Advanced Stem Cell & Islet Regeneration for Diabetes: Beta-Cell Engineering, Organoids, CRISPR & Regenerative Medicine

This 3-day online workshop explores regenerative biotechnology for diabetes, focusing on stem cell therapy and pancreatic islet regeneration to restore insulin-producing beta cells. Tailored for faculty, researchers, healthcare professionals, industry experts, and undergraduate/postgraduate students, the program utilizes web-based demonstrations, virtual laboratory resources, stem cell databases, and scientific visualization tools. No programming or laboratory experience is required.

This workshop aims to provide participants with a comprehensive understanding of stem cell-based therapies, pancreatic islet regeneration, and regenerative biotechnology for diabetes treatment. Through expert-led sessions and browser-based hands-on activities, participants will explore the latest breakthroughs in stem cell biology, pancreatic tissue engineering, and clinical translation without requiring laboratory access or programming skills.

• Introduce stem cell technologies for diabetes therapy.
• Explain pancreatic islet biology and regeneration.
• Demonstrate tissue engineering approaches for diabetes.
• Explore clinical translation of stem cell therapies.
• Introduce gene editing and organoid technologies.
• Provide practical exposure through online demonstrations and scientific resources.

📅Day 1: Pancreatic Beta-Cell Gene Expression Analysis & Stem Cell Differentiation

• Core Topics
• Stem cell-derived pancreatic beta cells and diabetes: Overview of diabetes-related beta-cell loss, regenerative medicine approaches, stem cell-derived insulin-producing cells, and their potential applications in diabetes therapy. Beta-cell differentiation and maturation: Key developmental stages involved in pancreatic lineage specification, differentiation into insulin-producing cells, and the biological characteristics of mature beta cells. Gene expression signatures of pancreatic cells: Introduction to gene expression analysis and the use of marker genes, including INS, PDX1, NKX6-1, MAFA, and GCG, to investigate beta-cell identity and pancreatic cell populations. Public gene expression datasets: Understanding prepared gene expression matrices, sample annotations, cell population labels, and the interpretation of publicly available biological datasets. Visualizing beta-cell identity and maturation: Interpreting gene expression patterns to understand differences between pancreatic cell populations and identify signatures associated with beta-cell differentiation and maturation.
• 🔬 Hands-on Session (Google Colab-Based Gene Expression Analysis)
• Using a prepared, publicly sourced pancreatic gene expression dataset in Google Colab , participants will explore gene expression patterns across selected pancreatic cell populations. With Python, pandas, and seaborn, they will examine INS, PDX1, NKX6-1, MAFA, and GCG expression, generate a gene expression heatmap, and compare marker signatures across cell populations. The exercise will demonstrate how computational analysis can help characterize beta-cell identity and maturation.
• Practical Output: A beta-cell marker expression heatmap, comparative gene expression analysis, and a brief interpretation of pancreatic cell identity.

📅 Day 2: AI-Based Pancreatic Cell Classification & Islet Heterogeneity Analysis

• Core Topics
• Pancreatic islet cell diversity: Biological roles of alpha, beta, and delta cells, their characteristic gene expression profiles, and the importance of cellular heterogeneity in pancreatic islet function. Stem cell-derived islets and cellular identity: Understanding how gene expression signatures can be used to characterize differentiated pancreatic cells and evaluate the identity of stem-cell-derived islet-like populations. Dimensionality reduction and data visualization: Introduction to Principal Component Analysis (PCA), feature representation, and the visualization of similarities and differences among pancreatic cell populations. Machine learning for cell classification: Fundamentals of supervised learning, labelled gene expression data, training and testing datasets, and the application of Random Forest classification to pancreatic cell identification. Feature importance and model interpretation: Examining which gene expression features contribute to classification predictions, interpreting model performance, and understanding the limitations of AI-based cellular classification.
• 🔬 Hands-on Session (AI-Powered Pancreatic Cell Classification)
• Using a prepared pancreatic single-cell gene expression dataset in Google Colab , participants will visualize selected pancreatic cell populations using PCA and develop a beginner-friendly Random Forest classifier with Python and scikit-learn. They will evaluate classification results, explore feature importance, and investigate how gene expression characteristics distinguish alpha, beta, and delta cell populations. The exercise will emphasize appropriate data splitting and the importance of avoiding data leakage when evaluating machine learning models.
• Practical Output: A PCA visualization, a trained pancreatic cell classification model, classification performance results, and a gene feature-importance chart.

📅 Day 3: AI-Assisted Biomarker Discovery for Beta-Cell Regeneration & Maturation

• Core Topics
• Beta-cell regeneration and regenerative biomarkers: Introduction to beta-cell differentiation, maturation, regenerative medicine, and the potential use of molecular signatures to investigate cellular development and therapeutic readiness. Gene expression comparisons across cell states: Exploring prepared gene expression datasets to compare selected genes between defined differentiation stages, maturation states, or pancreatic cell populations. Differential expression and fold-change analysis: Understanding fold changes, basic statistical comparisons, expression variability, and the interpretation of differences between biological groups. Computational prioritization of candidate biomarkers: Ranking genes using expression differences and basic statistical measures to identify candidates associated with beta-cell differentiation or maturation. Biological interpretation and translational limitations: Assessing the biological relevance of candidate genes, distinguishing association from causation, and understanding why computational predictions require independent experimental validation.
• 🔬 Hands-on Session (Google Colab-Based Biomarker Discovery)
• Participants will use a prepared gene expression dataset in Google Colab to compare selected pancreatic cell states and prioritize candidate beta-cell differentiation and maturation biomarkers. Using pandas, SciPy, and matplotlib, they will calculate fold changes, perform appropriate basic statistical comparisons, generate biomarker ranking plots, and prepare a concise interpretation of their findings. The exercise will demonstrate how computational workflows can support early-stage biomarker discovery in regenerative medicine.
• Practical Output: A ranked candidate biomarker table, comparative gene expression plots, and a concise research summary identifying promising candidates for further investigation.

• Understand stem cell biology and pancreatic regeneration principles.
• Explain current stem cell-based therapies for diabetes.
• Understand pancreatic islet engineering strategies.
• Explore clinical applications of regenerative medicine.
• Interpret ongoing stem cell clinical trials.
• Evaluate different transplantation and encapsulation technologies.
• Understand emerging gene-editing and organoid approaches.
• Design a conceptual regenerative medicine workflow for diabetes treatment.

This workshop is designed for students and professionals from Biotechnology, Life Sciences, Biomedical Sciences, Pharmacy, and Medical fields , along with Faculty Members, Research Scholars, PhD Candidates, Clinical Researchers, Healthcare Professionals, and Biotechnology Industry Professionals seeking advanced knowledge and practical exposure in modern biomedical and translational research.