CAREER: Decoding Tissue Architecture Through Spatial Transcriptomics Integration and 3D Reconstruction

Understanding how cells are organized within tissues is fundamental to biology. New technologies can measure gene activity while preserving the spatial location of cells, creating unprecedented opportunities to study how complex tissues are assembled and function. However, most current analyses are limited to two-dimensional tissue sections, making it difficult to understand the three-dimensional organization of biological systems. This CAREER project will develop computational tools that will enable researchers to integrate information across multiple tissue sections and reconstruct three-dimensional tissue architecture. The resulting open-source software will provide broadly applicable tools for analyzing spatial genomics data and will accelerate discovery across many areas of biological research. The project also integrates research with education through research internships for high school students, online bioinformatics micro-credentials, and science communication activities that engage students in computational biology and help prepare the next generation of scientists. This project will develop a unified computational framework for integrating spatial transcriptomics data across multiple tissue sections and reconstructing biologically meaningful three-dimensional tissue architectures. The framework combines graph representation learning, local alignment, contrastive learning, and optimization methods to preserve spatial organization while integrating data collected from multiple sections. The resulting computational tools will support analyses of spatial organization, tissue topography, developmental trajectories, and cellular interactions in three dimensions. The methods will be evaluated using diverse datasets representing multiple biological systems to ensure broad applicability and scalability. All software, documentation, and educational resources developed through this project will be released as open-source resources to maximize their impact on the biological research community. The project advances national interest by promoting scientific progress in biotechnology and AI and strengthening the computational biology workforce training through education and participation in science, technology, engineering, and mathematics (STEM) activities for students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.