Program Structure

Module 1: Introduction to Single-Cell Genomics

• What is single-cell genomics? Overview of high-resolution cellular analysis methods.
• Key applications: studying gene expression, cellular heterogeneity, and dynamic cellular processes.
• Technological advancements: how single-cell genomics has revolutionized cellular engineering and precision medicine.
• Key challenges: sensitivity, scalability, and data interpretation in single-cell technologies.

Module 2: High-Throughput Single-Cell Assays

• Single-cell RNA sequencing (scRNA-seq): principles, methodology, and applications for transcriptomic profiling.
• Single-cell ATAC-seq: epigenomic profiling to understand chromatin accessibility and regulatory regions.
• Single-cell proteomics and metabolomics: combining genomics with other cellular assays for comprehensive cellular analysis.
• Challenges in high-throughput single-cell assays: sequencing depth, technical noise, and scalability.

Module 3: Single-Cell Data Analysis and Interpretation

• Data preprocessing: quality control, normalization, and filtering for single-cell RNA-seq and other assays.
• Clustering and dimensionality reduction: identifying cell subpopulations and defining cellular states.
• Differential gene expression analysis: understanding how to identify genes that are differentially expressed between cell types or conditions.
• Pathway analysis: identifying key biological pathways and networks using single-cell data.

Module 4: CRISPR and Other Tools for Single-Cell Genetic Engineering

• Introduction to CRISPR-Cas9 and its applications in single-cell genetic modifications.
• Gene editing strategies for single-cell manipulation: knock-in, knock-out, and base editing techniques.
• Single-cell transfection and delivery methods: viral vectors, electroporation, and lipid-based transfection.
• Challenges in single-cell genetic engineering: off-target effects, efficiency, and delivery optimization.

Module 5: Engineering Immune Cells for Therapy

• Immunotherapy applications: engineering immune cells for targeted cancer therapy, CAR-T cells, and checkpoint inhibitors.
• Single-cell analysis of immune responses: tracking T-cell activation, differentiation, and function.
• Designing cell therapies: using single-cell genomics to enhance the efficacy and persistence of engineered immune cells.
• Challenges in immune cell engineering: safety, persistence, and tumor microenvironment complexity.

Module 6: Cancer Genomics and Single-Cell Applications

• Understanding cancer heterogeneity: how single-cell genomics uncovers intra-tumor variation.
• Using single-cell technologies to map cancer evolution, metastasis, and drug resistance mechanisms.
• Single-cell approaches to identifying cancer stem cells and targeted therapies.
• Challenges in cancer single-cell analysis: tumor microenvironment interactions and heterogeneity at the molecular level.

Module 7: Stem Cells and Regenerative Medicine

• Single-cell technologies for stem cell research: lineage tracing, differentiation, and reprogramming analysis.
• Understanding cellular plasticity and the role of epigenetic regulation in stem cell differentiation.
• Applications in regenerative medicine: using single-cell genomics to identify therapeutic targets for tissue repair.
• Challenges in stem cell engineering: efficiency, safety, and long-term engraftment issues.

Module 8: Ethical, Safety, and Regulatory Considerations in Single-Cell Engineering

• Ethical issues: modifying the genome of individual cells, gene therapy risks, and germline editing concerns.
• Regulatory challenges: guidelines for gene editing, stem cell research, and clinical trials.
• Public perception: communicating the benefits and risks of single-cell engineering in medicine and biotechnology.
• Balancing innovation with safety: developing robust, reproducible single-cell engineering systems for clinical use.

Module 9: Future Directions in Single-Cell Genomics

• Advancements in single-cell technologies: long-read sequencing, multi-omics integration, and spatial genomics.
• The role of AI and machine learning in single-cell data interpretation and prediction.
• The potential for personalized medicine: tailoring therapies based on individual single-cell profiles.
• Future challenges and opportunities in applying single-cell genomics to agriculture, disease research, and therapy development.

Final Project

• Create a Single-Cell Engineering Strategy for a specific biological system (e.g., cancer cells, stem cells, immune cells, or tissue regeneration).
• Include: system goal, target cell type, genetic engineering approach, experimental design, data analysis plan, and potential therapeutic or research application.
• Example projects: single-cell analysis of cancer drug resistance pathways, designing stem cell differentiation protocols, or engineering immune cells for cancer immunotherapy.