Multi-Center Study on Sample Preparation and Feature Selection Effects in Reproducible Mass Spectrometry Imaging for Clinical Translation
6 affiliations
- 1Technical University of Munich, TUM School of Medicine and Health, Institute of Pathology, Munich, Germany
- 2Imaging Mass Spectrometry Unit, Berlin Institute of Health (BIH) at Charité, Berlin, Germany
- 3Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Technische Hochschule Mannheim, Mannheim, Germany
- 4CeMOS Research and Transfer Center, Mass Spectrometry and Optical Spectroscopy, Technical University Mannheim, Mannheim, Germany
- 5Mannheim Center for Translational Neuroscience (MCTN), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- 6Medical Faculty Heidelberg, Heidelberg University, Heidelberg, 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 artifacts, 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. Eventhough sample preparation remains a major source of variability, it 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.
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BibTeX
@article{goncalves2026multicenter,
title = {{Multi-Center Study on Sample Preparation and Feature Selection Effects in Reproducible Mass Spectrometry Imaging for Clinical Translation}},
author = {Juliana P.L. Gonçalves and Marta Grzeski and Gentiane Krasniqi and Carsten Hopf and Oliver Klein and Kristina Schwamborn},
journal = {Molecular \& Cellular Proteomics : MCP},
year = {2026},
month = sep,
doi = {10.1016/j.mcpro.2026.101658},
url = {https://doi.org/10.1016/j.mcpro.2026.101658},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Multi-Center Study on Sample Preparation and Feature Selection Effects in Reproducible Mass Spectrometry Imaging for Clinical Translation
AU - Juliana P.L. Gonçalves
AU - Marta Grzeski
AU - Gentiane Krasniqi
AU - Carsten Hopf
AU - Oliver Klein
AU - Kristina Schwamborn
PY - 2026
DA - 2026/09/10
JO - Molecular & Cellular Proteomics : MCP
DO - 10.1016/j.mcpro.2026.101658
UR - https://doi.org/10.1016/j.mcpro.2026.101658
AB - 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 artifacts, 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. Eventhough sample preparation remains a major source of variability, it 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.
SN - 1535-9476
SN - 1535-9484
ER -