scRNA-seq for High Resolution Cell-Cell Interactions in Head and Neck Tumors

scRNA-seq for High Resolution Cell-Cell Interactions in Head and Neck Tumors

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scRNA-seq for High Resolution Cell-Cell Interactions in Head and Neck Tumors

By Fionn O’Sullivan
January 24, 2022

Cancerous Cellular Changes

How does the environment of a tissue become a cancerous change, and how do these changes affect the rest of the tissue? When the cells that comprise a tissue transform into tumors, complex alterations can occur in how neighboring non-cancerous cells interact both with each other and with the cancerous cells. Getting a handle on these alterations is essential for obtaining a more holistic understanding of different cancer types.

In a recent study byKürten et al, scientists characterized these kinds of changes in head and neck squamous cell carcinoma (HNSCC), a common type of cancer that can affect the mouth and throat. Here, the flattened or ‘squamous’ sheets of cells in the outer layers of the tissue can turn cancerous either due to exposure to environmental toxins (e.g. tobacco smoke), or infection with viruses such as human papillomavirus (HPV), two of the main causes of HNSCC.

scRNA-seq: Ideal for High Resolution Cellular Interactions

A high-resolution description of the cell types involved and the changes they undergo can reveal promising targets for future HNSCC treatments. To obtain this level of detail, the researchers utilized single-cell RNA sequencing (scRNA-seq) to collect data from over a hundred thousand cells.

However, mountains of complex data do little on their own, and analytical techniques to rearrange the data for human comprehension are essential. One method the authors employed for data visualization was UMAP (Uniform Manifold Approximation and Projection), which reveals distinct sub-populations of cells as clusters in a landscape of cell types—like a map of different islands of an archipelago.

Viral Genes Remain Active in Some Cancer Types

Doing this revealed that viral genes were still active in HPV+ HNSCC patients even though the virus does not fully assemble. Combined with other differences, these data showed that HPV- and HPV+ HNSCC are two distinct cancer types, suggesting future treatments should be tailored to one or the other for greater efficacy.

They also showed the anti-cancer activity of the immune system in HNSCC is primarily carried out by CD8+ T-cells. Applying algorithms that predict cell-cell interactions from their scRNA-seq dataset, they identified a list of potential CD8+ T-cells receptors that other immune cells may act on to prevent the T-cells from killing the tumor cells, providing further targets for checkpoint inhibitor therapies.

The Reproducibility of Bioinformatic Analysis

This kind of complex data analysis requires the development of a clean and comprehensive bioinformatics pipeline, which also allows for reproducibility. After clearly outlining their analysis steps, the authors hope other groups will be able to easily use their dataset, paving the way for future immune-checkpoint receptor discoveries relevant to HNSCC.

Fionn O’Sullivan, Neuroscientist & Content Writer, Bridge Informatics

Fionn is a Content Writer at Bridge Informatics, a professional services firm that helps biotech customers implement advanced techniques in the management and analysis of genomic data. Bridge Informatics focuses on data mining, machine learning, and various bioinformatic techniques to discover biomarkers and companion diagnostics. If you’re interested in reaching out, please email [email protected] or [email protected].


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