Integrative Analysis of Spatial and Single-cell RNA-seq Datasets to Characterize Tumor Microenvironment

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.

Challenges Around Utilization of Spatial Transcriptomics Data

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:

1. Limited Expertise: Given the relative nascency of high-throughput ST technologies, the average research laboratory lacks the bioinformatics expertise to effectively analyze spatial datasets. Furthermore, new computational methods tailored towards spatial data are actively being developed. These methods require systematic evaluation to determine their reliability.
2. Low resolution: Popular platforms like 10X Visium sample the gene expression on a tissue section in “spots’’ containing 7–10 cells on average. Deconvolution of the cell type composition within each spot is an initial hurdle in using this data, for which accuracy completely dictates the quality of data output. Accurate deconvolution requires reference single cell datasets that biologically match the tumor samples of interest. This in turn requires auditing multiple public sources, a significant amount of metadata curation, and benchmarking single-cell data integration.

Source URL:https://www.elucidata.io/blog/integrative-analysis-of-spatial-and-single-cell-rna-seq-datasets-to-characterize-tumor-microenvironment