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

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.

Uncovering Molecular Heterogeneity in the Kidney With Spatially Targeted Mass Spectrometry

“…Most protein IMS experiments rely on exact mass matching within a certain ppm error for identification. Recent computational tools allow for high throughput matching of m/z values with candidate identifications based upon intact mass and spatial correlation (Guo et al, 2021). Thus, advances in sample preparation, instrumentation, and computation are improving the feasibility and interpretation of protein and peptide IMS.…”

Cyclic multiplex fluorescent immunohistochemistry and machine learning reveal distinct states of astrocytes and microglia in normal aging and Alzheimer’s disease

“…Although this complexity is currently emerging with the advent of single-nuclei and single-cell RNA sequencing (snRNA-seq and scRNA-seq) [ 8 – 14 ], one limitation of such methods is their lack of spatial information. While the spatial resolution of tissue-based spatial transcriptomics and proteomics techniques is rapidly improving [ 15 , 16 ], methods to comprehensively phenotype brain cells at a single-cell resolution while preserving cellular spatial relationships with neuropathological lesions remain an urgent need.…”

ImShot: An Open-Source Software for Probabilistic Identification of Proteins In Situ and Visualization of Proteomics Data

“…The others relied either on customized experimental approaches or specific IMS platforms. Moreover, almost all the studies lacked a defined and conveniently executable strategy that could be used for integrating the two complementary datasets in general, especially in the diseased context ( 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 59 ) ( supplemental Table S2 ). Implementation of ImShot is independent of IMS platforms used to generate the data.…”

“…In fact, most of the attempts to combine the two modalities involved considerable manual curation leading to very limited number of identified discriminative masses ( 23 , 24 , 29 ). More successful approaches were associated with measurements of in situ tryptic peptides with very high mass accuracy (comparable to LC–MS) leading to the analysis method being particularly resource intensive ( 25 , 26 , 30 , 31 , 32 ). None of the approaches developed so far has a defined integrated “one-in-all” workflow/software in the form of a graphic user interface (GUI) leading to the tedious task of combining multiple platforms with substantial manual input.…”

“…None of the approaches developed so far has a defined integrated “one-in-all” workflow/software in the form of a graphic user interface (GUI) leading to the tedious task of combining multiple platforms with substantial manual input. Only recently, a command line–based package ( 32 ) has been proposed to identify IMS peptides from very high-resolution IMS data, but the workflow does not account for deisotoping IMS spectra, which as we demonstrate in this article could be pivotal in removing false-positive identifications. In addition, these approaches were exclusive of the two-group comparison scenario (healthy versus diseased), thereby providing very limited biological insights as part of a data analysis pipeline.…”