Periodic Reporting for period 2 - ENLIGHT-TENplus (European Network Linking Informatics and Genomics of Helper T cells in Tissues)
Reporting period: 2023-03-01 to 2025-02-28
Autoimmune diseases arise from aberrant immune responses against the body’s own tissues. There are more than 80 different types of autoimmune disease affecting various organs, and their prevalence is increasing. Recent findings suggest that the chronic nature of autoimmune diseases largely depends on the reactivation of tissue-resident memory T (TRM) cells, which are present in almost all tissues in the body. Therefore, TRM cells are a key target in the development of precision diagnostic and therapeutic strategies for autoimmune diseases.
TRM cells are strongly influenced by the ‘conditioning’ in the tissue. In healthy individuals, this conditioning tailors the immune response to the precise needs of organ-specific defence. However, in patients with chronic autoimmune diseases, environmental conditioning can modulate an ongoing tissue-resident memory response, triggering unexpected pathology. The molecular factors controlling tissue-specific conditioning and the reactivation of TRM cells in the context of autoimmunity are often poorly understood, as the interaction between TRM cells and their microenvironment is extremely complex. Consequently, there is an urgent need to apply cellular immunology and omics approaches to TRM cells derived from steady-state and autoimmune conditions to improve our understanding of their identity and function and fully realise their therapeutic potential.
However, a crucial missing link in the current research environment is the lack of qualified individuals with the cellular and molecular immunology skills to recognise and define important scientific questions that can be answered using omics approaches, as well as the bioinformatics expertise to analyse and interpret the resulting Big Data. Consequently, although large datasets can be acquired on particular TRM cell populations, they cannot be utilised optimally due to a lack of bioinformatics expertise.
The ENLIGHT-TEN+ project (European Network Linking Informatics and Genomics of Helper T cells in Tissues comprising TEN European countries) aimed to train a cohort of early-stage researchers (ESRs) with an in-depth understanding of T cell immunology and expertise in the bioinformatic processing of large datasets.
ENLIGHT-TEN+ had the following major objectives:
- Integrate cross-disciplinary, highly innovative technologies to generate a novel view of TRM cells, understanding the microenvironmental cues and molecular factors that shape their unique functional properties with unprecedented resolution.
- Apply this understanding to immune-mediated pathologies, enabling selective manipulation of TRM cells in autoimmune diseases.
- Foster a new generation of enthusiastic young researchers who can analyse Big Data and implement novel concepts of T cell biology in biomedical applications.
In conclusion, the programme equipped ESRs with in-depth scientific training and advanced technological skills, as well as broad exposure to experimental and computational approaches in T cell research. The findings have significantly advanced our understanding of TRM cell biology and pathology, while generating innovative tools and datasets that will support the development of future therapies for immune-related diseases.
TRM cells are strongly influenced by the ‘conditioning’ in the tissue. In healthy individuals, this conditioning tailors the immune response to the precise needs of organ-specific defence. However, in patients with chronic autoimmune diseases, environmental conditioning can modulate an ongoing tissue-resident memory response, triggering unexpected pathology. The molecular factors controlling tissue-specific conditioning and the reactivation of TRM cells in the context of autoimmunity are often poorly understood, as the interaction between TRM cells and their microenvironment is extremely complex. Consequently, there is an urgent need to apply cellular immunology and omics approaches to TRM cells derived from steady-state and autoimmune conditions to improve our understanding of their identity and function and fully realise their therapeutic potential.
However, a crucial missing link in the current research environment is the lack of qualified individuals with the cellular and molecular immunology skills to recognise and define important scientific questions that can be answered using omics approaches, as well as the bioinformatics expertise to analyse and interpret the resulting Big Data. Consequently, although large datasets can be acquired on particular TRM cell populations, they cannot be utilised optimally due to a lack of bioinformatics expertise.
The ENLIGHT-TEN+ project (European Network Linking Informatics and Genomics of Helper T cells in Tissues comprising TEN European countries) aimed to train a cohort of early-stage researchers (ESRs) with an in-depth understanding of T cell immunology and expertise in the bioinformatic processing of large datasets.
ENLIGHT-TEN+ had the following major objectives:
- Integrate cross-disciplinary, highly innovative technologies to generate a novel view of TRM cells, understanding the microenvironmental cues and molecular factors that shape their unique functional properties with unprecedented resolution.
- Apply this understanding to immune-mediated pathologies, enabling selective manipulation of TRM cells in autoimmune diseases.
- Foster a new generation of enthusiastic young researchers who can analyse Big Data and implement novel concepts of T cell biology in biomedical applications.
In conclusion, the programme equipped ESRs with in-depth scientific training and advanced technological skills, as well as broad exposure to experimental and computational approaches in T cell research. The findings have significantly advanced our understanding of TRM cell biology and pathology, while generating innovative tools and datasets that will support the development of future therapies for immune-related diseases.
Throughout the four summer schools, the ESRs deepened their knowledge of T cell immunology and were introduced to data resources and bioinformatic tools for analysing diverse Big Data sets related to T cells. They gained an understanding of the translational processes involved in taking a drug from discovery to market approval. They were also informed about various career options.
ESRs were equipped with a broad range of technologies relevant to their individual projects, including multi-colour flow cytometry, murine models, various in vivo and in vitro techniques, advanced bioinformatics methods such as programming in R, and imaging technologies.
Key regulatory mechanisms of TRM cells have been identified. It was shown that B cells shape the T cell receptor repertoire, thereby reducing self-reactivity. Epigenetic changes were found to influence TRM cell function in the gut, while HDAC1/2 was found to modulate TRM cell seeding. An intrinsic ageing signature was found in colonic memory T cells to be independent of external factors. The development of TRM cells in the lung was mapped, and a method to assess their metabolic wiring was developed.
The TRM cells associated with human diseases were characterised in the liver, gut, pancreas, and lung. Distinct T cell signatures were identified in response to checkpoint inhibitors. It was found that inflammatory and inhibitory programmes in gut TRM cells were linked to treatment outcomes. The immune architecture of the lungs was analysed, as was the memory formation therein, and platforms were developed to study immune modulators. These findings revealed disease-related features of TRM cells and potential therapeutic targets.
New bioinformatics tools and machine learning approaches were developed to facilitate multi-omics data integration and analysis. These tools allowed sensitive single-cell data integration, T cell receptor motif inference, and improved ATAC-seq workflows. Gene expression prediction and immune QTL mapping were also carried out. Several immune cell atlases were generated, covering topics such as autoimmune diseases, immunotherapy, healthy ageing, and pathogen responses. These outputs produced valuable datasets and tools for studying immune regulation.
The ESRs disseminated their findings by publishing scientific papers and presenting them at numerous conferences and symposia.
ESRs were equipped with a broad range of technologies relevant to their individual projects, including multi-colour flow cytometry, murine models, various in vivo and in vitro techniques, advanced bioinformatics methods such as programming in R, and imaging technologies.
Key regulatory mechanisms of TRM cells have been identified. It was shown that B cells shape the T cell receptor repertoire, thereby reducing self-reactivity. Epigenetic changes were found to influence TRM cell function in the gut, while HDAC1/2 was found to modulate TRM cell seeding. An intrinsic ageing signature was found in colonic memory T cells to be independent of external factors. The development of TRM cells in the lung was mapped, and a method to assess their metabolic wiring was developed.
The TRM cells associated with human diseases were characterised in the liver, gut, pancreas, and lung. Distinct T cell signatures were identified in response to checkpoint inhibitors. It was found that inflammatory and inhibitory programmes in gut TRM cells were linked to treatment outcomes. The immune architecture of the lungs was analysed, as was the memory formation therein, and platforms were developed to study immune modulators. These findings revealed disease-related features of TRM cells and potential therapeutic targets.
New bioinformatics tools and machine learning approaches were developed to facilitate multi-omics data integration and analysis. These tools allowed sensitive single-cell data integration, T cell receptor motif inference, and improved ATAC-seq workflows. Gene expression prediction and immune QTL mapping were also carried out. Several immune cell atlases were generated, covering topics such as autoimmune diseases, immunotherapy, healthy ageing, and pathogen responses. These outputs produced valuable datasets and tools for studying immune regulation.
The ESRs disseminated their findings by publishing scientific papers and presenting them at numerous conferences and symposia.
The ENLIGHT-TEN+ project aimed to improve our understanding of TRM cells and the molecular and microenvironmental factors that influence their function. This research will contribute to the development of new potential therapies for autoimmune diseases.
The network successfully integrated experimental and computational disciplines by fostering collaboration between academic and non-academic partners. A structured mentoring programme, outreach activities, and international secondments provided the ESRs with comprehensive training. They developed strong expertise in both T cell biology and computational analysis, gaining exposure to diverse cultural and research environments across ten European countries.
The institutions involved benefited from close collaboration among leading European research groups, each of which brought complementary expertise. The ESRs acquired critical skills in bridging the gap between experimental and computational sciences, positioning them to meet the growing demand in academia and industry for researchers who can analyse and interpret large-scale biological data.
The network successfully integrated experimental and computational disciplines by fostering collaboration between academic and non-academic partners. A structured mentoring programme, outreach activities, and international secondments provided the ESRs with comprehensive training. They developed strong expertise in both T cell biology and computational analysis, gaining exposure to diverse cultural and research environments across ten European countries.
The institutions involved benefited from close collaboration among leading European research groups, each of which brought complementary expertise. The ESRs acquired critical skills in bridging the gap between experimental and computational sciences, positioning them to meet the growing demand in academia and industry for researchers who can analyse and interpret large-scale biological data.