This Review provides a comprehensive discussion of how methods in machine learning and computer vision have been used to improve super-resolution microscopy to gain insights into subcellular biology.

Complex digital representations of organs were reconstructed by computationally generating virtual slices from sparsely sampled planar spatial transcriptomic data, exemplified by a 38-million-cell ...