Identify functional modules in spatial omics atlas with cellular community motifs - emerging spatial omics atlases, including human cell atlas, hubmap, and human tumor atlas, open new opportunities to…

The National Institute of Environmental Health Sciences awarded $2,759,326 to Indiana University Indianapolis on July 1, 2026, under the Medical Library Assistance program (CFDA 93.879) to develop a computational framework for identifying functional modules in spatial omics atlases using cellular community motifs.

The recipient will build a scalable computational framework and AI-enabled web portal to study conservative spatial organizations as functional modules across emerging spatial omics atlases including the Human Cell Atlas, HubMAP, and Human Tumor Atlas. The work focuses on understanding relationships between cell spatial arrangement and tissue functions in various biological systems and diseased microenvironments. The project proposes a computationally efficient search algorithm (CC-INDEX) to identify conservative cellular community motifs as functional modules across multiple spatial omics modalities including spatial transcriptomics, proteomics, epigenomics, and histopathological images. The framework will enable quantitative inquiry, analysis, and curation of knowledge on functional modules at the atlas level to enhance interpretability, generalizability, and sensitivity in comparative analyses.

Performance occurs at Indiana University Indianapolis in Indianapolis, Indiana. The period of performance runs from July 1, 2026, through June 30, 2030. The Medical Library Assistance program funds discrete scientific research projects focused on using biomedical informatics and data science methods and tools to improve human health.

Identify functional modules in spatial omics atlas with cellular community motifs - emerging spatial omics atlases, including human cell atlas, hubmap, and human tumor atlas, open new opportunities to investigate the relationships between cell spatial arrangement and tissue functions in various biological systems and diseased microenvironments. However, topological coordinating rules among different cell types, such as tissue spatial patterns in functional modules, are still under-investigated as the general unit across various tissues and diseases. Different from clustering cell type composition from classical top-down multicellular neighborhood analysis, bottom-up methods formulate the cell organization from cellular community (CC) motifs as discrete conservative patterns of recurring interconnections of various cell types. We hypothesize that CC motifs surrogating functional modules serve as precise modular markers, enhancing interpretability, generalizability, and sensitivity in comparative analyses and facilitating detailed mechanistic understanding in spatial omics studies, including spatial transcriptomics, proteomics, epigenomics, and histopathological images. In this project, we propose building a scalable and generalizable computational framework to comprehensively study conservative spatial organizations as functional modules, alongside an AI-innate web portal to quantitatively inquire, analyze, and curate the knowledge on functional modules in atlas-level spatial omics studies. We first analyze spatial multicellular neighborhoods by proposing a computationally efficient search algorithm CC-index identifying various conservative CC motifs as functional modules. In the triangulated tessellation space from spatial omics at the atlas level, the proposed approach is specially optimized to make precise multi-size CC motif identification computationally feasible (aim 1). These functional modules facilitate further spatial omics research expanding across multiple markers, samples, modalities, longitudes, resolutions, and scales. They can be further generalized to compare different biological statuses within multiple markers in continuous transcriptomics and proteomics expression. These functional modules will be used to facilitate various methodological and biomedical applications in spatial omics research and different applications in spatial omics research (aim 2). A large language model (LLM)-powered web portal, fmportal, will provide researchers with a user-friendly interface to query, analyze, and organize functional modules through a vector database across spatial omics atlases. This AI-innate system includes natural language interaction and empowered information summarization with RAG and reasoning (aim 3). All the generated results and knowledge will be carefully evaluated at multiple levels and through validations. Enabled by fast accumulating data, this work is likely a game changer, transforming spatial omics data into functional modules that reveal the broad and fundamental roles of spatial organization in tissue differentiation, organ development, disease progression, and drug response across diverse biomedical and pathological contexts.