Center for Functional Nanomaterials Seminar

A novel nanosensor enabling ultra-sensitive detection of antibody-ligand and receptor-ligand interactions is presented. The nanosensor detects optical and electrical molecular signatures to characterize both low-affinity (µM) and high-affinity (nM) interactions, as well as separate specific from non-specific interactions. This novel technology's name originates from the fact it uses a Self-Induced Back-Action (SIBA) mechanism for optical trapping to enable SIBA-Actuated Nanopore Electrophoresis (SANE) sensing. Our high-affinity model consisted of T-cell receptor-like antibodies engineered to target peptide-presenting Major Histocompatibility Complex ligands, representing a model of target ligands presented on the surface of cancer cells. Our low-affinity model of consisted of a natural-killer cell receptor targeting peptide-presenting receptors reconstituted from cancer cells, as a simplified model of interactions that occur during cancer immunotherapy. We were able to detect ligand-receptor complex binding at concentrations up to 1000 times lower than the free solution equilibrium binding constant (KD) for both low-affinity and high-affinity interactions, potentially offering very significant savings in expensive protein reagents. Additionally, SANE sensor measurements enabled estimation of the fast dissociation rate (koff) for the low-affinity specific ligand-receptor system, previously shown to be challenging to quantify with commercial technologies. The koff value of targeted peptide-presenting ligands is known to correlate with the subsequent activation of immune cells in vivo, suggesting the potential utility of the SANE senor as a screening tool in cancer immunotherapy. At the end of this talk I will also be presenting emerging new applications of the SANE sensing technology for the characterization of adeno-associated virus preparations for gene therapy and the label-free readout of their genetic content as a future low-cost approach to single virus DNA sequencing.

https://bnl.zoomgov.com/j/1617324438?pwd=aEtGa0hGK1dvSGJrNkRrZlhoZ3BIUT09
Meeting ID: 161 732 4438
Passcode: 241672