Vito RT Zanotelli

The paper integrates multi-omics data from pediatric sepsis patients to identify a reproducible inflammatory host-response axis associated with disease severity.

The paper compares in vitro and in vivo macrophage phenotypes using mass cytometry, finding distinct surface marker expression patterns between macrophages derived from culture and those from tumor-associated environments.

The paper shows that MTHFR phosphorylation depends on methionine levels and identifies MTHFR interactions with MTHFD1 and GCN1.

The paper introduces a workflow for processing and analyzing multiplexed imaging data, integrating image processing with single-cell analysis tools.

The paper quantifies how environment, neighborhood, and cell state influence phenotypic variability in 3D spheroids using mass cytometric imaging and a linear model.

The paper analyzes the human kinome and phosphatome to identify signaling networks altered by protein overexpression, revealing novel relationships and mechanisms of cancer progression.

The paper introduces a bead-based compensation workflow for mass cytometry to correct channel crosstalk, improving data quality and panel development.

The paper introduces histoCAT, a tool for analyzing single-cell spatial omics data to explore cell phenotypes and interactions in tissues, demonstrating its application on human breast cancer mass cytometry images.