International Journal of Molecular Zoology 2024, Vol.14, No.1, 31-43 http://animalscipublisher.com/index.php/ijmz 40 By comparing the differences and conservatism of different species during development, scientists can better understand the differences between human individuals and provide guidance for personalized healthcare. For example, using cross species developmental biology models, scientists can evaluate the manifestations and developmental processes of specific diseases in different individuals (Vecchia et al., 2018), providing a basis for personalized treatment. Cross species developmental biology models also have important application value for rare and hereditary diseases. By constructing corresponding disease models, scientists can delve deeper into the pathogenesis of these diseases and provide targeted treatment strategies for patients. These models can also be used for the development of cutting-edge technologies such as gene editing and cell therapy, providing new ideas and methods for disease treatment. 4 Challenges and Prospects In single-cell omics research, sample preparation is a crucial step that directly affects the quality of subsequent data and analysis results (Galazzi et al., 2018). At present, sample preparation still faces some technical challenges, such as cell acquisition, separation, labeling, and sequencing. How to efficiently and accurately obtain sufficient cells for certain rare cell types or specific tissues is an urgent problem to be solved. The quality of single-cell omics data is crucial for subsequent analysis and interpretation. However, due to technical limitations and experimental conditions, there are often issues with noise, batch effects, and non-specific signals in the data. How to improve data quality and reduce technical errors is a direction that needs continuous efforts in the field of single-cell omics. Single cell omics data has extremely high dimensions and complexity (Naz et al., 2019), making it a huge challenge to extract useful information from it and provide effective explanations. Existing data analysis methods often struggle to cope with such complex data structures, requiring the development of more advanced and efficient analysis tools and methods. In single-cell omics research, human samples are typically required. This involves a series of ethical issues such as privacy protection, informed consent, and data security. How to use and store human samples in a reasonable and compliant manner, ensuring the rights of researchers and the security of data, is a problem that must be taken seriously in research. With the development of technology, integrating multiple omics information such as genomics, transcriptomics, and epigenetics will become an important direction for the development of single-cell omics. By integrating multiple omics data, scientists can gain a more comprehensive understanding of the state and behavior of cells during development and disease processes, providing richer information for revealing the mysteries of life activities. The development of artificial intelligence and machine learning technology provides new opportunities for the analysis of single-cell omics data. By applying these technologies, we can more efficiently and accurately process and analyze large-scale single-cell omics data, extract useful information, and provide effective explanations. This will effectively promote the development of single-cell omics and make greater contributions to the progress of human health. References Assou S., Boumela I., Haouzi D., Anahory T., Déchaud H., Vos J., and Hamamah S., 2011, Dynamic changes in gene expression during human early embryo development: from fundamental aspects to clinical applications, Human reproduction update, 17(2): 272-290. https://doi.org/10.1093/humupd/dmq036 PMid:20716614 PMCid:PMC3189516 Barriuso J., Nagaraju R., and Hurlstone A., 2015, Zebrafish: a new companion for translational research in oncology, Clinical Cancer Research, 21: 969-975. https://doi.org/10.1158/1078-0432.CCR-14-2921 PMid:25573382 PMCid:PMC5034890
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