3D imaging flow cytometry by point-spread-function engineering

Flow cytometry has become a mainstay in the biologist’s toolkit by enabling characterization of large sample populations. Recently, imaging flow cytometry (IFC) has added subsample, 2D spatial information by replacing the canonical point detectors with a 2D detector, i.e. a camera. Here we describe how to extend the capabilities of flow-based imaging to 3D by introducing point-spread-function engineering to IFC. This method requires a unique calibration procedure in which we rely on statistical distributions within the flowing sample rather than the unattainable static ground-truth of a fixed sample. We validate our approach on fluorescent beads and DNA nanorods, then demonstrate the applicability of our system to live-cell imaging by measuring the 3D positions of labeled DNA loci in yeast at throughputs orders of magnitude higher than previous methods. Furthermore, our approach is fully compatible with existing IFC systems after slight modification, thereby increasing its potential for implementation.