GEM-IL: A highly responsive fluorescent lactate indicator.

Lactate metabolism has been shown to have increasingly important implications in cellular functions as well as in the development and pathophysiology of disease. The various roles as a signaling molecule and metabolite have led to interest in establishing a new method to detect lactate changes in live cells. Here we report our development of a genetically encoded metabolic indicator specifically for probing lactate (GEM-IL) based on superfolder fluorescent proteins and mutagenesis. With improvements in its design, specificity, and sensitivity, GEM-IL allows new applications compared with the previous lactate indicators, Laconic and Green Lindoblum. We demonstrate the functionality of GEM-IL to detect differences in lactate changes in human oncogenic neural progenitor cells and mouse primary ventricular myocytes. The development and application of GEM-IL show promise for enhancing our understanding of lactate dynamics and roles.

Raman image-activated cell sorting.

The advent of image-activated cell sorting and imaging-based cell picking has advanced our knowledge and exploitation of biological systems in the last decade. Unfortunately, they generally rely on fluorescent labeling for cellular phenotyping, an indirect measure of the molecular landscape in the cell, which has critical limitations. Here we demonstrate Raman image-activated cell sorting by directly probing chemically specific intracellular molecular vibrations via ultrafast multicolor stimulated Raman scattering (SRS) microscopy for cellular phenotyping. Specifically, the technology enables real-time SRS-image-based sorting of single live cells with a throughput of up to ~100 events per second without the need for fluorescent labeling. To show the broad utility of the technology, we show its applicability to diverse cell types and sizes. The technology is highly versatile and holds promise for numerous applications that are previously difficult or undesirable with fluorescence-based technologies.

Virtual-freezing fluorescence imaging flow cytometry.

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s-1 without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.