Jon Bezney

PHAROS: turning single-cell perturbation models into target-directed drug-combination screens

PHAROS uses a pretrained single-cell perturbation model to predict and prioritize effective drug combinations without retraining, showing success in cell lines and patient-derived tumors.

Innate immune responses to Plasmodium falciparum disrupt the blood-brain barrier

The paper investigates how innate immune responses to Plasmodium falciparum contribute to blood-brain barrier disruption in cerebral malaria using an in vitro model.

A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics

The paper introduces scCLEAN, a CRISPR/Cas9-based method to enhance single-cell transcriptomics by improving library complexity and isoform detection.

Perturbing RNA localization for functional study in neurons

This study introduces CRISPR-TO to manipulate RNA localization in neurons, showing it affects neurite outgrowth and axonal regeneration.

Simultaneous CRISPR screening and spatial transcriptomics reveals intracellular, intercellular, and functional transcriptional circuits.

This paper introduces Perturb-FISH, a method combining spatial transcriptomics and CRISPR screening to study transcriptional circuits, showing intracellular and intercellular effects in immune and neurological contexts.

Single-cell multi-omics map of human foetal blood in Down's Syndrome

This study integrates single-cell multi-omics data from human foetal blood to show that trisomy restructures enhancer-gene maps, leading to dysregulated erythroid differentiation and haematological abnormalities in Down's Syndrome.

Compressed Perturb-seq: highly efficient screens for regulatory circuits using random composite perturbations

Compressed Perturb-seq reduces cost and complexity of CRISPR screens by using random composite perturbations and computational decomposition, achieving similar accuracy to conventional methods with greater power to detect genetic interactions.

Compressed sensing for imaging transcriptomics

The paper introduces CISI, a method that uses compressed sensing to efficiently recover spatial gene expression data from composite measurements, achieving high efficiency and scalability.