Hannah N. Miles
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hnmiles.bsky.social
Hannah N. Miles
@hnmiles.bsky.social
PhD candidate, UW-Madison
Li Research Group & Ricke Lab
Reposted by Hannah N. Miles
Warmest congratulations to Dr. Hannah Miles @hnmiles.bsky.social, the 69th Ph.D. from the Li Lab 🥳🥳🥳Your support for fellow lab members, triathlon training inspiration, and fruitful collabs with Ricke Lab have truly left a lasting impact. Wishing you the best on your next adventure 🍾🍰
June 11, 2025 at 11:05 AM
Happy to see the platform @chemistlauren.bsky.social and I have been working on published at last!
MSIght: A Modular Platform for Improved Confidence in Global, Untargeted Mass Spectrometry Imaging Annotation #JProteomeRes pubs.acs.org/doi/10.1021/...
MSIght: A Modular Platform for Improved Confidence in Global, Untargeted Mass Spectrometry Imaging Annotation
Mass spectrometry imaging (MSI) has gained popularity in clinical analyses due to its high sensitivity, specificity, and throughput. However, global profiling experiments are often still restricted to LC-MS/MS analyses that lack spatial localization due to low-throughput methods for on-tissue peptide identification and confirmation. Additionally, the integration of parallel LC-MS/MS peptide confirmation, as well as histological stains for accurate mapping of identifications, presents a large bottleneck for data analysis, limiting throughput for untargeted profiling experiments. Here, we present a novel platform, termed MSIght, which automates the integration of these multiple modalities into an accessible and modular platform. Histological stains of tissue sections are coregistered to their respective MSI data sets to improve spatial localization and resolution of identified peptides. MS/MS peptide identifications via untargeted LC-MS/MS are used to confirm putative MSI identifications, thus generating MS images with greater confidence in a high-throughput, global manner. This platform has the potential to enable large-scale clinical cohorts to utilize MSI in the future for global proteomic profiling that uncovers novel biomarkers in a spatially resolved manner, thus widely expanding the utility of MSI in clinical discovery.
pubs.acs.org
April 11, 2025 at 2:50 AM