International Journal of Molecular Medical Science, 2024, Vol.14, No.4, 239-251 http://medscipublisher.com/index.php/ijmms 241 3 Single-Cell Sequencing Technologies 3.1 Introduction to single-cell sequencing 3.1.1 Historical perspective The study of cellular heterogeneity in cancer has long been hindered by the limitations of bulk sequencing methods, which average out the signals from diverse cell populations, masking the complexity within tumors. The advent of single-cell sequencing technologies has revolutionized this field by enabling the analysis of individual cells, thus providing unprecedented insights into tumor biology. Early applications of single-cell sequencing focused on understanding the genomic and transcriptomic landscapes of individual cells, which has since expanded to include epigenomic and multi-omics approaches (Ren et al., 2018; Lei et al., 2021; Bowes et al., 2022). Figure 1 shows the workflow of single-cell sequencing. Figure 1 Workflow of single-cell sequencing. 3.1.2 Technological advancements Technological advancements in single-cell sequencing have been rapid and transformative. Initial methods were limited by low throughput and high costs, but recent innovations have significantly improved the efficiency, accuracy, and scalability of these techniques. For instance, the development of microfluidic platforms and droplet-based methods has enabled the high-throughput sequencing of thousands of individual cells simultaneously. Additionally, advancements in computational algorithms have enhanced the ability to analyze and interpret the complex data generated by single-cell sequencing, leading to more accurate clustering and characterization of cellular subpopulations (Ren et al., 2018; Ahmed et al., 2022; Bowes et al., 2022). 3.2 Types of single-cell sequencing 3.2.1 Single-cell RNA sequencing (scRNA-seq) Single-cell RNA sequencing (scRNA-seq) is a powerful technique for profiling the transcriptomes of individual cells. This method has been instrumental in uncovering the cellular diversity within tumors and understanding the functional roles of different cell types in cancer progression. scRNA-seq has revealed distinct subpopulations of cancer cells and their interactions with the tumor microenvironment, providing insights into mechanisms of metastasis and therapy resistance (Li et al., 2017; Levitin et al., 2018; Ahmed et al., 2022). Recent advancements in scRNA-seq include the integration with spatial transcriptomics, which allows for the mapping of gene expression in the spatial context of tissue architecture (Ahmed et al., 2022).
RkJQdWJsaXNoZXIy MjQ4ODYzNQ==