A new single-cell sequencing tool brings fresh insight to functional T cell studies

Resolving cell phenotype is a common single-cell analysis goal in immunology since an organism’s immune system summons all cell subtypes to fight invaders. Random rearrangement of T cell receptor V(D)J gene regions drives the complex variability in T cell populations. Such germline gene changes generate and deploy armies of T cell clones to identify specific foreign proteins called antigens in order to mount an effective immune response to them.

To date, studying and identifying clonal T cell receptor (TCR) response has involved sequencing the receptor α and β chains. Yet fully capturing T cell response also requires identifying phenotypes within clonal populations. A promising computational tool called TraCeR has the power to advance research efforts by providing both T cell phenotype and TCR sequence at the single-cell level from standard C1™ mRNA Seq datasets.

Senior Staff Scientist Michael Stubbington, PhD, and Postdoctoral Fellow Tapio Lönnberg, PhD, of Sarah Teichmann’s lab at EMBL-EBI and the Wellcome Trust Sanger Institute in Cambridge, England, developed TraCeR to link T cell specificity with functional response by revealing clonal relationships among cells and their transcriptional profiles.

Stubbington, who specializes in computational TCR sequencing analysis of transcriptional phenotypes in T cell populations, studies the molecular function of recombined single-cell receptor sequences as they relate to antigen receptor diversity in immune response and disease. He believes single-cell full-length mRNA sequencing that accesses the whole transcriptome adds real value to detailed T cell receptor investigations by showing the phenotypic landscape during immune responses. While targeted PCR and end-counting RNA sequencing approaches provide general information, TraCeR is a method that stands to power discovery.

Lönnberg uses bioinformatics and computational biology to analyze transcriptomic data involved with kinetics and gene expression during T cell differentiation. The barcode-like trackability of TCR seq sparked his interest in T cell receptors and cell population variation. T cells in vivo respond relationally to health and disease states, he explained. “I am hopeful that one day we will come up with an atlas of all possible regulatory states of T cells, understand the transitions and use the information to identify and target pathogenic T cell subsets.”

Get TraCeR

TraCeR reconstructs T cell receptor sequences from single-cell RNA seq data such as C1 full-transcript reads. The tool includes reference files for use with human and mouse sequencing data. Stubbington and Lönnberg intend to add a module that constructs synthetic genomes and indices from user-supplied collections of V and J gene sequences to permit extension of the method to other organisms. TraCeR is compatible with the standard C1 mRNA protocol and comes with a sample dataset to test installation and configuration file setup and resource locations. Download the TraCeR application installation instructions and C1 protocol documentation for free at Script Hub.

Single-cell value

“There is a huge diversity of T cell receptor sequences within an organism,” Stubbington explained. “Almost every cell has a different sequence, so being able to look within individual cells is powerful. Also, single-cell approaches allow us to see the paired sequences from two separate chromosomes that make up the TCR in each cell.”

To Lönnberg, the value of single-cell analysis for TCR sequencing is the fact that T cell activation and differentiation occurs at the individual-cell level. “Using single-cell sequencing,” he said, “we can pinpoint the exact sequences associated with a specific response, rather than simply measuring enrichment at the level of TCR gene segments.”

While there are several published methods to identify T cell receptors at the single-cell level, Stubbington pointed out the difference with applying TraCeR to C1 mRNA Seq: There’s no need to target specific TCR sequences. “You get them for free whenever you perform single-cell RNA seq, so it adds an extra dimension of analysis to scRNA seq experiments using T cells. Furthermore,” he added, “it gives you the transcriptional state of every cell described by thousands of expressed genes.”

This is important as single-cell sequencing spreads and per-cell costs begin dropping. “Our method can be applied a posteriori to datasets that were not generated with TCR sequencing in mind,” Lönnberg noted.

Given the group’s current T cell receptor work, single-cell analysis could push this research to the next level in terms of translational research. Stubbington foresees numerous applications for T cell populations from human patients with a variety of disease states. Lönnberg pointed to antigen-specific cells that react to vaccines and generate T cell memories. He also expects age-related loss of T cell diversity to become a bigger focus.

“Single-cell analysis is changing the way we think about variability within cell populations,” remarked Stubbington. “That’s really important in immunology. It allows me to pursue fascinating problems in new, efficient ways.”

Lönnberg believes this is only the beginning and the impact of single-cell analysis will be profound. “For me, after studying large populations of T cells for my PhD, it has been a revelation to finally be able to take a look inside a single cell and see, metaphorically, what it thinks.”

Future clinical applications

Both scientists believe that single-cell analysis has the potential to guide care in the future. “Understanding heterogeneous lymphocyte populations present during disease could be very informative in cancer, autoimmunity and infectious disease,” said Stubbington.

“It will also allow researchers to study rare cell populations that have so far been inaccessible from clinical samples,” Lönnberg added. “Examples are rare subpopulations of T cells, or circulating tumor cells. Studying these cells from individual patients could allow more informed decisions regarding courses of treatment.”

The two believe TraCeR already offers real value, and they are already applying it in their other ongoing studies into differentiation of T helper cell subsets during infection.

“TraCeR demonstrates the potential for extending analyses of single-cell RNA seq in lymphocytes to gain more biological insights,” said Stubbington. “We’re aiming to expand its application to B cell receptors and antibodies, so it’s useful to investigate this arm of the adaptive immune system as well.”

“The possibilities are endless,” Lönnberg said. “At the moment, restrictions are mostly associated with relatively low throughput. Increasing throughput will allow denser time courses and more sensitive identification of rare subpopulations.” He cited the parallel study of adaptive and innate immune immunity during pathogen challenge as an overlooked opportunity. Lönnberg also mentioned antigen-presenting cell and T cell interactions as an example.

TraCeR has the potential to change our collective understanding of gene expression and immune response. The importance of T cells is that they’re at the center of the network of cell-cell interactions happening during both the mounting and the resolution of immune responses.

The team’s next step is to apply similar methods to the study of B cells to better understand the adaptive immune system as a whole. TraCeR offers sensitivity, specificity and simplicity. As scRNA seq throughput capabilities increase and costs decrease, the group plans to survey the entire immune repertoire for studies of autoimmune disease, tumor immunology and the immune system. Call it a dream or call it destiny—Stubbington and Lönnberg intend to manifest it.

For more on the validated TraCeR method and how to apply it to T cell immune investigations, read their Nature Methods paper, “T cell fate and clonality inference from single-cell transcriptomes.”