Deep learning reduces data requirements and allows real-time measurements in imaging FCS.

Imaging fluorescence correlation spectroscopy (FCS) is a powerful tool to extract information on molecular mobilities, actions, and interactions in live cells, tissues, and organisms. Nevertheless, several limitations restrict its applicability. First, FCS is data hungry, requiring 50,000 frames at 1-ms time resolution to obtain accurate parameter estimates. Second, the data size makes evaluation slow. Third, as FCS evaluation is model dependent, data evaluation is significantly slowed unless analytic models are available. Here, we introduce two convolutional neural networks-FCSNet and ImFCSNet-for correlation and intensity trace analysis, respectively. FCSNet robustly predicts parameters in 2D and 3D live samples. ImFCSNet reduces the amount of data required for accurate parameter retrieval by at least one order of magnitude and makes correct estimates even in moderately defocused samples. Both convolutional neural networks are trained on simulated data, are model agnostic, and allow autonomous, real-time evaluation of imaging FCS measurements.

Slow muscles guide fast myocyte fusion to ensure robust myotome formation despite the high spatiotemporal stochasticity of fusion events.

Skeletal myogenesis is dynamic, and it involves cell-shape changes together with cell fusion and rearrangements. However, the final muscle arrangement is highly organized with striated fibers. By combining live imaging with quantitative analyses, we dissected fast-twitch myocyte fusion within the zebrafish myotome in toto. We found a strong mediolateral bias in fusion timing; however, at a cellular scale, there was heterogeneity in cell shape and the relationship between initial position of fast myocytes and resulting fusion partners. We show that the expression of the fusogen myomaker is permissive, but not instructive, in determining the spatiotemporal fusion pattern. Rather, we observed a close coordination between slow muscle rearrangements and fast myocyte fusion. In mutants that lack slow fibers, the spatiotemporal fusion pattern is substantially noisier. We propose a model in which slow muscles guide fast myocytes by funneling them close together, enhancing fusion probability. Thus, despite fusion being highly stochastic, a robust myotome structure emerges at the tissue scale.

Simultaneous spatiotemporal super-resolution and multi-parametric fluorescence microscopy.

Super-resolution microscopy and single molecule fluorescence spectroscopy require mutually exclusive experimental strategies optimizing either temporal or spatial resolution. To achieve both, we implement a GPU-supported, camera-based measurement strategy that highly resolves spatial structures (~100 nm), temporal dynamics (~2 ms), and molecular brightness from the exact same data set. Simultaneous super-resolution of spatial and temporal details leads to an improved precision in estimating the diffusion coefficient of the actin binding polypeptide Lifeact and corrects structural artefacts. Multi-parametric analysis of epidermal growth factor receptor (EGFR) and Lifeact suggests that the domain partitioning of EGFR is primarily determined by EGFR-membrane interactions, possibly sub-resolution clustering and inter-EGFR interactions but is largely independent of EGFR-actin interactions. These results demonstrate that pixel-wise cross-correlation of parameters obtained from different techniques on the same data set enables robust physicochemical parameter estimation and provides biological knowledge that cannot be obtained from sequential measurements.