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Updated: Sep 12, 2026

Optimal Preparation of Formalin Fixed Samples for Peptide Based Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging Workflows
Published on: January 16, 2018
Multi-Center Study on Sample Preparation and Feature Selection Effects in Reproducible Mass Spectrometry Imaging for
Juliana P L Gonçalves1, Marta Grzeski2, Gentiane Krasniqi3
1Institute of Pathology, School of Medicine and Health, Technical University of Munich, Trogerstraße 18, 81675 Munich, Germany.
Abstract:
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) provides spatially resolved proteomic information that is valuable for understanding disease development, identifying biomarkers, and predicting treatment responses. Despite this potential, MALDI-MSI has not yet been adopted as part of routine diagnostic workflows, primarily due to challenges in analytical validation and robustness. Although sample preparation workflows for MALDI-MSI generally follow the same core steps, there is still a lack of robust data processing frameworks capable of minimizing technical artefacts, improving feature selection, and reducing inter-laboratory variability. To assess reproducibility and robustness of in situ proteomics across laboratories, we investigated formalin-fixed paraffin-embedded human tissue samples from spleen, intestine, and pancreas using MALDI-MSI. Two complementary workflows were evaluated: (i) decentralized sample preparation with data acquisition performed with one instrument, and (ii) centralized sample preparation with data acquisition carried out on instruments across participating laboratories. Data analysis focused on evaluating strategies for feature selection that preserved morphological information. Our findings demonstrate that, with adherence to a standardized protocol, reproducible results across different laboratories can be achieved. However, sample preparation remains a major source of variability, which can be mitigated through appropriate data analysis strategies, particularly in the choice of features used for analysis. Most importantly, biological differences between tissues were consistently identifiable across all datasets, further underscoring the reliability of the approach for clinical translation, especially when sources of variability are properly controlled.
