Anthony J. Cesnik

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.

SubCell is a deep learning model for fluorescence microscopy that captures cellular morphology and protein organization, outperforming existing methods and enabling a multimodal approach to gene function.

Bento enables subcellular analysis of spatial transcriptomics data by defining domains, annotating localization patterns, and quantifying gene colocalization, demonstrating its use on multiple datasets.

Spritz generates sample-specific proteogenomic databases annotated with sequence variations and PTMs from RNA-seq data, enabling improved identification of proteoforms in mass spectrometry analyses.