Periodic Reporting for period 2 - CELLULO-EPI (Celluloepidemiology: a novel paradigm for modeling T-cell responses on a population level.)
Berichtszeitraum: 2021-09-01 bis 2023-02-28
Celluloepidemiology is a term invented to describe an interdisciplinary approach combining unique cellular immune responses against pathogens on a population level with mathematical modeling, thereby generating unique and otherwise not obtainable multidimensional T-cell profiles.
CELLULO-EPI will develop and use such a highly innovative model to simulate how T-cells against pathogens evolve in a synthetic population as a function of age, gender, time since infection and other relevant variables. This model will be parameterized and fitted by cross sectional T-cell data against a wide set of pathogens from 500 individuals (sampled again after 1 year), unique data from individuals with known first infections with chikungunya and longitudinal data from individuals re-exposed to chickenpox and measles.
The insights of CELLULO-EPI will be pivotal for public health. One important example: Varicella-zoster virus (VZV) causes chickenpox but also shingles after VZV reactivation. Vaccination can prevent chickenpox, but the predicted increase in shingles incidence has blocked chickenpox vaccination in many EU-countries. Indeed, re-exposure to chickenpox is hypothesized to protect against shingles through boosting of T-cells. Unfortunately, none of the available epidemiological or immunological tools allow for adequate validation of the boosting hypothesis. However, CELLULO-EPI will be able to solve this persisting VZV vaccination dilemma. Furthermore, CELLULO-EPI will also redefine infectious disease epidemiology, for example by allowing us to back-calculate the time since last exposure.
CELLULO-EPI will develop and use such a highly innovative model to simulate how T-cells against pathogens evolve in a synthetic population as a function of age, gender, time since infection and other relevant variables. This model will be parameterized and fitted by cross sectional T-cell data against a wide set of pathogens from 500 individuals (sampled again after 1 year), unique data from individuals with known first infections with chikungunya and longitudinal data from individuals re-exposed to chickenpox and measles.
The insights of CELLULO-EPI will be pivotal for public health. One important example: Varicella-zoster virus (VZV) causes chickenpox but also shingles after VZV reactivation. Vaccination can prevent chickenpox, but the predicted increase in shingles incidence has blocked chickenpox vaccination in many EU-countries. Indeed, re-exposure to chickenpox is hypothesized to protect against shingles through boosting of T-cells. Unfortunately, none of the available epidemiological or immunological tools allow for adequate validation of the boosting hypothesis. However, CELLULO-EPI will be able to solve this persisting VZV vaccination dilemma. Furthermore, CELLULO-EPI will also redefine infectious disease epidemiology, for example by allowing us to back-calculate the time since last exposure.
We have been able to recruit > 600 participants and obtained PBMC and serum from these individuals.
- We have analysed a large cohort of > 600 participants for which we performed cytof mass cytometry to obtain best T-cell markers to analyse T-cell responses against SARS-CoV-2.
- Next, we used these markers on a novel flow cytometer allowing in-depth high-throughput T-cell characterization for more than 600 samples (each 5 stimuli).
- We developed a R based program (including automatic gating) allowing us to appropriately and timely analyse these 3000 flow cytometry data files using the Flemish Super Computer.
- Next, we used FLOWSOM (again supported by the Flemish Super Computer) to define T-cell subclusters.
- Finally, we used machine learning algorithms to infer fundamental immunological differences between the different population groups
- We have analysed a large cohort of > 600 participants for which we performed cytof mass cytometry to obtain best T-cell markers to analyse T-cell responses against SARS-CoV-2.
- Next, we used these markers on a novel flow cytometer allowing in-depth high-throughput T-cell characterization for more than 600 samples (each 5 stimuli).
- We developed a R based program (including automatic gating) allowing us to appropriately and timely analyse these 3000 flow cytometry data files using the Flemish Super Computer.
- Next, we used FLOWSOM (again supported by the Flemish Super Computer) to define T-cell subclusters.
- Finally, we used machine learning algorithms to infer fundamental immunological differences between the different population groups
We have been able to show that T-cell based epidemiology might be a validate alternative for antibody based epidemiology.