Biology subjects

Strauss, M. E.

Publications and source records attributed to Strauss, M. E..

7 recordsLinked to original sources

bioRxiv2026

Comparative Single-Cell Profiling of CRISPR Knockout and Interference Defines Modality-Specific Strengths in Functional Genomics

Pooled CRISPR screens coupled with single-cell RNA sequencing enable high-throughput functional interrogation of gene regulatory networks, yet systematic comparisons of CRISPR knockout (CRISPRko) and CRISPR interference (CRISPRi) remain limited. We established a single-cell CRISPRko workflow and benchmarked it against CRISPRi using 87 sgRNAs targeting 29 unfolded protein response genes. CRISPRko generated transcriptional phenotypes are highly concordant with CRISPRi, and induced comparable pathway-level responses. While CRISPRi allows direct assessment of target gene repression, CRISPRko provides an effective complementary approach for complete loss-of-function studies, expanding the toolkit for single-cell functional genomics.

genomics↗

bioRxiv2026

Saturation-seq integrates single-cell saturation genome editing and RNA-seq to quantify NFE2L2 (NRF2) variant effects

Interpreting the functional consequences of variants remains one of the central unsolved problems in genomics and clinical genetics. Compounding this, most existing approaches rely on reductive, one-dimensional proxies such as cell growth to score variant effects, which can be a poor substitute for the rich, multidimensional phenotyping that is ultimately needed to understand how variants alter biology. This is especially true for variants known to act through gain-of-function/neomorphic mechanisms. We developed Saturation-seq, a high-throughput platform that combines saturation genome editing with single-cell DNA and RNA profiling to systematically map variant effects. Using CRISPR-based editing in a barcoded haploid cell line, we install hundreds of variants directly into endogenous genomic loci, testing them in multiplex and preserving the native coding and regulatory context. Single-cell amplicon and transcriptome sequencing enables direct linkage of each genomic edit to its transcriptional impact. We apply Saturation-seq to comprehensively characterize 230 variants in the recurrently mutated N-terminal region of NFE2L2 (NRF2), a master regulator of oxidative stress and an oncogene mutated in lung cancer. We define variant function with disruption scores computed from misregulation of known NRF2 targets in single-cell transcriptomes; scores separate pathogenic/benign truthset variants with >90% accuracy and enabled interpretation of TCGA and TRACERx patient tumor data, as well as a rare NFE2L2 germline variant linked to a developmental syndrome. Thus, we establish a broadly applicable high-resolution single-cell variant-to-function platform with a rich phenotypic readout.

genomics↗

bioRxiv2025

Convergent DNA Methylation Abnormalities at Bivalent Chromatin in Human Growth Disorders

Loss-of-function mutations in DNMT3A, a DNA methyltransferase, or NSD1, a histone methyltransferase, cause overgrowth syndromes. Conversely, disruption of the DNMT3A domain that binds NSD1-deposited H3K36 dimethylation (H3K36me2) results in growth restriction. To investigate the molecular basis of these opposing growth outcomes, we generated isogenic human embryonic stem cells carrying growth syndrome-associated mutations in DNMT3A and NSD1. Unexpectedly, both overgrowth- and growth restriction- associated DNMT3A mutations led to DNA hypomethylation in a shared subset of active enhancers, implicating H3K36me2 in directing enhancer methylation maintenance. In contrast, bivalent chromatin--marked by both active and repressive chromatin modifications--showed divergent DNA methylation changes: hypermethylation in growth restriction-associated DNMT3A mutants and hypomethylation in overgrowth-associated DNMT3A or NSD1 loss-of-function mutants. These findings identify locus-specific DNA methylation defects as a common molecular feature and nominate dysregulated DNA methylation at bivalent chromatin as a potential driver of abnormal growth phenotypes.

genomics↗

bioRxiv2024

A genome-scale single cell CRISPRi map of trans gene regulation across human pluripotent stem cell lines

Population-scale resources of genetic, molecular, and cellular information form the basis for understanding human genomes, charting the heritable basis of disease, and tracing the effects of mutations. Pooled perturbation assays applied to cellular models, probing the effect of many perturbations coupled with an scRNA-seq readout (Perturb-seq), are especially potent references for interpreting disease-linked mutations or gene expression changes. However, the utility of existing maps has been limited by the comprehensiveness of perturbations possible, and the relevance of their cell line context. Here, we present the first genome-scale CRISPR interference (CRISPRi) perturbation map with single-cell RNA sequencing readout across many human genetic backgrounds in human pluripotent cells. To do so, we establish large-scale CRISPRi screening in human induced pluripotent stem cells from healthy donors, using over 20,000 guide RNAs to target 7,226 genes across 34 cell lines from 26 genetic backgrounds, and gather expression data from nearly 2 million cells. We comprehensively map trans expression changes induced by the target knockdowns, which complement co-expression patterns in unperturbed cells and facilitate the functional annotation of target genes to biological processes and complexes. Consistency of targeting protein complex members point to protein complexes as a nexus for aggregating transcriptional variation, revealing novel interaction partners. We characterise variation in perturbation effects across donors, with expression quantitative trait loci linked to higher genetic modulation of perturbation effects but overall low replication of trans effects due to knockdown of their corresponding cis regulators. This study pioneers population-scale CRISPR perturbations with single cell readouts that will fuel foundation models for the future of effective modulation of cellular disease phenotypes.

genetics↗

bioRxiv2023

Systematic characterisation of perturbations in complex developing cell populations reveals mechanisms underpinning development and disease

Perturbation studies using gene knockouts have become a key tool for understanding the roles of regulatory genes in development and disease. Here we systematically characterise the knockout effects of the key developmental regulators T and Mixl1 in chimeric mouse embryos during gastrulation and organogenesis. We present a comprehensive and effective suite of statistical tools for systematic characterisation of effects at the level of differential abundance of cell types, lineage development, and gene dysregulation. Applying our computational approach to a novel chimera data set with Mixl1 knockout reveals a disruption in Epicardium development in the absence of Mixl1, characterized by lack of upregulation of the key transcription factor Tbx18 and the Wnt regulator Sfrp5, and by dysregulation of the recently identified juxta-cardiac field. Finally, we demonstrate the wider utility of our framework by applying it to published acute myeloid leukemia (AML) patient data, and show how different responses to therapy are reflected in changes in gene expression along the myeloid trajectory between healthy and AML patients.

genomics↗

bioRxiv2023

High-throughput phenotyping of single nucleotide variants by linking transcriptomes to genotypes in single cells

CRISPR screens with single-cell transcriptomic readouts are a valuable tool to understand the effect of genetic perturbations, but are currently limited because genotypes are inferred from the guide RNA identity. We have developed a technique that couples single-cell genotyping to transcriptomics of the same cells to enable screening for the effects of single nucleotide variants. Analysis of variants tiling across the JAK1 gene demonstrates the importance of determining the precise genetic perturbation and classifies missense variants into three functional categories.

genetics↗

bioRxiv2017

GPseudoRank: MCMC for sampling from posterior distributions of pseudo-orderings using Gaussian processes

MotivationA number of pseudotime methods have provided point estimates of the ordering of cells for scRNA-seq data. A still limited number of methods also model the uncertainty of the pseudotime estimate. However, there is still a need for a method to sample from complicated and multi-modal distributions of orders, and to estimate changes in the amount of the uncertainty of the order during the course of a biological development, as this can support the selection of suitable cells for the clustering of genes or for network inference.\n\nResultsIn an application to a microarray data set our proposed method, GPseudoRank, identifies two modes of the distribution, each of them corresponding to point estimates of orders obtained by a different established method. In an application to scRNA-seq data we demonstrate the potential of GPseudoRank to identify phases of lower and higher pseudotime uncertainty during a biological process. GPseudoRank also correctly identifies cells precocious in their antiviral response.\n\nAvailability and implementationOur method is available on github: https://github.com/magStra/GPseudoRank.\n\nContactmagdalena.strauss@mrc-bsu.cam.ac.uk\n\nSupplementary informationSupplementary materials are available.

bioinformatics↗