iPSC, Organoids, Microphysiology & Organ-on-Chip: Experimental Design, Validation & Drug Testing

To provide participants with a practical understanding of iPSC, spheroid, organoid, microphysiology and organ-on-chip experimental workflows, with emphasis on experimental design, quality control, validation, troubleshooting and drug-response interpretation.

• Understand iPSC culture, differentiation and quality-control workflows
• Design healthy, disease and isogenic experimental groups
• Understand spheroid and organoid development and maturation
• Evaluate morphology, viability and molecular-marker data
• Understand tissue microphysiology, barriers, gradients and mechanical forces
• Design MPS and organ-on-chip experiments
• Interpret TEER, permeability, viability and biomarker readouts
• Identify experimental failures, variability and technical artefacts
• Compare 2D, organoid and MPS models for different research applications
• Apply reproducibility and validation principles to experimental studies

• Undergraduate & Postgraduate Students — Biotechnology, Life Sciences, Biomedical Sciences, Bioengineering, Pharmacy and related disciplines
• PhD Scholars & Researchers — Stem cells, tissue engineering, organoids, toxicology, pharmacology and disease modeling
• Faculty & Academicians — Teaching or conducting research in advanced cell and tissue models
• Pharma & Biotechnology Professionals — Drug discovery, preclinical research, assay development and translational biology
• CRO & R&D Professionals — Human-relevant models, toxicology and preclinical testing

• Design an iPSC-based disease-model experiment
• Evaluate iPSC quality and experimental suitability
• Assess spheroid and organoid development and validation
• Interpret microscopy, viability and marker-expression results
• Explain key principles of human tissue microphysiology
• Understand flow, shear stress and barrier functions in MPS
• Analyze organ-on-chip drug-response datasets
• Distinguish biological effects from technical problems
• Select appropriate controls, replicates and experimental endpoints
• Decide whether an experiment should proceed, be optimized or be repeated
• Design an improved follow-up experiment based on the results