Kathryn Geiger-Schuller

Perturb-ME combines CRISPR screening and single-cell profiling to identify regulatory modules governing gene programs in melanoma.

The paper introduces PerturbPair, a platform integrating transcriptomic and optical screens to map macrophage regulatory networks, revealing cross-modal relationships and disease-relevant gene programs.

This paper develops a platform for longitudinal imaging and transcriptomics of live cells, revealing adaptive drug-resistant states and key drivers of cellular function through integrated analysis.

This study uses dynamic caging to link live-cell behavior with transcriptional responses across perturbations, revealing new insights into immune and inflammatory gene regulation.

This study identifies spatially organized cDC1-T cell clusters within tumors as critical for enhancing CTL-mediated tumor rejection through local costimulation.

The paper identifies regulators of inflammatory macrophage states and shows that TNF mRNA m6A modification controls its stability and contributes to disease.

PerturbView enables highly multiplexed optical pooled screens in primary cells and tissues by improving barcode amplification, revealing regulators of NFκB signaling and enabling spatial transcriptomics integration.

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.

The paper uses Perturb-seq to systematically study 1,130 E3 ligases and their roles in inflammatory responses in dendritic cells, revealing co-functional modules and genetic interactions.

The paper benchmarks a new low-cost sequencing by synthesis method for scRNA-seq, showing comparable results to existing technologies and enabling cost-effective large-scale applications.

The paper uses Perturb-CITE-Seq to identify novel immune evasion mechanisms in cancer by analyzing multi-modal single-cell data from patient-derived models.