Raymond J. Monnat

The paper characterizes the IOMM-Lee cell line as a model for NF2-wildtype malignant meningioma, identifying its genomic features, radiosensitivity, and drug response to multiple anti-cancer agents.

This paper characterizes IDH+ glioma stem cell cultures to model GBM biology and therapeutic response, showing reproducible survival curves and methods to track cellular heterogeneity.

This paper uses a computational model to show that gene-corrected cells must have strong proliferative advantages to spread in the oral epithelium and that delivering correction to many cells reduces stochastic loss.

The paper uses a combined single-cell genetic and chemical screening platform to analyze kinase dependencies in glioblastoma response to targeted therapy, identifying adaptive signaling and potential combination therapies.

This paper introduces UPSIDE, a deep learning platform that identifies dynamic cell phenotypic states from transmitted light movies by clustering latent representations and inferring transition rates.

The paper develops a Functional Module States framework to predict drug sensitivity by analyzing transcriptional states from gene expression data in breast cancer cell lines.

This paper introduces a method to physically separate live cells based on visual phenotypes and uses it to study nuclear localization sequences and paclitaxel resistance in human cancers.

The paper identifies 35 new human safe harbor sites for genome engineering and validates their utility through experimental tests and toolkit development.

The paper examines how mutations during transcription and genome replication can affect HR-mediated gene drive systems based on different nuclease platforms, highlighting the risk of retargeting and the need for risk assessment.

This study examines mRNA and miRNA expression changes in Werner syndrome patient fibroblasts and WRN-depleted cells, revealing WRN's role in modulating G4 DNA-dependent transcription and distinct senescence-associated gene programs.