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

G. Guo, M. Papanicolaou, N. J. Demarais, Z. Wang, K. L. Schey, P. Timpson, T. R. Cox () and A. C. Grey ()
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G. Guo: University of Auckland
M. Papanicolaou: UNSW Sydney
N. J. Demarais: University of Auckland
Z. Wang: Vanderbilt University
K. L. Schey: Vanderbilt University
P. Timpson: UNSW Sydney
T. R. Cox: UNSW Sydney
A. C. Grey: University of Auckland

Nature Communications, 2021, vol. 12, issue 1, 1-16

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-23461-w

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