Cancer Genetics and Epigenetics 2024, Vol.12, No.1, 55-65 http://medscipublisher.com/index.php/cge 60 The capture and lysis of single cells is a delicate and complex task. Because cells are small in size, they are easily disturbed by the external environment. The RNA content in single cells is extremely low, which brings great difficulties to the reverse transcription and amplification processes. Low-abundance RNA can easily lead to low reverse transcription efficiency and amplification bias, thus affecting the accuracy and reliability of sequencing results (Isoda et al., 2017). In order to overcome these challenges, researchers continue to optimize capture platforms such as microfluidic chips and droplet microfluidic technology to improve the capture efficiency and lysis effect of single cells, and explore adding exogenous RNA or using pre-amplification strategies. method to increase the strength and stability of sequencing signals. Kiselev et al. (2019) discuss the challenges of unsupervised clustering of single-cell RNA sequencing data, which is a challenge in analyzing these data from a computational perspective, and the aspects with the data that make it challenging. Vallejos et al. (2017) explore the challenges and opportunities of normalizing single-cell transcriptome data, highlighting how using traditional methods risks producing misleading results, as well as providing alternatives and recommendations for single-cell RNA sequencing users. The massive data generated by single-cell RNA sequencing technology brings huge challenges to data analysis. How to extract meaningful biological information from huge data and accurately interpret the differences and regulatory relationships between cells are important tasks facing researchers. 3 Application of Single-cell RNA Sequencing in Studying Tumor Heterogeneity 3.1 Identification and classification of cell subpopulations within tumors Tumor is a complex biological system, and the heterogeneity of its internal structure has always been a difficult and hot topic in medical research. Traditional sequencing methods often can only give the average gene expression of the entire tumor tissue, and cannot accurately depict the characteristics of different cell subpopulations within the tumor. However, it is the differences in these cell subpopulations that determine the tumor's growth rate, ability to invade, and response to treatment. In this context, single-cell RNA sequencing technology emerged as the times require, opening a new door for the study of cell subpopulations within tumors. This technology is able to capture the gene expression information of single cells and can comprehensively and deeply explore the heterogeneity within tumors under unbiased conditions. Through single-cell RNA sequencing, it is not only possible to discover rare cell subpopulations that are masked in traditional sequencing, but also to accurately identify the unique molecular markers of each cell subpopulation, thereby accurately classifying them. Wang et al. (2021) conducted a comprehensive analysis of consensus molecular subtypes (CMS) of colorectal cancer (CRC) through single-cell RNA sequencing data, revealing the heterogeneity of gene regulatory networks and identifying key regulators of CRC. Ye et al. (2020) proposed a new learning framework to detect interactive gene groups in scRNA-seq data based on co-expression network analysis and subgraph learning, providing a systematic gene ontology for the detection of interactive gene groups in different cancer subtypes. Enrichment analysis. Such as pancreatic cancer research: In a study of pancreatic ductal adenocarcinoma (PDAC), researchers used single-cell RNA sequencing technology to identify different cell subpopulations in the tumor, including type 1 ductal cells and type 2 ductal cells. wait. Type 2 duct cells were found to have significantly higher levels of chromosomal copy number variations (CNV) than other cell types, suggesting that this cell subset plays an important role in the progression of PDAC. Type 2 ductal cells are mainly enriched in functional genes related to cancer, such as cell proliferation, migration, and hypoxia. This classification is not only based on similarities in gene expression, but also takes into account the functional status, differentiation stage, and potential response to treatment of the cells. Therefore, the classification of cell
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