Researchers at Brown University and the University of Michigan 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.
“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 Ying Ma, assistant professor of biostatistics at the Brown University School of Public Health and co-developer of IRIS. “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.”
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, who worked closely with Ma 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.
