Publication: Automated annotation and visualisation of high-resolution spatial proteomic mass spectrometry imaging data using HIT-MAP

Thomas R. Cox, May 2021

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

Just published in Nature Communications is our new tool for automated annotation and visualisation of MALDI Mass Spec Imaging data.

Spatial proteomics is a powerful tool to directly analyse and map the distribution of proteins in tissues at the single cell scale in an unbiased manner. We have created a new platform to identify and spatially map peptides and proteins that is applicable to a wide scope of applications studying normal and diseased tissues. This technology can be integrated with other established and/or emerging technology platforms (such as spatial transcriptomics) to significantly increase our understanding of health and disease.

Abstract

Spatial proteomics has the potential to significantly advance our understanding of biology, physiology and medicine. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) is a powerful tool in the spatial proteomics field, enabling direct detection and registration of protein abundance and distribution across tissues. MALDI-MSI preserves spatial distribution and histology allowing unbiased analysis of complex, heterogeneous tissues. However, MALDI-MSI faces the challenge of simultaneous peptide quantification and identification. To overcome this, we develop and validate HIT-MAP (High-resolution Informatics Toolbox in MALDI-MSI Proteomics), an open-source bioinformatics workflow using peptide mass fingerprint analysis and a dual scoring system to computationally assign peptide and protein annotations to high mass resolution MSI datasets and generate customisable spatial distribution maps. HIT-MAP will be a valuable resource for the spatial proteomics community for analysing newly generated and retrospective datasets, enabling robust peptide and protein annotation and visualisation in a wide array of normal and disease contexts.

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Citation

Guo G*, Papanicolaou M* et al.
Nature Communications (2021) |doi: 10.1038/s41467-021-23461-w