Computational Imaging Approaches for Integrated Spatial Omics and Tissue Architecture

Recent progress in the field of spatial omics, multiplexed imaging, and computational analysis has profoundly enhanced our ability to decipher the organizational complexity of tissues at single-cell and subcellular resolution. While advanced technologies like spatial transcriptomics, in situ sequencing, imaging mass cytometry, and multiplexed immunofluorescence have made it possible to simultaneously assess molecular states and spatial organization within intact tissues, they also produce vast, high-dimensional datasets. A central challenge in the current landscape is the development and standardization of computational frameworks that can harmonize these multimodal datasets, spanning imaging, transcriptomics, proteomics, and clinical data, and transform them into actionable biological and clinical insights. Although promising strides have been made with machine learning, spatial statistics, and integrated modeling, robust analytical pipelines, interoperability between platforms, and best practices for reproducible research remain open questions and are the subject of ongoing debate.

This Research Topic aims to foster methodological advancements that bridge spatial omics with tissue architecture and function. The primary objective is to facilitate the development of robust algorithms, scalable pipelines, and comprehensive data integration strategies that can not only map but also model biological phenomena mechanistically or predictively. By inviting contributions that range from deep learning approaches for feature extraction to innovative frameworks for multimodal data harmonization, this Research Topic seeks to answer key questions about how best to unlock the full potential of spatial omics technologies for both systems biology and clinical application. Special attention will be given to research addressing reproducibility, interoperability, and the practical implementation of FAIR data principles in computational imaging.

Within the boundaries of computational frameworks for integrating spatial omics and tissue architecture, submissions should focus on methodological innovation and practical workflow development, while excluding purely descriptive studies or those limited to a single modality without integrative or computational novelty. To gather further insights in this rapidly evolving domain, we welcome articles addressing, but not limited to, the following themes:
- Machine learning and deep learning for spatial feature extraction and cell-state classification
- Graph-based and spatial statistical models for neighborhood and cell-cell interaction analysis
- Multimodal integration strategies combining transcriptomics, proteomics, and advanced imaging data
- Computational reconstruction and modeling of tissue architecture and cellular spatial trajectories
- Development of scalable, interoperable, and reproducible pipelines, benchmarking strategies, and adherence to FAIR data standards

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: computational imaging, spatial omics, spatial transcriptomics, multiplex imaging, tissue architecture, multimodal integration, single-cell analysis, graph neural networks, cell-cell interactions

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.