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Biology subjects

Stauber, D.

Publications and source records attributed to Stauber, D..

2 recordsLinked to original sources

CRISPR-Cas13d-mediated targeting of a context-specific essential gene enables selective elimination of uveal melanoma

Uveal melanoma, the most common eye cancer in adults, remains limited to surgical intervention and chemotherapy, with a dismal survival rate that has not improved in over 50 years. To address this therapeutic impasse, we systematically analyzed public gene expression, RNAi, and CRISPR knockout datasets and identified RASGRP3 as an essential gene specifically for uveal melanoma. RasGRP3 is uniquely overexpressed and essential for survival in uveal melanoma cells, but dispensable in healthy cells. RasGRP3 remains "undruggable" due to its intracellular localization and lack of targetable binding pockets. To overcome this, we developed a CRISPR-Cas13d RNA-targeting therapeutic that specifically knocks down RasGRP3 mRNA. This Cas13d-based therapeutic mediates selective uveal melanoma killing through two synergistic mechanisms: (i) direct silencing of the essential RasGRP3 transcript, and (ii) collateral RNA degradation triggered by the cleavage of overexpressed RasGRP3. When delivered via optimized lipid nanoparticles encoding Cas13d mRNA and guide RNA, this strategy eliminated >97% of uveal melanoma cells while sparing healthy cells, including retinal pigment epithelial cells. This approach outperformed conventional Cas9 and siRNA methods in potency without inducing permanent genomic alterations. Our findings establish a RNA-targeting therapeutic for uveal melanoma and a framework for Cas13d-based interventions against broad "undruggable" cancers.

synthetic biology↗

PreciCE: Precision engineering of cell fates via data-driven multi-gene control of transcriptional networks

The directed differentiation of stem cells into specific cell types is critical for regenerative medicine and cell-based applications. However, current methods for cell fate control are inefficient, imprecise, and rely on laborious trial-and-error. To address these limitations, we present a method for data-driven multi-gene modulation of transcriptional networks. We develop bidirectional CRISPR-based tools based on dCas12a, Cas13d, and dCas9 for simultaneously activating and repressing many genes. Due to the vast combinatorial complexity of multi-gene regulation, we introduce a machine learning-based computational algorithm that uses single-cell RNA sequencing data to predict multi-gene perturbation sets for converting a starting cell type into a desired target cell type. By combining these technologies, we establish a unified workflow for data-driven cell fate engineering and demonstrate its efficacy in controlling early stem cell differentiation while suppressing alternative lineages through logic-based cell fate operations. This approach represents a significant advancement in the use of synthetic biology to engineer cell identity.

synthetic biology↗