Joseph M. Replogle

The paper develops a contrastive learning model to integrate scRNA-seq data and perturbation responses, revealing conserved and cell-type-specific cellular responses to genetic perturbations.

The paper introduces TRADE, a statistical framework to detect transcriptome-wide differential expression effects from noisy Perturb-seq data, showing that many true effects are not statistically significant but detectable in aggregate.

The paper introduces a dual sgRNA library design for CRISPRi screens to identify genetic modifiers, showing improved sensitivity and specificity in detecting epistatic relationships.

This study uses Perturb-seq to functionally interrogate SARS-CoV-2 host factors in human lung epithelial cells, identifying key factors that influence infection and host response.

The paper introduces PIP-seq, a microfluidics-free method for single-cell RNA sequencing that enables high-throughput, scalable, and flexible processing of samples.

This paper develops compact CRISPRi sgRNA libraries and optimal effectors to improve knockdown consistency and efficiency in mammalian cells.

The paper performs genome-scale Perturb-seq to map genotype-phenotype relationships, uncovering new gene functions and stress-specific mitochondrial regulation.

The paper introduces dynamo, a framework for estimating RNA velocities and predicting cell fates using single-cell RNA-seq data, applied to various biological processes.

The paper identifies GIGYF2 and 4EHP as translation inhibitors that suppress re-initiation of defective mRNAs, aiding ribosome-associated quality control and preventing toxic protein accumulation.

This paper introduces a method to directly capture CRISPR guides in single-cell RNA-sequencing libraries, enhancing Perturb-seq scalability and enabling multi-omic measurements.

The paper reports a detailed genetic and genomic map of multiple sclerosis, identifying susceptibility genes in peripheral immune cells and brain-resident microglia.

The paper explores genetic interaction manifolds using rich phenotypes from Perturb-seq to classify and mechanistically understand genetic interactions.