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ABSTRACT Nonmuscle myosin II (NMII) generates cytoskeletal forces that drive cell division, embryogenesis, muscle contraction and many other cellular functions. However, at present there is no method that can directly measure the forces generated by myosins in living cells. Here, we describe a Förster resonance energy transfer (FRET)-based tension sensor that can detect myosin-associated force along the filamentous actin network. Fluorescence lifetime imaging microscopy (FLIM)-FRET measurements indicate that the forces generated by NMII isoform B (NMIIB) exhibit significant spatial and temporal heterogeneity as a function of donor lifetime and fluorophore energy exchange. These measurements provide a proxy for inferred forces that vary widely along the actin cytoskeleton. This initial report highlights the potential utility of myosin-based tension sensors in elucidating the roles of cytoskeletal contractility in a wide variety of contexts. 

Abstract The development of high-resolution microscopes has made it possible to investigate cellular processes in 3D and over time. However, observing fast cellular dynamics remains challenging because of photobleaching and phototoxicity. Here we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo and Depth-Aware Video Frame Interpolation, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series post-acquisition. We show that CAFI is capable of understanding the motion context of biological structures and can perform better than standard interpolation methods. We benchmark CAFI’s performance on 12 different datasets, obtained from four different microscopy modalities, and demonstrate its capabilities for single-particle tracking and nuclear segmentation. CAFI potentially allows for reduced light exposure and phototoxicity on the sample for improved long-term live-cell imaging. The models and the training and testing data are available via the ZeroCostDL4Mic platform.