International Journal of Molecular Medical Science, 2024, Vol.14, No.5, 293-304 http://medscipublisher.com/index.php/ijmms 296 In summary, spatial transcriptomics represents a significant advancement in the field of cancer research, offering unparalleled insights into the spatial organization of gene expression within the TME. Its integration with other omics technologies holds great promise for advancing our understanding of tumor biology and developing more effective cancer therapies. 4 Application of Spatial Transcriptomics in Colon Cancer Research 4.1 Mapping cellular heterogeneity in TME Spatial transcriptomics (ST) has significantly advanced our understanding of the tumor microenvironment (TME) by enabling the mapping of cellular heterogeneity within tumors. This technology allows for the visualization and quantification of gene expression across different spatial regions of the tumor, providing insights into the complex cellular composition and interactions within the TME. For instance, the development of tools like spatialGE facilitates the quantification and visualization of tumor heterogeneity, offering spatial heterogeneity statistics and spot-level cell deconvolution to better understand the TME (Ospina et al., 2022). Additionally, ST has been used to profile spatial heterogeneity in various cancers, revealing unique tumor microenvironments and aiding in the identification of novel prognostic factors (Yu et al., 2022). 4.2 Identifying spatial gene expression patterns Identifying spatial gene expression patterns is crucial for understanding the functional architecture of tumors. ST technology enables the detection of gene expression gradients and spatially distinct gene expression profiles within the tumor. For example, studies have shown that spatial transcriptomics can delineate the extent of cancer foci more accurately than traditional pathologist annotations, highlighting gene expression changes during cancer progression (Berglund et al., 2018). Moreover, spatial transcriptomics has been applied to various cancer types to uncover spatially correlated patterns in gene expression, which are essential for understanding tumor growth dynamics and cancer hallmarks (Berglund et al., 2022). 4.3 Deciphering cell-cell interactions Deciphering cell-cell interactions within the TME is vital for understanding tumor progression and response to treatment. Spatial transcriptomics provides a powerful tool to infer molecular changes resulting from tumor and immune cell interactions. For instance (Figure 2), novel bioinformatics pipelines have been developed to infer biological patterns from ST data, enabling the identification of molecular interactions within the TME (Berglund et al., 2018). These interactions can be further analyzed to understand the pathways involved in tumorigenesis and resistance to immune attack, providing insights into the key drivers of cancer progression (Berglund et al., 2018). Additionally, spatial transcriptomics has been used to study the interactions between tumor cells and the extracellular matrix, revealing spatial patterns that impact tumor growth and immune modulation (Berglund et al., 2022). 4.4 Case studies in colon cancer Several case studies have demonstrated the application of spatial transcriptomics in colon cancer research. For example, spatialGE has been used to visualize and quantify the heterogeneity of the colon cancer TME (Figure 3), providing insights into the spatial distribution of gene expression and its association with clinical data (Ospina et al., 2022). Another study highlighted the use of spatial transcriptomics to profile the spatial heterogeneity of immune cell infiltration in colon cancer, revealing distinct immune cell patterns that correlate with tumor regions (Yu et al., 2022). These case studies underscore the potential of spatial transcriptomics to enhance our understanding of colon cancer biology and improve diagnostic and therapeutic strategies. 5 Technological and Methodological Considerations 5.1 Sample preparation and tissue handling Effective sample preparation and tissue handling are critical for the success of spatial transcriptomics in mapping the tumor microenvironment (TME) in colon cancer. Proper fixation and embedding of tissue samples are essential to preserve the spatial context of gene expression. Techniques such as formalin-fixed paraffin-embedded
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