Real-Time Quantitative In-Cell NMR: Ligand Binding and Protein Oxidation Monitored in Human Cells Using Multivariate Curve Resolution.
Enrico LuchinatLetizia BarbieriTimothy F CampbellLucia BanciPublished in: Analytical chemistry (2020)
In-cell NMR can investigate protein conformational changes at atomic resolution, such as those changes induced by drug binding or chemical modifications, directly in living human cells, and therefore has great potential in the context of drug development as it can provide an early assessment of drug potency. NMR bioreactors can greatly improve the cell sample stability over time and, more importantly, allow for recording in-cell NMR data in real time to monitor the evolution of intracellular processes, thus providing unique insights into the kinetics of drug-target interactions. However, current implementations are limited by low cell viability at >24 h times, the reduced sensitivity compared to "static" experiments and the lack of protocols for automated and quantitative analysis of large amounts of data. Here, we report an improved bioreactor design which maintains human cells alive and metabolically active for up to 72 h, and a semiautomated workflow for quantitative analysis of real-time in-cell NMR data relying on Multivariate Curve Resolution. We apply this setup to monitor protein-ligand interactions and protein oxidation in real time. High-quality concentration profiles can be obtained from noisy 1D and 2D NMR data with high temporal resolution, allowing further analysis by fitting with kinetic models. This unique approach can therefore be applied to investigate complex kinetic behaviors of macromolecules in a cellular setting, and could be extended in principle to any real-time NMR application in live cells.
Keyphrases
- high resolution
- magnetic resonance
- single cell
- solid state
- cell therapy
- electronic health record
- single molecule
- big data
- binding protein
- data analysis
- emergency department
- machine learning
- protein protein
- stem cells
- oxidative stress
- hydrogen peroxide
- molecular dynamics
- transcription factor
- mass spectrometry
- deep learning
- bone marrow
- signaling pathway