SkinTERM ESR6: PhD candidate 'Spatially resolved -omics of tissue regeneration and scarring'

Updated: about 2 months ago
Deadline: 14 Mar 2021

We have an open PhD position at the Helmholtz Zentrum München in the Schiller lab (Twitter: SchillerLab; Pubmed: Schiller HB - http://bit.ly/3aCbp3l ) on spatially resolved omics in organ regeneration.

The position is part of the Innovative Training Network “SkinTERM ”, Skin Tissue Engineering and Regenerative Medicine, an EU Horizon 2020 Marie Skłodowska-Curie Actions funded project. This network will train a new generation of entrepreneurial, multidisciplinary and intersectorially scientists able to drive this research area further towards clinical translation in Europe.

The ESR6 project will be executed in collaboration with the University of Algarve  (UALG, Portugal), the VU Medical Center Amsterdam  (VUMC, The Netherlands), and the company CUTISS AG  (CUTISS, Switzerland).

Background
In metazoan evolution, humans acquired ~300 large multidomain extracellular matrix (ECM) proteins, which interact with each other and cells to form elaborate composite biomaterials that shape both the form and function of tissues. This project will focus on the application of spatial omics methods for the molecular analysis of extracellular matrix niches to understand how tissue regeneration is modulated by ECM components.

The project
The student will use laser capture microdissection and LC-MS based proteomics as well as novel spatial transcriptomic single cell methods. In an integrative systems biology approach the pro-regenerative tissue niches in the African spiny mouse (Acomys), which is able to fully regenerate skin wounds and other organ injuries, will be compared to scarring tissues in the ordinary mouse (Mus), as well as different model systems of regeneration and fibrosis.

Methods:

  • Laser capture microdissection coupled to mass spectrometry based proteomics.
  • Application of spatial transcriptomic methods.
  • Confocal and light sheet 3D microscopy in cleared tissues.
  • Bioinformatic data analysis.

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