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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Published on: July 6, 2022

Riemannian metric learning for alignment of spatial multiomics.

Peter Halmos1, Yufan Xia1, Benjamin J Raphael1

  • 1Department of Computer Science, Princeton University, Princeton, NJ 08544, United States.

Bioinformatics (Oxford, England)
|July 7, 2026
PubMed
Summary
This summary is machine-generated.

Manifold Gromov-Wasserstein (MGW) aligns diverse spatial multiomics data. This novel framework integrates different molecular layers, enabling accurate tissue structure reconstruction and biological discovery across various scales.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Data Science

Background:

  • Emerging spatial technologies enable multi-modal measurements (transcriptome, epigenome, proteome, etc.) from thousands of cells within a tissue.
  • Integrating spatial data from heterogeneous feature spaces is challenging, as most assays profile only one modality per tissue slice.
  • Existing multi-modal integration techniques often struggle with spatial alignment across diverse data types, particularly when incorporating both spatial and feature information.

Purpose of the Study:

  • To develop a novel computational framework for aligning spatial multiomics data from arbitrary modalities.
  • To address the challenge of integrating heterogeneous feature spaces in spatial biology.
  • To enable robust reconstruction of tissue structures and biological insights from multi-modal spatial datasets.

Main Methods:

  • Introduction of Manifold Gromov-Wasserstein (MGW), a metric-learning framework leveraging the product structure of spatial multiomics.
  • Inference of modality-specific Riemannian pull-back metrics using neural fields.
  • Alignment of Riemannian distances via Gromov-Wasserstein optimal transport, providing a hyperparameter-free cost across modalities with a shared spatial base.

Main Results:

  • MGW demonstrates theoretical invariances, including orthogonal transformations and global feature scalings.
  • Successful application of MGW to diverse alignment tasks: mouse embryo spatiotemporal transcriptomics (Stereo-Seq), colorectal cancer spatial transcriptomics (Xenium, Visium), and human striatum/kidney cancer spatial metabolomics-transcriptomics.
  • MGW recovers biologically meaningful correspondences and spatially coherent tissue structures, outperforming existing optimal transport (OT) and non-OT multi-modal baselines.

Conclusions:

  • MGW offers a powerful and versatile solution for spatial multiomics data integration.
  • The framework facilitates the alignment of diverse spatial datasets, leading to improved biological interpretation.
  • MGW advances the field of spatial multiomics by enabling more comprehensive analysis of tissue architecture and cellular interactions.