FCS videos: Fluorescence correlation spectroscopy in space and time

• Published in: Biochimica et biophysica acta. General subjects Vol. 1868; no. 11; p. 130716
• Main Authors: Wohland, Thorsten, Sim, Shao Ren, Demoustier, Marc, Pandey, Shambhavi, Kulkarni, Rutuparna, Aik, Daniel
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.11.2024
• Subjects: Cell Membrane - chemistry; Cell Membrane - metabolism; Deep Learning; Fluorescence correlation spectroscopy; Lipid bilayer; Lipid Bilayers - chemistry; Lipid Bilayers - metabolism; Membrane structure and dynamics; Spatiotemporal super-resolution; Spectrometry, Fluorescence - methods; Spatiotemporal super-resolution; Lipid bilayer; Fluorescence correlation spectroscopy; Membrane structure and dynamics
• ISSN: 0304-4165; 1872-8006; 1872-8006
• DOI: 10.1016/j.bbagen.2024.130716

Fluorescence Correlation Spectroscopy (FCS), invented more than 50 years ago is a widely used tool providing information on molecular processes in a variety of samples from materials to life sciences. In the last two decades FCS was multiplexed and ultimately made into an imaging technique that provided maps of molecular parameters over whole sample cross-section. However, it was still limited by a measurement time on the order of minutes. With the improvement of FCS time resolution to seconds using deep learning, we extend here FCS to so-called FCS videos that can provide information how the molecular parameters determined by Imaging FCS change in space and time. This opens up new possibilities for the investigation of molecular processes. Here, we demonstrate the feasibility of the approach and show FCS video applications to lipid bilayers and cell membranes. [Display omitted] •First CNN for number concentration predictions in Imaging FCS.•Establishment of FCS videos for diffusion coefficient (D) and number concentration (N) maps at 2.5 s per frame.•Long-term recording of D and N videos for up to 15 min on a sample.•Access to new information on molecular dynamics of a sample through Correlations of FCS videos (D and N).