Extracellular vesicles (EVs) secreted by cancer cells provide an important insight into cancer biology and could be leveraged to enhance diagnostics and disease monitoring. This paper details a high-throughput label-free extracellular vesicle analysis approach to study fundamental EV biology, toward diagnosis and monitoring of cancer in a minimally invasive manner and with the elimination of interpreter bias. We present the next generation of our single particle automated Raman trapping analysis─SPARTA─system through the development of a dedicated standalone device optimized for single particle analysis of EVs. Our visualization approach, dubbed dimensional reduction analysis (DRA), presents a convenient and comprehensive method of comparing multiple EV spectra. We demonstrate that the dedicated SPARTA system can differentiate between cancer and noncancer EVs with a high degree of sensitivity and specificity (>95% for both). We further show that the predictive ability of our approach is consistent across multiple EV isolations from the same cell types. Detailed modeling reveals accurate classification between EVs derived from various closely related breast cancer subtypes, further supporting the utility of our SPARTA-based approach for detailed EV profiling.

Noble metal nanoparticles (NMNPs) have become firmly established as effective agents to detect various biomolecules with extremely high sensitivity. This ability stems from the collective oscillation of free electrons and extremely large electric field enhancement under exposure to light, leading to various light–matter interactions such as localized surface plasmon resonance (LSPR) and surface-enhanced Raman scattering. A remarkable feature of NMNPs is their customizability by mechanisms such as particle etching, growth, and aggregation/dispersion, yielding distinct color changes and excellent opportunities for colorimetric biosensing in user-friendly assays and devices. They are readily functionalized with a large variety of capping agents and biomolecules, with resultant bioconjugates often possessing excellent biocompatibility, which can be used to quantitatively detect analytes from physiological fluids. Furthermore, they can possess excellent catalytic properties that can achieve significant signal amplification through mechanisms such as the catalytic transformation of colorless substrates to colored reporters. The various excellent attributes of NMNP biosensors have put them in the spotlight for developing high-performance in vitro diagnostic (IVD) devices that are particularly well-suited to mitigate the societal threat that infectious diseases pose. This threat continues to dominate the global health care landscape, claiming millions of lives annually. NMNP IVDs possess the potential to sensitively detect infections even at very early stages with affordable and field-deployable devices, which will be key to strengthening infectious disease management. This has been the major focal point of current research, with a view to new avenues for early multiplexed detection of infectious diseases with portable devices such as smartphones, especially in resource-limited settings. In this Account, we provide an overview of our original inspiration and efforts in NMNP-based assay development, together with some more sophisticated IVD assays by ourselves and many others. Our work in the area has led to our recent efforts in developing IVDs for high-profile infectious diseases, including Ebola and HIV. We emphasize that integration with digital platforms represents an opportunity to establish and efficiently manage widespread testing, tracking, epidemiological intelligence, and data sharing backed by community participation. We highlight how digital technologies can address the limitations of conventional diagnostic technologies at the point of care (POC) and how they may be used to abate and contain the spread of infectious diseases. Finally, we focus on more recent integrations of noble metal nanoparticles with Raman spectroscopy for accurate, noninvasive POC diagnostics with improved sensitivity and specificity.

Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a nondestructive, label-free manner. Many applications entail the unmixing of signals from mixtures of molecular species to identify the individual components present and their proportions, yet conventional methods for chemometrics often struggle with complex mixture scenarios encountered in practice. Here, we develop hyperspectral unmixing algorithms based on autoencoder neural networks, and we systematically validate them using both synthetic and experimental benchmark datasets created in-house. Our results demonstrate that unmixing autoencoders provide improved accuracy, robustness, and efficiency compared to standard unmixing methods. We also showcase the applicability of autoencoders to complex biological settings by showing improved biochemical characterization of volumetric Raman imaging data from a monocytic cell.

.: Ammonia emission reduces the reliability and nitrogen (N) fertilizer efficiency of animal manure and mineral fertilizers applied to fields. The loss of ammonia to the atmosphere is frequently compensated for by costly over-application of N fertilizers. New technologies to reduce ammonia emission are regularly developed, and their efficacy needs to be tested using accurate methods. To date, a major obstacle to many available emission measurement techniques is the requirement of large plot sizes of homogeneous surface characteristics, which particularly is a challenge to the number of plot-level replicates that can be carried out on a field providing uniform surface characteristics throughout. The objectives of this research were to test three different methods for measuring NH₃ flux when applied to small plots (<315 m²) by comparison with conventional micrometeorological methods and to determine the labor intensity and expenses related to the respective methods in their entirety. The integrated horizontal flux (IHF) method and the ZINST method were used with passive flux Leuning samplers as micrometeorological reference methods. As examples of conventional small-plot emission measurement techniques, wind tunnels measuring gas-phase ammonia using ALPHA passive diffusion samplers and a flux chamber method using Dräger tubes for measurements of ammonia concentration (DTM) were used. As an inexpensive alternative small-plot method, we studied the feasibility of applying ALPHA passive diffusion samplers and battery-driven cup anemometers at ZINST height on small source areas (<315 m²), coupled with a backward Lagrangian stochastic (bLS) dispersion model to calculate emission fluxes (referred to as the AbLS method). When exposure duration was appropriate and weather conditions were not extreme, tests showed no significant difference in NH₃ emission fluxes measured with AbLS, compared to those obtained with IHF and ZINST using Leuning samplers. However, the AbLS method did not give reliable emission measurements in periods with high wind speeds and heavy rain. It was also shown that the AbLS method provided valid results when reducing the plot radius from the standard 20 m to 10 m, or even 5 m, provided that the ALPHA samplers were exposed for at least 5 or 6 h. Emission from 200 kg urea-N ha^-1 was between 20 and 30 kg N ha^-1 in the two trials. The cost for one study running for one week using the ZINST or bLS methodology, including equipment for four plots and eight measurement intervals, was $2785 if horizontal fluxes were measured using the ALPHA samplers, compared to $12,301 using the Leuning samplers and $13,928 using gas washing bottles. Using the DTM flux chamber method once is a little more expensive than using the AbLS method, but less expensive if the cost of purchasing the equipment is distributed over five studies in five years. Using wind tunnels is as costly as measuring emissions with the Leuning samplers or gas washing bottles using the bLS or ZINST method. Keywords: ALPHA samplers, Ammonia emission, AbLS, bLS method, DTM method, IHF method, Labor cost, Passive ammonia samplers, Wind tunnels.