Gastone Castellani

This study classifies and maps cell types in normal human lymph nodes using high-dimensional spatial proteomics, revealing novel cell subsets and interactions.

The paper evaluates biases in hyperplexed spatial proteomics, showing human eye limitations and demonstrating the value of computational tools like BRAQUE for analyzing low-signal data.

DNetPRO identifies low-dimensional signatures from high-throughput data by combining discriminant analysis with network processing, outperforming existing methods with fewer variables.

The paper identifies three tumor-specific gene signatures through network analysis of multi-tumor omics data and validates them with in vitro experiments to suggest novel drug targets and combinations.