Tracing T Cells

For all that we have learned about the immune system, much of it remains a mystery to us. What goes on behind the scenes in a T cell is a prime example of this – we don’t know how these cells’ fates are determined, nor do we understand the clonal relationship between different T cell types. But a new computational approach (1) from the Wellcome Genome Campus’ Single-Cell Genomics Centre could shed new light on the immune response.

“TraCeR is a software tool that finds and reconstructs the recombined T cell receptor (TCR) sequences that are present within single-cell RNA sequencing data without needing any modifications to the RNA sequencing method,” explains Mike Stubbington, who led the research. This tool allows researchers to do two things: first, they can find the sequence of both chains that comprise the TCR – essential to understanding what antigen that particular TCR can detect. Second, the huge possible diversity of TCR DNA sequences means that cells that share sequences are likely to be “siblings” that derive from the same original progenitor cell. This means that they can now track the family relationships between T cells within a sample. “Crucially, we get the information about TCR sequences in parallel to the RNA-seq gene expression profiles of each cell,” says Stubbington. “This means that we can find out whether all the ‘children’ of a particular T cell do the same thing at the same time, and whether the antigen detected by a T cell can influence its fate choices.” The tool relies on the presence of recombined and expressed DNA sequences that occur naturally within cells of interest – which means that it can be used in B cells as well as T cells, a step the researchers plan to take soon. “This enables us to think about the relationship between T cell specificity and cellular choices. It also allows us to think about how sibling cells make different ‘life choices’ and perform different roles during an immune response.” Unfortunately, the cost of TraCeR is currently prohibitive for routine clinical use, but if technological advances bring the price tag down, it might one day provide useful information about patient responses to vaccination or drug treatment in infectious disease, cancer or autoimmunity.

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References

1. MJ Stubbington et al., “T cell fate and clonality inference from single-cell transcriptomes”, Nat Methods, 13, 329–332 (2016). PMID: 26950746.

About the Author(s)

Michael Schubert

While obtaining degrees in biology from the University of Alberta and biochemistry from Penn State College of Medicine, I worked as a freelance science and medical writer. I was able to hone my skills in research, presentation and scientific writing by assembling grants and journal articles, speaking at international conferences, and consulting on topics ranging from medical education to comic book science. As much as I’ve enjoyed designing new bacteria and plausible superheroes, though, I’m more pleased than ever to be at Texere, using my writing and editing skills to create great content for a professional audience.

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