Researchers have unveiled a method that uses artificial intelligence to augment the latest spatial transcriptomics technologies. The research focuses on more recent technologies that produce images at a much closer scale, allowing for subcellular resolution (or multiple measurements per cell). While these techniques solve the resolution issue, they present new challenges because the resulting images are so close-up that rather than capturing 15 to 50 cells per image, they capture only a few genes. This reversal of the previous problem creates difficulties in identifying the individual components and determining how to group these measurements to learn about specific cells. It also obscures the big picture.
Researchers in Carnegie Mellon University's School of Computer Science have developed a method that uses artificial intelligence to augment how cells are studied and could help scientists better understand and eventually treat disease.
Many current spatial transcriptomics platforms still lack the resolution required for closer, more detailed analysis. These technologies often group cells in clusters that range from several to 50 cells for each measurement, a resolution that may be sufficient for well-represented large cells but that is problematic for small cells or ones that aren't well represented. These rare cells may be the most critical for the disease or condition being studied.
The algorithm developed by the CBD researchers, called subcellular spatial transcriptomics cell segmentation , harnesses AI and advanced deep neural networks to adaptively identify cells and their constituent parts. SCS uses transformer models, similar to those used by large language models like ChatGPT, to gather information from the area surrounding each measurement.
"The ability to use the most recent advances in AI to aid the study of the human body opens the door to several downstream applications of spatial transcriptomics to improve human health," said Ziv Bar-Joseph, the FORE Systems Professor of Machine Learning and Computational Biology at CMU.
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