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Labelling and tracking fluorescently conjugated antibody in Zebrafish xenograft model using robotic microinjection technique
Linköping University, Department of Physics, Chemistry and Biology. Linköping University.
2024 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Cancer disease is considered as the most challenging disease since it has been affecting enormous number of people around the world. In this thesis, MDA-MB-231 cell line has been used which is a type of triple negative breast Cancer. Many treatments have been developed in recent decades, but currently many scientists focus on immuno therapy specifically the immune checkpoint inhibitors that is considered to be a suitable treatment to restore the ordinary immune system. Nowadays, the most efficient model for studying cancer drug treatment is the zebrafish xenograft model due to several advantages. This project has been based on two objectives: using different types of injection method for the cancer cells as well as using different types of labelling methods with three different antibodies.Two methods have been applied to inject the tumor cells into the model. The efficiency of manual and the robotic injection methods have been calculated and the result ensures that the manual injection is more efficient than the robotic. However, the robotic injection needs least time to do the injection. In addition, the cancer cells were labelled with different types of PD-L1 checkpoint inhibitors in three different approaches. Successful results were obtained using Atezolizumab for labelling the tumor cell after fixing the embryo and co-inject the antibody with the cancer cells. Moreover, Alexa fluor 488 anti PDL1antibody clone 28-8 and the Rb mAb to PD-L1 clone 28-8, also showed positive results in particular concentration levels with particular labelling methods while negative results were obtained from other experiments .

Place, publisher, year, edition, pages
2024. , p. 50
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:liu:diva-200866ISRN: LITH-IFM-A-EX--23/4371--SEOAI: oai:DiVA.org:liu-200866DiVA, id: diva2:1837176
Presentation
2024-01-23, 10:15 (English)
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Available from: 2024-02-13 Created: 2024-02-13 Last updated: 2024-02-13Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Output format
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