Cancer Genetics and Epigenetics 2024, Vol.12, No.1, 55-65 http://medscipublisher.com/index.php/cge 59 Next, the released RNA is converted into cDNA by reverse transcriptase. In this step, reverse transcriptase uses RNA as a template to synthesize a complementary DNA strand, called cDNA. This process retains the information of the original RNA but converts it into a more stable form of DNA that is easier to manipulate and sequence later. Since the RNA content in a single cell is extremely low, it is often difficult to obtain sufficient data by direct sequencing. Therefore, cDNA usually needs to be amplified to increase its quantity before sequencing. This is usually achieved through PCR (polymerase chain reaction) technology, which can exponentially amplify specific DNA fragments in a short time (Picelli, 2017). Finally, the amplified cDNA is sent to a high-throughput sequencer for sequencing. Sequencers can read the sequence information of millions of DNA fragments simultaneously, thereby generating large amounts of data. These data are then subjected to bioinformatics analysis to reveal key information such as gene expression in individual cells, cell type, functional status, and interaction with other cells. 2.2 Advantages of single-cell RNA sequencing technology The advantages of single-cell RNA sequencing technology are particularly prominent compared with traditional sequencing methods based on cell populations. In traditional methods, researchers can usually only obtain the average gene expression of a group of cells, and such data often obscures the differences between individual cells. However, every cell in an organism is unique, and small differences between them can be critical to the function and behavior of the entire organism. The emergence of single-cell RNA sequencing technology has broken this limitation. It can capture the gene expression information of individual cells, thereby revealing the heterogeneity between cells. This heterogeneity plays a key role in complex biological processes, such as embryonic development, immune response, disease development, etc. Through single-cell RNA sequencing, researchers can more accurately understand cell types and status changes during these processes, providing new ideas for the diagnosis and treatment of diseases. AlJanahi et al. (2018) provide basic principles of the new technology, focusing on important concepts in single-cell RNA sequencing data analysis, such as quality control of data, normalization and standardization methods, and clustering for data dimensionality reduction. and visualization algorithms. Zhang et al. (2021) study used single-cell RNA sequencing technology to deeply analyze the heterogeneity of cancer cells, revealing the existence of different cell subpopulations and their association with tumor occurrence and development. It provides new ideas and methods for precise diagnosis and treatment of cancer. Zheng and Wang (2019) studied the application prospects of single-cell RNA sequencing technology in early diagnosis, prognosis assessment and new drug development of tumors, providing an important basis for the formulation of treatment strategies for solid tumors. This research lays the foundation for understanding the complexity of solid tumors and advancing personalized treatments. Single-cell RNA sequencing technology also has extremely high sensitivity. It is able to detect very rare cell types or low-abundance transcripts that are often difficult to detect with traditional methods. Single-cell RNA sequencing technology is still developing, and combined with other technologies such as spatial transcriptomics and single-cell multi-omics, it provides more powerful tools for life science research. With the continuous advancement of technology and reduction of costs, it is believed that single-cell RNA sequencing technology will play an increasingly important role in future biomedical research. 2.3 Technical challenges and solutions Although single-cell RNA sequencing technology has brought revolutionary progress to biomedical research, it still faces some technical challenges in practical application. These challenges mainly come from the complexity of single-cell operations, the scarcity of RNA, and the difficulty of data analysis.
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