ArticleNature communications2026
Development of a universal imaging "phenome" using shape, appearance and motion (SAM) features and the SAM Phenotype Observation Tool (SPOT).
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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Authors and funding
6 authors.
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Abstract
Cells are plastic, highly heterogeneous and change over time. High-content timelapse imaging promises to reveal dynamic cell behaviors, enabling more accurate identification of cell state and cell fate prediction for biological hypothesis generation and perturbation screens. To empower live-cell imaging based screening, we report the development of (1) a Shape, Appearance, Motion (SAM) "phenome"; a universal set of 2185 image-derived features that act as a image-"transcriptome" to comprehensively quantify an object's instantaneous phenotype; (2) the SAM-Phenotype-Observation-Tool (SPOT), for image-"sequencing" analysis of phenomes. We validate the effectiveness of unbiased SAM-SPOT workflow on publicly available computer vision and 2D single cell imaging datasets. Importantly, we demonstrate that SAM-phenome outperforms features generated by deep learning AI models trained on >1 million fixed single cell and >5000 single cell video frames, respectively. SAM-phenome and SPOT deliver high-throughput, object-treatment-agnostic, comprehensive screening readouts of dynamics, promising to advance novel molecular target discovery and new medicine development.
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