Biomedical subjects

Yumi Kwon

Publications and source records attributed to Yumi Kwon.

3 recordsLinked to original sources

PubMed2026

Unbiased Spatial Proteomics Uncovers Hepatic in Situ Regulation in Alcohol-Associated Hepatitis.

Alcohol-associated hepatitis (AH) is an acute inflammatory form of alcohol-associated liver disease. Previous studies have explored molecular mechanisms associated with AH pathogenesis through bulk liver tissue analysis; however, the heterogeneity of liver tissue and hence the spatial regulation within the AH liver microenvironment remained unaddressed. Here, an unbiased spatial proteomics analysis on the pathologic regions (PRs) of AH liver tissue is presented, including immune cell infiltration foci, lipid droplets, chicken-wire fibrosis, and fibrotic bands. Through combining a highly efficient nanodroplet processing in one pot for trace samples platform with ultrasensitive liquid chromatography-mass spectrometry, this study identified and quantified a total of 5186 unique proteins from PRs isolated in 200-μm-long × 200-μm-wide × 10-μm-thick areas. This in-depth spatial proteome coverage allowed us to discover mechanistic regulations within individual PRs, including compromised resolution of inflammation with infiltrated neutrophils at infiltration foci, increase of mitochondrial and peroxisomal fatty acid β-oxidation at lipid droplets, and differential cellular and extracellular regulations between chicken-wire fibrosis and fibrotic bands. Overall, this study demonstrated a new capability for AH research, revealed the significance of understanding spatial regulation within AH liver tissue, and further facilitated the development of therapeutic strategies at high resolution.

Proteomics↗

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↗