Advances in 3D cell models for cancer biology using real-time live-cell analysis
More clinically translational models and novel technologies are on the rise as valuable research tools. Prevalent in the fields of oncology and immuno-oncology these model systems utilize relevant cell types and organization to potentially enhance validation studies, disease recapitulation and drug discovery. In this webinar, we describe 3D cell culture methodologies and live-cell analysis solutions for investigating: tumor spheroid proliferation and death, tumor spheroid metastatic potential, multi-cell type models and organoid formation and growth within Matrigel domes.
What will you learn?
- Quantify tumor spheroid growth, morphology and viability using scaffold and scaffold-free 3D culture models.
- Study the invasive potential and capacity of malignant tumor cells.
- Study the impact of stromal cells and immune cells on 3D tumor biology.
- Real-time kinetic imaging and quantification of organoid growth in Matrigel domes.
Who might this interest?
• People considering or developing spheroid based drug discovery and organoid research models.
• Researchers working with automation in advanced cellular models.
• People that desire to implement live-cell imaging into their workflows.
Manager, Cell Imaging Applications
Sartorius (London, UK)
Kalpana is a Manager within the Cell Imaging Applications unit of BioAnalytics at Sartorius. She is a highly experienced industrial pharmacologist with over 25 years of experience gained from pharmaceutical, contract research and biotechnology laboratories. Trained initially as an in vitro pharmacologist, Kalpana specialized in designing and developing plate-based cellular assays to support automated high-throughput screening, compound profiling and mechanism of action follow-up studies. Within Sartorius, she’s applied her extensive experience to the development and validation of cellular assays for quantitative live-cell analysis and currently leads an R&D team focused on the development of advanced cellular models for oncology.