The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform
Christopher J Petzold - One of the best experts on this subject based on the ideXlab platform.
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a Rapid Methods development workflow for high throughput quantitative proteomic applications
PLOS ONE, 2019Co-Authors: Yan Chen, Mitchell G Thompson, William A Sharpless, Leanne Jade G Chan, Jennifer Gin, Jay D Keasling, Paul D Adams, Christopher J PetzoldAbstract:Recent improvements in the speed and sensitivity of liquid chromatography-mass spectrometry systems have driven significant progress toward system-wide characterization of the proteome of many species. These efforts create large proteomic datasets that provide insight into biological processes and identify diagnostic proteins whose abundance changes significantly under different experimental conditions. Yet, these system-wide experiments are typically the starting point for hypothesis-driven, follow-up experiments to elucidate the extent of the phenomenon or the utility of the diagnostic marker, wherein many samples must be analyzed. Transitioning from a few discovery experiments to quantitative analyses on hundreds of samples requires significant resources both to develop sensitive and specific Methods as well as analyze them in a high-throughput manner. To aid these efforts, we developed a workflow using data acquired from discovery proteomic experiments, retention time prediction, and standard-flow chromatography to Rapidly develop targeted proteomic assays. We demonstrated this workflow by developing MRM assays to quantify proteins of multiple metabolic pathways from multiple microbes under different experimental conditions. With this workflow, one can also target peptides in scheduled/dynamic acquisition Methods from a shotgun proteomic dataset downloaded from online repositories, validate with appropriate control samples or standard peptides, and begin analyzing hundreds of samples in only a few minutes.
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A Rapid Methods development workflow for high-throughput quantitative proteomic applications - Fig 4
2019Co-Authors: Yan Chen, Mitchell G Thompson, William A Sharpless, Leanne Jade G Chan, Jay D Keasling, Paul D Adams, Jennifer W. Gin, Christopher J PetzoldAbstract:(A) Central carbon pathways (glycolysis, lysine degradation, aromatic monomer degradation pathways, and tricarboxylic acid (TCA) cycle) in P. putida; (B-E) comparison of the relative protein abundances of P. putida grown on 10 mM of glucose, p-coumarate, and 5-aminovalerate carbon sources in MOPS media. The error bar shows the standard deviation of measured peak area of three biological replicates. Statistical significance of p-coumarate and 5-aminovalerate against glucose were calculated by moderated t-test with the limma package in R, and resulting p-values were adjusted using the Benjamini-Hochberg (BH) method. *, **, and *** indicate adjusted P < 0.05, 0.01 and 0.001, respectively.
Mitchell G Thompson - One of the best experts on this subject based on the ideXlab platform.
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a Rapid Methods development workflow for high throughput quantitative proteomic applications
PLOS ONE, 2019Co-Authors: Yan Chen, Mitchell G Thompson, William A Sharpless, Leanne Jade G Chan, Jennifer Gin, Jay D Keasling, Paul D Adams, Christopher J PetzoldAbstract:Recent improvements in the speed and sensitivity of liquid chromatography-mass spectrometry systems have driven significant progress toward system-wide characterization of the proteome of many species. These efforts create large proteomic datasets that provide insight into biological processes and identify diagnostic proteins whose abundance changes significantly under different experimental conditions. Yet, these system-wide experiments are typically the starting point for hypothesis-driven, follow-up experiments to elucidate the extent of the phenomenon or the utility of the diagnostic marker, wherein many samples must be analyzed. Transitioning from a few discovery experiments to quantitative analyses on hundreds of samples requires significant resources both to develop sensitive and specific Methods as well as analyze them in a high-throughput manner. To aid these efforts, we developed a workflow using data acquired from discovery proteomic experiments, retention time prediction, and standard-flow chromatography to Rapidly develop targeted proteomic assays. We demonstrated this workflow by developing MRM assays to quantify proteins of multiple metabolic pathways from multiple microbes under different experimental conditions. With this workflow, one can also target peptides in scheduled/dynamic acquisition Methods from a shotgun proteomic dataset downloaded from online repositories, validate with appropriate control samples or standard peptides, and begin analyzing hundreds of samples in only a few minutes.
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A Rapid Methods development workflow for high-throughput quantitative proteomic applications - Fig 4
2019Co-Authors: Yan Chen, Mitchell G Thompson, William A Sharpless, Leanne Jade G Chan, Jay D Keasling, Paul D Adams, Jennifer W. Gin, Christopher J PetzoldAbstract:(A) Central carbon pathways (glycolysis, lysine degradation, aromatic monomer degradation pathways, and tricarboxylic acid (TCA) cycle) in P. putida; (B-E) comparison of the relative protein abundances of P. putida grown on 10 mM of glucose, p-coumarate, and 5-aminovalerate carbon sources in MOPS media. The error bar shows the standard deviation of measured peak area of three biological replicates. Statistical significance of p-coumarate and 5-aminovalerate against glucose were calculated by moderated t-test with the limma package in R, and resulting p-values were adjusted using the Benjamini-Hochberg (BH) method. *, **, and *** indicate adjusted P < 0.05, 0.01 and 0.001, respectively.
Paul D Adams - One of the best experts on this subject based on the ideXlab platform.
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a Rapid Methods development workflow for high throughput quantitative proteomic applications
PLOS ONE, 2019Co-Authors: Yan Chen, Mitchell G Thompson, William A Sharpless, Leanne Jade G Chan, Jennifer Gin, Jay D Keasling, Paul D Adams, Christopher J PetzoldAbstract:Recent improvements in the speed and sensitivity of liquid chromatography-mass spectrometry systems have driven significant progress toward system-wide characterization of the proteome of many species. These efforts create large proteomic datasets that provide insight into biological processes and identify diagnostic proteins whose abundance changes significantly under different experimental conditions. Yet, these system-wide experiments are typically the starting point for hypothesis-driven, follow-up experiments to elucidate the extent of the phenomenon or the utility of the diagnostic marker, wherein many samples must be analyzed. Transitioning from a few discovery experiments to quantitative analyses on hundreds of samples requires significant resources both to develop sensitive and specific Methods as well as analyze them in a high-throughput manner. To aid these efforts, we developed a workflow using data acquired from discovery proteomic experiments, retention time prediction, and standard-flow chromatography to Rapidly develop targeted proteomic assays. We demonstrated this workflow by developing MRM assays to quantify proteins of multiple metabolic pathways from multiple microbes under different experimental conditions. With this workflow, one can also target peptides in scheduled/dynamic acquisition Methods from a shotgun proteomic dataset downloaded from online repositories, validate with appropriate control samples or standard peptides, and begin analyzing hundreds of samples in only a few minutes.
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A Rapid Methods development workflow for high-throughput quantitative proteomic applications - Fig 4
2019Co-Authors: Yan Chen, Mitchell G Thompson, William A Sharpless, Leanne Jade G Chan, Jay D Keasling, Paul D Adams, Jennifer W. Gin, Christopher J PetzoldAbstract:(A) Central carbon pathways (glycolysis, lysine degradation, aromatic monomer degradation pathways, and tricarboxylic acid (TCA) cycle) in P. putida; (B-E) comparison of the relative protein abundances of P. putida grown on 10 mM of glucose, p-coumarate, and 5-aminovalerate carbon sources in MOPS media. The error bar shows the standard deviation of measured peak area of three biological replicates. Statistical significance of p-coumarate and 5-aminovalerate against glucose were calculated by moderated t-test with the limma package in R, and resulting p-values were adjusted using the Benjamini-Hochberg (BH) method. *, **, and *** indicate adjusted P < 0.05, 0.01 and 0.001, respectively.
Yan Chen - One of the best experts on this subject based on the ideXlab platform.
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a Rapid Methods development workflow for high throughput quantitative proteomic applications
PLOS ONE, 2019Co-Authors: Yan Chen, Mitchell G Thompson, William A Sharpless, Leanne Jade G Chan, Jennifer Gin, Jay D Keasling, Paul D Adams, Christopher J PetzoldAbstract:Recent improvements in the speed and sensitivity of liquid chromatography-mass spectrometry systems have driven significant progress toward system-wide characterization of the proteome of many species. These efforts create large proteomic datasets that provide insight into biological processes and identify diagnostic proteins whose abundance changes significantly under different experimental conditions. Yet, these system-wide experiments are typically the starting point for hypothesis-driven, follow-up experiments to elucidate the extent of the phenomenon or the utility of the diagnostic marker, wherein many samples must be analyzed. Transitioning from a few discovery experiments to quantitative analyses on hundreds of samples requires significant resources both to develop sensitive and specific Methods as well as analyze them in a high-throughput manner. To aid these efforts, we developed a workflow using data acquired from discovery proteomic experiments, retention time prediction, and standard-flow chromatography to Rapidly develop targeted proteomic assays. We demonstrated this workflow by developing MRM assays to quantify proteins of multiple metabolic pathways from multiple microbes under different experimental conditions. With this workflow, one can also target peptides in scheduled/dynamic acquisition Methods from a shotgun proteomic dataset downloaded from online repositories, validate with appropriate control samples or standard peptides, and begin analyzing hundreds of samples in only a few minutes.
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A Rapid Methods development workflow for high-throughput quantitative proteomic applications - Fig 4
2019Co-Authors: Yan Chen, Mitchell G Thompson, William A Sharpless, Leanne Jade G Chan, Jay D Keasling, Paul D Adams, Jennifer W. Gin, Christopher J PetzoldAbstract:(A) Central carbon pathways (glycolysis, lysine degradation, aromatic monomer degradation pathways, and tricarboxylic acid (TCA) cycle) in P. putida; (B-E) comparison of the relative protein abundances of P. putida grown on 10 mM of glucose, p-coumarate, and 5-aminovalerate carbon sources in MOPS media. The error bar shows the standard deviation of measured peak area of three biological replicates. Statistical significance of p-coumarate and 5-aminovalerate against glucose were calculated by moderated t-test with the limma package in R, and resulting p-values were adjusted using the Benjamini-Hochberg (BH) method. *, **, and *** indicate adjusted P < 0.05, 0.01 and 0.001, respectively.
Yanbin Li - One of the best experts on this subject based on the ideXlab platform.
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Rapid Methods for detecting acrylamide in thermally processed foods a review
Food Control, 2015Co-Authors: Qinqin Hu, Xiahong Xu, Yingchun Fu, Yanbin LiAbstract:Abstract Acrylamide (AA), a neurotoxin and potential carcinogen, has been found in various thermally processed foods such as potato chips, biscuits, and coffee. LC–MS/MS and GC–MS as standard detection Methods show high sensitivity, selectivity, stability, and repeatability. However, these Methods require expensive instruments, skilled technicians in laboratories, and high testing costs, and cannot meet the needs for real-time and on-line detection of AA in foods. Therefore, Rapid detection Methods with merits of simplicity and portability such as computer vision, ELISA, electrochemical biosensing, and fluorescent biosensing have obtained an increasing amount of attention. Reported research on Rapid Methods has shown similar sensitivity and selectivity, but requires less time and cost in comparison with standard Methods through the use of nanomaterials and biomolecules with high affinity to AA. These improvements show great promise for high-throughput, real-time, and on-line detection of AA. This paper provides a comprehensive overview of Rapid detection Methods for AA in foods with comparison between Rapid and standard Methods. Meanwhile, suggestions for further research on Rapid Methods for detecting AA are also discussed based on technical challenges and industry needs.
