A Collaborative Study on the Classification of Silicone Oil Droplets and Protein Particles Using Flow Imaging Method.

In this study, we conducted a collaborative study on the classification between silicone oil droplets and protein particles detected using the flow imaging (FI) method toward proposing a standardized classifier/model. We compared four approaches, including a classification filter composed of particle characteristic parameters, principal component analysis, decision tree, and convolutional neural network in the performance of the developed classifier/model. Finally, the points to be considered were summarized for measurement using the FI method, and for establishing the classifier/model using machine learning to differentiate silicone oil droplets and protein particles.

Development of a platform for activatable fluorescent substrates of glucose transporters (GLUTs).

We have developed a platform for activatable fluorescent substrates of glucose transporters (GLUTs). We firstly conjugated fluorescein to glucosamine via an amide or methylene linker at the C-2 position of d-glucosamine, but the resulting compounds, FLG1 and FLG2, showed no uptake into MIN6 cells. So, we changed the fluorophore moiety to a fluorescein analogue, 2-Me TokyoGreen, which is less negatively charged. TokyoGreen-conjugated glucosamines TGG1 and TGG2 were successfully taken up into cells via GLUT. We further derivatized TGG1 and TGG2, and among the synthesized compounds, 2-Me-4-OMe TGG showed weak fluorescence under the acidic conditions of the extracellular environment inside tumors and in gastric cancers, and strong fluorescence at the intracellular physiological pH, under the control of a photoinduced electron transfer (PeT) process. This fluorogenic platform should be useful for developing a range of activatable fluorescent substrates targeting GLUTs, as well as derivatives that would be fluorescently activated by various intracellular enzymes, such as esterases, ß-galactosidase and bioreductases.