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The Analytical Scientist / Issues / 2026 / November / Thyra Opens MSI to Multimodal Analysis
Mass Spectrometry Clinical News and Research

Thyra Opens MSI to Multimodal Analysis

The open-source library connects mass spectrometry imaging with histology and spatial transcriptomics

11/04/2026 2 min read
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Clinical Report: Thyra Opens MSI to Multimodal Analysis

Overview

Thyra is an open-source software library that converts mass spectrometry imaging (MSI) data into SpatialData, enabling integration with histology, immunofluorescence, and spatial transcriptomics. The authors demonstrated the workflow by combining MSI, hematoxylin and eosin histology, and Xenium spatial transcriptomics in a previously published mouse brain dataset; they note that interpretation remains constrained by pixel size, tissue deformation, and image-registration accuracy.

Background

MSI maps the distribution of lipids, metabolites, peptides, and other molecules across tissue sections. Integrating these maps with microscopy and transcriptomic data can support analysis of molecular profiles in spatially defined tissue regions, but vendor-specific formats, differing coordinate systems, and large datasets complicate such work. Thyra addresses this interoperability challenge by converting supported MSI data into the SpatialData framework rather than introducing a new analytical platform. The provided standards context also identifies standardized data structures and experimental reporting as important to interoperability and reproducibility.

Data Highlights

AreaReported detail
Input dataContinuous raw vendor files and imzML datasets
Data conversionFull spectra, instrument metadata, and spatial coordinates are converted into SpatialData objects; instrument-specific resampling harmonizes mass-to-charge axes.
Data handling and outputA sparse matrix engine and single-pass processing support datasets larger than available computer memory. Outputs use Xarray for ion images, AnnData for spectral matrices, shared physical coordinates, and cloud-compatible Zarr v3 under the OME next-generation file format standard.
DemonstrationMALDI MSI, hematoxylin and eosin histology, and Xenium spatial transcriptomics were combined from a previously published mouse brain dataset.
Current limitationThe current release does not preserve ion-mobility data; the authors state that this is planned for a future version.

Key Findings

  • Thyra automatically detects supported MSI formats and converts spectra, instrument metadata, and spatial coordinates into SpatialData objects.
  • Instrument-specific resampling strategies harmonize mass-to-charge axes across different mass analyzers.
  • The resulting data structure supports use with scverse tools including Scanpy, Squidpy, napari, and Vitessce, and allows ion images, spectra, and other spatial layers to be inspected together.
  • The authors describe scripted quality-control checks for total-ion-current stability, mass-calibration drift, and registration errors.
  • In the mouse brain demonstration, registration enabled regions identified on histology or transcriptomic images to be used to query corresponding metabolic profiles.
  • The authors caution that interpretation is limited by MSI pixel size, tissue deformation, and image-registration accuracy; the current release also does not preserve ion-mobility data.

Clinical Implications

Thyra provides an interoperability workflow for researchers seeking to combine MSI with histology and spatial transcriptomics, including analysis of molecular profiles in tissue regions defined by other modalities. The source describes potential research applications, such as examining lipid distributions at tumor margins, but does not report clinical validation or establish diagnostic use.

Conclusion

Thyra converts supported MSI datasets into a shared spatial data framework and was demonstrated in a multimodal mouse brain workflow. Its use remains subject to the stated limitations of spatial resolution, tissue deformation, registration accuracy, and current ion-mobility support.

Related Resources & Content

  1. Visvikis T, et al., Nature Methods, year not provided -- Thyra Opens MSI to Multimodal Analysis
  2. SpatialData authors, Nature Methods, 2024 -- SpatialData: an open and universal data framework for spatial omics
  3. HUPO Proteomics Standards Initiative, year not provided -- mzIdentML: exchange format for peptides and proteins identified from mass spectra
  4. ASCO Publications — Multimodal machine learning analysis of more than 220,000 tumor profiles to diagnose molecular and morphological subtypes of cancer.
  5. The Analytical Scientist — MultiModal Molecular Imaging
  6. ASCO Publications — Multimodal machine learning analysis of more than 220,000 tumor profiles to diagnose molecular and morphological subtypes of cancer.
  7. ASCO Publications — Validation of omics based MSI calling to improve upon traditional methods of MSI detection.
  8. SpatialData: an open and universal data framework for spatial omics
  9. mzIdentML: exchange format for peptides and proteins identified from mass spectra
  10. mzIdentML: exchange format for peptides and proteins identified from mass spectra
  11. Intraoperative Evaluation of Breast Tissues During Breast Cancer Operations Using the MasSpec Pen - PMC
  12. SpatialData: an open and universal data framework for spatial omics | Nature Methods

This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.

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