Jongmin Jacob Woo

This study develops a high-sensitivity spatial proteomics method, SP-Max, to analyze protein expression changes in pancreatic tissues from normal to cancerous states.

This study profiles proteins and glycoproteins in pancreatic neoplasms to identify expression patterns and potential progression markers.

The paper develops an automated sample preparation method (AUTO-SP) for proteomic and PTM analysis, showing high correlation and identification of ubiquitinated peptides in breast cancer PDX models.

The paper identifies over 12,000 blood proteins using proteomic strategies and develops a database to catalog their detectability and abundance across different conditions.

This study uses single-cell proteomics and RNA-seq to analyze drug-resistant prostate cancer cells, identifying proteomic sub-clusters and gene expression changes linked to morphological and functional traits.

This study benchmarks label-free and TMT-MS2 methods for spatial proteome mapping, showing label-free provides deeper coverage and higher dynamic range, while TMT-MS2 offers faster throughput.

This paper introduces TIFF, a method enhancing single-cell proteomics sensitivity and accuracy by filtering background ions and using 3D MS1 feature matching, achieving deep proteome analysis in single cells.

This paper introduces a nested nanowell chip to improve single-cell proteomics throughput and robustness, demonstrating the ability to quantify ~1500 proteins from ~100 cells with low abundance variation.