Single-cell systems biology of cancer requires methods to measure multiple markers within tumors, quantitatively and at single-cell resolution. To this end, the Bodenmiller group has pioneered a single-cell mass spectrometric approach called mass cytometry (CyTOFTM). This technology allows the simultaneous and high-throughput quantification of approximately 50 markers, including proteins and their modifications, on single cells. We employ mass cytometry-based methods to study the cellular composition and regulation of tissue ecosystems, for insights into health and disease.

Drug development in breast cancer suffers from a lack of faithful models able to recapitulate breast cancer heterogeneity at the single-cell level. To overcome this limitation, we have generated a patient-derived breast cancer organoid biobank from fresh and frozen breast cancer samples. We are using this model to study the different populations composing the tumor and their response to drug treatments. It is our conviction that the study of the different cellular populations composing breast tumors, and their interactions, will lead to a better understanding of the tumor ecosystem and to the identification of therapies targeting specific cellular populations that could trigger tumor demise.

My current research focuses on understanding the molecular changes over breast cancer organoid growth. I'm integrating imaging mass cytometry (IMC) with single-cell based statistical approaches to model spatial heterogeneity in organoids. As part of handling IMC data, I develop software for image and single-cell analysis.

In the past, I have completed my PhD in the Marioni group at the European Bioinformatics Institute and the CRUK Cambridge Institute at the University of Cambridge.

Link to website: nilseling.github.io
Link to Google Scholar: https://scholar.google.com/citations?user=kBIvrFoAAAAJ&hl=de
Link to Github: https://github.com/nilseling