Automatized and optimized analysis of video-registered volume time courses of aquaporin-expressing Xenopus oocytes

GIBSON, OLIVER M.; GUASTAFERRI, FLORENCIA V.; CAVIGLIA, AGUSTÍN F.; AMODEO, GABRIELA; OZU, MARCELO; GALIZIA, LUCIANO

Congreso; 20th IUPAB Congress - 45th Annual Meeting of SBBF - 50th Annual Meeting of SBBq; 2021

Biophysical characterization of aquaporins expressed in oocytes is based on monitoring cell volume changes. This model allows the recording of sequential images or videos with low-cost videomicroscopy equipment. However, one bottleneck in the experimental workflow is image processing.  Timelapses recorded at high temporal resolution may contain thousands of images from where oocyte volume information must be extracted. The first step is oocyte segmentation, a user-dependent process that requires training and experimental criteria and is prone to bias included by the operator. Afterwards, oocyte area in each image is computed and converted to a relative volume curve (Vt/V0), assuming spherical shape of oocytes. Then, the osmotic permeability coefficient (Pf) is calculated using the slope of the Vt/V0 curve. To achieve an accurate Pf estimation, detection of the initial frame is mandatory, and not trivial to determine. This initial frame corresponds to an in-focus and stable oocyte image. Moreover, this frame must be temporarily located as near as possible to the instant the oocyte is subjected to the osmotic gradient. The challenge of this work is to present a new Phyton-based tool which allows us to process images, extract required information, and calculate the initial frame, Vt/V0 curves and Pf of each oocyte. We developed this new script to produce an automatic, faster, and operator-independent end-to-end solution to perform Pf analysis. To develop this tool we used various image and data processing techniques such as shape recognition algorithms and in-depth classification of image focus using cluster analysis. Comparison of results with the traditional approach show equal Pf values but with better determination of the initial frame and significantly lower time of analysis. With this tool it is possible to greatly increase the number of oocytes per experiment, gaining statistical power and substantially reducing the efforts spent in manual analysis.