Dr. W.A.A. (Wouter) de Steenhuijsen Piters

Vaccines save millions of lives yearly. Despite that, vaccine responses vary greatly between populations. Reduced vaccine responses or ‘vaccine hyporesponsiveness’ is especially common in (rural) populations living in low- and middle-income countries. In our group, headed by prof. dr. Maria Yazdanbakhsh, we aim to understand the immunological basis of this problem. We do so using single-cell technologies, such as mass cytometry and single-cell sequencing.
As a medical doctor and data scientist, I am particularly interested in predicting and possibly preventing vaccine hyporesponsiveness. Therefore, I am currently setting up a research line to explore the role of microbial communities (‘microbiome’) in modulating vaccine responses. In addition, together with a team of (clinical) researchers, I am setting up a proof-of-concept trial where we aim to improve the vaccine response using immunotherapy.
I invest part of my time in education and developing (online) workshops on microbiome data analyses and supervision of masters and PhD students.

I studied medicine and gained clinical experience working as a resident in paediatrics and in pulmonology. I received my PhD cum laude from the University of Utrecht (main supervisor: prof. dr. Debby Bogaert) where I worked on the characterization of the bacterial communities (‘microbiota’) residing in the respiratory tract and how these microbiota were related to respiratory infections. Through this work, I discovered my enthusiasm for data science. I expanded my data science repertoire during a postdoctoral position where I investigated the associations between early-life viral infections and the development of mucosal immunity and respiratory microbiota. In addition, I assessed mother-to-infant transmission of microbes. Ongoing research at the LUMC focusses on understanding vaccine hyporesponiveness across populations. Specifically, I apply and benchmark data-integration methods to draw powerful conclusions from multiple mass cytomet071-5261404ry datasets at once.