Multi-parametric analysis of the cryopreserved bovine semen using imaging flow cytometry with the application of machine learning tools

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dc.contributor.authorUmirbayeva, Anel
dc.date.accessioned2024-05-04T07:24:17Z
dc.date.available2024-05-04T07:24:17Z
dc.date.issued2024-04-16
dc.description.abstractCryopreservation is an essential technique used in the agricultural sector to preserve cattle semen for a long period of time and further use it for artificial insemination. However, the quality of semen upon thawing decreases drastically, and there are no standardised procedures that could analyse sperm’s morphological and metabolic parameters simultaneously. Based on literature, high variations in methods and results of semen evaluations among studies were determined. Therefore, a meta-analysis was conducted to determine what quality parameters were the main informative ones for a multi-parametric sperm evaluation and to analyse their associations with each other. As results of meta-analysis showed that DNA integrity, mitochondrial membrane potential, and morphology of spermatozoa are important factors for sperm quality evaluation. Advanced technique Imaging Flow Cytometry was used for a rapid, high-throughput, and accurate assessment of each cell’s quality parameters. A novel sperm thawing and staining protocol for multi-parametric evaluation under IFC was developed, and about 100,000 cells were analysed for each quality parameter with the following statistical analysis of obtained quantitative data. This analysis allowed us to see the relationship between mitochondrial membrane potential and abnormal morphology of spermatozoa as well as to characterise a biological mechanism for sperm bundle formation. Image database is currently under creation for the development and application of a comprehensive machine learning algorithm for sperm quality prediction.en_US
dc.identifier.citationUmirbaeva, A. (2024) Multi-parametric analysis of the cryopreserved bovine semen using imaging flow cytometry with the application of machine learning tools. Nazarbayev University School of Sciences and Humanitiesen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/7636
dc.language.isoenen_US
dc.publisherNazarbayev University School of Sciences and Humanitiesen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectType of access: Restricteden_US
dc.subjectsperm cryopreservationen_US
dc.subjectbovine semen qualityen_US
dc.subjectflow cytometryen_US
dc.subjectmeta-analysisen_US
dc.subjectimaging flow cytometryen_US
dc.titleMulti-parametric analysis of the cryopreserved bovine semen using imaging flow cytometry with the application of machine learning toolsen_US
dc.typeBachelor's thesisen_US
workflow.import.sourcescience

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