ACS Measurement Science Au最新文献

Sample normalization is a crucial step in metabolomics for fair quantitative comparisons. It aims to minimize sample-to-sample variations due to differences in the total metabolite amount. When samples lack a specific metabolic quantity to accurately represent their total metabolite amounts, post-acquisition sample normalization becomes essential. Despite many proposed normalization algorithms, understanding remains limited of their differences, hindering the selection of the most suitable one for a given metabolomics study. This study bridges this knowledge gap by employing data simulation, experimental simulation, and real experiments to elucidate the differences in the mechanism and performance among common post-acquisition sample normalization methods. Using public datasets, we first demonstrated the dramatic discrepancies between the outcomes of different sample normalization methods. Then, we benchmarked six normalization methods: sum, median, probabilistic quotient normalization (PQN), maximal density fold change (MDFC), quantile, and class-specific quantile. Our results show that most normalization methods are biased when there is unbalanced data, a phenomenon where the percentages of up- and downregulated metabolites are unequal. Notably, unbalanced data can be sourced from the underlying biological differences, experimental perturbations, and metabolic interference. Beyond normalization algorithms and data structure, our study also emphasizes the importance of considering additional factors contributed by data quality, such as background noise, signal saturation, and missingness. Based on these findings, we propose an evidence-based normalization strategy to maximize sample normalization outcomes, providing a robust bioinformatic solution for advancing metabolomics research with a fair quantitative comparison.

Sample normalization is a crucial step in metabolomics for fair quantitative comparisons. It aims to minimize sample-to-sample variations due to differences in the total metabolite amount. When samples lack a specific metabolic quantity to accurately represent their total metabolite amounts, post-acquisition sample normalization becomes essential. Despite many proposed normalization algorithms, understanding remains limited of their differences, hindering the selection of the most suitable one for a given metabolomics study. This study bridges this knowledge gap by employing data simulation, experimental simulation, and real experiments to elucidate the differences in the mechanism and performance among common post-acquisition sample normalization methods. Using public datasets, we first demonstrated the dramatic discrepancies between the outcomes of different sample normalization methods. Then, we benchmarked six normalization methods: sum, median, probabilistic quotient normalization (PQN), maximal density fold change (MDFC), quantile, and class-specific quantile. Our results show that most normalization methods are biased when there is unbalanced data, a phenomenon where the percentages of up- and downregulated metabolites are unequal. Notably, unbalanced data can be sourced from the underlying biological differences, experimental perturbations, and metabolic interference. Beyond normalization algorithms and data structure, our study also emphasizes the importance of considering additional factors contributed by data quality, such as background noise, signal saturation, and missingness. Based on these findings, we propose an evidence-based normalization strategy to maximize sample normalization outcomes, providing a robust bioinformatic solution for advancing metabolomics research with a fair quantitative comparison.

Single cell Amperometry (SCA) is a powerful, sensitive, high temporal resolution electrochemical technique used to quantify secreted molecular messengers from individual cells and vesicles. This technique has been extensively applied to study the process of exocytosis, and it has also been applied, albeit less frequently, to investigate insulin exocytosis from single pancreatic beta cells. Insufficient insulin release can lead to diabetes, a chronic lifestyle disorder that affects millions of people worldwide. This review aims to summarize and highlight electrochemical measurements of insulin via monitoring its secretion from beta cells by SCA with micro- and nanoelectrodes since the 1990s and to explain how and why serotonin is used as a proxy for monitoring insulin during exocytosis from single beta cells. Finally, we describe how the combination of SCA measurements with the intracellular vesicle impact electrochemical cytometry (IVIEC) technique has led to important findings regarding fractional release types in beta cells. These findings, reported recently, have opened a new window in the study of pore formation, exocytosis from single vesicles, and the mechanisms of insulin secretion. This sensitive cellular electroanalysis approach should help in the development of novel therapeutic strategies targeting diabetes in the future.

The gas-liquid-solid interface plays a crucial role in various electrochemical energy conversion devices, including fuel cells and electrolyzers. Understanding the effect of gas transfer on the electrochemistry at this three-phase interface is a grand challenge. Scanning electrochemical cell microscopy (SECCM) is an emerging technique for mapping the heterogeneity in electrochemical activity; it also inherently features a three-phase boundary at the nanodroplet cell. Herein, we quantitatively analyze the role of the three-phase boundary in SECCM involving gas via finite element simulation. Oxygen reduction reaction is used as an example for reaction with a gas reactant, which shows that interfacial gas transfer can enhance the overall mass transport of reactant, allowing measuring current density of several A/cm². The hydrogen evolution reaction is used as an example for reaction with a gas product, and fast interfacial gas transfer kinetics can significantly reduce the concentration of dissolved gas near the electrode. This helps to measure electrode kinetics at a high current density without the complication of gas bubble formation. The contribution of interfacial gas transfer can be understood by directly comparing its kinetics to the mass transfer coefficient from the solution. Our findings aid the quantitative application of SECCM in studying electrochemical reactions involving gases, establishing a basis for investigating electrochemistry at the three-phase boundary.

Single cell Amperometry (SCA) is a powerful, sensitive, high temporal resolution electrochemical technique used to quantify secreted molecular messengers from individual cells and vesicles. This technique has been extensively applied to study the process of exocytosis, and it has also been applied, albeit less frequently, to investigate insulin exocytosis from single pancreatic beta cells. Insufficient insulin release can lead to diabetes, a chronic lifestyle disorder that affects millions of people worldwide. This review aims to summarize and highlight electrochemical measurements of insulin via monitoring its secretion from beta cells by SCA with micro- and nanoelectrodes since the 1990s and to explain how and why serotonin is used as a proxy for monitoring insulin during exocytosis from single beta cells. Finally, we describe how the combination of SCA measurements with the intracellular vesicle impact electrochemical cytometry (IVIEC) technique has led to important findings regarding fractional release types in beta cells. These findings, reported recently, have opened a new window in the study of pore formation, exocytosis from single vesicles, and the mechanisms of insulin secretion. This sensitive cellular electroanalysis approach should help in the development of novel therapeutic strategies targeting diabetes in the future.

The gas–liquid–solid interface plays a crucial role in various electrochemical energy conversion devices, including fuel cells and electrolyzers. Understanding the effect of gas transfer on the electrochemistry at this three-phase interface is a grand challenge. Scanning electrochemical cell microscopy (SECCM) is an emerging technique for mapping the heterogeneity in electrochemical activity; it also inherently features a three-phase boundary at the nanodroplet cell. Herein, we quantitatively analyze the role of the three-phase boundary in SECCM involving gas via finite element simulation. Oxygen reduction reaction is used as an example for reaction with a gas reactant, which shows that interfacial gas transfer can enhance the overall mass transport of reactant, allowing measuring current density of several A/cm². The hydrogen evolution reaction is used as an example for reaction with a gas product, and fast interfacial gas transfer kinetics can significantly reduce the concentration of dissolved gas near the electrode. This helps to measure electrode kinetics at a high current density without the complication of gas bubble formation. The contribution of interfacial gas transfer can be understood by directly comparing its kinetics to the mass transfer coefficient from the solution. Our findings aid the quantitative application of SECCM in studying electrochemical reactions involving gases, establishing a basis for investigating electrochemistry at the three-phase boundary.

The COVID-19 outbreak has led to notable developments in point-of-care (POC) diagnostic devices, as they can be valuable resources in identifying and managing the spread of the pandemic. Currently, the majority of techniques demand advanced laboratory equipment and professionals to execute precise, efficient, accurate, and sensitive testing. In this work, we report a new method to significantly enhance the sensitivity of microwave sensing of the SARS-CoV-2 virus by functionalizing the sensor surface using anti-SARS-CoV-2 spike antibody-gold nanoparticle (AuNPs) conjugates. AuNPs were surface-functionalized with the antispike antibody by EDC/NHS chemistry via PEG as a linker to form the conjugate (Ab-PEG-AuNPs). The Ab-PEG-AuNPs nanoconjugate was then coated onto the sensor through APTES and used for selectively capturing the spike protein on the SARS-CoV-2 virus. The sensing performance of the modified sensor was demonstrated via both experimental measurements and numerical simulations. Our sensor exhibited high sensitivity, achieving a limit of detection of 1,000 copies/mL of the SARS-CoV-2 virus within a 60 min time frame while requiring a minimal sample volume of 100 μL. The sensor exhibits outstanding specificity in distinguishing SARS-CoV-2 from other viruses, including influenza A and B, SARS-CoV-1, and MERS-CoV. Overall, this sensor provides a sensitive and label-free alternative for COVID-19 POC diagnosis.

The COVID-19 outbreak has led to notable developments in point-of-care (POC) diagnostic devices, as they can be valuable resources in identifying and managing the spread of the pandemic. Currently, the majority of techniques demand advanced laboratory equipment and professionals to execute precise, efficient, accurate, and sensitive testing. In this work, we report a new method to significantly enhance the sensitivity of microwave sensing of the SARS-CoV-2 virus by functionalizing the sensor surface using anti-SARS-CoV-2 spike antibody-gold nanoparticle (AuNPs) conjugates. AuNPs were surface-functionalized with the antispike antibody by EDC/NHS chemistry via PEG as a linker to form the conjugate (Ab-PEG-AuNPs). The Ab-PEG-AuNPs nanoconjugate was then coated onto the sensor through APTES and used for selectively capturing the spike protein on the SARS-CoV-2 virus. The sensing performance of the modified sensor was demonstrated via both experimental measurements and numerical simulations. Our sensor exhibited high sensitivity, achieving a limit of detection of 1,000 copies/mL of the SARS-CoV-2 virus within a 60 min time frame while requiring a minimal sample volume of 100 μL. The sensor exhibits outstanding specificity in distinguishing SARS-CoV-2 from other viruses, including influenza A and B, SARS-CoV-1, and MERS-CoV. Overall, this sensor provides a sensitive and label-free alternative for COVID-19 POC diagnosis.

Detecting medically important biomarkers in complex biological samples without prior treatment or extraction poses a major challenge in biomedical analysis. Electrochemical methods, specifically electrochemiluminescence (ECL), show potential due to their high sensitivity, minimal background noise, and straightforward operation. This study investigates the ECL performance of screen-printed electrodes (SPEs) modified with the conductive polymer poly(3,4-ethylenedioxythiophene) (PEDOT) and its derivatives for dopamine (DA) detection. PEDOT modification significantly enhances ECL intensity, improves sensitivity, and expands the linear range for DA detection. Functionalizing PEDOT with ethylene glycol (EG) further enhances stability, specificity, and resistance to interferences for DA detection. These modified SPEs demonstrate the linear range of 1-200 μM and a detection limit as low as 0.887 nM (S/N = 3), surpassing many previous studies using SPEs. Moreover, the PEDOT-EG₄-OMe-modified SPEs can reliably detect DA in solutions with high protein concentrations or artificial cerebrospinal fluid. These results suggest that the PEDOT derivative-modified SPE can serve as reusable and sensitive DA sensors in complex biological environments, highlighting the potential of the ECL system for a range of challenging applications.

Assessing the quality of wheat, one of humanity’s most important crops, in a straightforward manner, is essential. In this study, analysis of variance (ANOVA) simultaneous component analysis (ASCA) paired with near-infrared spectroscopy (NIRS) was used as an easy-to-implement and environmentally friendly tool for this purpose. The capabilities of combining NIRS with ASCA were demonstrated by studying the effects of sampling site and year on the quality of 180 Austrian wheat samples across four sites over 3 years. It was found that the year, sample site, and their combination significantly (p < 0.001) affect the NIR spectra of wheat. NIR spectral preprocessing tools, usually employed in chemometric workflows, notably influence the results obtained by ASCA, particularly in terms of the variance attributed to annual and regional effects. The influence of the year was identified as the dominant factor, followed by region and the combined effect of year and sampling site. Interpretation of the loading plots obtained by ASCA demonstrates that wheat components such as proteins, carbohydrates, moisture, or fat contribute to annual and regional differences. Additionally, the protein, starch, moisture, fat, fiber, and ash contents of wheat samples obtained using a NIR-based calibration were found to be significantly influenced by year, sampling site, or their combination using ANOVA. This study shows that the combination of ASCA with NIRS simplifies NIR-based quality assessment of wheat without the need for time- and chemical-consuming calibration development.