Seeing ghosts In fluorescence-activated cell sorting, characteristic target features are labeled with a specific fluorophore, and cells displaying different fluorophores are sorted. Ota et al. describe a technique called ghost cytometry that allows cell sorting based on the morphology of the cytoplasm, labeled with a single-color fluorophore. The motion of cells relative to a patterned optical structure provides spatial information that is compressed into temporal signals, which are sequentially measured by a single-pixel detector. Images can be reconstructed from this spatial and temporal information, but this is computationally costly. Instead, using machine learning, cells are classified directly from the compressed signals, without reconstructing an image. The method was able to separate morphologically similar cell types in an ultrahigh-speed fluorescence imaging–activated cell sorter. Science , this issue p. 1246