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Related Concept Videos

MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...

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Related Experiment Video

Updated: Jun 19, 2026

MALDI-Mass Spectrometric Imaging for the Investigation of Metabolites in Medicago truncatula Root Nodules
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One section, two worlds: single-cell integration of MALDI-MSI and spatial transcriptomics on the same single tissue

Tim F E Hendriks1, Gert B Eijkel1, Theodoros Visvikis1

  • 1The Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry (IMS), Maastricht University, 6229 ER, Maastricht, The Netherlands.

Scientific Reports
|November 29, 2025
PubMed
Summary

This study integrates mass spectrometry imaging with spatial transcriptomics for pixel-scale multi-omics analysis. The workflow reveals metabolic heterogeneity within cell types, enhancing understanding of cellular function and disease.

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Area of Science:

  • Spatial Biology
  • Multi-omics Integration
  • Biochemistry and Molecular Biology

Background:

  • Understanding tissue complexity necessitates spatially resolved multi-omics data at single-cell resolution.
  • Current methods often struggle with precise spatial alignment between different molecular datasets.
  • Integrating metabolic and transcriptomic information is crucial for a holistic view of cellular function.

Purpose of the Study:

  • To develop and validate a workflow integrating matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) with Xenium spatial transcriptomics (SPT) on a single tissue section.
  • To achieve pixel-scale spatial correspondence between metabolic and transcriptomic features.
  • To enable per-cell MALDI spectra extraction aligned with gene expression for enhanced cell-type resolution and metabolic heterogeneity identification.

Main Methods:

  • Integration of high-resolution MALDI-MSI with Xenium SPT on identical tissue sections.
  • Assessment of MALDI-MSI compatibility with downstream SPT, evaluating transcript counts, cell recovery, and cell-type assignments.
  • Pixel-scale modality co-registration and integrated clustering for joint analysis of metabolic and transcriptomic data.

Main Results:

  • The integrated workflow achieved pixel-scale co-registration of metabolic and transcriptomic data without significant misalignment.
  • MALDI-MSI reduced transcript counts by approximately 30% but preserved cell recovery and cell-type assignments for downstream SPT.
  • Integrated clustering identified enhanced cell-type resolution and revealed metabolic heterogeneity within transcriptionally defined cell populations.

Conclusions:

  • The presented workflow enables precise correlations between a cell's biochemical state and its function, providing a holistic view of cellular heterogeneity.
  • This approach advances data integration for multi-omic atlases, facilitating translational research in health and disease.
  • The method offers a scalable solution for comprehensive spatial multi-omics analysis at single-cell resolution.