Neural network informed photon filtering reduces fluorescence correlation spectroscopy artifacts.

ABSTRACT: Fluorescence correlation spectroscopy (FCS) techniques are well-established tools to investigate molecular dynamics in confocal and super-resolution microscopy. In practice, users often need to handle a variety of sample- or hardware-related artifacts, an example being peak artifacts created by bright, slow-moving clusters. Approaches to address peak artifacts exist, but measurements suffering from severe artifacts are typically nonanalyzable. Here, we trained a one-dimensional U-Net to automatically identify peak artifacts in fluorescence time series and then analyzed the purified, nonartifactual fluctuations by time-series editing. We show that, in samples with peak artifacts, the transit time and particle number distributions can be restored in simulations and validated the approach in

SUBMITTER: Seltmann A 

PROVIDER: S-EPMC10995453 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature