José L. McFaline‐Figueroa

The paper uses in vivo single-cell CRISPR screens to study how tumor suppressor loss and genetic perturbations affect glioma states and immune recruitment in mice.

SHERLOCK is a deep generative framework for single-cell perturbation analysis that learns interpretable perturbation representations and enables causal analysis of downstream effects.

The paper identifies an endodermal subpopulation in the chick embryo that transitions into neuromesodermal progenitors, contributing to multiple germ layers.

This study shows that soft hydrogels promote tertiary lymphoid structure formation, while stiff hydrogels hinder it, improving antitumor immunity.

This study uses single-cell transcriptomics to analyze how diverse SHP2 variants affect gene expression and cellular phenotypes in relation to disease.

The paper develops a CRISPR screening framework to map kinase-dependent immune evasion mechanisms in glioblastoma by integrating single-cell transcriptomics and deep generative models.

The paper introduces TRTL, a modular method for targeted single-cell transcriptomics that enables scalable, atlas-aligned, and clonotype-resolved analysis of user-defined transcript panels.

This paper develops a human airway organoid platform to expand and map the differentiation trajectories of pulmonary neuroendocrine cells.

The paper profiles molecular responses to HDAC inhibitors in diverse cell lines, revealing heterogeneous protein remodeling and cell-type specific effects.

MrVI is a deep generative model that identifies sample-level stratifications and cellular differences without prior cell-type grouping, applied to COVID-19 and IBD data.

This study uses spatiotemporal single-cell analysis to track T cell clonal dynamics and phenotypic plasticity in GVHD, revealing links between TCR features, clonal expansion, and disease severity.

This study maps brain-wide, cell-type-specific gene expression changes in mice following LC-NE system activation or inhibition using single-nucleus RNA sequencing.

This study uses SCHEMATIC to analyze the molecular response of glioblastoma to EGFR inhibitors, identifying chemotype-specific programs and showing that combined EGFR/PI3K inhibition reduces adaptive transcription.

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.

The paper introduces sciPlex-ATAC-seq for high-capacity multiplexing of single-cell chromatin accessibility, demonstrating its use in chemical epigenomics and immune stimulation studies.

Starfysh integrates spatial transcriptomics and histology to analyze tumor microenvironments, identifying disease-specific spatial hubs and immune interactions in breast cancers.

This paper introduces Scribe, a toolkit for causal gene regulatory network inference from single-cell data, showing that pseudotime data performs worse than true time series data.

The paper investigates how thyroid hormone regulates different maturation paths in zebrafish pigment cell lineages through transcriptomic profiling and trajectory analysis.

This study uses single-cell RNA-seq to analyze gene expression in A. thaliana root cells, identifying cell-type-specific genes and developmental trajectories, and shows cell-type differences in heat stress responses.

This paper introduces crisprQTL mapping to identify gene regulation by using CRISPR perturbations and single-cell RNA-seq in human cells, finding 128 cis-regulatory elements affecting nearby genes.

The paper identifies a major issue with CRISPR-based single cell screens due to guide-barcode swapping and presents an optimized method to improve accuracy.

The paper introduces Cicero, a statistical method to link regulatory elements to target genes using single-cell chromatin accessibility data, and applies it to study myogenesis.