Taking Microcavity Label-Free Single Molecule Detection Deep into the Protein Realm: Cancer Marker Detection at the Ultimate Sensitivity
Finding a method for label-free sensing of individual bio-nanoparticles is considered the “Holy Grail” in the bio-sensing field. An ideal technology that could do this would be able to follow the sensing of biological antigen-antibody interactions in their native form and in real-time without interfering tags. It has been over 10 years since the possibility of label-free microcavity detection of single virus or single protein binding by a reactive frequency shift was theorized, and over 5 years since the non-specific detection and sizing of Influenza A (InfA) was demonstrated using this mechanism. The signal to noise ratio in that experiment was only 3. Detecting the smallest virus MS2 with a mass only one hundredth of InfA, therefore seemed hopeless. The prospect of detecting an intermediate size protein such BSA was anticipated to be even further out of reach, since its mass is 5,000× smaller than InfA. However, within the last 2 years both were detected with an extraordinary microcavity that marries micro-cavity photonics with nano-plasmonic receptors. The following article chronicles this advance.
KeywordsSingle molecule detection Whispering gallery mode Plasmonic enhancement Microcavity Gold nanoshell Biosensing Reactive sensing principle Smallest virus Cancer marker Microresonator Ring resonator WGM LSP Plasmonic epitope WGM-nanoplasmonic-hybrid resonator WGM-nph Label-free single molecule detection Thyroglobulin Bovine serum albumin BSA
Steve Arnold thanks Rino DiBartolo for his kind invitation to lecture at this NATO advanced study institute. He would also like to think all of the other instructors for teaching him about their research and especially for introducing him to the interesting world of plasmonics. In this respect he would also like to especially thank Mark Stockman for many conversations over pasta and wine. The research described herein was supported by the National Science Foundation grants CBET 0933531 and EECS 1303499.
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