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Biology subjects

Woappi, Y.

Publications and source records attributed to Woappi, Y..

2 recordsLinked to original sources

RESTRICT-seq enables time-gated CRISPR screens and uncovers novel epigenetic dependencies of SCC resistance

Cancer cell evasion of therapy is a highly adaptive process that undermines the efficacy of many treatment strategies. A significant milestone in the study of these mechanisms has been the advent of pooled CRISPR knockout screens, which enable high-throughput, genome-wide interrogations of tumor dependencies and synthetic lethal interactions, advancing our understanding of how cancer cells adapt to and evade therapies. However, the utility of this approach diminishes when applied to dynamic biological contexts, where processes are transient and sensitive to routine cell culture manipulations that introduce noise and limit meaningful discoveries. To overcome these limitations, we present RESTRICT-seq, a next-generation pooled screening methodology that restricts Cas9 nuclear activation in controlled, repeated cycles. By confining Cas9 catalytic activity to strict temporal windows, RESTRICT-seq mitigates undesired fitness penalties that accumulate throughout CRISPR screens. When benchmarked against conventional pooled screens and standard inducible CRISPR protocols, RESTRICT-seq revealed significantly fewer divergent cell clones and increased signal-to-noise ratio, overcoming a key limitation of traditional methods. Leveraging RESTRICT-seq, we conducted a comprehensive functional survey of the druggable mammalian epigenome, uncovering several elusive epigenetic drivers of treatment resistance in cutaneous squamous cell carcinoma (cSCC). This revealed PAK1 as a previously unrecognized mediator of cSCC resistance in human and mouse SCC, offering new insights into a prognostic marker and therapeutic target of high clinical significance. Our findings establish RESTRICT-seq as a powerful tool for extending the applicability of pooled CRISPR screens to dynamic and previously intractable biological contexts.

cancer biology↗

Patches: A Representation Learning framework for Decoding Shared and Condition-Specific Transcriptional Programs in Wound Healing

Single-cell genomics enables the study of cell states and cell state transitions across biological conditions like aging, drug treatment, or injury. However, existing computational methods often struggle to simultaneously disentangle shared and condition-specific transcriptional patterns, particularly in experimental designs with missing data, unmatched cell populations, or complex attribute combinations. To address these challenges, Patches identifies universal transcriptomic features alongside condition-dependent variations in scRNA-seq data. Using conditional subspace learning, Patches enables robust integration, cross-condition prediction, and biologically interpretable representations of gene expression. Unlike prior methods, Patches excels in experimental designs with multiple attributes, such as age, treatment, and temporal dynamics, distinguishing general cellular mechanisms from condition-dependent changes. We applied Patches to both simulated data and real transcriptomic datasets from skin injury models, focusing on the effects of aging and drug treatment. Patches revealed shared wound healing patterns and condition-specific changes in cell behavior and extracellular matrix remodeling. These insights deepen our understanding of tissue repair and can identify potential biomarkers for therapeutic interventions, particularly in contexts where the experimental design is complicated by missing or difficult-to-collect data.

genomics↗