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Detection of Metabolites Near or Inside Cells with Machine-learning Driven SERS Optophysiology
Wednesday, March 10, 2021: 9:05 AM - 9:40 AM
Speaker(s)
Description
We recently introduced the concept of surface enhanced Raman scattering (SERS) optophysiology, which combines a SERS nanosensor on the tip of a pulled fiber to provide spatially and temporally specific molecular information near or inside biological material. To accomplish this, a SERS nanofiber decorated with a dense and well dispersed array of Au NP has been developed for the measurement of neurotransmitters and other metabolites in proximity of cells. The nanosensors are thus highly compatible with current physiology experiments also relying on similar nanosensors based on electrochemistry and electrophysiology. Specifically, we will show that the SERS optophysiology nanosensor can measure a panel of metabolites near cells in a single experiment. The SERS spectra of these neurotransmitters were identified with a barcoding data processing method, processed with TensorFlow using a convolutional neural network architecture. This machine-learning driven data processing significantly improved the positive assignment rates for a series of metabolites and allows for complex measurements of the cell’s biochemistry. In addition to these untargeted SERS nanosensors, we also designed molecularly specific sensors to measure pH, H2O2 and heavy metals inside cells using the same nanosensor architecture. This suite of SERS nanosensor will open the door to survey molecular changes in proximity of healthy and diseased cells.
Additional Info
Keywords: Please select up to 4 keywords ONLY:
Data Analysis and Manipulation,Instrumentation,Raman/Surface Enhanced Raman Spectroscopy
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