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MSc Thesis Defense: Yeşim Erdoğan, 3D BIOPRINTED MESENCHYMAL STEM CELL DIFFERENTIATION INTO NEURONS, Date & Time: June 15, 2026 – 12:00 PM, Place: FENS G035

3D BIOPRINTED MESENCHYMAL STEM CELL DIFFERENTIATION INTO NEURONS

 

 

Yeşim Erdoğan
Molecular Biology, Genetics and Bioengineering, MSc Thesis, 2026

 

Thesis Jury

     Prof. Bahattin Koç (Thesis Advisor)

  Asst. Prof. Nur Mustafaoğlu Varol (Thesis Co-Advisor)

Asst. Prof. Sibel Çetinel

Prof. Gamze Torun Köse

  Asst. Prof. Ece Öztürk

  

 

Date & Time: June 15th, 2026 – 12.00 PM

Place: FENS G035

Keywords: neural tissue modeling, stem cells, hydrogels, 3D bioprinting

 

 

Abstract

 

Neurological diseases are one of the main causes of mortality worldwide. Understanding the underlying disease mechanisms is crucial for developing efficient therapeutic approaches. Despite advancements in the field, current disease and tissue modeling systems remain insufficient in replicating in vivo conditions accurately. The sensitivity and limited lifespan of neurons, along with the complex brain environment, present significant challenges in brain tissue modeling. To address these limitations, the combination of stem cell technology and three-dimensional (3D) bioprinting provides a promising approach for mimicking physiologically relevant neural tissues. In this thesis, bone marrow-derived mesenchymal stem cells (bm-MSCs) were bioprinted in a gelatin methacrylate (GelMA) hydrogel matrix. These bioprinted constructs were cultured for seven days in neuronal differentiation medium (DM) or DM combined with astrocyte-conditioned medium (DCM). These constructs were analyzed by cell viability, immunocytochemistry, calcium imaging and LC-MS-based proteomics. Through this systematic characterization, we demonstrated the formation of morphologically relevant and functionally active neural networks in this 3D constructs. Consequently, we proposed a neuronal differentiation approach within a hydrogel environment, providing a 3D platform for neural tissue engineering and disease modeling.

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