Emerging Computational Methods in Mass Spectrometry Imaging
Year: 2022
Type: review
Abstract: Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.
Source: Advanced Science
Authors: Hang Hu, Julia Laskin
Institution: Purdue University West Lafayette
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $5,200
Awards: Novel Platform for Quantitative Subcellular Resolution Imaging of Human Tissues Using Mass Spectrometry, Development of a high throughput system for molecular imaging of different cell types in mouse brain tissues, G3110643040, Novel Platform for Quantitative Subcellular Resolution Imaging of Human Tissues Using Mass Spectrometry, Pushing the Frontiers of Identification and Coverage in High-Resolution Spatially-Resolved Lipidomics
OpenAlex ID: W4306653410