Erica A. K. DePasquale

The paper integrates multiple datasets to create a comprehensive human liver cell atlas, identifying new cell types and associations with demographic factors.

MisTIC corrects transcript misassignment in spatial transcriptomics data, improving cell type identification, differential expression analysis, and RNA localization.

The paper investigates the effects of co-stimulatory blockade on B cell accrual and T cell subsets during T-cell-mediated renal allograft rejection using single-cell RNA sequencing.

This study defines the transcriptional landscape of CD8+ T cells in pediatric liver transplant rejection using single-cell sequencing and identifies persistent CD8+ clones associated with graft failure.

This study identifies EZH2 as an oncogenic driver in hepatoblastoma, showing its role in promoting tumor progression through epigenetic silencing and noncanonical signaling, and highlights its potential as a therapeutic target.

The paper identifies EIF4A1 as a vulnerability in AML cells and shows that combining Zotatifin with Venetoclax enhances apoptosis and survival in AML models.

The paper develops a method to detect mitochondrial mutations from single-cell RNA-seq to identify clonal expansions in human hematopoiesis.

KRSA predicts differential kinase activity from kinome array data by analyzing phosphorylation differences and comparing results to gene expression databases.

DoubletDecon identifies and removes doublets from scRNA-seq data using deconvolution and gene expression analysis.

The paper identifies lamina-specific gene expression changes in pyramidal neurons from schizophrenia patients compared to controls, highlighting immune and synaptic pathway disruptions.

DoubletDecon identifies and removes doublets in scRNA-Seq data using deconvolution and cell-state gene expression analysis, improving cell state delineation.

cellHarmony matches and compares single-cell transcriptomes to identify disease-specific gene regulatory programs and cell-state changes.

The paper identifies PPAR agonists as potential treatments for schizophrenia by analyzing bioenergetic changes in the DLPFC using bioinformatics and experimental validation.