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- Identification of fungal species in patient tissue using MALDI-Imaging and Deep Learning approaches
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Created: 17th Feb 2021 at 10:07
Last updated: 17th Feb 2021 at 10:08
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Roles: Postdoc
Expertise: MALDI-MS Imaging, Proteomics
Tools: Cell and tissue culture, Cell biology, Image analysis
I am responisble for the spatial proteomics part in Z2. We provide a proteomic platform to study spatial changes of the proteome during host-fungal pathogen interaction using MALDI-MS Imaging and Lasermicrodissection. As type of biological sample we can work with cryo and formalin fixed tissue, as well as cell cultures and lysed sample solutions.
Please get into contact with me if you are interested in MALDI-MS Imaging for your research project.
Projects: B4, FungiNet B - Bioinformatics projects, FungiNet total
Institutions: Leibniz-Institute for Natural Product Research and Infection Biology Hans Knöll Institute (HKI)

Roles: PhD Student
Tools: deep learning, Machine Learning, Image analysis
Automated analysis of microscopic image data of host-pathogen interaction
In project Z2 a sophisticated proteome analysis platform will be provided based on 2D-gel electrophoresis (2D-GE), MALDI-TOF and LC-MS/MS techniques to specific A and Cprojects.
This service and research topic deals with the spatially resolved field of proteomics which allows targetet and untargeted host-pathogen interaction studies.
Please get into contact with Franziska Hoffmann and Ferdinand von Eggeling if you are interested in spatial proteomics.
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Abstract (Expand)
Authors: Ferdinand Von Eggeling, Franziska Hoffmann
Date Published: 25th Jun 2020
Journal: Proteomics
PubMed ID: 32578340
Citation: Proteomics. 2020 Sep;20(17-18):e2000077. doi: 10.1002/pmic.202000077. Epub 2020 Jul 6.