Computer vision meets microfluidics: a label-free method for high-throughput cell analysis

In this paper, we review the integration of microfluidic chips and computer vision, which has great potential to advance research in the life sciences and biology, particularly in the analysis of cell imaging data. Microfluidic chips enable the generation of large amounts of visual data at the single-cell level, while computer vision techniques can rapidly process and analyze these data to extract valuable information about cellular health and function. One of the key advantages of this integrative approach is that it allows for noninvasive and low-damage cellular characterization, which is important for studying delicate or fragile microbial cells. The use of microfluidic chips provides a highly controlled environment for cell growth and manipulation, minimizes experimental variability and improves the accuracy of data analysis. Computer vision can be used to recognize and analyze target species within heterogeneous microbial populations, which is important for understanding the physiological status of cells in complex biological systems. As hardware and artificial intelligence algorithms continue to improve, computer vision is expected to become an increasingly powerful tool for in situ cell analysis. The use of microelectromechanical devices in combination with microfluidic chips and computer vision could enable the development of label-free, automatic, low-cost, and fast cellular information recognition and the high-throughput analysis of cellular responses to different compounds, for broad applications in fields such as drug discovery, diagnostics, and personalized medicine.

A high-throughput screening platform for pigment regulating agents using pluripotent stem cell-derived melanocytes

In this study, we describe a simple and straightforward assay using induced pluripotent stem cell-derived melanocytes and high-throughput flow cytometry, to identify the effect induced by pigment regulating agents on melanin content. The assay is based on the correlation between forward light-scatter characteristics and melanin content, with pigmented cells displaying high light absorption/low forward light scatter, while the opposite is true for lowly pigmented melanocytes, as a result of genetic background or chemical treatments. Orthogonal validation is then performed by regular melanin quantification. Such approach was validated using a set of 80 small molecules and yielded a confirmed hit. The assay described in this study may prove a useful tool to identify modulators of melanogenesis in human melanocytes.