Image-Based Live Cell Sorting. Issue 6 (June 2021) - LaBelle, Cody A.; Massaro, Angelo; Cortés-Llanos, Belén; Sims, Christopher E.; Allbritton, Nancy L.
Abstract : Technologies capable of cell separation based on cell images provide powerful tools enabling cell selection criteria that rely on spatially or temporally varying properties. Image-based cell sorting (IBCS) systems utilize microfluidic or microarray platforms, each having unique characteristics and applications. The advent of IBCS marks a new paradigm in which cell phenotype and behavior can be explored with high resolution and tied to cellular physiological and omics data, providing a deeper understanding of single-cell physiology and the creation of cell lines with unique properties. Cell sorting guided by high-content image information has far-reaching implications in biomedical research, clinical medicine, and pharmaceutical development. Highlights: Advancements in imaging and computational decision-making have given rise to a new method for classifying and isolating single cells from samples, which addresses major limitations of commonly used technologies, for example, fluorescence activated cell sorting (FACS). Microfluidic flow, microfluidic containment, and microarray-based systems have been utilized to separate cells utilizing high-spatial resolution and real-time kinetic information. From inexpensive and flexible in-laboratory devices to precise clinical tools, image-based cell sorting (IBCS) systems have broad potential. Machine learning and deep neural networks can be combined with IBCS for intelligent cell selection and isolation. Microfluidics and microarrays can be combined in single IBCS platforms, taking advantage of both technologies.