ACS Measurement Science Au最新文献

Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.

O-glycosylation is a common post-translational modification that is essential for the defensive properties of mucus barriers. Incomplete and altered O-glycosylation is often linked to severe diseases, such as cancer, cystic fibrosis, and chronic obstructive pulmonary disease. Originating from a nontemplate-driven biosynthesis, mucin-type O-glycan structures are very complex. They are often present as heterogeneous mixtures containing multiple isomers. Therefore, the analysis of complex O-glycan mixtures usually requires hyphenation of orthogonal techniques such as liquid chromatography (LC), ion mobility spectrometry, and mass spectrometry (MS). However, MS-based techniques are mainly qualitative. Moreover, LC separation of O-glycans often lacks reproducibility and requires sophisticated data treatment and analysis. Here we present a mucin-type O-glycomics analysis workflow that utilizes hydrophilic interaction liquid chromatography for separation and fluorescence labeling for detection and quantification. In combination with mass spectrometry, a detailed analysis on the relative abundance of specific mucin-type O-glycan compositions and features, such as fucose, sialic acids, and sulfates, is performed. Furthermore, the average number of monosaccharide units of O-glycans in different samples was determined. To demonstrate universal applicability, the method was tested on mucins from different tissue types and mammals, such as bovine submaxillary mucins, porcine gastric mucins, and human milk mucins. To account for day-to-day retention time shifts in O-glycan separations and increase the comparability between different instruments and laboratories, we included fluorescently labeled dextran ladders in our workflow. In addition, we set up a library of glucose unit values for all identified O-glycans, which can be used to simplify the identification process of glycans in future analyses.

This proof-of-principle study presents the ability of the recently developed iLovEnose to measure ultratrace levels of volatile organic compounds (VOCs) in simulated human breath based on the combination of multiple gas sensors. The iLovEnose was developed by our research team as a test bed for gas sensors that can be hosted in three serially connected compact low-volume and temperature-controlled compartments. Herein, the eNose system was equipped with conventional semiconducting metal oxide (MOX) gas sensors using a variety of base technologies providing 11 different sensor signals that were evaluated to determine six VOCs of interest at eight low to ultralow concentration levels (i.e., ranging from 3 to 0.075 ppm) at humid conditions (90% rh at 22 °C). The measurements were randomized and performed four times over a period of 2 weeks. Partial least-squares regression analysis was applied to estimate the concentration of these six analytes. It was shown that the iLovEnose system is able to discriminate between these VOCs and provide reliable quantitative information relevant for future applications in exhaled breath analysis as a diagnostic disease detection or monitoring device.

Relaxation rate dispersion, i.e., nonexponential or multicomponent kinetics, is observed in complex systems when measuring relaxation kinetics. Often, the origin of rate dispersion is associated with the heterogeneity in the system. However, both homogeneous (where all molecules experience the same rate but inherently nonexponential) and heterogeneous (where all molecules experience different rates) systems can exhibit rate dispersion. A multidimensional correlation analysis method has been demonstrated to detect and quantify rate dispersion observed in molecular rotation, diffusion, solvation, and reaction kinetics. One-dimensional (1D) autocorrelation function detects rate dispersion and measures its extent. Two-dimensional (2D) autocorrelation function measures the origin of rate dispersion and distinguishes homogeneous from heterogeneous. In a heterogeneous system, implicitly there exist subensembles of molecules experiencing different rates. A three-dimensional (3D) autocorrelation function measures subensemble exchange if present and reveals if the system possesses static or dynamic heterogeneity. This perspective discusses the principles, applications, and potential and also presents a future outlook of two-dimensional fluctuation correlation spectroscopy (2D-FlucCS). The method is applicable to any experiment or simulation where a time series of fluctuation in an observable (emission, scattering, current, etc.) around a mean value can be obtained in steady state (equilibrium or nonequilibrium), provided the system is ergodic.

The development of artificial receptors has great significance in measurement science and technology. The need for a robust version of natural receptors is getting increased attention because the cost of natural receptors is still high along with storage difficulties. Aptamers, imprinted polymers, and nanozymes are some of the matured artificial receptors in analytical chemistry. Recently, a new direction has been discovered by organic chemists, who can synthesize robust, activity-based, self-immolative organic molecules that have artificial receptor properties for the targeted analytes. Specifically designed trigger moieties implant selectivity and sensitivity. These latent electrochemical redox substrates are highly stable, mass-producible, inexpensive, and eco-friendly. Combining redox substrates with the merits of electrochemical techniques is a good opportunity to establish a new direction in artificial receptors. This Review provides an overview of electrochemical redox substrate design, anatomy, benefits, and biosensing potential. A proper understanding of molecular design can lead to the development of a library of novel self-immolative redox molecules that would have huge implications for measurement science and technology.

Changes in the levels of lipid sn-positional isomers are associated with perturbation of the physiological environment within the biological system. Consequently, knowing the concentrations of these lipids holds significant importance for unraveling their involvement in disease diagnosis and pathological mechanisms. However, existing methods for lipid quantification often fall short in accuracy due to the structural diversity and isomeric forms of lipids. To address this challenge, we have developed an aziridine-based isobaric tag labeling strategy that allows (i) differentiation and (ii) enhanced relative quantification of lipid sn-positional isomers from distinct samples in a single run. The methodology enabled by aziridination, isobaric tag labeling, and lithiation has been applied to various phospholipids, enabling the determination of the sn-positions of fatty acyl chains and enhanced relative quantification. The analysis of Escherichia coli lipid extracts demonstrated the enhanced determination of the concentration ratios of lipid isomers by measuring the intensity ratios of mass reporters released from sn-positional diagnostic ions. Moreover, we applied the method to the analysis of human colon cancer plasma. Intriguingly, 17 PC lipid sn-positional isomers were identified and quantified simultaneously, and among them, 7 showed significant abundance changes in the colon cancer plasma, which can be used as potential plasma markers for diagnosis of human colon cancer.

In this study, a micro helium dielectric barrier discharge (μHDBD) plasma device fabricated using 3D printing and molding techniques was coupled with a mini spectrometer to detect and identify different classes of volatile organic compounds (VOCs) using optical emission spectrometry (OES). We tested 11 VOCs belonging to three different classes (straight-chain alkanes, aromatics, and polar organic compounds). Our results clearly demonstrate that the optical emission spectra of different classes of VOCs show clear differences, and therefore, can be used for identification. Additionally, the emission spectra of VOCs with a similar structure (such as n-pentane, n-hexane, n-heptane, n-octane, and n-nonane) have similar optical emission spectrum shape. Acetone and ethanol also have similar emission wavelengths, but they show different line intensities for the same concentrations. We also found that the side-chain group of aromatics will also affect the emission spectra even though they have a similar structure (all have a benzene ring). Moreover, our μHDBD-OES system can also identify multiple compounds in VOC mixtures. Our work also demonstrates the possibility of identifying different classes of VOCs by the OES shape.

The main cause of mortality among cancer patients is metastatic disease. Metastasis develops from cancer cells that invade the stromal tissue and intravasate the circulatory or lymphatic systems to eventually form new tumors in other organs. Blocking cancer cell invasion can potentially prevent or reduce the metastatic progression of cancers. Testing different chemical compounds against cell invasion in three-dimensional cultures is a common laboratory technique. The efficacy of the treatments is often evaluated from confocal microscopic images of the cells using image processing. However, the analysis approaches are often subject to variations and inconsistencies due to user decisions that must be made while processing each image. To overcome this limitation, we developed a fully automated method to quantify the invasion of cancer cells from a 3D tumor spheroid into the surrounding extracellular matrix. We demonstrated that this method resolves cell invasion from spheroids of different shapes and sizes and from cells that invade as a cluster or individually. We also showed that this approach can help quantify the dose-dependent anti-invasive effects of a commonly used chemotherapy drug. Our automated method significantly reduces the time and increases the consistency and accuracy of cancer cell invasion analysis in three-dimensional cultures.

Electrochemical paper-based analytical devices represent an important platform for portable, low-cost, affordable, and decentralized diagnostics. For this kind of application, chemical functionalization plays a pivotal role to ensure high clinical performance by tuning surface properties and the area of electrodes. However, controlling different surface properties of electrodes by using a single functionalization route is still challenging. In this work, we attempted to tune the wettability, chemical composition, and electroactive area of carbon-paper-based devices by thermally treating polydopamine (PDA) at different temperatures. PDA films were deposited onto pyrolyzed paper (PP) electrodes and thermally treated in the range of 300–1000 °C. After deposition of PDA, the surface is rich in nitrogen and oxygen, it is superhydrophilic, and it has a high electroactive area. As the temperature increases, the surface becomes hydrophobic, and the electroactive area decreases. The surface modifications were followed by Raman, X-ray photoelectron microscopy (XPS), laser scanning confocal microscopy (LSCM), contact angle, scanning electron microscopy (SEM-EDS), electrical measurements, transmission electron microscopy (TEM), and electrochemical experiments. In addition, the chemical composition of nitrogen species can be tuned on the surface. As a proof of concept, we employed PDA-treated surfaces to anchor [AuCl₄]⁻ ions. After electrochemical reduction, we observed that it is possible to control the size of the nanoparticles on the surface. Our route opens a new avenue to add versatility to electrochemical interfaces in the field of paper-based electrochemical biosensors.

Nitrite is a compound used as a food additive for its preservative action and coloring capability, as well as an industrial agent for its antifreezing action and for preventing corrosion, and it is also used as a pharmaceutical in cyanide detoxification therapy. However, even recently, because of its high toxicity, it has been used as a murder and suicidal agent due to its affordability and ready availability. In this technical report, we describe an electrochemical paper-based device for selectively determining nitrite in complex biofluids, such as blood, cadaveric blood, vitreous humor, serum, plasma, and urine. The approach was validated in terms of the linearity of response, selectivity, and sensitivity, and the accuracy of the determination was verified by comparing the results with a chromatographic instrumental method. A linear response was observed in the micromolar range; the sensitivity of the method expressed as the limit of detection was 0.4 μM in buffer measurements. The simplicity of use, the portability of the device, and the performance shown make the approach suitable for detecting nitrite in complex biofluids, including contexts of forensic interest, such as murders or suicides in which nitrite is used as a toxic agent. Limits of detection of ca. 1, 2, 4, 5, 3, and 4 μM were obtained in vitreous humor, urine, serum and plasma, blood, and cadaveric blood, also highlighting a satisfactory accuracy comprised between 91 and 112%.