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Protein biomarkers on tissue as imaged via MALDI mass spectrometry: A systematic approach to study the limits of detection
Author(s) -
de Ven Stephanie M. W. Y.,
Bemis Kyle D.,
Lau Kenneth,
Adusumilli Ravali,
Kota Uma,
Stolowitz Mark,
Vitek Olga,
Mallick Parag,
Gambhir Sanjiv S.
Publication year - 2016
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201500515
Subject(s) - biomarker discovery , maldi imaging , proteomics , mass spectrometry imaging , computational biology , biomarker , mass spectrometry , quantitative proteomics , chemistry , matrix assisted laser desorption/ionization , bioinformatics , biology , chromatography , biochemistry , gene , organic chemistry , adsorption , desorption
MALDI mass spectrometry imaging (MSI) is emerging as a tool for protein and peptide imaging across tissue sections. Despite extensive study, there does not yet exist a baseline study evaluating the potential capabilities for this technique to detect diverse proteins in tissue sections. In this study, we developed a systematic approach for characterizing MALDI‐MSI workflows in terms of limits of detection, coefficients of variation, spatial resolution, and the identification of endogenous tissue proteins. Our goal was to quantify these figures of merit for a number of different proteins and peptides, in order to gain more insight in the feasibility of protein biomarker discovery efforts using this technique. Control proteins and peptides were deposited in serial dilutions on thinly sectioned mouse xenograft tissue. Using our experimental setup, coefficients of variation were <30% on tissue sections and spatial resolution was 200 μm (or greater). Limits of detection for proteins and peptides on tissue were in the micromolar to millimolar range. Protein identification was only possible for proteins present in high abundance in the tissue. These results provide a baseline for the application of MALDI‐MSI towards the discovery of new candidate biomarkers and a new benchmarking strategy that can be used for comparing diverse MALDI‐MSI workflows.