The generation of long-lasting high affinity antibodies is one of the core pillars of our immune system ensuring life-long protection against infection. Dysregulation of this process can cause severe pathological conditions, such as alloimmunization during blood transfusions or self-reactivity in auto-immune disease.  High affinity antibodies are formed as product of a germinal center (GC) reaction, in which activated B cells proliferate and differentiated into antibodies-secreting cells (ASC). Understanding the exact mechanisms controlling this process within the GC is fundamental to devise strategies to modulate antibodies formation in both health and disease

During the past Sars-CoV2 pandemic we had the unique opportunity to investigate B cell differentiation dynamics in human settings by specifically analyzing Sars-CoV2 spike-specific B cells isolated from infected individuals or individuals undergoing mRNA vaccination. In particular, we generated a multi-omics single-cell dataset containing more than 40’000 spike-specific B cells from 24 individuals at three timepoints after vaccination of which we analyzed transcriptome, surface protein expression and B-cell receptor (BCR) sequence.

In this project we will use our multi-omics single cell dataset to investigate how metabolism impacts B cell differentiation into ASC. It is already known that metabolic changes are key to the generation of ASC, as extensive metabolic reprogramming is needed in differentiating B cells to sustain the massive antibody production carried out by ASC. Additionally, ASC can be short-lived or long-lived; while it is still unknown which mechanisms determine the longevity of ASC, some have hypothesize that metabolism might play a central role.

Your task within this project will be to analyze the metabolic state of our spike-specific B cells. You will evaluate different published computational approaches to obtain metabolic state information from single-cell transcriptomic data. Finally, you apply the best performing methodology to compare the metabolic state in the different B cell subsets identified within our dataset. Knowledge of R/Phyton is required.