S5E12

Abstract

Simultaneous delivery of mRNA to multiple populations of antigen (Ag)–specific CD8+ T cells is challenging given the diversity of peptide epitopes and polymorphism of class I major histocompatibility complexes (MHCI). We have recently developed Ag-presenting nanoparticles (APNs) for mRNA delivery using pMHCI molecules that were refolded with photocleavable peptides to allow rapid ligand exchange by UV light and site-specifically conjugated with a lipid tail for postinsertion into preformed mRNA lipid nanoparticles. Across different TCR transgenic mouse models (P14, OT-1, and Pmel), UV-exchanged APNs bound and transfected their cognate Ag-specific CD8+ T cells equivalent to APNs produced using conventionally refolded pMHCI molecules. In mice infected with PR8 influenza, multiplexed delivery of UV-exchanged APNs against three immunodominant epitopes led to transfection of a VHH mRNA reporter in cognate Ag-specific CD8+ T cells. Together, our data support the use of APNs for multiplexed mRNA delivery to virus-specific T cells, which can potentially be expanded to engineer broader Ag-specific T cell subsets in vivo.

Introduction of speaker

Dr. Fang-Yi “Ida” Su is a postdoctoral fellow in the laboratory of Dr. Gabe Kwong at Georgia Tech and Emory School of Medicine. Her research interests include nanomaterials, gene/drug delivery, and cell engineering. Prior to her postdoctoral training, she received her Ph.D. in bioengineering from the University of Washington with Dr. Patrick Stayton, and her M.S. in chemical engineering with Dr. Hsing-Wen Sung from National Tsing Hua University in Taiwan. In recognition of her work, Dr. Su has been honored with notable awards including a postdoctoral fellowship provided by Georgia Tech and Peking University (China), the HHMI International Student Research Fellowship, and the MOE Technologies Incubation Scholarship from the Taiwanese Government. Her career goals are to lead a research lab and address unmet clinical needs in cancer, infectious diseases, and autoimmunity.

Abstract

Metal additive manufacturing (AM) offers the potential to change the manufacturing engineering landscape by producing parts with tailored performance and intricate geometries with minimal loss of material. However, limited material selection and non-optimal process parameters might lead to defects, such as porosity, roughness, and cracking, which will affect mechanical properties. The ability to accurately predict the extremely variable temperature field in detail, and relate it quantitatively to structure and properties, is a key step in predicting part performance and optimizing process design. This talk will cover the study of melt pool dynamics and microstructure evolution using mechanistic models with machine learning. Specifically, I will present a systematic modeling and experimental study on the relationship between the thermal characteristics of AM and the resultant microstructure and properties.

Introduction of speaker

Dr. Lichao Fang is a Postdoctoral Scholar in the Department of Material Science & Engineering at Stanford University. She completed her PhD studies in the Department of Mechanical Engineering at Northwestern University. Her research focuses on physical models, data-driven models, and dark-field X-ray imaging experiments in metal additive manufacturing.