ENDpoiNTs

Institutionen
  • FB Biologie
Publikationen
  Dreser, Nadine; Holzer, Anna-Katharina; Kapitza, Marion; Scholz, Christopher; Kranaster, Petra; Gutbier, Simon; Klima, Stefanie; Kolb, David; Dietz, Christian; Trefzer, Timo; Berthold, Michael R.; Waldmann, Tanja; Leist, Marcel(2019): Development of a neural rosette formation assay (RoFA) to identify neurodevelopmental toxicants and to characterize their transcriptome disturbances Archives of Toxicology ; 2019. - ISSN 0340-5761. - eISSN 1432-0738

Development of a neural rosette formation assay (RoFA) to identify neurodevelopmental toxicants and to characterize their transcriptome disturbances

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The first in vitro tests for developmental toxicity made use of rodent cells. Newer teratology tests, e.g. developed during the ESNATS project, use human cells and measure mechanistic endpoints (such as transcriptome changes). However, the toxicological implications of mechanistic parameters are hard to judge, without functional/morphological endpoints. To address this issue, we developed a new version of the human stem cell-based test STOP-tox<sub>(UKN)</sub>. For this purpose, the capacity of the cells to self-organize to neural rosettes was assessed as functional endpoint: pluripotent stem cells were allowed to differentiate into neuroepithelial cells for 6 days in the presence or absence of toxicants. Then, both transcriptome changes were measured (standard STOP-tox(UKN)) and cells were allowed to form rosettes. After optimization of staining methods, an imaging algorithm for rosette quantification was implemented and used for an automated rosette formation assay (RoFA). Neural tube toxicants (like valproic acid), which are known to disturb human development at stages when rosette-forming cells are present, were used as positive controls. Established toxicants led to distinctly different tissue organization and differentiation stages. RoFA outcome and transcript changes largely correlated concerning (1) the concentration-dependence, (2) the time dependence, and (3) the set of positive hits identified amongst 24 potential toxicants. Using such comparative data, a prediction model for the RoFA was developed. The comparative analysis was also used to identify gene dysregulations that are particularly predictive for disturbed rosette formation. This ‘RoFA predictor gene set’ may be used for a simplified and less costly setup of the STOP-tox<sub>(UKN)</sub> assay.

Forschungszusammenhang (Projekte)

    Karreman, Christiaan; Kranaster, Petra; Leist, Marcel(2019): SUIKER : Quantification of antigens in cell organelles, neurites and cellular sub-structures by imaging Alternatives to Animal Experimentation : ALTEX ; 36 (2019), 3. - S. 518-520. - ISSN 1868-596X. - eISSN 1868-8551

SUIKER : Quantification of antigens in cell organelles, neurites and cellular sub-structures by imaging

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Quantification of fluorescence colocalization and intensity of strongly overlapping cells, e.g., neuronal cultures, is challenging for programs that use image segmentation to identify cells as individual objects. Moreover, learning to use and apply one of the large imaging packages can be very time- and/or resource-demanding. Therefore, we developed the free and highly interactive image analysis program SUIKER (program for SUperImposing KEy Regions) that quantifies colocalization of different proteins or other features over an entire image field. The software allows definition of cellular subareas by subtraction ("punching out") of structures identified in one channel from structures in a second channel. This allows, e.g., definition of neurites without cell bodies. Moreover, normalization to live or total cell numbers is possible. Providing a detailed manual that contains image analysis examples, we demonstrate how the program uses a combination of colocalization information and fluorescence intensity to quantify carbohydrate-specific stains on neurites. SUIKER can import any multichannel histology or cell culture image, builds on user-guided threshold setting, batch processes large image stacks, and exports all data (including the settings, results and metadata) in flexible formats to be used in Excel.

Forschungszusammenhang (Projekte)

Mittelgeber
NameKennzifferBeschreibungLaufzeit
Europäische Union427/19keine Angabe
Weitere Informationen
Laufzeit: 01.01.2019 – 31.12.2023