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.

I am combining highly multiplexed imaging, data analysis and experimental biology to characterize type 1 diabetes (T1D) progression and beta cell heterogeneity.

I did my PhD with Prof. Pedro Herrera at the University of Geneva, studying regeneration of insulin-producing beta cells by transdifferentiation of the closely related alpha cells. During my thesis, I used confocal microscopy and lineage tracing in complex transgenic mouse models and primary human pancreatic islets. This work sparked my interest into both T1D research and image analysis of single cells, two elements that are still at the center of my current projects.

In the Bodenmiller lab, I use imaging mass cytometry to analyze pancreas sections of individuals with or at risk for type 1 diabetes. The multiplexing capacity of this technology enables deep phenotyping of beta cells and of infiltrating immune cells, and mapping of their interactions. The goal of this project is to gain a better understanding of T1D development in the pancreas.

In parallel, I'm combining primary human islet culture with imaging mass cytometry to study how beta cell subpopulations respond to external stimuli relevant to T1D. The aim is to identify beta cell subsets that are more sensitive to destructive signals or, conversely, more responsive to regenerative cues. This work is supported by a JDRF postdoctoral fellowship.

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

As a research assistant and former Master's student in the lab, I am currently analyzing IMC data with a focus on the immune system and its relation to the protein family of chemokines. I am investigating whether and to what extent the spatial context of chemokine expression can contribute to understanding differences in the immune landscape of patients.

I like to develop high throughput methods together with tailored data analysis approaches to better understand how cells perceive their environment and interact.