posted on 2024-07-05, 15:47authored byMax FK Wills, Carlos Bueno Alejo, Nikolas Hundt, Marina Santana-Vega, Andrea Taladriz-Sender, Alasdair W Clark, Andrew HudsonAndrew Hudson, Ian EperonIan Eperon
The identification of photobleaching steps in single molecule fluorescence imaging is a well-established procedure for analysing the stoichiometries of molecular complexes. Nonetheless, the method is challenging with protein fluorophores because of the high levels of noise, rapid bleaching and highly variable signal intensities, all of which complicate methods based on statistical analyses of intensities to identify bleaching steps. It has recently been shown that deep learning by convolutional neural networks can yield an accurate analysis with a relatively short computational time. We describe here an improved use of such an approach that detects bleaching events even in the first time point of observation, and we have included this within an integrated software package incorporating fluorescence spot detection, colocalisation, tracking, FRET and photobleaching step analyses of single molecules or complexes. This package, known as FluoroTensor, is written in Python with a self-explanatory user interface.
History
Author affiliation
College of Life Sciences
College of Science & Engineering
Molecular & Cell Biology
Chemistry
Version
VoR (Version of Record)
Published in
Computational and Structural Biotechnology Journal
FluoroTensor is available on GITHUB with a CC BY-NC 4.0 licence (https://github.com/LISCB/FluoroTensor). A detailed User Guide has been made available on the SpliceSelect website (https://www.spliceselect.org/research/). There is a link in the GitHub repository.