Biomedical subjects

Jongmin Woo

Publications and source records attributed to Jongmin Woo.

3 recordsLinked to original sources

PubMed2025

Multi-omic profiling of intraductal papillary neoplasms of the pancreas reveals distinct patterns and potential markers of progression.

To enable early detection of pancreatic cancer from precancerous lesions, we analyze proteins and glycoproteins from 64 intraductal papillary mucinous neoplasms (IPMNs), 55 cyst fluid samples, 104 pancreatic ductal adenocarcinomas (PDACs), and various types of normal samples using mass spectrometry. High-grade IPMNs show enrichment of glycosylation level and tumor progression pathways compared to low-grade lesions. High-grade IPMN associated proteins, such as PLOD3, IRS2, LGALS9, and Trop-2, are identified and validated using immunolabeling and laser microdissection. Some high-grade associated proteins are also detected in pancreatic cyst fluids, which allows us to link proteins and glycoproteins expressed in neoplastic cells to clinically accessible biospecimens. Altered glycosylation level of extracellular matrix (ECM) proteins is observed in IPMNs compared to normal ducts. Additionally, we identify a subset of IPMNs with PDAC-like features, including elevated expression of ECM proteins. These findings offer insight into progression-associated proteins and emphasize the diagnostic and therapeutic potential of these proteins in pancreatic tumors.

Humans↗

PubMed2024

Proteome-Scale Tissue Mapping Using Mass Spectrometry Based on Label-Free and Multiplexed Workflows.

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ∼3500 proteins at a spatial resolution of 50 μm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provides robust protein quantifications in identifying differentially abundant proteins and spatially covariable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial coexpression analysis.

Proteome↗

PubMed2024

Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows.

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ~3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.

Journal Article↗