Scoping Review: Methods and Applications of Spatial Transcriptomics in Tumor Research.

Simple Summary: Spatial transcriptomics is a technique of measuring gene expression with spatial resolution on a tissue slide, which is especially useful in cancer research. This scoping study reviews 41 articles published by the end of 2023 to examine current trends for employed methods, applications, and data analysis approaches for spatial transcriptomics, identifying challenges and practical uses of this technique in studying cancer. The research shows that cancer is a major focus in spatial transcriptomics studies, with certain tools and methods being particularly popular among scientists. However, many studies do not fully explain their data processing methods, making it hard to reproduce their results. Emphasis on transparent sharing of analysis scripts and tailored single-cell analysis methods for ST is recommended to enhance study reproducibility and reliability in the domain. This work may stand as a spatial transcriptomics developmental snapshot and reference to contemporary neoplasm biology research conducted through this technique. Spatial transcriptomics (ST) examines gene expression within its spatial context on tissue, linking morphology and function. Advances in ST resolution and throughput have led to an increase in scientific interest, notably in cancer research. This scoping study reviews the challenges and practical applications of ST, summarizing current methods, trends, and data analysis techniques for ST in neoplasm research. We analyzed 41 articles published by the end of 2023 alongside public data repositories. The findings indicate cancer biology is an important focus of ST research, with a rising number of studies each year. Visium (10x Genomics, Pleasanton, CA, USA) is the leading ST platform, and SCTransform from Seurat R library is the preferred method for data normalization and integration. Many studies incorporate additional data types like single-cell sequencing and immunohistochemistry. Common ST applications include discovering the composition and function of tumor tissues in the context of their heterogeneity, characterizing the tumor microenvironment, or identifying interactions between cells, including spatial patterns of expression and co-occurrence. However, nearly half of the studies lacked comprehensive data processing protocols, hindering their reproducibility. By recommending greater transparency in sharing analysis methods and adapting single-cell analysis techniques with caution, this review aims to improve the reproducibility and reliability of future studies in cancer research. [ABSTRACT FROM AUTHOR]