filoVision - using deep learning and tip markers to automate filopodia analysis.

Filopodia are slender, actin-filled membrane projections used by various cell types for environment exploration. Analyzing filopodia often involves visualizing them using actin, filopodia tip or membrane markers. Due to the diversity of cell types that extend filopodia, from amoeboid to mammalian, it can be challenging for some to find a reliable filopodia analysis workflow suited for their cell type and preferred visualization method. The lack of an automated workflow capable of analyzing amoeboid filopodia with only a filopodia tip label prompted the development of filoVision. filoVision is an adaptable deep learning platform featuring the tools filoTips and filoSkeleton. filoTips labels filopodia tips and the cytosol using a single tip marker, allowing information extraction without actin or membrane markers. In contrast, filoSkeleton combines tip marker signals with actin labeling for a more comprehensive analysis of filopodia shafts in addition to tip protein analysis. The ZeroCostDL4Mic deep learning framework facilitates accessibility and customization for different datasets and cell types, making filoVision a flexible tool for automated analysis of tip-marked filopodia across various cell types and user data.

Putting the brakes on a myosin motor.

Insulin-stimulated trafficking of GLUT4 requires the myosin motor Myo1C and signaling adaptor 14-3-3ß. Originally, it was thought that 14-3-3ß promotes GLUT4 transport by binding the Myo1C lever arm and activating the Myo1C motor. New work by Ji and Ostap using in vitro assays reveals that 14-3-3ß binding actually inhibits Myo1C motility, prompting reconsideration of the functional relationship between 14-3-3ß and Myo1C and the regulatory potential of atypical light chains.