Re(defining) CD4+ T Cell Identities One Cell at a Time

Project Information

ThDEFINE

Grant agreement ID: 646794

DOI

10.3030/646794

Project closed

EC signature date 19 October 2015

Start date 1 January 2016

End date 30 June 2022

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 980 685,00

EU contribution

€ 1 980 685,00

Coordinated by

GENOME RESEARCH LIMITED LBG
United Kingdom

Periodic Reporting for period 5 - ThDEFINE (Re(defining) CD4+ T Cell Identities One Cell at a Time)

Reporting period: 2021-01-01 to 2022-06-30

Summary of the context and overall objectives of the project

The immune system is the body’s primary defence against infection. In mammals it has two different branches, an “innate” branch that allows a quick response to danger signals and an “adaptive” branch that can mount a strong and highly specific response to infections. T cells are key regulators of adaptive immunity. They develop into specific subtypes that are particularly suited to different infections or specific tissue contexts. For example, T helper cells (Th1, Th2 and Th17 cells) differ in their ability to fight bacteria, parasites or viruses, and Tregs and nTregs are thought to be of particular importance in shutting down immune responses.

Traditionally, to understand the functions of different immune cell types, surface markers are used to purify subgroups. Molecular techniques like sequencing expressed genes (RNAseq) can then characterise the function of the chosen subpopulation. While informative, this can only identify subpopulations that are already defined by a marker - thus, rarer subpopulations can be missed. Furthermore, each T cell expresses a different T cell receptor (TCR) that determines which invader it will respond to, and since TCRs are unique to each T cell clone, it is very hard to study its role in mixtures of otherwise similar cells.

Cutting edge advances have recently allowed RNAseq experiments to be carried out with tiny amounts of material: it is now possible to sequence single cells (scRNAseq). When we started this project, the Teichmann lab had just demonstrated scRNAseq for 100s of cells isolated from mice to profile cell-to-cell variability. Rapid advances since then mean that it is now routine in the lab to sequence thousands of cells from a given mouse or human sample, transforming the analysis of different T cell subpopulations. This provides new challenges in data analysis.

The aims of the study are to (1) use scRNAseq to analyse T cell subpopulations in mice and humans, (2) examine how these subpopulations differ when mice experience different types of infections and during human disease, (3) develop computational methods to interpret the highly complex data, and (4) combine the knowledge gleaned from 1-3 to define the key regulators of different T cell subpopulations. Genetic engineering techniques will be used to alter such regulators, validating their importance as potential targets for drug development.

Infection is a major cause of disease, so it is vital that we understand, at the molecular level, how the body fights it. T cells are central to this process - by learning how T cells develop and function the immune response can be modulated, e.g. increasing the immune response during infection or dampening the response in autoimmune disorders.

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

During the first three years of this grant the team used animal models to study T cell populations, including Salmonella and malaria infections. By focussing on the TCR in the Salmonella model, we found that a number of functionally distinct Th subpopulations developed from a single precursor cell. We developed a computational model, called TraCeR, that is able to identify specific regions of the TCR gene that are activated in any given single cell (Stubbington 2016 Nat Methods).

In the malaria model, populations of malaria-specific Th cells were analysed at different time points after exposure to the parasite. We developed a computational tool that allowed us to draw a continuous timeline (pseudotime) of a response even though we only tested cells at pre-defined time points. This helped us to better understand the process of cell activation and identified the point at which two distinct subpopulations arose from a shared group of precursor cells (Lönnberg 2017 Sci Immunol).

We then went onto study the relationship between cell cycling, proliferation and differentiation during in vivo CD4+ T cell responses in the malaria model. We investigated the development of T helper memory responses with a combination of FACS indexing, gene perturbation, scRNAseq and computational modelling. These results were published in (Soon 2020 Nat Immunol).

Another line of investigation concentrated on the role of different pathways and transcription factors in Th2 responses. We demonstrated the role of XBP-1 in co-ordinating the unfolded protein response, driving Th2 specific gene expression and accelerating proliferation (Pramanik 2018 Genome Medicine). Additionally we published a new method for single-cell ATAC-seq (Chen 2018 Nat Commun).

Our advances in T cell biology during the initial 3 years of the project meant we were able to direct this project towards studying T cells in human tissues. The single-cell transcriptome profile of the thymus, the organ responsible for T cell development, across the human lifetime and across mouse and human is a high resolution census of T cell development within the native tissue microenvironment (Park 2020 Science).

We investigated the activation and migration profiles of helper T cells along the human colon and characterised the transcriptional adaptation of regulatory T cells between the colon and associated mesenteric lymph nodes (James et al., 2020 Nature Immunology).

Expanding this work, we used single-cell genomics to examine immune cell identity across multiple tissues of the human body (Dominguez-Conde 2022 Science). This generated a map of human immune cells of unprecedented size and detail, revealing under-appreciated cell states, in particular in the memory T cell compartment. The processed data can be accessed at https://www.tissueimmunecellatlas.org/ and the raw data has been deposited in ArrayExpress. We also developed an immune cell type classifier (CellTypist) which includes a large variety of T cell types and cell states. This tool enables the fast and accurate prediction of cell identity and is accessible via https://www.celltypist.org/. We envision that both data and CellTypist will be a great resource for the community.

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)

By describing different subpopulations during immune responses we furthered knowledge of how Th cells are activated. To date the greatest impact has been the development of the computational methods mentioned above. TraCeR has been used by many researchers around the globe interested in how TCR recognises foreign invaders. We plan to develop TRaCeR to further expand its use. The concept of pseudo time discussed above is widely applicable to many different systems that use single cell sequencing to understand biology.

Widening the scope and impact of our cross-tissue study of human T cells, we are further dissecting the transcriptional and epigenetic programs that control cell identity of tissue-resident and circulatory T cell populations. We have initiated the analysis and aim to prepare a manuscript for the end of 2022.

We have expanded our analysis into diseases associated with T cell compartments. We are investigating how T cell development within the thymus is perturbed in genetic and non-genetic conditions including Myasthenia Gravis, Down’s syndrome, and Patau syndrome. Data analysis is ongoing and a manuscript detailing our findings is expected imminently.

We have also initiated investigation into T cell lymphoma in an attempt to identify insights to aid clinical diagnosis and treatment. We have started initial data generation and aim to accomplish publications in the future to directly inform clinical management of the disease.

Constructing the human thymus atlas