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The surface properties of biologically active nanoparticles (NPs) are often dictated by synthetic ligands that are grafted to the NP core to form a protecting monolayer. Ligand selection is thus critical in determining NP surface properties and corresponding interactions at the nano-bio interface, which are relevant to numerous applications including drug delivery and biosensing. However, chemically specific structure–property relationships for rationally selecting ligands to achieve desired biointeractions are largely lacking. In this Focus Article, we review the challenges associated with relating ligand chemical properties to monolayer-protected NP surface properties due to the interplay of ligand–ligand, ligand–solvent, and ligand–biomolecule interactions that are difficult to anticipate. In particular, we highlight unexpected spatially varying properties that emerge even for uniformly functionalized NPs due to the fluctuations of ligands at the nanoscale. We further review the capability of physics-based molecular simulations to reveal these unexpected behaviors, providing powerful computational methods to predict NP properties. Finally, we discuss the opportunity for such simulations to be combined with machine-learning methods to guide the computational design of monolayer-protected NPs prior to synthesis.
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Mass Spectrometry Measurements of Neuropeptides: From Identification to Quantitation
Neuropeptides (NPs), a unique class of neuronal signaling molecules, participate in a variety of physiological processes and diseases. Quantitative measurements of NPs provide valuable information regarding how these molecules are differentially regulated in a multitude of neurological, metabolic, and mental disorders. Mass spectrometry (MS) has evolved to become a powerful technique for measuring trace levels of NPs in complex biological tissues and individual cells using both targeted and exploratory approaches. There are inherent challenges to measuring NPs, including their wide endogenous concentration range, transport and postmortem degradation, complex sample matrices, and statistical processing of MS data required for accurate NP quantitation. This review highlights techniques developed to address these challenges and presents an overview of quantitative MS-based measurement approaches for NPs, including the incorporation of separation methods for high-throughput analysis, MS imaging for spatial measurements, and methods for NP quantitation in single neurons.
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- Award ID(s):
- 1735252
- PAR ID:
- 10334576
- Date Published:
- Journal Name:
- Annual Review of Analytical Chemistry
- Volume:
- 15
- Issue:
- 1
- ISSN:
- 1936-1327
- Page Range / eLocation ID:
- 83 to 106
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1146/annurev-anchem-061020-022048
