Scalable software engineering and methods for large-scale subcellular spatial omics

Boston Medical Center Corporation has been awarded $3,371,645 by the National Human Genome Research Institute (NHGRI) under the Human Genome Research program (CFDA 93.172) to develop scalable software engineering methods and computational tools for large-scale subcellular spatial omics analysis. The four-year project, initiated September 1, 2025, and concluding August 31, 2029, addresses critical barriers in spatial subcellular transcriptomics research by creating: (1) a scalable, extensible database infrastructure supporting interactive analysis of large datasets on standard computing systems without requiring specialized high-performance computing resources; and (2) novel computational methods that leverage high-resolution transcript spatial distribution information to enable transcript-level analysis beyond current single-cell resolution approaches.

The project includes sub-awards to the Trustees of Boston University for specialized statistical and computational expertise. Boston University Medical Campus (UEI: FBYMGMHW4X95), under Site Principal Investigator Dr. Shariq Mohammed, will provide statistical analysis oversight, study design development, data analysis execution, and manuscript preparation contributions. Additionally, Boston University's Computer Science and Engineering Department (UEI: THL6A6JLE1S7), led by Dr. Crovella, will develop novel analytical methods for improved subcellular spatial transcriptomics insights through graduate student researcher Wonyl Choi, and contribute to manuscript preparation and results interpretation. These coordinated efforts aim to systematically integrate and analyze spatial gene expression data across multiple samples to enable robust statistical analyses and link spatial patterns to clinical and experimental outcomes.

Abstract

Emerging spatial subcellular transcriptomics technologies represent a major advancement in biological research, offering to study the relationship of gene expression and tissue architecture at unprecedented resolution and scale. These technologies promise to transform fields across the biomedical research by providing detailed insights into cellular behavior and tissue architecture in health and disease. However, despite their transformative potential, the ability to fully leverage these new technologies is currently limited by several critical barriers. First, no scalable computational tools exist that facilitate an interactive and efficient analysis of the massive datasets generated by these platforms, making IT difficult for researchers to explore and extract meaningful insights. Second, the current analytical approaches are largely focused on single-cell resolution and fail to utilize the rich transcript-level information that is encoded in the data, limiting our ability to understand complex tissue organization and the molecular underpinnings of biological processes and morphology. Finally, there is a pressing need for systematic methods that integrate and analyze spatial data across multiple samples, which would enable robust statistical analyses and link spatial gene expression patterns to clinical or experimental outcomes. This project will directly address these challenges by developing a comprehensive suite of computational tools designed to unlock the full potential of spatial subcellular data. 1) we will create a scalable and extensible database infrastructure that supports interactive analysis of large datasets on standard computing systems, removing the need for specialized high-performance computing resources. Additionally, 2) we will develop novel methods that fully harness the high-resolution transcript spatial distribution information to improve the identification of cells, boundaries, and spatial domains within tissues. These methods will also extend to 3D datasets, allowing for more accurate modeling of complex tissue structures. Finally, 3) we will build a robust statistical framework that integrates spatial data from multiple samples, enabling researchers to associate detailed spatial gene expression patterns with clinical features or experimental outcomes. These tools will be available as standalone tools and integrated into the widely used giotto suite, thereby democratizing access to advanced spatial data analysis and facilitating novel discoveries in both basic and clinical research.