Assessing the Performance of 1D-Convolution Neural Networks to Predict Concentration of Mixture Components from Raman Spectra

An emerging application of Raman spectroscopy is monitoring the state of chemical reactors during biologic drug production. Raman shift intensities scale linearly with the concentrations of chemical species and thus can be used to analytically determine real-time concentrations using non-destructive light irradiation in a label-free manner. Chemometric algorithms are used to interpret Raman spectra produced from complex mixtures of bioreactor contents as a reaction evolves. Finding the optimal algorithm for a specific bioreactor environment is challenging due to the lack of freely available Raman mixture datasets. The RaMix Python package addresses this challenge by enabling the generation of synthetic Raman mixture datasets with controllable noise levels to assess the utility of different chemometric algorithm types for real-time monitoring applications. To demonstrate the capabilities of this package and compare the performance of different chemometric algorithms, 48 datasets of simulated spectra were generated using the RaMix Python package. The four tested algorithms include partial least squares regression (PLS), a simple neural network, a simple convolutional neural network (simple CNN), and a 1D convolutional neural network with a ResNet architecture (ResNet). The performance of the PLS and simple CNN model was found to be comparable, with the PLS algorithm slightly outperforming the other models on 83\% of the data sets. The simple CNN model outperforms the other models on large, high noise datasets, demonstrating the superior capability of convolutional neural networks compared to PLS in analyzing noisy spectra. These results demonstrate the promise of CNNs to automatically extract concentration information from unprocessed, noisy spectra, allowing for better process control of industrial drug production. Code for this project is available at github.com/DexterAntonio/RaMix.Comment: 7 pages, 7 figure

Enhancing Radiotherapy by Lipid Nanocapsule-Mediated Delivery of Amphiphilic Gold Nanoparticles to Intracellular Membranes

Amphiphilic gold nanoparticles (amph-NPs), composed of gold cores surrounded by an amphiphilic mixed organic ligand shell, are capable of embedding within and traversing lipid membranes. Here we describe a strategy using crosslink-stabilized lipid nanocapsules (NCs) as carriers to transport such membrane-penetrating particles into tumor cells and promote their transfer to intracellular membranes for enhanced radiotherapy of cancer. We synthesized and characterized interbilayer-crosslinked multilamellar lipid vesicles (ICMVs) carrying amph-NPs embedded in the capsule walls, forming Au-NCs. Confocal and electron microscopies revealed that the intracellular distribution of amph-NPs within melanoma and breast tumor cells following uptake of free particles vs Au-NCs was quite distinct and that amph-NPs initially delivered into endosomes by Au-NCs transferred over a period of hours to intracellular membranes through tumor cells, with greater intracellular spread in melanoma cells than breast carcinoma cells. Clonogenic assays revealed that Au-NCs enhanced radiotherapeutic killing of melanoma cells. Thus, multilamellar lipid capsules may serve as an effective carrier to deliver amphiphilic gold nanoparticles to tumors, where the membrane-penetrating properties of these materials can significantly enhance the efficacy of frontline radiotherapy treatments.United States. Army Research Office (Contract W911NF-13-D-0001)United States. Army Research Office (Contract W911NF-07-D-0004

Recent developments in biosensing methods for extracellular vesicle protein characterization

Research into extracellular vesicles (EVs) has grown significantly over the last few decades with EVs being widely regarded as a source of biomarkers for human health and disease with massive clinical potential. Secreted by every cell type in the body, EVs report on the internal cellular conditions across all tissue types. Their presence in readily accessible biofluids makes the potential of EV biosensing highly attractive as a noninvasive diagnostic platform via liquid biopsies. However, their small size (50-250 nm), inherent heterogeneity, and the complexity of the native biofluids introduce challenges for effective characterization, thus, limiting their clinical utility. This has led to a surge in the development of various novel EV biosensing techniques, with capabilities beyond those of conventional methods that have been directly transferred from cell biology. In this review, key detection principles used for EV biosensing are summarized, with a focus on some of the most recent and fundamental developments in the field over the last 5 years. This article is categorized under: Diagnostic Tools > Biosensing Diagnostic Tools > In Vitro Nanoparticle-Based Sensing

Determination of nanoparticle size distribution together with density or molecular weight by 2D analytical ultracentrifugation

Nanoparticles are finding many research and industrial applications, yet their characterization remains a challenge. Their cores are often polydisperse and coated by a stabilizing shell that varies in size and composition. No single technique can characterize both the size distribution and the nature of the shell. Advances in analytical ultracentrifugation allow for the extraction of the sedimentation (s) and diffusion coefficients (D). Here we report an approach to transform the s and D distributions of nanoparticles in solution into precise molecular weight (M), density (ρP) and particle diameter (dp) distributions. M for mixtures of discrete nanocrystals is found within 4% of the known quantities. The accuracy and the density information we achieve on nanoparticles are unparalleled. A single experimental run is sufficient for full nanoparticle characterization, without the need for standards or other auxiliary measurements. We believe that our method is of general applicability and we discuss its limitations

Orthogonal analysis reveals inconsistencies in cargo loading of extracellular vesicles

Since extracellular vesicles (EVs) have emerged as a promising drug delivery system, diverse methods have been used to load them with active pharmaceutical ingredients (API) in preclinical and clinical studies. However, there is yet to be an engineered EV formulation approved for human use, a barrier driven in part by the intrinsic heterogeneity of EVs. API loading is rarely assessed in the context of single vesicle measurements of physicochemical properties but is likely administered in a heterogeneous fashion to the detriment of a consistent product. Here, we applied a suite of single-particle resolution methods to determine the loading of rhodamine 6G (R6G) surrogate cargo mimicking hydrophilic small molecule drugs across four common API loading methods: sonication, electroporation, freeze-thaw cycling and passive incubation. Loading efficiencies and alterations in the physical properties of EVs were assessed, as well as co-localization with common EV-associated tetraspanins (i.e., CD63, CD81 and CD9) for insight into EV subpopulations. Sonication had the highest loading efficiency, yet significantly decreased particle yield, while electroporation led to the greatest number of loaded API particles, albeit at a lower efficiency. Moreover, results were often inconsistent between repeated runs within a given method, demonstrating the difficulty in developing a rigorous loading method that consistently loaded EVs across their heterogeneous subpopulations. This work highlights the significance of how chosen quantification metrics can impact apparent conclusions and the importance of single-particle characterization of EV loading

Effect of Particle Diameter and Surface Composition on the Spontaneous Fusion of Monolayer-Protected Gold Nanoparticles with Lipid Bilayers

Anionic, monolayer-protected gold nanoparticles (AuNPs) have been shown to nondisruptively penetrate cellular membranes. Here, we show that a critical first step in the penetration process is potentially the fusion of such AuNPs with lipid bilayers. Free energy calculations, experiments on unilamellar and multilamellar vesicles, and cell studies all support this hypothesis. Furthermore, we show that fusion is only favorable for AuNPs with core diameters below a critical size that depends on the monolayer composition.National Science Foundation (U.S.). Graduate Research Fellowship ProgramNational Science Foundation (U.S.). Materials Research Science and Engineering Centers (Program) (Award DMR-0819762)National Cancer Institute (U.S.) (Award U54CA143874)United States. Army Research Office (Contract W911NF-13-D-0001)United States. Army Research Office (Contract W911NF-07-D-0004, T.O. 8

Nanoplasmonic Approaches for Sensitive Detection and Molecular Characterization of Extracellular Vesicles

All cells release a multitude of nanoscale extracellular vesicles (nEVs) into circulation, offering immense potential for new diagnostic strategies. Yet, clinical translation for nEVs remains a challenge due to their vast heterogeneity, our insufficient ability to isolate subpopulations, and the low frequency of disease-associated nEVs in biofluids. The growing field of nanoplasmonics is poised to address many of these challenges. Innovative materials engineering approaches based on exploiting nanoplasmonic phenomena, i.e., the unique interaction of light with nanoscale metallic materials, can achieve unrivaled sensitivity, offering real-time analysis and new modes of medical and biological imaging. We begin with an introduction into the basic structure and function of nEVs before critically reviewing recent studies utilizing nanoplasmonic platforms to detect and characterize nEVs. For the major techniques considered, surface plasmon resonance (SPR), localized SPR, and surface enhanced Raman spectroscopy (SERS), we introduce and summarize the background theory before reviewing the studies applied to nEVs. Along the way, we consider notable aspects, limitations, and considerations needed to apply plasmonic technologies to nEV detection and analysis

Synthesis and Characterization of Janus Gold Nanoparticles

When gold nanoparticles are coated with binary mixtures of dislike ligand molecules, separation in the ligand shell occurs; if the particles are smaller than a threshold size the separation is solely enthalpy driven leading to the spontaneous formation of Janus particles

Superparamagnetic Nanoparticles as High Efficiency Magnetic Resonance Imaging T-2 Contrast Agent

Nanoparticle-based magnetic resonance imaging T-2 negative agents are of great interest, and much effort is devoted to increasing cell loading capability while maintaining low cytotoxicity. Herein, two classes of mixed-ligand protected magnetic-responsive, bimetallic gold/iron nano particles (Au/Fe NPs) synthesized by a two-step method are presented. Their structure, surface composition, and magnetic properties are characterized. The two classes of sulfonated Au/Fe NPs, with an average diameter of 4 nm, have an average atomic ratio of Au to Fe equal to 7 or 8, which enables the Au/Fe NPs to be superparamagnetic with a blocking temperature of 56 K and 96 K. Furthermore, preliminary cellular studies reveal that both Au/Fe NPs show very limited toxicity. MRI phantom experiments show that r(2)/r(1) ratio of Au/Fe NPs is as high as 670, leading to a 66% reduction in T-2 relaxation time. These nanoparticles provide great versatility and potential for nanopartide-based diagnostics and therapeutic applications and as imaging contrast agents