Targeted Gene Delivery in Central Nervous Systems (CNS)
Machine-learning Guided Design of Next-Generation Vectors
Owing to a convergence of factors, machine learning (ML) is being increasingly leveraged in earnest to improve protein engineering efforts. In parallel, advances in deep sequencing technologies allow millions of sequences to be assayed and used for training supervised ML models for prediction of protein properties. To this end, we aim to merge modern day machine learning predictive modeling with ML-based ‘predictive model inversion’, to improve vector engineering in a therapeutically relevant system.
To engineer these vectors, we will apply experimental and in silico approaches for generating diverse libraries and selecting variants with improved and desired properties. We envision that these ML-designed next-generation vectors will have broad utility in targeting and manipulating various cells/tissues for therapeutic applications, for AAVs and beyond.
Translational Gene Therapy in Neurological Disorders & Brain Aging
As a brain-resident macrophage, microglia unfortunately have been implicated in many neurological diseases, such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, Glioblastoma, and others etc. Therefore, genetically manipulating endogenous microglia is a promising therapeutic approach to counteract disease pathology. The integration of microglial vectors with disease-associated molecular targets will open new avenues for therapeutic modalities in many neurological diseases. We aim to engineer a new generation of microglia-based technologies and cell therapies for specific targeting and manipulation of the dynamic interaction between diseased cells and immune cells in the context of various pathologies.
Identifying Molecular Targets in Controlling Neuro-Immune Interactions
Engineering Materials to Recapitulate Cellular Microenvironment
We leverage protein-engineered materials in combination with bio-orthogonal synthetic chemistries to recapitulate the microenvironment of neuro-immune interactions, to identify how aberrant biochemical as well as mechanotransduction can alter microglia and other brain cells’ functions. We aim to harness well-defined engineered microenvironment to identify synergistic interactions among stiffness, adhesive ligands, and soluble factors with other cell types that modulate microglia activation and immune functions. Specifically, we can create customizable with independently tunable biochemical and mechanical properties that mimic the essential properties of human brain (i.e., elasticity, proteolytic remodeling, and cell signaling), which will provide unique insight into biological mechanisms of these cell-microenvironment interactions.
Combining single-cell RNA sequencing with high-throughput niche screening platform, we aim to investigate how cells respond to dynamic niche changes, how environmental risk factors interact with other genetic factors, and to predict cell fates from surrounding niche cues, in order to guide future materials’ design and regenerative therapies.
High-throughput CRISPR-based Functional Genomic Screening
The rapid development of next-generation sequencing has greatly expanded our knowledge of the human genome. Thousands of disease-associated mutations have been identified by large-scale human sequencing efforts. However, the majority of these newly proposed features await experimental validation in a cell-type and disease-specific context.
Dysregulation of microglia contributes to numerous neurological diseases, while selective reduction of its activation has shown neuroprotective effects. As a therapeutic target, the goal is to agonize molecular targets that enhance the neuroprotective roles of microglia and antagonize those that drive neuroinflammation. We aim to use CRISPR-guided mutagenesis as well as expression systems (CRISPRi & CRISPRa), to establish systematically a collection of human microglial mutations on genomic regions relevant for human neurological diseases, and to elucidate how disease-associated changes in gene expressions can pinpoint causal determinants in disease states. This will serve as a versatile platform for systematically assess functions of genomic features on gene regulation, cell fate control, disease ontology, etc., and to deconvolute the contributions of different cell types and their interactions in diseases.