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ThDEFINE Report Summary

Project ID: 646794
Funded under: H2020-EU.1.1.

Periodic Reporting for period 1 - ThDEFINE (Re(defining) CD4+ T Cell Identities One Cell at a Time)

Reporting period: 2016-01-01 to 2016-04-30

Summary of the context and overall objectives of the project

Background:
The immune system is the body’s primary defence against infection. In mammals, the immune system has two different branches, an “innate” branch that allows a very quick response to so-called danger signals and an “adaptive” branch that can mount a strong and highly specific response to infections.
One of the key cell types that regulates the adaptive immune response are so-called T helper or Th cells. To deal with the differing demands during infection, i.e. rapid and strong response, no response to molecules of the body itself, and the ability to shut down the immune response, Th cells can fulfil different roles in different tissues and are able to develop into specific subtypes that are particularly suited to deal with different infections. For example, Th1, Th2 and Th17 cells differ in their ability to fight bacteria, parasites or viruses. Tregs and nTregs are thought to be of particular importance in shutting down immune responses.
What is the problem?
Traditionally, to understand the functions of different immune cell types, markers on the surface of a particular subpopulation have been used to purify subgroups and then molecular techniques, especially the sequencing of the expressed genes (RNAseq), can characterise the chosen subpopulation and investigate its function. This has been informative, but has a number of shortcomings: it is only possible to identify subpopulations that are already defined by a marker and rarer subpopulations will be lost. Furthermore, on the surface of each Th cell a different T cell receptor, or TCR, is found that determines the kind of invader this T cell will respond to. Since the TCR is unique to every T cell clone, it is very hard to study its role in mixtures of otherwise similar cells.
How is the issue being addressed?
Over the last decade a new concept for studying the function of subpopulations has become possible. Cutting edge techniques have been developed that now allow RNAseq experiments to be carried out with tiny amounts of material. In fact, it is now possible, although technically challenging, to sequence single cells (scRNAseq). Members of the Teichmann lab have recently demonstrated that they are able to carry out scRNAseq for 100s of cells isolated from mice and profile cell-to-cell variability. This is transformational for the analysis of different Th subpopulations. At the same time this new method throws up new problems in how to analyse the data that is generated.
What are the overall objectives?
The aims of the study are (1) to use scRNAseq to analyse existing Th subpopulations in healthy mice and identify new subpopulations if they exist, (2) to examine how these subpopulations differ when mice experience different types of infections, (3) to develop computational methods that will help to interpret the very complex data that is generated, and lastly (4) to combine the knowledge gleaned from (1-3) to define the key regulators that govern the function of different Th subpopulations. Such regulators would be good candidates targets for drug development. Genetic engineering techniques will be used to alter the regulators and demonstrate their importance.
Why is this important for society?
Infection, for example by bacteria, parasite or viruses, is one of the major causes of disease. It is therefore vital that we understand, at the molecular level, how the body fights these invaders. We know that Th cells are central to this process. By learning exactly how Th cells develop and function, we will be able to modulate the immune response. For example, we may wish to increase the immune response when the body is fighting an infection or trying to eradicate a tumour, or dampen the immune response when it attacks itself, as is the case in autoimmune disorders such as diabetes or arthritis.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

By the end of the reporting period, the team was able to generate scRNA-seq data for different Th subpopulations. The generation of these Th subpopulations in response to a bacterial infection (Salmonella) was studied in mice. By focussing on the TCR, we demonstrated that a number of functionally distinct Th subpopulations all developed from a single precursor cell, giving insights into the developmental pathways that give rise to the distinct populations. We achieved this by developing a computational model, called TraCeR, that is able to identify specific regions of the TCR gene that are activated in any given single cell. We also studied the development of Th subpopulations when mice were infected with a parasite (malaria). Populations of malaria-specific Th cells were analysed at different time points after exposure to the parasite. To analyse the data another new computational tool was developed that allows us to draw a continuous time line (pseudo time) of a response even though we only tested the cells at pre-defined time points. This tool helps us to better understand the process of cell activation and identified the time point at which two distinct subpopulations arose from a shared group of precursor cells.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The description of the different subpopulation during an immune response has furthered our knowledge of how Th cells are activated. To date the greatest impact has been the development of the computational methods mentioned above. Although only described recently, the TraCeR algorithm has already been used by many researchers around the globe who are interested in understanding how the TCR recognises foreign invaders. We plan to develop this method further to make it even more widely used.

Furthermore, the concept of pseudo time, i.e. the description of a continuous time line that captures the changes that single cells undergo as they are either activated or differentiate into new cell types is widely applicable to many different systems that use single cell sequencing to understand biology.
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