High-throughput screening platform for quantitative phenotype analysis of Xenopus laevis with deep learning

Hyunmo Yang; Sanzhar Askaruly; Seongmin Yun; Geoseong Na; Taejoon Kwon; Woonggyu Jung

doi:10.1117/12.2624884

11 January 2022 High-throughput screening platform for quantitative phenotype analysis of Xenopus laevis with deep learning

Hyunmo Yang, Sanzhar Askaruly, Seongmin Yun, Geoseong Na, Taejoon Kwon, Woonggyu Jung

Author Affiliations +

Proceedings Volume 12159, SPIE Advanced Biophotonics Conference (SPIE ABC 2021); 1215905 (2022) https://doi.org/10.1117/12.2624884
Event: SPIE Advanced Biophotonics Conference (SPIE ABC 2021), 2021, Busan, Republic of Korea

Abstract

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.

Citation Download Citation

Hyunmo Yang, Sanzhar Askaruly, Seongmin Yun, Geoseong Na, Taejoon Kwon, and Woonggyu Jung "High-throughput screening platform for quantitative phenotype analysis of Xenopus laevis with deep learning", Proc. SPIE 12159, SPIE Advanced Biophotonics Conference (SPIE ABC 2021), 1215905 (11 January 2022); https://doi.org/10.1117/12.2624884

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KEYWORDS

Scanners

Biological research

Microscopes

Statistical analysis

Convolutional neural networks

Image processing

Matrices

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