T. Mamie Lih

SPOTTER enables automated, high-plex spatial proteomics and phosphoproteomics by barcoding proteins on tissue sections, achieving deep coverage with LC-MS/MS.

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.

The paper uses proteomics data and machine learning to identify protein signatures distinguishing renal cell carcinomas from normal tissues and different subtypes.

The paper optimizes data-independent acquisition (DIA) mass spectrometry for single-cell proteomics, achieving reliable quantification of proteins and post-translational modifications at sub-nano-gram levels.

Omic-Sig uses multi-omics data to identify and visualize kinase-substrate interactions by analyzing differential abundances between case and control samples.