Quantifying Phytohormones in Vivo with FRET Biosensors and the FRETENATOR Analysis Toolset

Next-generation ABACUS biosensors reveal cellular ABA dynamics driving root growth at low aerial humidity

“…FRETENATOR segment and ratio analysis was compared to the commercial software Imaris 8.2 (https://imaris.oxinst.com/) for validation and to ensure comparable results. Buffer exchange and segmentation were performed as previously described 26,62 . Segmentation in Imaris was performed using the surfaces wizard on the AxAm channel, with background subtraction and object splitting.…”

“…Nuclear localization allows easy discrimination of the fluorescence of neighbouring cells and the exclusion of non-nuclear background and auto-fluorescence during image processing 11 . To accelerate this image processing, we developed a comprehensive image analysis toolset to quickly analyse confocal stacks in 3D/4D, allowing us to robustly quantify and visualize nuclear emission ratios within moments (See supplemental methods and 26 ).…”

Focus on biosensors: Looking through the lens of quantitative biology

“…Notes: Several image acquisition channels are used in FRET biosensor analysis. Here, the new FRETENATOR analysis pipeline for Fiji was used (Rowe et al, 2021 ). For segmentation, the following steps are performed: first a difference of Gaussian filter is applied to the acceptor-excited acceptor-emission channel to remove background and smooth noise, then Otsu’s method is used to threshold the filtered image based on signal intensity.…”

“…Biosensor images present further challenges for analysis, as they often require multichannel imaging and sequential acquisition, which must be taken at high speed when measuring fast dynamics. Because of the complexity of the imaging setup, a compromise is often made between acquisition speed, resolution and signal-to-noise ratio (Rizza et al, 2019 ; Rowe et al, 2021 ). This compromise means accurate fully automated segmentation and complete tissue reconstruction is difficult, but the development of deep learning segmentation tools, such as PlantSeg (Wolny et al, 2020 ) and Stardist (Weigert et al, 2020 ), may help increase segmentation accuracy and overcome some of these bottlenecks.…”

“…This compromise means accurate fully automated segmentation and complete tissue reconstruction is difficult, but the development of deep learning segmentation tools, such as PlantSeg (Wolny et al, 2020 ) and Stardist (Weigert et al, 2020 ), may help increase segmentation accuracy and overcome some of these bottlenecks. After segmentation steps, biosensor image analysis workflows also require additional bespoke steps, such as background subtraction and the calculation of emission ratios, to process the data, so it can be quantified meaningfully (Mahlandt & Goedhart, 2021 ; Rizza et al, 2017 ; Rowe et al, 2021 ; Waadt et al, 2020 ).…”