Yun Yang

This study develops a high-throughput single-cell proteomics workflow combining nPOP with quantitative hyperplexing, achieving high sensitivity and quantification across multiple cell types.

This paper introduces a method to control false discovery rates in deep-learning-based de novo peptide sequencing, improving reliability of peptide identifications.

The paper introduces SWIFT, a fast and efficient proteomic workflow for processing microgram-level FF and FFPE tissues, achieving high repeatability and deep proteome coverage.

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.

This paper introduces Integral-Hyperplex, a method combining isobaric tags and tandem mass tags to increase single-cell proteomics throughput by 30-fold, validated through quantification experiments and automated labeling.

This paper introduces a simple one-step vial-based pretreatment method for deep single-cell proteomics and applies it to study oocyte aging, identifying age-related protein markers.

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.

DeepRTAlign improves retention time alignment for large cohort LC-MS data, enhancing identification sensitivity without compromising quantitative accuracy and enabling predictive modeling for hepatocellular carcinoma recurrence.