International Journal of Molecular Medical Science, 2024, Vol.14, No.1, 29-41 http://medscipublisher.com/index.php/ijmms 37 many immune-related diseases. Subtle differences between different immune cell types in health and disease states can be found through single cell omics analysis, and these differences may be key to the differences in immune response. Wimmers et al. (2021) analyzed individual immune cells following influenza vaccination by single-cell sequencing and ATAC sequencing. They found that vaccines can induce long-lasting changes in the epigenetic and transcriptional states of immune cells, which provides important clues to understanding the molecular mechanisms of immune memory. This research not only reveals how vaccines affect the internal state of immune cells, but also provides a powerful tool for developing new treatments and vaccines. By delving into individual differences in vaccine reactivity, we can better understand the complexity of the immune system and potentially lead to new breakthroughs in personalized medicine and immunotherapy. Single-cell omics technology provides strong support for the study of individual differences in vaccine reactivity. 4 Challenges and Prospects 4.1 Technical challenges The starting point of monocytomic research is to obtain high quality single cell suspensions. In this process, sample acquisition, processing, and preservation methods are crucial, as they directly affect the state of the cell, which in turn affects the accuracy of the experimental results. If the cells are damaged or changed during harvesting or handling, the results of subsequent experiments can be skewed, leading to a misunderstanding of the internal mechanisms of the cells. At present, how to effectively isolate individual cells from solid tissues while maintaining their original state remains a technical challenge. This requires continuous exploration and innovation by researchers to seek more refined and efficient sample preparation methods. For example, some research teams are trying to use microfluidic technology or laser capture micro-cutting technology to achieve more accurate and efficient single cell separation. Wills and Mead (2015) mentioned in their study that the huge amount of data generated by single-cell omics experiments requires researchers to have highly complex computational methods in data analysis and interpretation. Extracting meaningful biological information from data from hundreds of thousands or even millions of cells is an extremely difficult task. This requires not only efficient algorithms, but also a lot of computing resources to support it. The high dimensional and sparse nature of single-cell data also creates additional challenges for data analysis. High dimensionality means that the data contains a large number of characteristic variables, while sparsity means that each cell may only express a small percentage of those genes. How to accurately classify, judge the state and interpret the function of cells in such data background is the key problem that needs to be solved. Yost et al. (2019) mentioned in their study that in order to address these challenges, researchers are constantly developing new data analysis methods and tools. For example, some teams are trying to leverage algorithms such as machine learning or deep learning to improve the efficiency and accuracy of data analysis. At the same time, several novel sequencing techniques are being developed to improve the resolution and data quality of single-cell omics experiments. Although monocytomics faces many technical challenges, with the continuous efforts and innovations of researchers, we believe that these problems will eventually be solved. 4.2 Biological challenges The state of cells is transient, and they make rapid and precise adjustments in response to subtle changes in the external environment. This dynamic nature makes it extremely difficult to capture the complete picture of a cell's state. Although single-cell omics techniques can provide researchers with accurate snapshots of cells at a moment in time, how to connect these isolated snapshots to form a continuous dynamic trajectory of cell behavior has become a huge challenge. This requires researchers not only to have high-precision technical means, but also to have deep biological insight and innovative thinking. Yost et al. (2019) argue that even if the states of cells can be accurately captured, understanding what those states mean biologically is a difficult task. The state of cells does not exist in isolation; they interact intricately with
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