Automated annotation and visualisation of high-resolution spatial proteomic mass spectrometry imaging data using...

Area of Science:

• Proteomics
• Biotechnology
• Bioinformatics

Background:

• Spatial proteomics offers significant potential for advancing biology, physiology, and medicine.
• Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) is a key technology for spatial proteomics, preserving tissue architecture.
• A major challenge in MALDI-MSI is the simultaneous quantification and identification of peptides.

Purpose of the Study:

• To develop and validate an open-source bioinformatics workflow for spatial proteomics using MALDI-MSI.
• To enable robust peptide and protein annotation and visualization in complex biological tissues.
• To address the challenge of simultaneous peptide quantification and identification in MALDI-MSI datasets.

Main Methods:

• Development of HIT-MAP (High-resolution Informatics Toolbox in MALDI-MSI Proteomics), an open-source bioinformatics workflow.
• Utilizing peptide mass fingerprint analysis and a dual scoring system for computational annotation.
• Application to high mass resolution MALDI-MSI datasets for spatial protein mapping.

Main Results:

• HIT-MAP successfully assigns peptide and protein annotations to MALDI-MSI data.
• The workflow generates customizable spatial distribution maps of proteins within tissues.
• Validated computational approach for analyzing complex, heterogeneous tissue samples.

Conclusions:

• HIT-MAP provides a valuable resource for the spatial proteomics community.
• Enables robust analysis of both new and existing MALDI-MSI datasets.
• Facilitates enhanced understanding of protein distribution in normal and disease contexts.