Zebrafish is a useful biological model for analyzing genetic modification and large-scale screening. Its morphological evaluation, carrying meaningful information about genotype-phenotype relationship, is equally important. However, analysis of large amounts across development stages is a labor-intensive task. Here, we suggest a high-throughput monitoring technique using office scanner. Moreover, we developed deep learning models for extraction and analysis of massive statistical information. CNN-based architecture, forming the core of segmentation, serves as a basis for quantitative analysis and an early signal for embryo’s abnormal growth. Finally, compared to conventional microscope imaging, our scanning technique offers high-throughput, accurate, and fast quantitative phenotype analysis.
Xenopus laevis are emerging models to study human diseases and to investigate pharmaceutical effects in vivo due to
smaller size and faster developmental rates. It is also an effective organism to observe drug effects on phenotypic
characteristics because it can provide many biological systems in a short time and remain optically accessible at the early
stages of development. Although morphological evaluation of massive Xenopus data is an essential procedure, it requires
labor-intensive and manual inspection under an optical microscope. In this study, we propose a high-throughput, widefield,
and time-lapse phenotype screening system modifying the office scanner. We also fabricated the customized
PDMS well plate for efficient and stress-free imaging of living Xenopus laevis samples in normal and drug environments.
With our manipulated device, we were successfully able to monitor the morphological changes of Xenopus laevis
embryos acquired from more than 180 wells throughout 72 hours post fertilization stage. Our home-built software
combines best practices of image processing and deep learning for automated accurate segmentation of large Xenopus
data. Importantly, phenotypic features are quantitatively extracted to monitor the early-stage morphological
abnormalities. In addition, the convolutional neural network (CNN) based algorithm enable to classify phenotype
precisely. In conclusion, compared to conventional microscope screening, our platform offers high-throughput, accurate,
and fast quantitative phenotype analysis. The suggested platform could become a promising tool in massive and dynamic
observation such as developmental studies, drug testing, and phenotype-genotype assays, where statistical knowledge is
critical.
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