Work Package 2

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TCR repertoires in health and disease in blood and tissues

ESRs in this WP will study the relationships between different memory T cell populations in and outside the circulation and will make in-depth studies of the TCR repertoires of purified naive and memory T cell populations (in health and disease) using NGS. An important problem that is encountered when analysing the great diversity of T cell repertoires is that artefacts introduced during reverse transcription, by PCR and during sequencing, can mistakenly be interpreted as additional receptors.

To tackle this problem, ESR7 and ESR8 will combine NGS with the recently developed technique of molecular barcoding, through which each starting molecule is tagged with a unique molecular identifier (UMI) during cDNA synthesis. ESR7 will carry out the experimental work of the TCR sequencing studies of memory T cells in blood and bone marrow in collaboration with ESR4. ESR8 will filter out erroneous variants using a recently developed bioinformatics pipeline at Utrecht.  ESR8 will also make use of recently (unpublished) developed methods to correct for erroneous barcodes (UMIs) generated by PCR and/or sequencing errors, and for impurities in the sorting of memory T cell subsets. These approaches will allow ESR8 to deduce which memory T cell subsets share many clones and are therefore likely dependent on each other.

ESR9 will make use of NGS data generated by ESR7 and from P4 to develop novel computational tools to analyse TCR repertoires and their  diversity, which will be used by ESR10 and ESR11. ESR9 will make use of probabilistic models to estimate selective effects from changes in relative TCR clonotype frequencies over time in longitudinal data generated by ESR7 and from P4.

ESR10 will focus on the development of mathematical descriptions of T cell clonal sizes and cross-reactivity in TCR repertoires, from birth to old age, making use of NGS data from P4. ESR10 will also make  use of the stochastic models developed at Leeds to custom-build computational ones of single-cell sampling.

ESR11 will apply a previously designed bioinformatics pipeline at CNRS to infer effective selection pressures on clonotypes for each sample (a given organ at a given time), and thus quantify the fold change in frequency of the observed clonotypes in a sample, relative to a null expectation estimated from the raw statistics of TCR recombination expected in healthy patients.