International Journal of Molecular Medical Science, 2024, Vol.14, No.1, 29-41 http://medscipublisher.com/index.php/ijmms 38 surrounding cells, tissues, organs, and the organism as a whole. The same cell state may have completely different functions and meanings in different disease contexts. By closely linking single cell omics data to biological function and disease processes, the state and function of cells can be read more accurately. Conklin et al. (2022) mentioned that in order to address these challenges, a combination of tools and approaches is needed. The dynamic changes of cell state were tracked by combining the single cell omics data of time series. Technical means such as gene knockout and gene editing can also be used to verify the key role of specific genes or molecules in cell state and function. Learn from the advanced algorithms and technologies in computer science and artificial intelligence to mine the hidden information and rules in the single cell omics data. 4.3 Future direction Ning et al. (2013) believe that with the continuous development of monocytomics technology, the state and function of cells can be comprehensively understood from different perspectives. In the future, integrating multimodal data will become a critical task. Through the combination of single-cell RNA sequencing, single-cell ATAC sequencing and single-cell proteomics and other single-cell omics technologies, we can more deeply explore the molecular mechanism inside the cell, so as to more comprehensively reveal the physiological and pathological processes of the cell. Kolodziejczyk et al. (2015) believe that improving spatial resolution is an important direction for future research. If a technology could be developed that could provide high spatial resolution while maintaining single-cell resolution information, it could know exactly where each cell is in the tissue, and how they interact with each other, like a GPS location. This is essential to reveal the mechanisms of immune regulation in the cellular microenvironment. Through such research, more in-depth understanding of the working principle of the immune system, so as to provide new ideas and methods for future immunotherapy. Of course, to achieve these goals, new tools and algorithms need to be constantly developed. Existing bioinformatics tools and algorithms are often inadequate in the face of large-scale single-cell data. There is a need to develop new tools that are more efficient and accurate to meet the growing data challenges. These new tools will be able to better process and analyze data, improve the accuracy and efficiency of the analysis, and thus further advance the development of immunology research. 4.4 Impact on disease treatment Single cell omics technology provides a strong support for the realization of personalized medicine. The traditional medical model often adopts a "one-size-fits-all" treatment plan, which lacks sufficient consideration of individual differences. However, single-cell omics technology, through the fine analysis of the patient's specific immune cell status and function, allows doctors to gain insight into the patient's disease characteristics and physiological status, so as to formulate a treatment plan that is more consistent with the individual characteristics of the patient. Wills and Mead (2015) mention that in cancer treatment, single-cell omics techniques can help physicians analyze the type and number of immune cells in a patient's tumor tissue, as well as their interactions with cancer cells. This helps doctors select the most appropriate immunotherapy approach for the patient, such as personalized immunocell therapy or immunomodulatory drugs, thereby improving the effectiveness and safety of the treatment. The application of single cell omics technology in the field of precision immunotherapy is also of great significance. Traditional immunotherapy approaches often rely on a general understanding of the overall immune system and lack a detailed grasp of the individual immune response. Single-cell omics technology can reveal the complexity and diversity of individual immune responses of patients, providing a strong support for precision immunotherapy. Macaulay and Voet (2014) suggest that this technique could help identify new immunotherapeutic targets. By analyzing the gene expression profile and protein composition of individual immune cells, researchers can discover key molecules closely related to the development of disease, thus providing new candidate targets for
RkJQdWJsaXNoZXIy MjQ4ODYzNQ==