Testing and validation of a new automatized analysis method for cell volume changes

GIBSON, OLIVER; AGUSTIN CAVIGLIA; MARCELO OZU; GALIZIA, LUCIANO; AMODEO, GABRIELA

Rosario

Congreso; L Reunión Anual de la Sociedad Argentina de Biofísica; 2022

Sociedad Argentina de Biofisica

Xenopus laevis oocytes is a widely used cellular model for aquaporin expression.Biophysical characterization of aquaporins expressed in oocytes is based on monitoringcell volume changes. This model allows recording sequential images or videos with lowcostvideomicroscopy equipment. Traditional image processing is performed manually.The first step is oocyte segmentation, which includes determination of an initial frame (ZF,Zero Frame) corresponding to an in-focus and stable oocyte image. Afterwards, the oocytearea in each image is computed and then converted to a relative volume curve (Vt/V0),assuming spherical shape of oocytes. These sequential steps are time-consuming.Recently we developed a new Phyton-based tool which allows us to process images,extract all the required information, and calculate Vt/V0 curves and Pf of each oocyte in anunsupervised manner.For testing and validation of the automatic analysis we compared the script output to thePf traditional estimation method. We analyzed a typical experimental dataset with videosobtained at 30 frame per seconds using oocytes injected with incremental masses ofAQP4-M23 cRNA. To test the method effectiveness, each obtained Vt/V0 curve wascompared to the manual determination. Then, the most accurate automatized curve ineach experiment was selected.. In this step we validated as accurate determinations 79.5± 3.6 % of the total analyzed videos (43 from a total of 54 videos), without any differencein the obtained Pf at every injected mass. We evaluated differences in the estimation of ZF(ΔZF), Pf (ΔPf) and R2between automatized and traditional analysis. ΔPf of eachexperimental group didn?t show any difference and the global value remains similar to Pfin native oocytes (ΔPf = 0.93 ± 0.15 x10-3 cm.s-1, n=43). The difference between themethods in the frame detection is estimated trough ΔZF (ΔZF = 80 ± 15 frames, n = 43)and corresponds to a time difference of 2.77 ± 0.35 seconds. This time differencecorresponds to approximately 7% of the entire recorded video. Interesting, determinationcoefficients from lineal fitting were lower in the automatized method (R2man = 0.95 ±0.03 vs R2script = 0,92 ± 0.03, n=43, p