Bio

Institute Affiliations


  • Member, Maternal & Child Health Research Institute (MCHRI)

Professional Education


  • Doctor of Philosophy, RadboudUniversityNijmegen (2020)
  • Master of Science, Katholieke Universiteit Leuven (2012)
  • Bachelor of Science, Katholieke Universiteit Leuven (2010)

Stanford Advisors


Publications

All Publications


  • Clusters of Tolerogenic B Cells Feature in the Dynamic Immunological Landscape of the Pregnant Uterus CELL REPORTS Benner, M., Feyaerts, D., Garcia, C., Inci, N., Lopez, S., Fasse, E., Shadmanfar, W., van der Heijden, O. H., Gorris, M. J., Joosten, I., Ferwerda, G., van der Molen, R. G. 2020; 32 (13): 108204

    Abstract

    Well-timed interaction of correctly functioning maternal immune cells is essential to facilitate healthy placenta formation, because the uterine immune environment has to tolerate the semi-allogeneic fetus and allow adequate trophoblast invasion. Here, we assess the uterine immune signature before and during pregnancy. Extensive supervised and unsupervised flow cytometry clustering strategies not only show a general increase in immune memory throughout pregnancy but also reveal the continuous presence of B cells. Contrary to the belief that B cells are merely a consequence of uterine pathology, decidual B cells produce IL-10 and are found to be localized in clusters, together with Foxp3pos T cells. Our findings therefore suggest a role for B cells in healthy pregnancy.

    View details for DOI 10.1016/j.celrep.2020.108204

    View details for Web of Science ID 000573722100014

    View details for PubMedID 32997982

  • VoPo leverages cellular heterogeneity for predictive modeling of single-cell data. Nature communications Stanley, N., Stelzer, I. A., Tsai, A. S., Fallahzadeh, R., Ganio, E., Becker, M., Phongpreecha, T., Nassar, H., Ghaemi, S., Maric, I., Culos, A., Chang, A. L., Xenochristou, M., Han, X., Espinosa, C., Rumer, K., Peterson, L., Verdonk, F., Gaudilliere, D., Tsai, E., Feyaerts, D., Einhaus, J., Ando, K., Wong, R. J., Obermoser, G., Shaw, G. M., Stevenson, D. K., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2020; 11 (1): 3738

    Abstract

    High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.

    View details for DOI 10.1038/s41467-020-17569-8

    View details for PubMedID 32719375

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