Pub Date : 2024-10-21DOI: 10.1021/acsmeasuresciau.4c0003510.1021/acsmeasuresciau.4c00035
Jian Yu, Haidy Metwally, Jennifer Kolwich, Hailey Tomm, Martin Kaufmann, Rachel Klotz, Chang Liu, J. C. Yves Le Blanc, Thomas R. Covey, John Rudan, Avena C. Ross and Richard D. Oleschuk*,
Ambient mass spectrometry (MS) technologies have been applied to spatial metabolomic profiling of various samples in an attempt to both increase analysis speed and reduce the length of sample preparation. Recent studies, however, have focused on improving the spatial resolution of ambient approaches. Finer resolution requires greater analysis times and commensurate computing power for more sophisticated data analysis algorithms and larger data sets. Higher resolution provides a more detailed molecular picture of the sample; however, for some applications, this is not required. A liquid microjunction surface sampling probe (LMJ-SSP) based MS platform combined with unsupervised multivariant analysis based hyperspectral visualization is demonstrated for the metabolomic analysis of marine bacteria from the genus Pseudoalteromonas to create a rapid and robust spatial profiling workflow for microbial natural product screening. In our study, metabolomic profiles of different Pseudoalteromonas species are quickly acquired without any sample preparation and distinguished by unsupervised multivariant analysis. Our robust platform is capable of automated direct sampling of microbes cultured on agar without clogging. Hyperspectral visualization-based rapid spatial profiling provides adequate spatial metabolite information on microbial samples through red–green–blue (RGB) color annotation. Both static and temporal metabolome differences can be visualized by straightforward color differences and differentiating m/z values identified afterward. Through this approach, novel analogues and their potential biosynthetic pathways are discovered by applying results from the spatial navigation to chromatography-based metabolome annotation. In this current research, LMJ-SSP is shown to be a robust and rapid spatial profiling method. Unsupervised multivariant analysis based hyperspectral visualization is proven straightforward for facile/rapid data interpretation. The combination of direct analysis and innovative data visualization forms a powerful tool to aid the identification/interpretation of interesting compounds from conventional metabolomics analysis.
{"title":"Rapid and Robust Workflows Using Different Ionization, Computation, and Visualization Approaches for Spatial Metabolome Profiling of Microbial Natural Products in Pseudoalteromonas","authors":"Jian Yu, Haidy Metwally, Jennifer Kolwich, Hailey Tomm, Martin Kaufmann, Rachel Klotz, Chang Liu, J. C. Yves Le Blanc, Thomas R. Covey, John Rudan, Avena C. Ross and Richard D. Oleschuk*, ","doi":"10.1021/acsmeasuresciau.4c0003510.1021/acsmeasuresciau.4c00035","DOIUrl":"https://doi.org/10.1021/acsmeasuresciau.4c00035https://doi.org/10.1021/acsmeasuresciau.4c00035","url":null,"abstract":"<p >Ambient mass spectrometry (MS) technologies have been applied to spatial metabolomic profiling of various samples in an attempt to both increase analysis speed and reduce the length of sample preparation. Recent studies, however, have focused on improving the spatial resolution of ambient approaches. Finer resolution requires greater analysis times and commensurate computing power for more sophisticated data analysis algorithms and larger data sets. Higher resolution provides a more detailed molecular picture of the sample; however, for some applications, this is not required. A liquid microjunction surface sampling probe (LMJ-SSP) based MS platform combined with unsupervised multivariant analysis based hyperspectral visualization is demonstrated for the metabolomic analysis of marine bacteria from the genus <i>Pseudoalteromonas</i> to create a rapid and robust spatial profiling workflow for microbial natural product screening. In our study, metabolomic profiles of different <i>Pseudoalteromonas</i> species are quickly acquired without any sample preparation and distinguished by unsupervised multivariant analysis. Our robust platform is capable of automated direct sampling of microbes cultured on agar without clogging. Hyperspectral visualization-based rapid spatial profiling provides adequate spatial metabolite information on microbial samples through red–green–blue (RGB) color annotation. Both static and temporal metabolome differences can be visualized by straightforward color differences and differentiating <i>m</i>/<i>z</i> values identified afterward. Through this approach, novel analogues and their potential biosynthetic pathways are discovered by applying results from the spatial navigation to chromatography-based metabolome annotation. In this current research, LMJ-SSP is shown to be a robust and rapid spatial profiling method. Unsupervised multivariant analysis based hyperspectral visualization is proven straightforward for facile/rapid data interpretation. The combination of direct analysis and innovative data visualization forms a powerful tool to aid the identification/interpretation of interesting compounds from conventional metabolomics analysis.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"4 6","pages":"668–677 668–677"},"PeriodicalIF":4.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsmeasuresciau.4c00035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-21eCollection Date: 2024-12-18DOI: 10.1021/acsmeasuresciau.4c00048
Jian Li, Jin Li, Xing-Hua Xia
Plasmon-enhanced infrared (IR) techniques have garnered significant interest for their ability to achieve greatly more sensitive IR detection than conventional surface enhanced IR techniques. However, the difficulty in electrically connecting antennas has limited their application in IR spectroelectrochemistry, a crucial field for catalysis, analysis, and energy storage. Recent technical advancements have enabled the successful application of electrochemical potentials to antennas, making plasmon-enhanced IR spectroelectrochemistry feasible. This perspective aims to summarize the latest strategies and offer insights into future improvements for better design of plasmon enhanced IR spectroelectrochemistry platforms and understanding of IR spectroelectrochemistry.
{"title":"Plasmon Enhanced IR Spectroelectrochemistry.","authors":"Jian Li, Jin Li, Xing-Hua Xia","doi":"10.1021/acsmeasuresciau.4c00048","DOIUrl":"10.1021/acsmeasuresciau.4c00048","url":null,"abstract":"<p><p>Plasmon-enhanced infrared (IR) techniques have garnered significant interest for their ability to achieve greatly more sensitive IR detection than conventional surface enhanced IR techniques. However, the difficulty in electrically connecting antennas has limited their application in IR spectroelectrochemistry, a crucial field for catalysis, analysis, and energy storage. Recent technical advancements have enabled the successful application of electrochemical potentials to antennas, making plasmon-enhanced IR spectroelectrochemistry feasible. This perspective aims to summarize the latest strategies and offer insights into future improvements for better design of plasmon enhanced IR spectroelectrochemistry platforms and understanding of IR spectroelectrochemistry.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"4 6","pages":"606-614"},"PeriodicalIF":4.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11659986/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-21eCollection Date: 2024-12-18DOI: 10.1021/acsmeasuresciau.4c00035
Jian Yu, Haidy Metwally, Jennifer Kolwich, Hailey Tomm, Martin Kaufmann, Rachel Klotz, Chang Liu, J C Yves Le Blanc, Thomas R Covey, John Rudan, Avena C Ross, Richard D Oleschuk
Ambient mass spectrometry (MS) technologies have been applied to spatial metabolomic profiling of various samples in an attempt to both increase analysis speed and reduce the length of sample preparation. Recent studies, however, have focused on improving the spatial resolution of ambient approaches. Finer resolution requires greater analysis times and commensurate computing power for more sophisticated data analysis algorithms and larger data sets. Higher resolution provides a more detailed molecular picture of the sample; however, for some applications, this is not required. A liquid microjunction surface sampling probe (LMJ-SSP) based MS platform combined with unsupervised multivariant analysis based hyperspectral visualization is demonstrated for the metabolomic analysis of marine bacteria from the genus Pseudoalteromonas to create a rapid and robust spatial profiling workflow for microbial natural product screening. In our study, metabolomic profiles of different Pseudoalteromonas species are quickly acquired without any sample preparation and distinguished by unsupervised multivariant analysis. Our robust platform is capable of automated direct sampling of microbes cultured on agar without clogging. Hyperspectral visualization-based rapid spatial profiling provides adequate spatial metabolite information on microbial samples through red-green-blue (RGB) color annotation. Both static and temporal metabolome differences can be visualized by straightforward color differences and differentiating m/z values identified afterward. Through this approach, novel analogues and their potential biosynthetic pathways are discovered by applying results from the spatial navigation to chromatography-based metabolome annotation. In this current research, LMJ-SSP is shown to be a robust and rapid spatial profiling method. Unsupervised multivariant analysis based hyperspectral visualization is proven straightforward for facile/rapid data interpretation. The combination of direct analysis and innovative data visualization forms a powerful tool to aid the identification/interpretation of interesting compounds from conventional metabolomics analysis.
{"title":"Rapid and Robust Workflows Using Different Ionization, Computation, and Visualization Approaches for Spatial Metabolome Profiling of Microbial Natural Products in <i>Pseudoalteromonas</i>.","authors":"Jian Yu, Haidy Metwally, Jennifer Kolwich, Hailey Tomm, Martin Kaufmann, Rachel Klotz, Chang Liu, J C Yves Le Blanc, Thomas R Covey, John Rudan, Avena C Ross, Richard D Oleschuk","doi":"10.1021/acsmeasuresciau.4c00035","DOIUrl":"10.1021/acsmeasuresciau.4c00035","url":null,"abstract":"<p><p>Ambient mass spectrometry (MS) technologies have been applied to spatial metabolomic profiling of various samples in an attempt to both increase analysis speed and reduce the length of sample preparation. Recent studies, however, have focused on improving the spatial resolution of ambient approaches. Finer resolution requires greater analysis times and commensurate computing power for more sophisticated data analysis algorithms and larger data sets. Higher resolution provides a more detailed molecular picture of the sample; however, for some applications, this is not required. A liquid microjunction surface sampling probe (LMJ-SSP) based MS platform combined with unsupervised multivariant analysis based hyperspectral visualization is demonstrated for the metabolomic analysis of marine bacteria from the genus <i>Pseudoalteromonas</i> to create a rapid and robust spatial profiling workflow for microbial natural product screening. In our study, metabolomic profiles of different <i>Pseudoalteromonas</i> species are quickly acquired without any sample preparation and distinguished by unsupervised multivariant analysis. Our robust platform is capable of automated direct sampling of microbes cultured on agar without clogging. Hyperspectral visualization-based rapid spatial profiling provides adequate spatial metabolite information on microbial samples through red-green-blue (RGB) color annotation. Both static and temporal metabolome differences can be visualized by straightforward color differences and differentiating <i>m</i>/<i>z</i> values identified afterward. Through this approach, novel analogues and their potential biosynthetic pathways are discovered by applying results from the spatial navigation to chromatography-based metabolome annotation. In this current research, LMJ-SSP is shown to be a robust and rapid spatial profiling method. Unsupervised multivariant analysis based hyperspectral visualization is proven straightforward for facile/rapid data interpretation. The combination of direct analysis and innovative data visualization forms a powerful tool to aid the identification/interpretation of interesting compounds from conventional metabolomics analysis.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"4 6","pages":"668-677"},"PeriodicalIF":4.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11659995/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-21DOI: 10.1021/acsmeasuresciau.4c0004810.1021/acsmeasuresciau.4c00048
Jian Li*, Jin Li and Xing-Hua Xia*,
Plasmon-enhanced infrared (IR) techniques have garnered significant interest for their ability to achieve greatly more sensitive IR detection than conventional surface enhanced IR techniques. However, the difficulty in electrically connecting antennas has limited their application in IR spectroelectrochemistry, a crucial field for catalysis, analysis, and energy storage. Recent technical advancements have enabled the successful application of electrochemical potentials to antennas, making plasmon-enhanced IR spectroelectrochemistry feasible. This perspective aims to summarize the latest strategies and offer insights into future improvements for better design of plasmon enhanced IR spectroelectrochemistry platforms and understanding of IR spectroelectrochemistry.
{"title":"Plasmon Enhanced IR Spectroelectrochemistry","authors":"Jian Li*, Jin Li and Xing-Hua Xia*, ","doi":"10.1021/acsmeasuresciau.4c0004810.1021/acsmeasuresciau.4c00048","DOIUrl":"https://doi.org/10.1021/acsmeasuresciau.4c00048https://doi.org/10.1021/acsmeasuresciau.4c00048","url":null,"abstract":"<p >Plasmon-enhanced infrared (IR) techniques have garnered significant interest for their ability to achieve greatly more sensitive IR detection than conventional surface enhanced IR techniques. However, the difficulty in electrically connecting antennas has limited their application in IR spectroelectrochemistry, a crucial field for catalysis, analysis, and energy storage. Recent technical advancements have enabled the successful application of electrochemical potentials to antennas, making plasmon-enhanced IR spectroelectrochemistry feasible. This perspective aims to summarize the latest strategies and offer insights into future improvements for better design of plasmon enhanced IR spectroelectrochemistry platforms and understanding of IR spectroelectrochemistry.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"4 6","pages":"606–614 606–614"},"PeriodicalIF":4.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsmeasuresciau.4c00048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14eCollection Date: 2024-12-18DOI: 10.1021/acsmeasuresciau.4c00047
Brian Low, Yukai Wang, Tingting Zhao, Huaxu Yu, Tao Huan
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.
{"title":"Closing the Knowledge Gap of Post-Acquisition Sample Normalization in Untargeted Metabolomics.","authors":"Brian Low, Yukai Wang, Tingting Zhao, Huaxu Yu, Tao Huan","doi":"10.1021/acsmeasuresciau.4c00047","DOIUrl":"10.1021/acsmeasuresciau.4c00047","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"4 6","pages":"702-711"},"PeriodicalIF":4.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11659990/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-13DOI: 10.1021/acsmeasuresciau.4c0004710.1021/acsmeasuresciau.4c00047
Brian Low, Yukai Wang, Tingting Zhao, Huaxu Yu and Tao Huan*,
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.
{"title":"Closing the Knowledge Gap of Post-Acquisition Sample Normalization in Untargeted Metabolomics","authors":"Brian Low, Yukai Wang, Tingting Zhao, Huaxu Yu and Tao Huan*, ","doi":"10.1021/acsmeasuresciau.4c0004710.1021/acsmeasuresciau.4c00047","DOIUrl":"https://doi.org/10.1021/acsmeasuresciau.4c00047https://doi.org/10.1021/acsmeasuresciau.4c00047","url":null,"abstract":"<p >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.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"4 6","pages":"702–711 702–711"},"PeriodicalIF":4.6,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsmeasuresciau.4c00047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10eCollection Date: 2024-12-18DOI: 10.1021/acsmeasuresciau.4c00058
Amir Hatamie, Xiulan He, Andrew Ewing, Patrik Rorsman
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.
{"title":"From Insulin Measurement to Partial Exocytosis Model: Advances in Single Pancreatic Beta Cell Amperometry over Four Decades.","authors":"Amir Hatamie, Xiulan He, Andrew Ewing, Patrik Rorsman","doi":"10.1021/acsmeasuresciau.4c00058","DOIUrl":"10.1021/acsmeasuresciau.4c00058","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"4 6","pages":"629-637"},"PeriodicalIF":4.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11659994/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10eCollection Date: 2024-12-18DOI: 10.1021/acsmeasuresciau.4c00061
C Hyun Ryu, Debasree Mandal, Hang Ren
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.
{"title":"Gas-Liquid-Solid Three-Phase Boundary in Scanning Electrochemical Cell Microscopy.","authors":"C Hyun Ryu, Debasree Mandal, Hang Ren","doi":"10.1021/acsmeasuresciau.4c00061","DOIUrl":"10.1021/acsmeasuresciau.4c00061","url":null,"abstract":"<p><p>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/cm<sup>2</sup>. 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.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"4 6","pages":"729-736"},"PeriodicalIF":4.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11659987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10DOI: 10.1021/acsmeasuresciau.4c0005810.1021/acsmeasuresciau.4c00058
Amir Hatamie*, Xiulan He, Andrew Ewing and Patrik Rorsman,
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.
{"title":"From Insulin Measurement to Partial Exocytosis Model: Advances in Single Pancreatic Beta Cell Amperometry over Four Decades","authors":"Amir Hatamie*, Xiulan He, Andrew Ewing and Patrik Rorsman, ","doi":"10.1021/acsmeasuresciau.4c0005810.1021/acsmeasuresciau.4c00058","DOIUrl":"https://doi.org/10.1021/acsmeasuresciau.4c00058https://doi.org/10.1021/acsmeasuresciau.4c00058","url":null,"abstract":"<p >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.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"4 6","pages":"629–637 629–637"},"PeriodicalIF":4.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsmeasuresciau.4c00058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10DOI: 10.1021/acsmeasuresciau.4c0006110.1021/acsmeasuresciau.4c00061
C. Hyun Ryu, Debasree Mandal and Hang Ren*,
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.
{"title":"Gas–Liquid–Solid Three-Phase Boundary in Scanning Electrochemical Cell Microscopy","authors":"C. Hyun Ryu, Debasree Mandal and Hang Ren*, ","doi":"10.1021/acsmeasuresciau.4c0006110.1021/acsmeasuresciau.4c00061","DOIUrl":"https://doi.org/10.1021/acsmeasuresciau.4c00061https://doi.org/10.1021/acsmeasuresciau.4c00061","url":null,"abstract":"<p >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/cm<sup>2</sup>. 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.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"4 6","pages":"729–736 729–736"},"PeriodicalIF":4.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsmeasuresciau.4c00061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}