---
title: "CPMsi for scalable Mass Spectrometry Imaging: fast, cohort-scale MSI data processing"
id: "biorxiv-3-cpmsi-a-data-driven-computational-pipeline-for-fast-large-scale-mass"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-3-cpmsi-a-data-driven-computational-pipeline-for-fast-large-scale-mass"
content_type: "clinical_feed_article"
specialty: "Radiology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.15.758756v1?rss=1"
published_at: "2026-10-21T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# CPMsi for scalable Mass Spectrometry Imaging: fast, cohort-scale MSI data processing
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-3-cpmsi-a-data-driven-computational-pipeline-for-fast-large-scale-mass
- **Specialty:** [Radiology](https://medichelpline.com/clinical-feed/radiology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.15.758756v1?rss=1)
- **Published At:** 2026-10-21T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The preprint describes **CPMsi**, a data-driven computational pipeline built to process raw **Mass Spectrometry Imaging** (MSI) data at cohort scale and to reduce computational bottlenecks for large datasets. - CPMsi performs global spectral alignment using a novel binning strategy; the authors reported that the pipeline processes raw MSI directly rather than relying on preprocessed files. - The pipeline identifies regions of molecular similarity and generates compact **consensus spectra** that capture spatial molecular heterogeneity across tissue. - CPMsi supports mapping of molecularly similar regions across consecutive tissue sections and integrates multimodal and multi-omics MSI acquisitions from the same section. - The authors applied CPMsi to three test cases: two consecutive brain MSI sections (~600,000 and ~500,000 pixels with ~20,000 m/z features), a multimodal lipidomic MSI acquired in both ionization modes from the same section, and a multi-omics dataset combining lipidomic, metabolomic, and glycomic MSI. - Performance claims include region identification reported as 30-times faster than the vendor-provided MSI analysis software used as comparator; consensus spectra reportedly reduced data complexity while preserving molecular differences. - Integration of complementary MSI modalities reportedly revealed molecular patterns not resolvable in single modalities. - The manuscript is a preprint and has not undergone peer review. The source did not report details on algorithmic parameters, exact computational resources used, or quantitative benchmarks beyond the reported 30-fold speed comparison and dataset descriptors.
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Paola Tasca 1 Leiden University Medical Center; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Paola%2BTasca%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Tasca%20P&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3APaola%2BTasca%2B) * [ORCID record for Paola Tasca](http://orcid.org/0009-0000-0079-8452 "Open in new tab") Iulia Sidorov 1 Leiden University Medical Center; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Iulia%2BSidorov%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Sidorov%20I&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AIulia%2BSidorov%2B) * [ORCID record for Iulia Sidorov](http://orcid.org/0009-0004-6998-4690 "Open in new tab") Hans Dalebout 1 Leiden University Medical Center; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Hans%2BDalebout%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Dalebout%20H&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AHans%2BDalebout%2B) Nina Ogrinc 1 Leiden University Medical Center; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Nina%2BOgrinc%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Ogrinc%20N&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ANina%2BOgrinc%2B) * [ORCID record for Nina Ogrinc](http://orcid.org/0000-0002-0773-0095 "Open in new tab") Ron Heeren 2 Maastricht MultiModal Molecular Imaging (M4I) Institute; 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* [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Martin%2BGiera%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Giera%20M&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AMartin%2BGiera%2B) * [ORCID record for Martin Giera](http://orcid.org/0000-0003-1684-1894 "Open in new tab") Yassene Mohammed 1 Leiden University Medical Center; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Yassene%2BMohammed%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Mohammed%20Y&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AYassene%2BMohammed%2B) * [ORCID record for Yassene Mohammed](http://orcid.org/0000-0003-3265-3332 "Open in new tab") * For correspondence: y.mohammed@lumc.nl * [Abstract](https://www.biorxiv.org/content/10.64898/2026.10.15.758756v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5890259/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.10.15.758756v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5890259/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.10.15.758756v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5890259/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.10.15.758756v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5890259/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.10.15.758756v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5890259/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## 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. ### Competing Interest Statement The authors have declared no competing interest. ## Funder Information Declared Dutch Kidney Foundation, 21OK+15 Novo Nordisk Foundation, NNF21CC0073729 European Union, SPARK 101140863 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-NC-ND 4.0 International license](http://creativecommons.org/licenses/by-nc-nd/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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