High-throughput cellular analytics to identify central nervous system targets for gene therapy
summary
Pipeline uses single-cell RNA-seq on thousands to tens of thousands of cells, reduces ~20,000 genes to ~10–25 principal components, then clusters with a shared nearest-neighbor graph and Louvain-style modularity to define cell types (each defined by about a dozen unique genes). Stimulus mapping reads immediate early genes to pick pain-activated cell cohorts. Therapeutic step uses CRISPR-Cas9 + AAV delivery via one-time stereotactic injection to reprogram or silence targets; in mice it achieves high targeting efficiency and behavior control for pain pathways.
AI-written summary · about
chapters (7)
- 0:00Introduction to precision gene therapies
- 1:54Identifying brain cell populations
- 3:34Single-cell RNA sequencing process
- 13:32Mapping cell types to functions
- 17:27Linking cell types to diseases
- 20:21Gene therapy precision challenges
- 23:09Conclusion and future work
related talks
30mMachine Learning: A New Approach to Drug Discovery
44mNext-Gen Therapies
33mUnderstanding Molecular Complexity for Precision Medicine
29mMachine Learning Techniques for Omics Data Analysis
29mHarnessing Multimodal Single-Cell Sequencing Data for Integrative Analysis
20mTranslating a Trillion Pointo Diseases of Data into Therapies
30mAI in Drug Discovery
36mFormatting Biological Big Data to Enable (Personalized) Systems Pharmacology