Circulating tumor cells (CTCs) are cancer cells that have shed from a primary tumor and entered the bloodstream, acting as the "seed" for metastases [1]. Their journey is perilous; CTCs must adapt to the harsh environment of the bloodstream, navigating circulating immune components [2] and physical challenges like shear stress and squeezing through constricted vessels [3]. These stresses often trigger biochemical changes, such as epithelial-mesenchymal transition (EMT) [3, 4], a process where epithelial cells lose their characteristics and gain mesenchymal features [4]. This transition enhances motility, invasion, and intra- and extravasation [5, 6]. To survive in the bloodstream, CTCs may adopt an EMT phenotype, avoiding anoikis (cell death due to detachment) and developing chemotherapy resistance [5, 6]. Ultimately, they may undergo mesenchymal-to-epithelial transition (MET) to colonize distant sites [7]. The CTC population is remarkably heterogeneous, reflecting the diversity within the original tumor [1]. Only a subset of CTCs contributes to metastasis, possessing unique biological characteristics resulting from genetic and transcriptional alterations [1]. The CTC microenvironment is a complex interplay of various cell types and soluble factors [8, 9], influencing CTC phenotype through biochemical adaptations and interactions [10, 11]. Interactions with hematopoietic and stromal cells provide further support and stability [12]. Current cancer therapies often target specific steps in the metastatic cascade, including primary tumor invasion, intravasation, vascular transit, extravasation, and colonization [13]. Two primary strategies involve preventing metastasis and suppressing existing metastases. To personalize treatment, identifying high-risk patients is crucial, necessitating effective biomarkers to guide therapy and avoid overtreatment [13]. CTCs offer a promising approach for such stratification and targeted therapy, since they represent the "seeds" of metastasis and eliminating them could prevent or eliminate secondary lesions [13]. Therefore, characterizing the molecular mechanisms driving CTC dissemination and seeding is paramount. To effectively study this complex process, high-resolution technologies are needed to analyze CTC shedding, invasion, and interactions with other cells [14]. Advances in next-generation sequencing (NGS) and bioinformatics have enabled a deeper understanding of the molecular mechanisms behind metastasis through genotypic and phenotypic characterization of CTCs [14]. Single-cell RNA sequencing (scRNA-seq) emerges as a particularly powerful tool, offering gene expression profiles at single-cell resolution, potentially revealing key regulatory pathways in metastasis.

The review extensively cites existing literature on CTCs, their biology, and the methods used to study them. The authors review methods for CTC enrichment and isolation, focusing on fluorescence-activated cell sorting (FACS), micromanipulation, laser capture microdissection (LCM), and microfluidic technologies. Each method is described in detail, highlighting advantages, limitations, and relevant publications. The literature review also covers computational methods for analyzing scRNA-seq data, including cell clustering techniques (PCA, t-SNE, UMAP, scCAEs, scDeepCluster, scVI, VAE, DCA, DESC, DDLK), differential gene expression analysis (DESeq, edgeR, Limma, SAMseq, BPSC, MAST, Monocle, DEsingle), and pseudotime analysis (Monocle, Palantir, Slingshot, PAGA, STREAM, Tempora, tradeSeq). The cited studies cover various cancer types, including breast, prostate, colorectal, pancreatic, and lung cancer, demonstrating the broad applicability of scRNA-seq to uncover CTC heterogeneity and biological mechanisms. The review synthesizes findings from these studies, highlighting the translational relevance of scRNA-seq for understanding tumor heterogeneity, predicting clinical outcomes, and identifying potential therapeutic targets.

The paper focuses on reviewing the existing methodologies rather than presenting a new research study. Therefore, its methodology lies primarily in the comprehensive review and synthesis of existing literature on CTCs and their analysis using scRNA-seq. The authors systematically examine the workflow for analyzing CTCs using scRNA-seq, breaking it down into three main stages: 1. **CTC Enrichment and Isolation:** The paper details several methods employed to enrich and isolate CTCs from the blood, including: * **FACS:** Utilizing fluorescently labeled antibodies to isolate CTCs based on surface markers. High-throughput FACS systems are described, along with their limitations concerning the required number of input cells. * **Micromanipulation:** Employing manual or automated robotic platforms (CellCollector, CytePicker) to visually select and isolate individual CTCs using capillary pipettes or needles. This approach offers high precision but can be labor-intensive and may not be suitable for large-scale studies. * **LCM:** Combining CTC enrichment methods with a laser to dissociate and collect single cells from a microscopic slide, achieving high purity. The NanoVelcro-LCM method is discussed, showcasing its compatibility with various sequencing techniques. * **Microfluidic Technology:** Leveraging microfluidic devices (e.g., 10X Genomics, Hydro-Seq, SCR-chip) for high-throughput single-cell isolation, RNA extraction, barcoding, and library preparation in a single integrated system. The advantages and limitations of different microfluidic platforms are compared, focusing on capture efficiency and potential for contamination. *The authors also provide an overview of different methods available for enrichment, pointing out differences in efficiency and success in capturing a significant portion of CTCs from various types of cancer. 2. **scRNA-Seq:** The paper explores the application of scRNA-seq in characterizing individual CTCs. The discussion focuses on the various platforms available and the process of obtaining gene expression profiles at the single-cell level. The challenges of low CTC numbers and the high level of technical noise associated with scRNA-seq are also addressed. 3. **Data Analysis:** The paper extensively describes bioinformatic tools and analysis methods employed to interpret the scRNA-seq data. Key steps in the data analysis workflow are outlined: * **Cellular Subpopulation Identification:** This stage involves clustering cells based on similarity in gene expression using techniques like PCA, t-SNE, UMAP, and various deep learning-based methods. The challenges associated with clustering rare cell populations are discussed. * **Differential Gene Expression Analysis:** This section details methods for identifying genes differentially expressed across different cell populations or conditions. Both traditional bulk RNA-seq methods and specialized scRNA-seq tools are reviewed, considering the challenges of technical noise and dropout events. * **Pseudotime Analysis:** The authors describe pseudotime analysis, a method used to infer the order of cell states and developmental trajectories based on gene expression patterns. This approach helps understand dynamic cellular processes and potentially track changes in CTCs over time or across different locations. The paper highlights the importance of selecting appropriate methods for each stage, considering the unique characteristics of CTCs and scRNA-seq data.

The paper synthesizes findings from numerous studies applying scRNA-seq to CTCs across various cancer types. Key findings include: * **Tumor Heterogeneity:** scRNA-seq reveals significant heterogeneity within CTC populations, reflecting the intratumoral heterogeneity of the primary tumor. This heterogeneity encompasses different phenotypic states, including epithelial, mesenchymal, and hybrid states, as well as cancer stem cell (CSC)-like phenotypes. * **Prognostic and Predictive Biomarkers:** Specific CTC subtypes, particularly those expressing mesenchymal markers (e.g., VIM, SPARC, ITGB1) or CSC markers (e.g., ALDH, CD44+/CD24-), are associated with poor prognosis and reduced survival across various cancers (colorectal, prostate, HCC). The presence of these markers can serve as a prognostic and predictive indicator for treatment outcomes. * **Treatment Response and Disease Progression:** scRNA-seq allows monitoring of treatment response and disease progression by identifying changes in gene expression profiles of CTCs over time. For example, a "pre-adapted (PA)" transcriptional state in breast CTCs, associated with chemotherapy resistance and metastatic spread, was discovered. Specific gene signatures (e.g., 6-gene resistance signature in breast cancer, RRM2 signature in enzalutamide-resistant prostate cancer) are correlated with treatment resistance and aggressive malignancy. * **Metastatic Mechanisms:** scRNA-seq reveals novel insights into the molecular mechanisms driving metastasis, revealing the role of extracellular matrix (ECM) genes (e.g., SPARC in pancreatic cancer) and interactions between CTCs and blood immune cells (e.g., platelets, Treg cells). Studies show altered expression of genes associated with stress responses, cell cycle, and immune evasion pathways. * **Drug Resistance Mechanisms:** scRNA-seq has uncovered the activation of non-canonical Wnt signaling in AR-targeted therapy-resistant prostate cancer CTCs, identifying potential drug targets. * **CTC Subtyping in Other Fluids:** scRNA-seq is not limited to blood samples and has been successfully used to study CTCs in other biological fluids (CSF, pleural effusion), revealing potential differences in gene expression profiles compared to blood-derived CTCs. For instance, CSF-CTCs from lung adenocarcinoma patients displayed high expression of lung-specific genes and proliferation markers. The paper emphasizes that scRNA-seq can aid in identifying druggable targets within CTCs and their microenvironment. This includes targeting mesenchymal markers, interfering with signaling pathways (Wnt, Notch, Hedgehog), using EGFR and TGF-β inhibitors, and disrupting CTC clustering with host cells (platelets). The potential of combining scRNA-seq data with patient-derived organoids for drug screening is highlighted.

This comprehensive review demonstrates the significant contribution of scRNA-seq in advancing our understanding of CTC biology and its implications for precision medicine. The findings presented highlight the power of scRNA-seq in dissecting tumor heterogeneity at the single-cell level, providing insights previously unobtainable using traditional bulk analysis methods. The identification of specific gene signatures associated with poor prognosis, treatment resistance, and metastatic potential offers crucial information for risk stratification and personalized therapeutic strategies. The exploration of CTC interactions with immune cells and the microenvironment opens new avenues for targeted therapies, such as immunotherapy and anti-platelet agents. The integration of scRNA-seq data with other techniques, like patient-derived organoids, promises to further revolutionize drug discovery and development. However, challenges remain, such as the need for more efficient CTC capture, improved bioinformatic tools, and cost-effective high-throughput technologies. Overcoming these limitations will be critical to fully translating the potential of scRNA-seq for CTC analysis into routine clinical practice.

This review comprehensively outlines the application of single-cell RNA sequencing (scRNA-seq) to the study of circulating tumor cells (CTCs). It showcases the technology's potential to revolutionize cancer diagnostics and treatment through the identification of novel biomarkers and therapeutic targets. The ability to decipher tumor heterogeneity, predict treatment response, and uncover underlying metastatic mechanisms provides a promising path toward more personalized and effective cancer therapies. Future research should focus on enhancing CTC capture efficiency, refining bioinformatic tools, and developing cost-effective, high-throughput methods to make scRNA-seq analysis of CTCs more readily accessible in clinical settings.

While scRNA-seq offers considerable advantages in analyzing CTCs, several limitations persist. The technology is heavily reliant on the successful enrichment and isolation of CTCs, which can be challenging due to their rarity in blood samples. The efficiency of various enrichment methods and potential for contamination can significantly impact downstream analysis. Furthermore, the high cost and complexity of scRNA-seq can limit its widespread application in clinical settings. Finally, the analysis of scRNA-seq data requires sophisticated bioinformatic tools and expertise, which are not universally accessible.