The tumor microenvironment (TME) is a specialized ecosystem created by and for tumor cells. It is a complex community composed of multiple cell types - tumor cells, immune cells, stromal cells, fibroblasts, and endothelial cells (blood vessels), and surrounding tissue components - the stroma and extracellular matrix.
A dynamic crosstalk between these components creates a unique microenvironment that is increasingly conducive to the development and progression of the tumor.
It has shown to be essential for generating heterogeneity, clonal evolution and enhancing multi-drug resistance in tumor cells. Variance of the TME composition between patients has also been linked to variance in therapeutic outcomes across a variety of cancers. Thus, the TME has attracted great research and clinical interest as a therapeutic target in cancer.
This blog explores the challenges around utilizing spatial transcriptomics data and offers solutions to mitigate them. Take a dig.
Traditionally, single-cell technologies have been used to unravel the cellular heterogeneity of the Tumor Microenvironment (TME) providing a more comprehensive understanding of tumor biology. However, the tissue context that emerges in the TME dictates how these cells interact with each other and with acellular components. This tissue context is lost in single-cell analyses. Phenotypes related to tumor organization such as delineating tumor edge vs core regions, tertiary lymphoid structures (TLS), etc., are difficult to evaluate. Additionally, rare cell types or those that cannot withstand harsh dissociation protocols are under-represented in single-cell data.
Spatial transcriptomics (ST) technologies are poised as powerful discovery tools for decoding the TME ecosystem and bringing the current therapeutic research into an entirely new paradigm. However, there are a few challenges to effectively utilizing spatial data:
Elucidata offers technology and services to help scientists go from data to insights. Our bioinformatics experts developed a customized pipeline to dissect tumor organization in spatial datasets by leveraging high-quality reference scRNA-seq datasets available on Elucidata’s data harmonization platform - Polly - to deconvolve the cell type composition and annotate the most malignant tumor regions, followed by annotation of the tumor core vs edge using a combination of gene expression and histology.
Key steps of the solution are as follows:
Connect with us or reach out to us at info@elucidata.io to learn more.
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