Max Planck Institute of Immunobiology and Epigenetics, Freiburg
Institute for Systems Immunology, Julius-Maximilians-University of Würzburg



In our current ILC research projects we are utilizing single-cell based methods to characterize the spectrum of ILC sub-types within tissues and inter-tissue heterogeneity of these sub-types. With the help of single-cell RNA-seq we are attempting to catalogue the full repertoire of mature and progenitor ILCs in mouse lung, small intestine and lymph node. Our previously developed algorithms predict ILC sub-types and –states and infer differentiation trajectories. We are particularly interested in the identification of tissue-specific naïve progenitors and the differentiation pathways they give rise to. We are also investigating these aspects upon perturbations, such as, for example, parasitic infection. In our research we complement cutting-edge single-cell RNA-seq methods for the characterization of cell states with single-molecule FISH-based spatial gene expression analysis for exploring niche-interactions, and lineage-tracing techniques to reveal temporal dynamics. We are collaborating with the labs Georg Gasteiger, Andreas Diefenbach, and Yakup Tanriver.


Dr. Dominic Grün
Project Leader
Patrice Zeis
PhD student
Prof. Dr. Georg Gasteiger
Project Leader
Mi Lian
PhD student







(*equal contributions, #co-corresponding authors)

Grün D#, Muraro MJ, Boisset JC, Wiebrands K, Lyubimova A, Dharmadhikari G, van den Born M, van Es J, Jansen E, Clevers H, de Koning EJP, van Oudenaarden A# (2016) De Novo Prediction of Stem Cell Identity Using Single-Cell Transcriptome Data. Cell Stem Cell 19(2): 266-77

Grün D and van Oudenaarden A. (2015) Design and analysis of single cell sequencing experiments. Cell 163(4): 799-810

Grün D*, Lyubimova A, Kester L, Wiebrands K, Basak O, Sasaki N, Clevers H, van Oudenaarden A. (2015) Single-cell mRNA sequencing reveals rare intestinal cell types. Nature 525: 251-255

Grün D*, Kester L*, van Oudenaarden A. (2014) Validation of noise models for single-cell transcriptomics enables genome-wide quantification of stochastic gene expression. Nature Methods 11(6): 637-40