Abstract

The survival rate of colorectal cancer has significantly improved in the past years; however, the 5-year survival of metastatic colorectal cancer is still lower than 20%. A better understanding of the molecular mechanisms underlying colorectal metastasis will provide new insights into the development of available therapeutic interventions. Single cell sequencing technology offers a powerful approach to characterizing the tumor microenvironment. This review will discuss the challenges in the integration of spatial and single-cell transcriptomics data to characterize the molecular characteristics of the metastasis-associated cell populations.

Keywords:Single-Cell Transcriptomics; Colorectal Cancer; Chemotherapy; Cellular heterogeneity

Low survival rate of metastatic colorectal cancer

As a common malignant tumor of the digestive system, colorectal cancer (CRC) is the third most common cancer and the leading contributor to cancer-related deaths worldwide [1]. In past decades, huge advances in treatment have continuously improved the survival rates of CRC patients [2-4]. However, the 5-year survival of patients diagnosed with metastatic CRC is less than 20% [5,6]. The main limitation of the current treatment regimen for metastatic CRC is the resistance to systemic chemotherapy and/or targeted therapy [7]. Immunotherapies hold promise for CRC metastasis, especially in patients with high microsatellite instability (MSI-H) or DNA mismatch repair deficiency (dMMR), with the overall response rate of 60-70% [8,9]. However, there is still a large number of CRC patients who have been diagnosed with other subsets of CRC, and the response rate for these patients is low. Developing new drugs to improve the effectiveness of immunotherapy is urged to improve outcomes of metastatic CRC.

The strategy for the integration of spatial transcriptome and scRNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology for profiling the molecular characteristics of the transcriptome at the single cell level to understand cellular heterogeneity and identify novel biomarkers for disease diagnosis, prognosis, and treatment [10,11]. While scRNA-seq provides high-resolution transcriptomic data at the single-cell level, it loses the spatial context of each cell within the tissue, which is crucial for understanding the cellular microenvironment and the organization and communication of cells in tumors. Although preserving spatial information, spatial transcriptomics often has lower resolution and coverage compared to scRNA-seq [12,13]. Therefore, the integration of spatial transcriptomics and scRNA-seq data could leverage their complementary strengths to understand the tissue architecture and the complex interactions between different cell types.

Currently, integration methods can be broadly categorized into two types: deconvolution (enhancing spatial resolution using scRNA-seq data) and mapping (mapping single-cell data to spatial locations) [14]. The deconvolution methods for spatial transcriptomic data mainly use scRNA-seq data as a reference to estimate the proportions of cell types in each spatial spot [15,16]. Each spot typically contains 1 to 10 cells, depending on the different technologies and tissue types, suggesting that this method fails to achieve single-cell resolution. Mapping-based integration method is to assign single cells from scRNA-seq to their spatial locations in tissue sections. The two key steps for integration are to build the expression matrix, including a set of gene features to classify different cell types, and to map the above matrix to spatial transcriptomics data [14,17]. Finally, each spatial location in spatial transcriptome data obtains high gene expression information. In practical applications for mapping, deep learning is emerging as a powerful tool to solve the need for large storage and time. The application of deep learning is a rapidly evolving field with great potential to advance the visualization of scRNAseq cell types in their spatial context [18-20].

The promise of integrating multi-omics data at the single cell level

Through the integration of spatial transcriptomics and scRNAseq data, Valdeolivas et al. systematically deciphered the spatial distribution of different CMS subtypes, which is crucial for the treatment and prognosis of CRC and identified crosstalk events of tumor and stroma regions in CMS2 carcinomas [21]. The C5AR1- RPS19 ligand-receptor pair was also identified to play important roles in cell communications between tumor and stroma cells [22]. In addition, the communication between tumor and immune cells has been investigated to find MDK-mediated immunosuppression with Treg cells in CRC [23]. The transcription factor BHLHE40 was identified as the key driver of liver metastasis in colorectal cancer [24]. The spatial characteristics of major cell types in TME, such as CAF, CD8+ T cells, and tumor-associated macrophages, have been investigated between the CRC primary tumor and liver metastasis to reveal potential targets for the development of the immunotherapy strategy for metastatic CRC [25].

Conclusion

Taken together, the integration between spatial transcriptomics and scRNA-seq data in CRC reveals the spatial distribution of molecular subtypes, cellular heterogeneity, and interactions within the TME. These findings pave the way for personalized and more effective therapies. Further developments in integrating multi-omics data are important for the improvement of the outcomes of metastatic CRC.

Acknowledgment

This work was supported by the Shanghai Pujiang Program (23PJ1411400), Science and Technology Development Fund from Science and Technology Commission of Fengxian District, Shanghai (20211709).

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