SPOmiAlign: a modality-agnostic computational framework for multimodal spatial omics alignment enabled by a feature matching foundation model.

Overview

1. Liangzhu Laboratory, Zhejiang University School of Medicine, No. 1369 Wenyi West Road, Yuhang District, Hangzhou, Zhejiang Province 311113, China
2. Zhejiang Key Laboratory of Multi-Omics in Infection and Immunity, Center for Infectious Disease Research, School of Medicine, Westlake University, No. 18 Shilongshan Street, Zhuantang Subdistrict, Xihu District, Hangzhou, Zhejiang Province 310024, China
3. Zhejiang University-University of Edinburgh Institute (ZJE), Zhejiang University School of Medicine, Zhejiang University, 718 East Haizhou Road, Haining, 314400, China
4. Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, 3 East Qingchun Road, Hangzhou, 310016, China

Abstract

Multimodal spatial omics enables systematic characterization of tissue organization by jointly profiling transcriptomic, proteomic, metabolomic, and other spatially resolved modalities within their spatial context. A central challenge in realizing this potential is achieving robust spatial alignment across modalities and sections. Although numerous alignment methods have been developed, most are designed for single-modality sections or specific modality combinations, with few enabling modality-agnostic alignment. Cross-modal alignment remains difficult due to the absence of shared molecular features, partial spatial overlap, and nonrigid tissue deformations. To address these challenges, we introduce SPOmiAlign, a modality-agnostic framework for spatial multimodal alignment, enabled by a feature-matching foundation model that serves as a general computational primitive for spatial multi-omics alignment. The framework enables accurate cross-modal spatial alignment without manual intervention or modality-specific tuning. Across diverse multimodal benchmarks, SPOmiAlign consistently achieves higher alignment accuracy than existing methods. We further demonstrate its utility through automated registration to a common coordinate framework, enabling standardized anatomical annotation. Finally, applying SPOmiAlign to integrate spatial transcriptomic, proteomic, and metabolomic data in mouse brain facilitates the identification of spatial domains that were difficult to resolve with less accurate registration, highlighting its utility for multi-omic integration and biological interpretation.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• alleninstitute.github.io/abc_atlas_access — at alleninstitute.github.io; found in “Data availability”
• zenodo:14986870 — at Zenodo; found in “Data availability”

Data availability

All datasets used in this study are publicly available. The Slide-seq dataset, a high-throughput sequencing-based spatial transcriptomics technology with near-cellular resolution comprising 101 adult mouse brain coronal sections that span the entire anteroposterior axis, was obtained from an online resource at https://docs.braincelldata.org/downloads/index.html/Slide-seq_Data. The MERFISH dataset, a high-throughput imaging-based spatial transcriptomics technology with single-cell resolution consisting of 25 adult mouse brain sagittal sections, was obtained from the Allen Brain Cell Atlas at https://alleninstitute.github.io/abc_atlas_access/descriptions/Zhuang-ABCA-3.html. The Allen Common Coordinate Framework (CCF) and the corresponding brain region annotations were obtained from the Allen Brain Atlas. Nissl-stained reference images of the adult mouse brain CCF used for alignment are publicly available for coronal views at https://mouse.brain-map.org/experiment/thumbnails/100048576?image_type=atlas and sagittal views at https://mouse.brain-map.org/experiment/thumbnails/100042147?image_type=atlas. Brain region annotations corresponding to the 25 m resolution CCF were downloaded from the file annotation_25.nrrd at https://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/annotation/ccf_2017/annotation_25.nrrd. The correspondence between the brain region identifiers and the anatomical labels was obtained from structure _tree _safe _2017.csv, available at https://github.com/cortex-lab/allenCCF/blob/master/structure_tree_safe_2017.csv. The MISAR-seq dataset, a spatial multiomic profiling dataset of the mouse brain that jointly measures chromatin accessibility and gene expression, was obtained from a public repository at https://github.com/gpenglab/MISAR-seq/blob/main/Data/Download. The cRCC R114 dataset used in this study, including the spatial transcriptomics and MALDI imaging mass spectrometry-based metabolomics data, was obtained from a public repository at https://zenodo.org/records/14986870. The spatial multiomic mouse brain dataset that integrates MALDI imaging mass spectrometry-based metabolomics, MAGIC-seq spatial transcriptomics, and PLATO spatial proteomics was obtained from a public repository at https://github.com/bioinfo-biols/Flow2Spatial/tree/main/datasets. Source data are provided with this paper. The code for the SPOmiAlign program is available on GitHub at https://github.com/wangyiyuyang/SPOmiAlign. All source code will be released publicly once the manuscript is published.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 10 MeSH terms, 2 funders, 42 references.

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This paper

Wang, Y., He, Z., & Yan, Y. (2026). SPOmiAlign: a modality-agnostic computational framework for multimodal spatial omics alignment enabled by a feature matching foundation model. Briefings in bioinformatics, 27(3), bbag331. https://doi.org/10.1093/bib/bbag331

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