Röllin, A. | bioRxiv Science

bioRxiv2023

Deep learning reduces data requirements and allows real-time measurements in Imaging Fluorescence Correlation Spectroscopy

Imaging Fluorescence Correlation Spectroscopy (Imaging 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. Thirdly, as FCS evaluation is model-dependent, data evaluation is significantly slowed unless analytic models are available. Here we introduce two convolutional neural networks (CNNs) - FCSNet and Im-FCSNet - 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 CNNs are trained on simulated data, are model-agnostic, and allow autonomous, real-time evaluation of Imaging FCS measurements.

biophysics↗

bioRxiv2020

SRRF 'n' TIRF - FCS: Simultaneous spatiotemporal super-resolution microscopy

Super-resolution microscopy and single molecule fluorescence spectroscopy require mutually exclusive experimental strategies optimizing either time or spatial resolution. To achieve both, we implement a GPU-supported, camera-based measurement strategy that highly resolves spatial structures (~60 nm), temporal dynamics ([≤] 2 ms), and molecular brightness from the exact same data set. We demonstrate the applicability and advantages of multi-parametric measurements to monitor the super-resolved structure and dynamics of two different biomolecules, the actin binding polypeptide LifeAct, and the epidermal growth factor receptor (EGFR). Simultaneous super-resolution of spatial and temporal details leads to an improved precision in estimating the diffusion coefficient of LifeAct in dependence of the cellular actin network. Multi-parametric analysis 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 new biological knowledge that cannot be obtained from sequential measurements.

biophysics↗