One section, two worlds: single-cell integration of MALDI-MSI and spatial transcriptomics on the same single tissue section

Understanding tissue complexity requires spatially resolved multi-omics data at single-cell resolution. Here, we present a workflow integrating high-resolution matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) with Xenium spatial transcriptomics (SPT) on a single tissue section. This strategy ensures pixel-scale spatial correspondence between metabolic and transcriptomic features, avoiding misalignment issues of serial sections, where even minor offsets result in sampling different cells. We investigated MALDI-MSI compatibility with downstream SPT revealing that the number of transcripts per cell decreased by ~ 30% after MSI, whilst cell recovery and cell-type assignments are preserved. Validated using mouse brain and demonstrated using human glioblastoma tissues, we achieved pixel-scale modality co-registration, enabling per-cell MALDI spectra extraction aligned with gene expression. Integrated clustering revealed enhanced cell-type resolution and identified metabolic heterogeneity within transcriptionally defined populations. This facilitates precise correlations of a cell's function and its biochemical state, providing a holistic view of cellular function, heterogeneity, and interaction in health and disease. Our workflow provides a scalable path to multi-omic atlases, advancing both data integration and translational research.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-11-28
DOI
https://doi.org/10.1038/s41598-025-26735-1
Citations
4
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
3.22

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

One section, two worlds: single-cell integration of MALDI-MSI and spatial transcriptomics on the same single tissue section

Benjamin Balluff, Tim Hendriks, Eva Cuypers, Ron M. A. Heeren et al.
4 citations
Scientific Reports
Single-cell and spatial transcriptomics
3.22
article

One section, two worlds: single-cell integration of MALDI-MSI and spatial transcriptomics on the same single tissue section

Benjamin Balluff, Tim Hendriks, Eva Cuypers, Ron M. A. Heeren, Gert B. Eijkel, Theodoros Visvikis
article en
4 citations

Abstract

Understanding tissue complexity requires spatially resolved multi-omics data at single-cell resolution. Here, we present a workflow integrating high-resolution matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) with Xenium spatial transcriptomics (SPT) on a single tissue section. This strategy ensures pixel-scale spatial correspondence between metabolic and transcriptomic features, avoiding misalignment issues of serial sections, where even minor offsets result in sampling different cells. We investigated MALDI-MSI compatibility with downstream SPT revealing that the number of transcripts per cell decreased by ~ 30% after MSI, whilst cell recovery and cell-type assignments are preserved. Validated using mouse brain and demonstrated using human glioblastoma tissues, we achieved pixel-scale modality co-registration, enabling per-cell MALDI spectra extraction aligned with gene expression. Integrated clustering revealed enhanced cell-type resolution and identified metabolic heterogeneity within transcriptionally defined populations. This facilitates precise correlations of a cell's function and its biochemical state, providing a holistic view of cellular function, heterogeneity, and interaction in health and disease. Our workflow provides a scalable path to multi-omic atlases, advancing both data integration and translational research.

Scientific ReportsVol. 15(1)
Maastricht University (NL)
Universiteit Maastricht, Fonds Wetenschappelijk Onderzoek, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, KU Leuven, Vlaamse regering, Interreg Vlaanderen-Nederland, Interreg
Openalex Percentile: Top 7%
Single-cell and spatial transcriptomics
3.22
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.