Protein Nanostructures Produce Self-Adjusting Hyperpolarized Magnetic Resonance Imaging Contrast through Physical Gas Partitioning
Source abstract
Abstract
Signal amplification strategies are critical for overcoming the intrinsically poor sensitivity of nuclear magnetic resonance (NMR) reporters in noninvasive molecular detection. A mechanism widely used for signal enhancement is chemical exchange saturation transfer (CEST) of nuclei between a dilute sensing pool and an abundant detection pool. However, the dependence of CEST amplification on the relative size of these spin pools confounds quantitative molecular detection with a larger detection pool typically making saturation transfer less efficient. Here we show that a recently discovered class of genetically encoded nanoscale reporters for 129Xe magnetic resonance overcomes this fundamental limitation through an elastic binding capacity for NMR-active nuclei. This approach pairs high signal amplification from hyperpolarized spins with ideal, self-adjusting saturation transfer behavior as the overall spin ensemble changes in size. These reporters are based on gas vesicles, i.e., microbe-derived, gas-filled protein nanostructures. We show that the xenon fraction that partitions into gas vesicles follows the ideal gas law, allowing the signal transfer under hyperpolarized xenon chemical exchange saturation transfer (Hyper-CEST) imaging to scale linearly with the total xenon ensemble. This conceptually distinct elastic response allows the production of quantitative signal contrast that is robust to variability in the concentration of xenon, enabling virtually unlimited improvement in absolute contrast with increased xenon delivery, and establishing a unique principle of operation for contrast agent development in emerging biochemical and in vivo applications of hyperpolarized NMR and magnetic resonance imaging.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Atomic and Subatomic Physics Research · Advanced NMR Techniques and Applications · Advanced MRI Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
California Institute of Technology, Division of Chemistry and Chemical Engineering, Pasadena, California 91125, United States
Leibniz-Forschungsinstitut für Molekulare Pharmarkologie (FMP), 13125 Berlin, Germany
Funding
Deutsche Forschungsgemeinschaft · Human Frontier Science Program
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