International Journal of Molecular Medical Science, 2024, Vol.14, No.4, 239-251 http://medscipublisher.com/index.php/ijmms 244 expression of chemokines that attract T cells, along with malignant and myeloid cells. By elucidating the interactions between different cellular programs, this study sheds light on the underlying logic that governs the spatial organization of immune-malignant cell networks. 4.4.3 Single cell sequencing reveals differences in immunotherapy efficacy ammong CRC patients Although most of dMMR CRC patients received immune checkpoint inhibition immunotherapy could achieve good therapeutic efficacy, there are still a small number of patients suffered from unsatisfactory tumor regression. Li et al. (2023) employed single-cell RNA sequencing to examine the dynamics of immune and stromal cells in 19 dMMR CRC patients who received neoadjuvant PD-1 blockade. This study revealed that in tumors achieving a pathological complete response (pCR), there is a coordinated decrease in CD8+ Trm-mitotic (tumor-resident memory T cells undergoing mitosis), CD4+ Tregs (regulatory T cells), proinflammatory IL1B+ Monocytes (monocytes expressing interleukin-1 beta), and CCL2+ Fibroblasts (fibroblasts expressing chemokine C-C motif ligand 2) following treatment. In contrast, the proportions of CD8+ Tem (effector memory T cells), CD4+ Th (helper T cells), CD20+ B (B cells expressing CD20), and HLA-DRA+ Endothelial cells (endothelial cells expressing human leukocyte antigen DRA) increased. They also discovered that proinflammatory features in the tumor microenvironment contribute to the persistence of residual tumors by modulating CD8+ T cells and other immune cell populations associated with treatment response. Chen et al. (2024) further explored the spatiotemporal cellular dynamics following neoadjuvant PD-1 blockade in CRC. They analyzed multiple sequential single-cell samples from 22 patients undergoing PD-1 blockade to map the evolution of local and systemic immunity in CRC patients. In this study, exhausted T (Tex) cells or tumor-reactive-like CD8+ T (Ttr-like) cells were found to be closely related to treatment efficacy. Additionally, Tex cells showed correlated proportion changes with multiple other tumor-enriched cell types following PD-1 blockade. These findings provide valuable insights into the mechanisms underlying the response to PD-1 blockade in CRC patients. Understanding the relationship between these cell types and their changes in response to treatment may help in the development of more effective and personalized immunotherapy strategies for patients with CRC. Furthermore, these studies highlights the importance of single-cell analysis in unraveling the complex interactions between immune cells and tumor cells in the tumor microenvironment, which may ultimately lead to improved patient outcomes. In conclusion, single-cell sequencing has provided profound insights into the cellular heterogeneity of colon cancer. By profiling the tumor microenvironment, identifying rare cell populations, understanding clonal evolution, and mapping the immune landscape, scRNA-seq has opened new avenues for research and therapeutic development in colon cancer. 5 Data Analysis and Interpretation 5.1 Preprocessing and quality control Preprocessing and quality control are critical steps in single-cell sequencing to ensure the reliability and accuracy of the data. Initial steps involve filtering out low-quality cells and reads, which can be achieved by assessing metrics such as the number of detected genes per cell, the proportion of mitochondrial gene expression, and the overall read quality. For instance, Figure 1 in the study by Bian et al. (2018) scTrio-seq was employed to examine mutations, transcriptome, and methylome within colorectal cancer tumors, ensuring high-quality data by optimizing single-cell multiomics sequencing techniques. Similarly, quality control measures such as normalization and batch-effect correction are essential to mitigate technical variations and enhance the comparability of data across different samples (Li et al., 2021). 5.2 Data integration and dimensionality reduction Data integration from multiple sources and dimensionality reduction are pivotal for managing the complexity of single-cell data. Techniques such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP) are commonly used to reduce the dimensionality of the data while preserving its intrinsic structure. For example, in colorectal cancer research, integrating single-cell RNA sequencing (scRNA-seq) with bulk RNA transcriptome sequencing has revealed a
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