Native, Memory-Bounded Conversion of Mass Spectrometry Imaging Data into the SpatialData Ecosystem.
- 1The Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry (IMS), Maastricht University, Maastricht6229 ER, The Netherlands.
- 2European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg69117, Germany.
Abstract
Mass spectrometry imaging (MSI) is now a routine modality for spatial molecular profiling and is increasingly combined with histology, multiplexed imaging, and spatial transcriptomics to build richer descriptions of tissue than any single technique can provide. The conceptual case for this multimodal paradigm was settled a decade ago, but the data infrastructure needed to operationalize it at scale has not kept up. Vendor binaries and the imzML exchange format keep MSI outside the OME-NGFF, Zarr, and SpatialData stacks on which the rest of spatial omics has converged, leaving each new experiment to rebuild ad hoc converters from scratch. Here, we present Thyra, an open-source Python library that converts raw vendor and imzML MSI data directly into the SpatialData format. A two-pass streaming sparse matrix engine writes the spectral matrix straight into the output Zarr store, keeping peak memory bounded near 200 MB regardless of data set size and supporting 100+ GB acquisitions. On a representative mouse brain MALDI data set, this yields three- to 4-fold storage reductions and an order-of-magnitude improvement in m/z range query latency, the bottleneck for ion image generation. Once converted, MSI data are directly composable with the scverse analysis ecosystem; we quantify this with an scverse readiness benchmark in which spatial autocorrelation, pixel clustering, and neighborhood enrichment, all standard operations in spatial transcriptomics, run as a one-liner on the converted object. A brief exploratory analysis of a skin biopsy from a psoriasis patient illustrates the analytical surface that the unified container exposes.
Related
- Tenfold Expansion Submicrometer MALDI Mass Spectrometry Imaging of Tissues and Cultured Cells
- Truthful visualizations for mass spectrometry imaging enable high-spatial-resolution interactive m/z mapping and exploration
- Single-Cell Mass Spectrometry Imaging Using Desorption Electrospray Ionization Coupled To Orbitrap Mass Spectrometers
- Continuous-wave laser ablation enables high-resolution ambient mass spectrometry imaging
- Automated Virtual Pathology Panels for ML-Assisted Interpretation of Mass Spectrometry Imaging
BibTeX
@article{visvikis2026native,
title = {{Native, Memory-Bounded Conversion of Mass Spectrometry Imaging Data into the SpatialData Ecosystem.}},
author = {Theodoros Visvikis and Wouter-Michiel Vierdag and Luca Marconato and Angeliki Birmpili and Ron M A Heeren and Eva Cuypers},
journal = {Journal of the American Society for Mass Spectrometry},
year = {2026},
month = oct,
doi = {10.1021/jasms.6c00169},
url = {https://europepmc.org/article/MED/42916767},
note = {CC BY},
}
RIS
TY - JOUR
TI - Native, Memory-Bounded Conversion of Mass Spectrometry Imaging Data into the SpatialData Ecosystem.
AU - Theodoros Visvikis
AU - Wouter-Michiel Vierdag
AU - Luca Marconato
AU - Angeliki Birmpili
AU - Ron M A Heeren
AU - Eva Cuypers
PY - 2026
DA - 2026/10/01
JO - Journal of the American Society for Mass Spectrometry
DO - 10.1021/jasms.6c00169
UR - https://europepmc.org/article/MED/42916767
AB - Mass spectrometry imaging (MSI) is now a routine modality for spatial molecular profiling and is increasingly combined with histology, multiplexed imaging, and spatial transcriptomics to build richer descriptions of tissue than any single technique can provide. The conceptual case for this multimodal paradigm was settled a decade ago, but the data infrastructure needed to operationalize it at scale has not kept up. Vendor binaries and the imzML exchange format keep MSI outside the OME-NGFF, Zarr, and SpatialData stacks on which the rest of spatial omics has converged, leaving each new experiment to rebuild ad hoc converters from scratch. Here, we present Thyra, an open-source Python library that converts raw vendor and imzML MSI data directly into the SpatialData format. A two-pass streaming sparse matrix engine writes the spectral matrix straight into the output Zarr store, keeping peak memory bounded near 200 MB regardless of data set size and supporting 100+ GB acquisitions. On a representative mouse brain MALDI data set, this yields three- to 4-fold storage reductions and an order-of-magnitude improvement in m/z range query latency, the bottleneck for ion image generation. Once converted, MSI data are directly composable with the scverse analysis ecosystem; we quantify this with an scverse readiness benchmark in which spatial autocorrelation, pixel clustering, and neighborhood enrichment, all standard operations in spatial transcriptomics, run as a one-liner on the converted object. A brief exploratory analysis of a skin biopsy from a psoriasis patient illustrates the analytical surface that the unified container exposes.
SN - 1044-0305
SN - 1879-1123
ER -