CPMsi: a data-driven Computational Pipeline for fast, large-scale Mass Spectrometry Imaging analysis
- 1Leiden University Medical Center
Abstract
Mass Spectrometry Imaging (MSI) is increasingly used in clinical research to characterize tissue heterogeneity and identify disease-associated molecular patterns while preserving spatial context. However, translating MSI to large patient cohort studies remains challenging because, among others, existing computational tools struggle to scale with the size and complexity of such datasets. To overcome this bottleneck, we developed CPMsi, a scalable, data-driven computational pipeline for cohort-scale MSI analysis. CPMsi directly processes raw MSI data, performs global spectral alignment using a novel binning strategy, identifies regions of molecular similarity, and generates consensus spectra that compactly capture spatial molecular heterogeneity. The pipeline supports mapping of regions across consecutive tissue sections, and integration of multi-modal and multi-omics MSI acquisitions from the same section. We tested CPMsi using (i) consecutive brain MSI sections comprising ~600,000 and ~500,000 pixels with ~20,000 m/z features, (ii) multimodal brain MSI lipidomic acquired from the same tissue section in positive and negative ionization modes, and (iii) a multi-omics brain MSI dataset of lipidomic, metabolomic, and glycomic measurements. CPMsi identified regions of molecular similarity 30-times faster than vendor-provided MSI analysis software. Consensus spectra substantially reduced data complexity while preserving molecular differences between tissue regions and enabled mapping regions of similar molecular composition across consecutive tissue sections. Integration of complementary MSI modalities further revealed molecular patterns that were not resolved by individual modalities alone. Overall, CPMsi enables efficient reduction, comparison, and integration of complex MSI datasets, providing a scalable framework for cohort-scale studies, biomarker discovery, and future clinical applications.
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BibTeX
@misc{tasca2026cpmsi,
title = {{CPMsi: a data-driven Computational Pipeline for fast, large-scale Mass Spectrometry Imaging analysis}},
author = {P. Tasca and I. Sidorov and H. Dalebout and N. Ogrinc and R. Heeren and M. Wuhrer and J. Kers and A. de Vries and T. Rabelink and M. Giera and Y. Mohammed},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.15.758756},
url = {https://www.biorxiv.org/content/10.64898/2026.10.15.758756v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - CPMsi: a data-driven Computational Pipeline for fast, large-scale Mass Spectrometry Imaging analysis
AU - P. Tasca
AU - I. Sidorov
AU - H. Dalebout
AU - N. Ogrinc
AU - R. Heeren
AU - M. Wuhrer
AU - J. Kers
AU - A. de Vries
AU - T. Rabelink
AU - M. Giera
AU - Y. Mohammed
PY - 2026
DA - 2026/10/15
PB - bioRxiv
DO - 10.64898/2026.10.15.758756
UR - https://www.biorxiv.org/content/10.64898/2026.10.15.758756v1
AB - Mass Spectrometry Imaging (MSI) is increasingly used in clinical research to characterize tissue heterogeneity and identify disease-associated molecular patterns while preserving spatial context. However, translating MSI to large patient cohort studies remains challenging because, among others, existing computational tools struggle to scale with the size and complexity of such datasets. To overcome this bottleneck, we developed CPMsi, a scalable, data-driven computational pipeline for cohort-scale MSI analysis. CPMsi directly processes raw MSI data, performs global spectral alignment using a novel binning strategy, identifies regions of molecular similarity, and generates consensus spectra that compactly capture spatial molecular heterogeneity. The pipeline supports mapping of regions across consecutive tissue sections, and integration of multi-modal and multi-omics MSI acquisitions from the same section. We tested CPMsi using (i) consecutive brain MSI sections comprising ~600,000 and ~500,000 pixels with ~20,000 m/z features, (ii) multimodal brain MSI lipidomic acquired from the same tissue section in positive and negative ionization modes, and (iii) a multi-omics brain MSI dataset of lipidomic, metabolomic, and glycomic measurements. CPMsi identified regions of molecular similarity 30-times faster than vendor-provided MSI analysis software. Consensus spectra substantially reduced data complexity while preserving molecular differences between tissue regions and enabled mapping regions of similar molecular composition across consecutive tissue sections. Integration of complementary MSI modalities further revealed molecular patterns that were not resolved by individual modalities alone. Overall, CPMsi enables efficient reduction, comparison, and integration of complex MSI datasets, providing a scalable framework for cohort-scale studies, biomarker discovery, and future clinical applications.
ER -