Meelad Amouzgar

This study identifies APOE4/4-driven dysfunctional microglial states in Alzheimer's disease through multiomic analysis, revealing shifts toward terminal states with metabolic disruption and inflammatory signaling.

This study investigates how cell cycle dynamics influence T cell fate and exhaustion by analyzing single-cell data and manipulating cell cycle progression and signaling.

This study analyzes multi-omic data from gliomas to identify therapeutic escape mechanisms and TME evolution, finding that N-glycosylation classifies tumor grade and immune transcriptome predicts GBM survival.

This paper develops a deep single-cell mass cytometry approach to capture both canonical and noncanonical cell cycle states across diverse cell systems.

This paper introduces a supervised dimensionality reduction method using hybrid subset selection with linear discriminant analysis to improve visualization and analysis of single-cell data.