International Journal of Molecular Medical Science, 2024, Vol.14, No.4, 239-251 http://medscipublisher.com/index.php/ijmms 242 3.2.2 Single-cell DNA sequencing (scDNA-seq) Single-cell DNA sequencing (scDNA-seq) focuses on the genomic landscape of individual cells, providing detailed information on genetic mutations, copy number variations, and other genomic alterations. This technique has been pivotal in studying intratumor heterogeneity and tumor evolution, as it allows for the reconstruction of clonal architectures and the tracing of evolutionary dynamics within tumors. scDNA-seq has also been used to identify rare subclones that may contribute to disease progression and resistance to therapy (Navin et al., 2015; Bian et al., 2018; Bowes et al., 2022). 3.2.3 Single-Cell ATAC sequencing (scATAC-seq) Single-cell ATAC sequencing (scATAC-seq) is used to profile the chromatin accessibility landscape at the single-cell level. This technique provides insights into the regulatory elements and epigenetic modifications that govern gene expression in individual cells. scATAC-seq has been applied to study the epigenetic heterogeneity within tumors, revealing how different chromatin states can influence cellular behavior and contribute to cancer development. The integration of scATAC-seq with other single-cell omics techniques, such as scRNA-seq, has further enhanced our understanding of the complex regulatory networks in cancer (Bian et al., 2018; Ren et al., 2018; García-Sanz and Jiménez, 2021). 3.3 Technical considerations and challenges Despite the significant advancements in single-cell sequencing technologies, several technical challenges remain. One major issue is the potential for technical noise and dropout events, where some transcripts or genomic regions may not be detected in every cell, leading to incomplete data. Additionally, the high cost and complexity of single-cell sequencing experiments can be limiting factors, particularly for large-scale studies. Another challenge is the need for robust computational tools to handle the vast amounts of data generated and to accurately interpret the results. Addressing these challenges will be crucial for the continued advancement and application of single-cell sequencing in cancer research (Ren et al., 2018; Lei et al., 2021; Ahmed et al., 2022; Bowes et al., 2022). 4 Application of Single-Cell Sequencing in CRC Single-cell sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in colon cancer. This technology allows for the dissection of complex tumor ecosystems at an unprecedented resolution, providing insights into the tumor microenvironment, identification of rare cell populations, clonal evolution, and the immune landscape. 4.1 Profiling tumor microenvironment The tumor microenvironment (TME) plays a crucial role in the progression and treatment response of colon cancer. Single-cell sequencing has enabled detailed profiling of the TME, revealing the diverse cellular components and their interactions. For instance, scRNA-seq has been used to map the cell type-specific transcriptome landscape in various cancers, including lung cancer, which shares similarities with colon cancer in terms of TME complexity (Wu et al., 2021). This approach has identified rare cell types and highlighted the heterogeneity in cellular composition and intercellular signaling networks (Wu et al., 2021). Additionally, single-cell sequencing has provided insights into the immune microenvironment, showing how immune cells interact with tumor cells and influence disease progression (Chen et. al., 2023). 4.2 Identification of rare cell populations One of the significant advantages of single-cell sequencing is its ability to identify rare cell populations within tumors. In advanced non-small cell lung cancer, scRNA-seq has uncovered rare cell types such as follicular dendritic cells and T helper 17 cells, which were not detected in previous studies using bulk sequencing methods (Wu et al., 2021). Similarly, in colon cancer, scRNA-seq can identify rare but potentially critical cell populations that contribute to tumor heterogeneity and treatment resistance. This capability is crucial for understanding the full spectrum of cellular diversity within tumors and for developing targeted therapies.
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