To grasp what propels disease progression in tissues, researchers require more than a mere glimpse of cells in seclusion — they must observe the cells’ locations, their interactions, and how this spatial arrangement varies across disease stages. A novel computational approach termed MESA (Multiomics and Ecological Spatial Analysis), outlined in a study published in Nature Genetics, is aiding scientists in examining diseased tissues with greater significance.
The research outlines findings from a partnership involving scholars from MIT, Stanford University, Weill Cornell Medicine, the Ragon Institute of MGH, MIT, and Harvard, alongside the Broad Institute of MIT and Harvard, coordinated by the Stanford group.
MESA incorporates an ecology-inspired perspective into tissue evaluation. It provides a framework for interpreting spatial omics data — the outcome of advanced technology that captures molecular insights alongside cellular locations in tissue samples. This information delivers a high-definition map of tissue “neighborhoods,” and MESA facilitates the comprehension of this map’s structure.
“By merging techniques from previously separate fields, MESA allows researchers to more effectively understand how tissues are systematically organized and how this organization evolves in various disease scenarios, enhancing the potential for new diagnostics and the discovery of novel prevention and treatment targets,” notes Alex K. Shalek, director of the Institute for Medical Engineering and Science (IMES), the J. W. Kieckhefer Professor at IMES and the Department of Chemistry, and an external member of the Koch Institute for Integrative Cancer Research at MIT, as well as an institute member of the Broad Institute and a member of the Ragon Institute.
“In ecology, researchers investigate biodiversity in different regions — how animal species are distributed and interact,” elaborates Bokai Zhu, an MIT postdoctoral researcher and co-author of the study. “We realized we could apply those concepts to cells within tissues. Instead of examining rabbits and snakes, we focus on T cells and B cells.”
By treating cell types as ecological species, MESA measures “biodiversity” within tissues and observes how that diversity shifts in disease states. For instance, in liver cancer specimens, the method disclosed areas where tumor cells consistently co-existed with macrophages, indicating that these regions might drive distinct disease outcomes.
“Our technique interprets tissues as ecosystems, revealing cellular ‘hotspots’ that signify early indicators of disease or treatment response,” Zhu comments. “This paves the way for innovative precision diagnostics and therapy development.”
MESA also presents another significant benefit: it can computationally enhance tissue data without requiring additional experiments. By leveraging publicly accessible single-cell datasets, the tool infuses extra information — such as gene expression patterns — onto established tissue samples. This methodology enriches understanding of how spatial domains operate, particularly when contrasting healthy and diseased tissue.
In evaluations across various datasets and tissue types, MESA discovered spatial configurations and crucial cell populations that had been previously unnoticed. It amalgamates diverse types of omics data, such as transcriptomics and proteomics, creating a multifaceted view of tissue architecture.
Currently available as a Python package, MESA is tailored for academic and translational studies. Although spatial omics remains too resource-heavy for routine clinical application in hospitals, the technology is gaining momentum within pharmaceutical companies, especially for drug trials where grasping tissue responses is essential.
“This is merely the start,” states Zhu. “MESA opens the path to employing ecological theory to decode the spatial intricacies of disease — and ultimately, to enhance prediction and treatment methods.”