Ruijun Tian

AutoPELSA automates the PELSA workflow for proteome-wide ligand-protein interaction detection, enabling high-throughput analysis of both strong and low-affinity ligands.

Spatial-DC improves spatial proteomics deconvolution by using deep learning to infer cell-type composition and reconstruct proteomic profiles from low-resolution data.

The paper introduces an automated system for high-throughput proteomic data generation, demonstrating its robustness and application in spatial proteomics and plasma/cell line profiling.

The paper identifies lysine-R2HGylation as a new post-translational modification driven by R2HG in cancers, showing its impact on GSTP1 activity and monocyte differentiation.

The paper evaluates a nanoLC-Zeno TOF platform for spatial proteomics, identifying protein groups and differential expression in gastric cancer tissue.

This study uses phosphotyrosine proteomics to analyze CAR-T cells from patients and donors, finding heightened tyrosine phosphorylation and tonic signaling linked to therapeutic efficacy.

The paper introduces SCPro, a multimodal spatial proteomics method, to profile pancreatic tumor heterogeneity by integrating imaging, flow cytometry, and protein aggregation capture, revealing spatial proteome variations and a novel regulatory T cell subtype.

This study creates a spatial proteomic atlas of lung tissues from COVID-19 victims, identifying protein dysregulations and markers of pathological changes.

The paper introduces an automated proteomics sample preparation platform that improves throughput for drug target identification.

The paper introduces Slim-TPCA, an optimized method for proteome-wide characterization of protein complex dynamics, showing improved throughput and accuracy in identifying complex dissociation and association under glucose deprivation.