International Journal of Molecular Medical Science, 2024, Vol.14, No.1, 29-41 http://medscipublisher.com/index.php/ijmms 39 drug development. Single-cell omics technology can also be used to monitor therapeutic effects and guide clinical treatment decisions. By comparing and analyzing the immune cell status of patients before and after receiving immunotherapy, doctors can timely understand the treatment effect and the patient's immune response, so as to adjust the treatment plan or change drugs to ensure the effectiveness and safety of treatment. Gawad et al. (2016) mentioned that despite the promising application of single-cell omics technology in immune system research and disease treatment, there are still technical and biological challenges. For example, there are still many difficulties in the acquisition and processing of single-cell samples, and the analysis and interpretation of data. However, with the continuous development of new technologies and the application of new algorithms, it is believed that these challenges will be gradually overcome in the future, and single cell omics technology will provide more in-depth insights and more effective solutions for immunological research and clinical treatment. References Bashford-Rogers R., Smith K., and Thomas D., 2018, Antibody repertoire analysis in polygenic autoimmune diseases, Immunology, 155: 3-17. https://doi.org/10.1111/imm.12927 PMid:29574826 PMCid:PMC6099162 Bettelli E., Carrier Y., Gao W., Korn T., Strom T., Oukka M., Weiner H., and Kuchroo V., 2006, Reciprocal developmental pathways for the generation of pathogenic effector TH17 and regulatory T cells, Nature, 441: 235-238. https://doi.org/10.1038/nature04753 PMid:16648838 Butler A., Hoffman P., Smibert P., Papalexi E., and Satija R., 2018, Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology, 36: 411-420. https://doi.org/10.1038/nbt.4096 PMid:29608179 PMCid:PMC6700744 Chen K.H., Boettiger A., Moffitt J., Wang S.S., and Zhuang X., 2015, Spatially resolved, highly multiplexed RNA profiling in single cells, Science, 348: 6233. https://doi.org/10.1126/science.aaa6090 PMid:25858977 PMCid:PMC4662681 Conklin K.Y., Zhang B., Knoten A., Diep D., Lake B., Jain S., and Zhang K., 2022, 10X Genomics Single-Nucleus Multiome (RNA+ATAC) Assay for Profiling Adult Human Tissues v1, 2: 18. https://doi.org/10.17504/protocols.io.b4dqqs5w Duhen T., Duhen R., Lanzavecchia A., Sallusto F., and Campbell, D., 2012, Functionally distinct subsets of human FOXP3+ Treg cells that phenotypically mirror effector Th cells, Blood, 19: 4430-4440. https://doi.org/10.1182/blood-2011-11-392324 PMid:22438251 PMCid:PMC3362361 Francisco L.M., Sage P.T., and Sharpe A.H., 2010, The PD-1 pathway in tolerance and autoimmunity https://doi.org/10.1111/j.1600-065X.2010.00923.x PMid:20636820 PMCid:PMC2919275 Gawad C., Koh, W., and Quake, S., 2016, Single-cell genome sequencing: current state of the science. Nature Reviews Genetics, 17: 175-188. https://doi.org/10.1038/nrg.2015.16 PMid:26806412 Giladi A., and Amit I., 2018, Single-Cell Genomics: A Stepping Stone for Future Immunology Discoveries, Cell, 172: 14-21. https://doi.org/10.1016/j.it.2019.09.004 PMid:31645299 Gomes T., Teichmann S., and Talavera-López C., 2019, Immunology Driven by Large-Scale Single-Cell Sequencing, Trends in Immunology, 40(11):1011-1021. https://doi.org/10.1038/nmeth.2772 PMid:24363023 Islam S., Zeisel A., Joost S., Manno G.L., Zając P., Kasper M., Lönnerberg P., and Linnarsson S., 2013, Quantitative single-cell RNA-seq with unique molecular identifiers, Nature Methods, 11: 163-166. https://doi.org/10.1038/s12276-020-00499-2 PMid:32929221 PMCid:PMC8080663 Kashima Y., Sakamoto, Y., Kaneko, K., Seki, M., Suzuki, Y., and Suzuki, A., 2020, Single-cell sequencing techniques from individual to multiomics analyses. Experimental & Molecular Medicine, 52: 1419-1427. https://doi.org/10.1038/s12276-020-00499-2 PMid:32929221 PMCid:PMC8080663
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