Biology subjects

Jost, M.

Publications and source records attributed to Jost, M..

26 records · Page 2Linked to original sources

bioRxiv2020

CRISPR-based functional genomics in human dendritic cells

Dendritic cells (DCs) regulate processes ranging from antitumor and antiviral immunity to host-microbe communication at mucosal surfaces. It remains difficult, however, to genetically manipulate human DCs, limiting our ability to probe how DCs elicit specific immune responses. Here, we develop a CRISPR/Cas9 genome editing method for human monocyte-derived DCs (moDCs) that mediates knockouts with a median efficiency of >93% across >300 genes. Using this method, we perform genetic screens in moDCs, identifying mechanisms by which DCs tune responses to lipopolysaccharides from the human microbiome. In addition, we reveal donor-specific responses to lipopolysaccharides, underscoring the importance of assessing immune phenotypes in donor-derived cells, and identify genes that control this specificity, highlighting the potential of our method to pinpoint determinants of inter-individual variation in immune responses. Our work sets the stage for a systematic dissection of the immune signaling at the host-microbiome interface and for targeted engineering of DCs for neoantigen vaccination.

immunology↗

bioRxiv2020

High-content Imaging-based Pooled CRISPR Screens in Mammalian Cells

CRISPR (clustered regularly interspaced short palindromic repeats) -based gene inactivation provides a powerful means of linking genes to particular cellular phenotypes. CRISPR-based screening has mostly relied upon using large genomic pools of single guide RNAs (sgRNAs). However, this approach is limited to phenotypes that can be enriched by chemical selection or FACS sorting. Here, we developed a microscopy-based approach, which we name optical enrichment, to computationally select cells displaying a particular CRISPR-induced phenotype, mark them by photo-conversion of an expressed photo-activatable fluorescent protein, and then isolate the fluorescent cells using fluorescence-activated cell sorting (FACS). A plugin was developed for the open source software Manager to automate the phenotypic identification and photo-conversion of cells, allowing ~1.5 million individual cells to be screened in 8 hr. We used this approach to screen 6092 sgRNAs targeting 544 genes for their effects on nuclear size regulation and identified 14 bona fide hits. These results present a highly scalable approach to facilitate imaging-based pooled CRISPR screens.

cell biology↗

bioRxiv2020

Pharmaceutical-grade Rigosertib is a Microtubule-destabilizing Agent

We recently used CRISPRi/a-based chemical-genetic screens and targeted cell biological, biochemical, and structural assays to determine that rigosertib, an anti-cancer agent in phase III clinical trials, kills cancer cells by destabilizing microtubules. In a recent manuscript, Reddy and co-workers suggest that this microtubule-destabilizing activity of rigosertib is mediated not by rigosertib itself but by a contaminating degradation product of rigosertib, ON01500, present in formulations obtained from commercial vendors (Baker et al., 2019). Here, we demonstrate that treatment of cells with pharmaceutical-grade rigosertib (>99.9% purity) results in qualitatively indistinguishable phenotypes as treatment with commercially obtained rigosertib across multiple assays. The two compounds have indistinguishable chemical-genetic interactions with genes involved in modulating the microtubule network (KIF2C and TACC3), both destabilize microtubules in cells and in vitro, and both show substantially reduced toxicity in cell lines expressing a rationally-designed mutant of tubulin (L240F TUBB mutant), in which the rigosertib binding site in tubulin is mutated. Importantly, the specificity of the L240F TUBB mutant for microtubule-destabilizing agents, which is disputed by Reddy and co-workers, was recently confirmed by an independent research group (Patterson et al., 2019). We conclude that rigosertib kills cancer cells by destabilizing microtubules, in agreement with our original findings.

cancer biology↗

bioRxiv2019

Modulated efficacy CRISPRi reveals evolutionary conservation of essential gene expression-fitness relationships in bacteria

Essential genes are the central hubs of cellular networks. Despite their importance, the lack of high-throughput methods for titrating their expression has limited our understanding of the fitness landscapes against which essential gene expression levels are optimized. We developed a modified CRISPRi system leveraging the predictable reduction in efficacy of imperfectly matched sgRNAs to generate specific levels of CRISPRi activity and demonstrate its broad applicability in bacteria. Using libraries of mismatched sgRNAs, we characterized the expression-fitness relationships of essential genes in Escherichia coli and Bacillus subtilis. Remarkably, these relationships co-vary by pathway and are predominantly conserved between E. coli and B. subtilis despite ~ 2 billion years of evolutionary separation, suggesting that deeply conserved tradeoffs underlie bacterial homeostasis.\n\nOne Sentence SummaryBacterial essential genes have varying responses to CRISPRi knockdown that are largely conserved across ~2 billion years of evolution.

systems biology↗

bioRxiv2019

Titrating gene expression with series of systematically compromised CRISPR guide RNAs

Biological phenotypes arise from the degrees to which genes are expressed, but the lack of tools to precisely control gene expression limits our ability to evaluate the underlying expression-phenotype relationships. Here, we describe a readily implementable approach to titrate expression of human genes using series of systematically compromised sgRNAs and CRISPR interference. We empirically characterize the activities of compromised sgRNAs using large-scale measurements across multiple cell models and derive the rules governing sgRNA activity using deep learning, enabling construction of a compact sgRNA library to titrate expression of [~]2,400 genes involved in central cell biology and a genome-wide in silico library. Staging cells along a continuum of gene expression levels combined with rich single-cell RNA-seq readout reveals gene-specific expression-phenotype relationships with expression level-specific responses. Our work provides a general tool to control gene expression, with applications ranging from tuning biochemical pathways to identifying suppressors for diseases of dysregulated gene expression.

genomics↗

bioRxiv2019

Exploring genetic interaction manifolds constructed from rich phenotypes

Synergistic interactions between gene functions drive cellular complexity. However, the combinatorial explosion of possible genetic interactions (GIs) has necessitated the use of scalar interaction readouts (e.g. growth) that conflate diverse outcomes. Here we present an analytical framework for interpreting manifolds constructed from high-dimensional interaction phenotypes. We applied this framework to rich phenotypes obtained by Perturb-seq (single-cell RNA-seq pooled CRISPR screens) profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g. identifying true suppressors), and mechanistic elucidation of synthetic lethal interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we apply recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.\n\nOne Sentence SummaryPrinciples and mechanisms of genetic interactions are revealed by rich phenotyping using single-cell RNA sequencing.

genomics↗

bioRxiv2018

Molecular recording of mammalian embryogenesis

Understanding the emergence of complex multicellular organisms from single totipotent cells, or ontogenesis, represents a foundational question in biology. The study of mammalian development is particularly challenging due to the difficulty of monitoring embryos in utero, the variability of progenitor field sizes, and the indeterminate relationship between the generation of uncommitted progenitors and their progression to subsequent stages. Here, we present a flexible, high information, multi-channel molecular recorder with a single cell (sc) readout and apply it as an evolving lineage tracer to define a mouse cell fate map from fertilization through gastrulation. By combining lineage information with scRNA-seq profiles, we recapitulate canonical developmental relationships between different tissue types and reveal an unexpected transcriptional convergence of endodermal cells from extra-embryonic and embryonic origins, illustrating how lineage information complements scRNA-seq to define cell types. Finally, we apply our cell fate map to estimate the number of embryonic progenitor cells and the degree of asymmetric partitioning within the pluripotent epiblast during specification. Our approach enables massively parallel, high-resolution recording of lineage and other information in mammalian systems to facilitate a quantitative framework for describing developmental processes.

developmental biology↗

bioRxiv2018

Approaches to maximize sgRNA-barcode coupling in Perturb-seq screens

Perturb-seq is a platform for single-cell gene expression profiling of pooled CRISPR screens. Like many functional genomics platforms, Perturb-seq relies on lentiviral transduction to introduce perturbation libraries to cells. On this platform, these are barcoded sgRNA libraries. A critical consideration for performing Perturb-seq experiments is uncoupling of barcodes from linked sgRNA expression cassettes, which can occur during lentiviral transduction of co-packaged libraries due to reverse transcriptase-mediated template switching. This problem is common to lentiviral libraries designed with linked variable regions. Here, we demonstrate that recombination between Perturb-seq vectors scrambles linked variable regions separated by 2 kb. This predicts information loss in Perturb-seq screens performed with co-packaged libraries. We also demonstrate ways to address this problem and discuss best practices for single-cell screens with transcriptional readouts.

genomics↗