Cell-Interaction Analysis and Functional Studies Using Circulating Tumor Cell Models

The purpose of this chapter is to provide a comprehensive, step-by-step methodology for the analysis of circulating tumor cell (CTC) interactions within the blood microenvironment, from the acquisition of CTCs to single-cell transcriptomic sequencing and subsequent data analysis. Our methodological approach involved the following steps: (1) Data Preprocessing: The raw sequencing data were subjected to stringent quality control and preprocessing to remove artifacts and outliers that could potentially skew the analysis. (2) Seurat analysis: Utilizing the Seurat package, we performed dimensionality reduction techniques such as principal component analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to visualize and cluster CTCs based on their gene expression profiles. (3) CellphoneDB integration: We employed CellphoneDB to analyze the interactions between CTCs and other cell types within the blood, elucidating potential interaction pairs that could be targeted for therapeutic intervention. (4) Statistical graphics: The ggplot2 package was used to create informative and visually appealing graphs that summarized the results of our analysis, facilitating the interpretation of complex data for both oncologists and biologists. By following this methodological framework, we were able to provide a comprehensive analysis of CTC interactions, offering valuable insights that could inform the development of targeted therapies in oncology.