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ABSTRACT Regeneration is the remarkable phenomenon through which an organism can regrow lost or damaged parts with fully functional replacements, including complex anatomical structures, such as limbs. In 2019, Development launched its ‘Model systems for regeneration’ collection, a series of articles introducing some of the most popular model organisms for studying regeneration in vivo. To expand this topic further, this Perspective conveys the voices of five expert biologists from the field of regenerative biology, each of whom showcases some less well-known, but equally extraordinary, species for studying regeneration. 

Image-based cell classification has become a common tool to identify phenotypic changes in cell populations. However, this methodology is limited to organisms possessing well characterized species-specific reagents (e.g., antibodies) that allow cell identification, clustering and convolutional neural network (CNN) training. In the absence of such reagents, the power of image-based classification has remained mostly off-limits to many research organisms. We have developed an image-based classification methodology we named Image3C (Image-Cytometry Cell Classification) that does not require species-specific reagents nor pre-existing knowledge about the sample. Image3C combines image-based flow cytometry with an unbiased, high-throughput cell cluster pipeline and CNN integration. Image3C exploits intrinsic cellular features and non-species-specific dyes to perform de novo cell composition analysis and to detect changes in cellular composition between different conditions. Therefore, Image3C expands the use of imaged-based analyses of cell population composition to research organisms in which detailed cellular phenotypes are unknown or for which species-specific reagents are not available.