Cell sorting in a Petri dish controlled by computer vision.

Abstract

Fluorescence-activated cell sorting (FACS) applying flow cytometry to separate cells on a molecular basis is a widespread method. We demonstrate that both fluorescent and unlabeled live cells in a Petri dish observed with a microscope can be automatically recognized by computer vision and picked up by a computer-controlled micropipette. This method can be routinely applied as a FACS down to the single cell level with a very high selectivity. Sorting resolution, i.e., the minimum distance between two cells from which one could be selectively removed was 50-70 micrometers. Survival rate with a low number of 3T3 mouse fibroblasts and NE-4C neuroectodermal mouse stem cells was 66 +/- 12% and 88 +/- 16%, respectively. Purity of sorted cultures and rate of survival using NE-4C/NE-GFP-4C co-cultures were 95 +/- 2% and 62 +/- 7%, respectively. Hydrodynamic simulations confirmed the experimental sorting efficiency and a cell damage risk similar to that of normal FACS