Trang H. Le

The paper presents a multimodal perturbation atlas using CRISPR knockouts in A549 cells, combining microscopy, phase imaging, and scRNA-seq to assess phenotypic resolution.

The paper uses single-cell spatial proteomics to show that metabolic enzyme expression and localization vary between cells, revealing autonomous metabolic heterogeneity.

The paper analyzes how cell shape correlates with proteomic features across multiple cell lines using image-based spatial proteomics data.

The paper uses Molecular Pixelation to analyze surface proteomic signatures of CAR T cells under acute and chronic stimulation, identifying spatial patterns linked to exhaustion.

The paper uses spatial proteomics to analyze primary cilia proteomes across cell lines, revealing cell-type specificity and single-cilia heterogeneity, and identifies novel cilia proteins and potential genetic variants.

This paper introduces a simplified VQGAN training scheme for generating multi-channel organelle staining from bright field images, achieving top performance in a benchmark challenge.

The paper describes a machine learning competition to develop automated image segmentation tools for anatomical structures across human organs, resulting in models that will be used in the Human Reference Atlas.