New AI-powered statistics method has potential to improve tissue and disease research
Research team hopeful that the method, called IRIS, can provide more detailed information for precision health treatment plans and health outcomes.
Researchers at the University of Michigan and Brown University have developed a new computational method, IRIS, to analyze complex tissue data which could transform our current understanding of diseases and how we treat them.
Integrative and Reference-Informed tissue Segmentation (IRIS) is a novel machine learning and artificial intelligence method that gives biomedical researchers the ability to view more precise information about tissue development, disease pathology and tumor organization.
The findings were published today in the journal Nature Methods.
IRIS draws from data generated by spatially resolved transcriptomics (SRT) and uniquely leverages single-cell RNA sequencing data as the reference to examine multiple layers of tissue simultaneously and distinguish various regions with unprecedented accuracy and computational speed.
Unlike traditional techniques that yield averaged data from tissue samples, SRT provides a much more granular view, pinpointing thousands of locations within a single tissue section. However, the challenge has always been to interpret this vast and detailed dataset, says Xiang Zhou, professor of Biostatistics at the University of Michigan School of Public Health and senior author of the paper. He worked with Ying Ma, assistant professor of Biostatistics at the Brown University School of Public Health, to develop IRIS.
Interpreting large and complex datasets is where IRIS becomes a helpful tool—its algorithms sort through the data to identify and segment various functional domains, such as tumor regions, and provide insights into cell interactions and disease progression mechanisms.
“Different from existing methods, IRIS directly characterizes the cellular landscape of the tissue and identifies biologically interpretable spatial domains, thus facilitating the understanding of the cellular mechanism underlying tissue function,” said Ma, who earned her Ph.D. in Biostatistics from the University of Michigan School of Public Health in 2023. “We anticipate that IRIS will serve as a powerful tool for large-scale multi-sample spatial transcriptomics data analysis across a wide range of biological systems.”
In the study, the researchers applied IRIS to six SRT datasets and compared its performance to other commonly used spatial domain methods. IRIS significantly outperformed other methods in accuracy. Ultimately as SRT technology continues to grow in popularity and use, the researchers hope to see methods like IRIS help to potentially develop targets for clinical interventions or drug targets, improving personalized treatment plans and patient health outcomes.
“The computational approach of IRIS pioneers a novel avenue for biologists to delve into the intricate architecture of complex tissues, offering unparalleled opportunities to explore the dynamic processes shaping tissue structure during development and disease progression. Through characterizing refined tissue structures and elucidating their alterations during disease states, IRIS holds the potential to unveil mechanistic insights crucial for understanding and combating various diseases.” said Zhou.
The study, “Accurate and efficient integrative reference-informed spatial domain detection for spatial transcriptomics,” was supported by grants from the National Institutes of Health.
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