powered single-cell CRISPR screen experiments.

genomics↗

bioRxiv2025

An unbiased survey of distal element-gene regulatory interactions with direct-capture targeted Perturb-seq

Identifying the impact of distal regulatory elements on gene expression is a core challenge in human genetics. Large-scale CRISPR screens have not captured lower effect size element-gene interactions due to selection bias and limited statistical power. We developed a framework for highly powered CRISPR screens, consisting of Direct-Capture Targeted Perturb-seq (DC-TAP-seq), unbiased target selection, and a pipeline accounting for statistical power. Surveying 10,000 random distal element-gene pairs revealed most element-gene interactions have effect sizes <10%, which were virtually undetectable in prior studies. Most interactions occur within 100kb, many elements bind CTCF without classical enhancer chromatin, and housekeeping genes have similar frequencies of distal regulatory elements but with weaker effects. We also highlight limitations of predictive models and suggest that new models consider elements with smaller effect sizes. Our study provides an expanded view of distal regulatory elements and a framework for building more comprehensive maps of distal regulation.

genomics↗

bioRxiv2024

GWAS-informed data integration and non-coding CRISPRi screen illuminate genetic etiology of bone mineral density

Over 1,100 independent signals have been identified with genome-wide association studies (GWAS) for bone mineral density (BMD), a key risk factor for mortality-increasing fragility fractures; however, the effector gene(s) for most remain unknown. Informed by a variant-to-gene mapping strategy implicating 89 non-coding elements predicted to regulate osteoblast gene expression at BMD GWAS loci, we executed a single-cell CRISPRi screen in human fetal osteoblasts (hFOBs). The BMD relevance of hFOBs was supported by heritability enrichment from stratified LD-score regression involving 98 cell types grouped into 15 tissues. 23 genes showed perturbation in the screen, with four (ARID5B, CC2D1B, EIF4G2, and NCOA3) exhibiting consistent effects upon siRNA knockdown on three measures of osteoblast maturation and mineralization. Lastly, additional heritability enrichments, genetic correlations, and multi-trait fine-mapping revealed unexpectedly that many BMD GWAS signals are pleiotropic and likely mediate their effects via non-bone tissues. Extending our CRISPRi screening approach to these tissues could play a key role in fully elucidating the etiology of BMD.

genomics↗

bioRxiv2023

Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection

Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method -- SCEPTRE low-MOI -- that resolves these analysis challenges and demonstrates improved calibration and power.

genomics↗

bioRxiv2021

Discovery of target genes and pathways of blood trait loci using pooled CRISPR screens and single cell RNA sequencing

The majority of variants associated with complex traits and common diseases identified by genome-wide association studies (GWAS) map to noncoding regions of the genome with unknown regulatory effects in cis and trans. By leveraging biobank-scale GWAS data, massively parallel CRISPR screens and single cell transcriptome sequencing, we discovered target genes of noncoding variants for blood trait loci. The closest gene was often the target gene, but this was not always the case. We also identified trans-effects networks of noncoding variants when cis target genes encoded transcription factors, such as GFI1B and NFE2. We observed that GFI1B trans-target genes were enriched for GFI1B binding sites and fine-mapped GWAS variants, and expressed in human bone marrow progenitor cells, suggesting that GFI1B acts as a master regulator of blood traits. This platform will enable massively parallel assays to catalog the target genes of human noncoding variants in both cis and trans.

genomics↗

bioRxiv2020

Conditional resampling improves sensitivity and specificity of single cell CRISPR regulatory screens

Single-cell CRISPR screens are the most promising biotechnology for mapping regulatory elements to their target genes at genome-wide scale. However, the analysis of these screens presents significant statistical challenges. For example, technical factors like sequencing depth impact not only expression measurement but also perturbation detection, creating a confounding effect. We demonstrate on two recent high multiplicity of infection single-cell CRISPR screens how these challenges cause calibration issues among existing analysis methods. To address these challenges, we propose SCEPTRE: analysis of single-cell perturbation screens via conditional re-sampling. This methodology, designed to avoid calibration issues due to technical confounders and expression model misspecification, infers associations between perturbations and expression by resampling the former according to a working model for perturbation detection probability in each cell. SCEPTRE demonstrates excellent calibration and sensitivity on the CRISPR screen data and yields hundreds of new regulatory relationships, supported by orthogonal functional evidence.

genomics↗

bioRxiv2019

Multi-resolution localization of causal variants across the genome

We present KnockoffZoom, a flexible method for the genetic mapping of complex traits at multiple resolutions. KnockoffZoom localizes causal variants by testing the conditional associations of genetic segments of decreasing width while provably controlling the false discovery rate using artificial genotypes as negative controls. Our method is equally valid for quantitative and binary phenotypes, making no assumptions about their genetic architectures. Instead, we rely on well-established genetic models of linkage disequilibrium. We demonstrate that our method can detect more associations than mixed effects models and achieve fine-mapping precision, at comparable computational cost. Lastly, we apply KnockoffZoom to data from 350k subjects in the UK Biobank and report many new findings.

genomics↗

bioRxiv2018

Exploratory Gene Ontology Analysis with Interactive Visualization

The Gene Ontology (GO) is a central resource for functional-genomics research. Scientists rely on the functional annotations in the GO for hypothesis generation and couple it with high-throughput biological data to enhance interpretation of results. At the same time, the sheer number of concepts (>30,000) and relationships (>70,000) presents a challenge: it can be difficult to draw a comprehensive picture of how certain concepts of interest might relate with the rest of the ontology structure. Here we present new visualization strategies to facilitate the exploration and use of the information in the GO. We rely on novel graphical display and software architecture that allow significant interaction. To illustrate the potential of our strategies, we provide examples from high-throughput genomic analyses, including chromatin immunoprecipitation experiments and genome-wide association studies. The scientist can also use our visualizations to identify gene sets that likely experience coordinated changes in their expression and use them to simulate biologically-grounded single cell RNA sequencing data, or conduct power studies for differential gene expression studies using our built-in pipeline. Our software and documentation are available at http://aegis.stanford.edu.

bioinformatics↗