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Anti-CD19 CAR-T cell immunotherapy is a hopeful treatment option for patients with B cell lymphomas, however it copes with partly severe adverse effects like neurotoxicity. Single-cell resolved molecular data sets in combination with clinical parametrization allow for comprehensive characterization of cellular subpopulations, their transcriptomic states, and their relation to the adverse effects. We here present a re-analysis of single-cell RNA sequencing data of 24 patients comprising more than 130,000 cells with focus on cellular states and their association to immune cell related neurotoxicity. For this, we developed a single-cell data portraying workflow to disentangle the transcriptional state space with single-cell resolution and its analysis in terms of modularly-composed cellular programs. We demonstrated capabilities of single-cell data portraying to disentangle transcriptional states using intuitive visualization, functional mining, molecular cell stratification, and variability analyses. Our analysis revealed that the T cell composition of the patient’s infusion product as well as the spectrum of their transcriptional states of cells derived from patients with low ICANS grade do not markedly differ from those of cells from high ICANS patients, while the relative abundancies, particularly that of cycling cells, of LAG3-mediated exhaustion and of CAR positive cells, vary. Our study provides molecular details of the transcriptomic landscape with possible impact to overcome neurotoxicity.

Résumé indisponible / The objective of this thesis focuses on the study and the development of bioinformatics platforms and tools on the grid. The second objective is to develop applications in molecular epidemiology and metagenomics based on these tools and platforms. Based on the studies of existing bioinformatics platforms and tools, we propose our solution: a platform and a portal for molecular epidemiology and high throughput metagenomics on the grid. The main idea of ​​our platform is to simplify the submission of jobs to the grid via the pilots jobs (jobs generic that can control and launch many real tasks) and the PULL model (tasks are retrieved and executed automatically). There are other platforms that have similar approaches but our platform focuses on the simplicity and the saving time for the submission of jobs. Bioinformatics tools chosen to deploy the platform are popular tools that can be used in many bioinformatics analyses. We apply a workflow engine in the platform so that users can make the analysis easier. Our platform can be seen as a generalized system that can be applied to both the epidemiological surveillance and metagenomics of which two use cases are deployed and tested on the grid. The first use case is used to monitor bird flu. The approach of this application is to federate data sequences of influenza viruses and provide a portal with tools on the grid to analyze these data. The second use case is used to apply the power of the grid in the analysis of high throughput sequencing of amplicon sequences. In this case, we prove the efficiency of the grid by using our platform to gridifier an existing application, which has much less performance than the gridified version.