Label free detection of specific protein binding using a microwave sensor

Label-Free Analysis of Multivalent Protein Binding Using Bioresponsive Nanogels and Surface Plasmon Resonance (SPR)

Precise identification of protein-protein interactions is required to improve our understanding of biochemical pathways for biology and medicine. In physiology, how proteins interact with other proteins or small molecules is crucial for maintaining biological functions. For instance, multivalent protein binding (MPB), in which a ligand concurrently interacts with two or more receptors, plays a key role in regulating complex but accurate biological functions, and its interference is related to many diseases. Therefore, determining MPB and its kinetics has long been sought, which currently requires complicated procedures and instruments to distinguish multivalent binding from monovalent binding. Here, we show a method for quickly evaluating the MPB over monovalent binding and its kinetic parameters in a label-free manner. Engaging pNIPAm-co-AAc nanogels with MPB-capable moieties (e.g., PD-1 antigen and biocytin) permits a surface plasmon resonance (SPR) instrument to evaluate the MPB events by amplifying signals from the specific target molecules. Using our MPB-based method, PD-1 antibody that forms a type of MPB by complexing with two PD-1 proteins, which are currently used for cancer immunotherapy, is detectable down to a level of 10 nM. In addition, small multivalent cations (e.g., Ca²⁺, Fe²⁺, and Fe³⁺) are distinguishably measurable over monovalent cations (e.g., Na⁺ and K⁺) with the pNIPAm-co-AAc nanogels.

Noninvasive In-Situ Measurement of Blood Lactate Using Microwave Sensors

GOAL

This paper reports a novel electromagnetic sensor technique for real-time noninvasive monitoring of blood lactate in human subjects.

METHODS

The technique was demonstrated on 34 participants who undertook a cycling regime, with rest period before and after, to produce a rising and falling lactate response curve. Sensors attached to the arm and legs of participants gathered spectral data, blood samples were measured using a Lactate Pro V2; temperature and heart rate data was also collected.

RESULTS

Pointwise mutual information and neural networks are used to produce a predictive model. The model shows a good correlation between the standard invasive and novel noninvasive electromagnetic wave based blood lactate measurements, with an error of 13.4% in the range of 0-12 mmol/L.

CONCLUSION

The work demonstrates that electromagnetic wave sensors are capable of determining blood lactate level without the need for invasive blood sampling.

SIGNIFICANCE

Measurement of blood metabolites, such as blood lactate, in real-time and noninvasively in hospital environments will reduce the risk of infection, increase the frequency of measurement and ensure timely intervention only when necessary. In sports, such tools will enhance training of athletes, and enable more effecting training regimes to be prescribed.