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.
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