Fakultät für Biologie

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Master thesis available in the Schiller lab @HelmholtzMunich

Computational analysis of single-cell spatial omics data to characterize cell-cell communication in pulmonary fibrosis



Dr. Laurens De Sadeleer (laurens.desadeleer@helmholtz-muenchen.de)

Dr Janine Gote-Schniering (janine.schniering@helmholtz-muenchen.de)

Dr. Herbert Schiller, Deputy Director Institute of Lung Health and Disease, Helmholtz Munich

Project background

Fibrogenesis after tissue injury is one of the most prevalent clinical complications and accounts for 45% of deaths in the developed world. One of the archetypic examples of a fibrotic disease is Idiopathic pulmonary fibrosis (IPF), for which the incidence rate dramatically increases with age. IPF is a relentless fibrotic lung disease characterized by the progressive scarring of alveolar tissues with dramatic changes in epithelial, endothelial and fibroblast cell states during disease progression1,2.

The main aim of our research is to understand how these aberrant cell states are wired together into circuits and influence each other throughout disease evolution3–5 and aging6 . To better understand the altered cell-cell communication inducing these disease-specific circuits, we use state-of-the art -omics technologies, organotypic ex vivo models and innovative imaging tools such as iterative highly multiplexed immunofluorescence (4i)7, in which the very same tissue section is stained iteratively with 20+ antibodies to characterize the tissue niche of aberrant cell states in lung fibrosis. Similarly, we are implementing targeted spatial transcriptomics methods (10x Xenium platform) with single cell resolved spatial information for hundreds of transcripts on large fields of view on the tissue section.

Project description

In this project, the student will implement and optimize image processing and spatial data analysis tools using scverse (https://scverse.org/) core tools (Scanpy8, Squidpy9) to characterize the cellular circuits in the lung and their alterations in lung aging and disease. The work will include implementing software for image alignments, cell segmentations, assessing co-localisation of specific cell states and characterizing subtissular niches using Squidpy. The lab is closely connected with the Institute of Computational Biology at Helmholtz (Theis lab) in order to use state of the art algorithmic developments for this project.

Tasks for the project will include:

  • Learning scverse tooling for spatially-resolved omics data
  • Implementing image alignment software
  • Implementing cell segmentation software
  • Analysis of cell-cell communication in subtissular niches

Project requirements

This project will suit a highly motivated student with a strong interest in computational biology and spatially-resolved omics. Experience with scanpy/squidpy and/or spatially resolved single cell omics data is beneficial but not necessary. There will be regular contact with supervisors but you should also be comfortable working independently. By completing this project the student will be exposed to a cutting edge experimental systems biology research group, develop a range of research and problem-solving skills and become familiar with the challenges of method development, data analysis, and biological interpretation of spatial omics datasets.


Women and people from other underrepresented groups are strongly encouraged to apply and we will seek to provide any support you require to complete the project.


  1. Adams, T. S. et al. Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis. Sci Adv 6, eaba1983 (2020).
  2. Habermann, A. C. et al. Single-cell RNA sequencing reveals profibrotic roles of distinct epithelial and mesenchymal lineages in pulmonary fibrosis. Sci Adv 6, eaba1972 (2020).
  3. Mayr, C. H. et al. Integrative analysis of cell state changes in lung fibrosis with peripheral protein biomarkers. EMBO Mol. Med. 13, e12871 (2021).
  4. Strunz, M. et al. Alveolar regeneration through a Krt8+ transitional stem cell state that persists in human lung fibrosis. Nat. Commun. 11, 3559 (2020).
  5. Mayr, C. H. et al. Autocrine Sfrp1 inhibits lung fibroblast invasion during transition to injury induced myofibroblasts. bioRxiv 2022.07.11.499594 (2022) doi:10.1101/2022.07.11.499594.
  6. Angelidis, I. et al. An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics. Nat. Commun. 10, 963 (2019).
  7. Gut, G., Herrmann, M. D. & Pelkmans, L. Multiplexed protein maps link subcellular organization to cellular states. Science 361, (2018).
  8. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
  9. Palla, G. et al. Squidpy: a scalable framework for spatial omics analysis. Nat. Methods 19, 171–178 (2022).