, determine their function and understand their organization. A technique called spatial transcriptomics brings these cells into focus by combining imaging with the ability to quantify the level of genes in each cell—giving researchers the ability to study in detail several key biological mechanisms, ranging from howMany current spatial transcriptomics platforms still lack the resolution required for closer, more detailed analysis.
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.
When applied to images of brain and liver samples with hundreds of thousands of cells, SCS accurately identified the exact location and type of each cell. SCS also identified several cells missed by current analysis approaches, such as rare and small cells that may play a crucial role in specific diseases or processes, including aging. SCS also provided information on location of molecules within cells, greatly improving the resolution at which researchers can study cellular organization.
"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," said Ziv Bar-Joseph, the FORE Systems Professor of Machine Learning and Computational Biology at CMU.
"By integrating state-of the-art biotechnology and AI, SCS helps unlock several open questions about cellular organization that are key to our ability to understand, and ultimately treat, disease," added Hao Chen, a Lane Postdoctoral Fellow in CBD.