To comprehend the factors that influence disease advancement in tissues, researchers require more than a mere snapshot of isolated cells — they must observe the positioning of cells, their interactions, and how this spatial arrangement evolves through various disease states. A novel computational technique named MESA (Multiomics and Ecological Spatial Analysis), elaborated upon in a study published in Nature Genetics, is aiding scientists in examining diseased tissues in more insightful manners.
This research outlines the findings of a partnership among scholars from MIT, Stanford University, Weill Cornell Medicine, the Ragon Institute of MGH, MIT, Harvard, and the Broad Institute of MIT and Harvard, and was spearheaded by the team from Stanford.
MESA introduces an ecology-inspired perspective to tissue examination. It provides a framework to interpret spatial omics data — the result of advanced technology that gathers molecular information alongside the cellular locations in tissue samples. These data yield a high-resolution blueprint of tissue “neighborhoods,” and MESA assists in deciphering the composition of that blueprint.
“By merging methodologies from typically separate fields, MESA allows researchers to gain a deeper insight into how tissues are organized locally and how that organization varies in distinct disease scenarios, fostering new diagnostics and the discovery of fresh targets for prevention and treatment,” states Alex K. Shalek, the head of the Institute for Medical Engineering and Science (IMES), the J. W. Kieckhefer Professor at IMES and the Department of Chemistry, an extramural member of the Koch Institute for Integrative Cancer Research at MIT, and also a member of the Broad Institute as well as the Ragon Institute.
“In ecology, researchers examine biodiversity across regions — looking at how animal species are distributed and interact,” notes Bokai Zhu, MIT postdoctoral fellow and co-author of the study. “We realized that we could apply those same concepts to cells within tissues. Instead of rabbits and snakes, we focus on T cells and B cells.”
By treating cell types as ecological species, MESA quantifies “biodiversity” within tissues and monitors how this diversity fluctuates in disease. For instance, in liver cancer samples, the method identified areas where tumor cells consistently co-occurred with macrophages, indicating that these zones may influence distinct disease outcomes.
“Our method interprets tissues like ecosystems, revealing cellular ‘hotspots’ that indicate early signs of disease or treatment response,” Zhu emphasizes. “This creates new opportunities for precision diagnostics and therapy formulation.”
MESA also presents another significant benefit: It can computationally enhance tissue data without necessitating additional experiments. Utilizing publicly accessible single-cell datasets, this tool imparts supplementary information — such as gene expression profiles — onto existing tissue samples. This strategy enriches understanding of how spatial domains operate, particularly when contrasting healthy and diseased tissues.
Through evaluations across various datasets and tissue types, MESA unveiled spatial configurations and critical cell populations that had been previously ignored. It integrates a range of omics data, including transcriptomics and proteomics, establishing a multilayered perspective of tissue architecture.
Currently available as a Python package, MESA is tailored for both academic and translational research. While spatial omics remains too resource-demanding for regular clinical application in hospitals, the technology is increasingly being adopted by pharmaceutical companies, particularly for drug trials where understanding tissue responses is paramount.
“This is merely the start,” concludes Zhu. “MESA paves the way for utilizing ecological theory to decipher the spatial intricacies of disease — ultimately enhancing prediction and treatment methods.”