The Experts below are selected from a list of 3633 Experts worldwide ranked by ideXlab platform
Christoph Roesli - One of the best experts on this subject based on the ideXlab platform.
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Label-Free Quantification using MALDI mass spectrometry: considerations and perspectives.
Analytical and Bioanalytical Chemistry, 2012Co-Authors: Amelie S. Benk, Christoph RoesliAbstract:Profound knowledge of protein abundances in healthy tissues and their changes in disease is crucial for understanding biological processes in basic science and for the development of novel diagnostics and therapeutics. Mass spectrometrybased Label-Free protein Quantification is used increasingly often to gain insights into physiological changes observed in perturbed systems. Although the soft ionization techniques electrospray ionization and matrix-assisted laser desorption/ionization have both been used for protein Quantification, this article focuses on instrumental setups with a MALDI ion source. Beside reviewing current bioinformatic data-processing tools for Label-Free Quantification and elaborating on the technical benefits of combining UHPLC and MALDI–MS, we outline the potential of state-of-the-art instruments by reporting unpublished results obtained from twenty-four complex biological samples. This review points out that the capabilities of LC–MALDI MS systems have not yet been fully utilized because of a lack of suitable software tools.
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DeepQuanTR: MALDI‐MS‐based label‐free Quantification of proteins in complex biological samples
Proteomics, 2010Co-Authors: Tim Fugmann, Dario Neri, Christoph RoesliAbstract:The Quantification of changes in protein abundance in complex biological specimens is essential for proteomic studies in basic and applied research. Here we report on the development and validation of the DeepQuanTR software for identification and Quantification of differentially expressed proteins using LC-MALDI-MS. Following enzymatic digestion, HPLC peptide separation and normalization of MALDI-MS signal intensities to the ones of internal standards, the software extracts peptide features, adjusts differences in HPLC retention times and performs a relative Quantification of features. The annotation of multiple peptides to the corresponding parent protein allows the definition of a Protein Quant Value, which is related to protein abundance and which allows inter-sample comparisons. The performance of DeepQuanTR was evaluated by analyzing 24 samples deriving from human serum spiked with different amounts of four proteins and eight complex samples of vascular proteins, derived from surgically resected human kidneys with cancer following ex vivo perfusion with a reactive ester biotin derivative. The identification and experimental validation of proteins, which were differentially regulated in cancerous lesions as compared with normal kidney, was used to demonstrate the power of DeepQuanTR. This software, which can easily be used with established proteomic methodologies, facilitates the relative Quantification of proteins derived from a wide variety of different samples.
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DeepQuanTR: MALDI-MS-based Label-Free Quantification of proteins in complex biological samples
Proteomics, 2010Co-Authors: Tim Fugmann, Dario Neri, Christoph RoesliAbstract:The Quantification of changes in protein abundance in complex biological specimens is essential for proteomic studies in basic and applied research. Here we report on the development and validation of the DeepQuanTR software for identification and Quantification of differentially expressed proteins using LC-MALDI-MS. Following enzymatic digestion, HPLC peptide separation and normalization of MALDI-MS signal intensities to the ones of internal standards, the software extracts peptide features, adjusts differences in HPLC retention times and performs a relative Quantification of features. The annotation of multiple peptides to the corresponding parent protein allows the definition of a Protein Quant Value, which is related to protein abundance and which allows inter-sample comparisons. The performance of DeepQuanTR was evaluated by analyzing 24 samples deriving from human serum spiked with different amounts of four proteins and eight complex samples of vascular proteins, derived from surgically resected human kidneys with cancer following ex vivo perfusion with a reactive ester biotin derivative. The identification and experimental validation of proteins, which were differentially regulated in cancerous lesions as compared with normal kidney, was used to demonstrate the power of DeepQuanTR. This software, which can easily be used with established proteomic methodologies, facilitates the relative Quantification of proteins derived from a wide variety of different samples.
Tim Fugmann - One of the best experts on this subject based on the ideXlab platform.
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DeepQuanTR: MALDI‐MS‐based label‐free Quantification of proteins in complex biological samples
Proteomics, 2010Co-Authors: Tim Fugmann, Dario Neri, Christoph RoesliAbstract:The Quantification of changes in protein abundance in complex biological specimens is essential for proteomic studies in basic and applied research. Here we report on the development and validation of the DeepQuanTR software for identification and Quantification of differentially expressed proteins using LC-MALDI-MS. Following enzymatic digestion, HPLC peptide separation and normalization of MALDI-MS signal intensities to the ones of internal standards, the software extracts peptide features, adjusts differences in HPLC retention times and performs a relative Quantification of features. The annotation of multiple peptides to the corresponding parent protein allows the definition of a Protein Quant Value, which is related to protein abundance and which allows inter-sample comparisons. The performance of DeepQuanTR was evaluated by analyzing 24 samples deriving from human serum spiked with different amounts of four proteins and eight complex samples of vascular proteins, derived from surgically resected human kidneys with cancer following ex vivo perfusion with a reactive ester biotin derivative. The identification and experimental validation of proteins, which were differentially regulated in cancerous lesions as compared with normal kidney, was used to demonstrate the power of DeepQuanTR. This software, which can easily be used with established proteomic methodologies, facilitates the relative Quantification of proteins derived from a wide variety of different samples.
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DeepQuanTR: MALDI-MS-based Label-Free Quantification of proteins in complex biological samples
Proteomics, 2010Co-Authors: Tim Fugmann, Dario Neri, Christoph RoesliAbstract:The Quantification of changes in protein abundance in complex biological specimens is essential for proteomic studies in basic and applied research. Here we report on the development and validation of the DeepQuanTR software for identification and Quantification of differentially expressed proteins using LC-MALDI-MS. Following enzymatic digestion, HPLC peptide separation and normalization of MALDI-MS signal intensities to the ones of internal standards, the software extracts peptide features, adjusts differences in HPLC retention times and performs a relative Quantification of features. The annotation of multiple peptides to the corresponding parent protein allows the definition of a Protein Quant Value, which is related to protein abundance and which allows inter-sample comparisons. The performance of DeepQuanTR was evaluated by analyzing 24 samples deriving from human serum spiked with different amounts of four proteins and eight complex samples of vascular proteins, derived from surgically resected human kidneys with cancer following ex vivo perfusion with a reactive ester biotin derivative. The identification and experimental validation of proteins, which were differentially regulated in cancerous lesions as compared with normal kidney, was used to demonstrate the power of DeepQuanTR. This software, which can easily be used with established proteomic methodologies, facilitates the relative Quantification of proteins derived from a wide variety of different samples.
Stefan Tenzer - One of the best experts on this subject based on the ideXlab platform.
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Label-Free Quantification in ion mobility-enhanced data-independent acquisition proteomics
Nature Protocols, 2016Co-Authors: Ute Distler, Jörg Kuharev, Pedro Navarro, Stefan TenzerAbstract:This protocol describes a data-independent acquisition workflow for Label-Free quantitative proteomics that integrates ion mobility separation and applies drift time–specific collision energies to improve precursor fragmentation efficiency.
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In‐depth evaluation of software tools for data‐independent acquisition based label‐free Quantification
Proteomics, 2015Co-Authors: Jörg Kuharev, Pedro Navarro, Ute Distler, Olaf Jahn, Stefan TenzerAbstract:Label-Free Quantification (LFQ) based on data-independent acquisition workflows currently experiences increasing popularity. Several software tools have been recently published or are commercially available. The present study focuses on the evaluation of three different software packages (Progenesis, synapter, and ISOQuant) supporting ion mobility enhanced data-independent acquisition data. In order to benchmark the LFQ performance of the different tools, we generated two hybrid proteome samples of defined quantitative composition containing tryptically digested proteomes of three different species (mouse, yeast, Escherichia coli). This model dataset simulates complex biological samples containing large numbers of both unregulated (background) proteins as well as up- and downregulated proteins with exactly known ratios between samples. We determined the number and dynamic range of quantifiable proteins and analyzed the influence of applied algorithms (retention time alignment, clustering, normalization, etc.) on Quantification results. Analysis of technical reproducibility revealed median coefficients of variation of reported protein abundances below 5% for MS(E) data for Progenesis and ISOQuant. Regarding accuracy of LFQ, evaluation with synapter and ISOQuant yielded superior results compared to Progenesis. In addition, we discuss reporting formats and user friendliness of the software packages. The data generated in this study have been deposited to the ProteomeXchange Consortium with identifier PXD001240 (http://proteomecentral.proteomexchange.org/dataset/PXD001240).
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Biomedical applications of ion mobility-enhanced data-independent acquisition-based Label-Free quantitative proteomics
Expert Review of Proteomics, 2014Co-Authors: Ute Distler, Jörg Kuharev, Stefan TenzerAbstract:Mass spectrometry-based proteomics greatly benefited from recent improvements in instrument performance and the development of bioinformatics solutions facilitating the high-throughput Quantification of proteins in complex biological samples. In addition to Quantification approaches using stable isotope labeling, Label-Free Quantification has emerged as the method of choice for many laboratories. Over the last years, data-independent acquisition approaches have gained increasing popularity. The integration of ion mobility separation into commercial instruments enabled researchers to achieve deep proteome coverage from limiting sample amounts. Additionally, ion mobility provides a new dimension of separation for the quantitative assessment of complex proteomes, facilitating precise Label-Free Quantification even of highly complex samples. The present work provides a thorough overview of the combination of ion mobility and data-independent acquisition-based Label-Free Quantification LC-MS and its applicatio...
Dario Neri - One of the best experts on this subject based on the ideXlab platform.
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DeepQuanTR: MALDI‐MS‐based label‐free Quantification of proteins in complex biological samples
Proteomics, 2010Co-Authors: Tim Fugmann, Dario Neri, Christoph RoesliAbstract:The Quantification of changes in protein abundance in complex biological specimens is essential for proteomic studies in basic and applied research. Here we report on the development and validation of the DeepQuanTR software for identification and Quantification of differentially expressed proteins using LC-MALDI-MS. Following enzymatic digestion, HPLC peptide separation and normalization of MALDI-MS signal intensities to the ones of internal standards, the software extracts peptide features, adjusts differences in HPLC retention times and performs a relative Quantification of features. The annotation of multiple peptides to the corresponding parent protein allows the definition of a Protein Quant Value, which is related to protein abundance and which allows inter-sample comparisons. The performance of DeepQuanTR was evaluated by analyzing 24 samples deriving from human serum spiked with different amounts of four proteins and eight complex samples of vascular proteins, derived from surgically resected human kidneys with cancer following ex vivo perfusion with a reactive ester biotin derivative. The identification and experimental validation of proteins, which were differentially regulated in cancerous lesions as compared with normal kidney, was used to demonstrate the power of DeepQuanTR. This software, which can easily be used with established proteomic methodologies, facilitates the relative Quantification of proteins derived from a wide variety of different samples.
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DeepQuanTR: MALDI-MS-based Label-Free Quantification of proteins in complex biological samples
Proteomics, 2010Co-Authors: Tim Fugmann, Dario Neri, Christoph RoesliAbstract:The Quantification of changes in protein abundance in complex biological specimens is essential for proteomic studies in basic and applied research. Here we report on the development and validation of the DeepQuanTR software for identification and Quantification of differentially expressed proteins using LC-MALDI-MS. Following enzymatic digestion, HPLC peptide separation and normalization of MALDI-MS signal intensities to the ones of internal standards, the software extracts peptide features, adjusts differences in HPLC retention times and performs a relative Quantification of features. The annotation of multiple peptides to the corresponding parent protein allows the definition of a Protein Quant Value, which is related to protein abundance and which allows inter-sample comparisons. The performance of DeepQuanTR was evaluated by analyzing 24 samples deriving from human serum spiked with different amounts of four proteins and eight complex samples of vascular proteins, derived from surgically resected human kidneys with cancer following ex vivo perfusion with a reactive ester biotin derivative. The identification and experimental validation of proteins, which were differentially regulated in cancerous lesions as compared with normal kidney, was used to demonstrate the power of DeepQuanTR. This software, which can easily be used with established proteomic methodologies, facilitates the relative Quantification of proteins derived from a wide variety of different samples.
Ute Distler - One of the best experts on this subject based on the ideXlab platform.
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Label-Free Quantification in ion mobility-enhanced data-independent acquisition proteomics
Nature Protocols, 2016Co-Authors: Ute Distler, Jörg Kuharev, Pedro Navarro, Stefan TenzerAbstract:This protocol describes a data-independent acquisition workflow for Label-Free quantitative proteomics that integrates ion mobility separation and applies drift time–specific collision energies to improve precursor fragmentation efficiency.
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In‐depth evaluation of software tools for data‐independent acquisition based label‐free Quantification
Proteomics, 2015Co-Authors: Jörg Kuharev, Pedro Navarro, Ute Distler, Olaf Jahn, Stefan TenzerAbstract:Label-Free Quantification (LFQ) based on data-independent acquisition workflows currently experiences increasing popularity. Several software tools have been recently published or are commercially available. The present study focuses on the evaluation of three different software packages (Progenesis, synapter, and ISOQuant) supporting ion mobility enhanced data-independent acquisition data. In order to benchmark the LFQ performance of the different tools, we generated two hybrid proteome samples of defined quantitative composition containing tryptically digested proteomes of three different species (mouse, yeast, Escherichia coli). This model dataset simulates complex biological samples containing large numbers of both unregulated (background) proteins as well as up- and downregulated proteins with exactly known ratios between samples. We determined the number and dynamic range of quantifiable proteins and analyzed the influence of applied algorithms (retention time alignment, clustering, normalization, etc.) on Quantification results. Analysis of technical reproducibility revealed median coefficients of variation of reported protein abundances below 5% for MS(E) data for Progenesis and ISOQuant. Regarding accuracy of LFQ, evaluation with synapter and ISOQuant yielded superior results compared to Progenesis. In addition, we discuss reporting formats and user friendliness of the software packages. The data generated in this study have been deposited to the ProteomeXchange Consortium with identifier PXD001240 (http://proteomecentral.proteomexchange.org/dataset/PXD001240).
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Biomedical applications of ion mobility-enhanced data-independent acquisition-based Label-Free quantitative proteomics
Expert Review of Proteomics, 2014Co-Authors: Ute Distler, Jörg Kuharev, Stefan TenzerAbstract:Mass spectrometry-based proteomics greatly benefited from recent improvements in instrument performance and the development of bioinformatics solutions facilitating the high-throughput Quantification of proteins in complex biological samples. In addition to Quantification approaches using stable isotope labeling, Label-Free Quantification has emerged as the method of choice for many laboratories. Over the last years, data-independent acquisition approaches have gained increasing popularity. The integration of ion mobility separation into commercial instruments enabled researchers to achieve deep proteome coverage from limiting sample amounts. Additionally, ion mobility provides a new dimension of separation for the quantitative assessment of complex proteomes, facilitating precise Label-Free Quantification even of highly complex samples. The present work provides a thorough overview of the combination of ion mobility and data-independent acquisition-based Label-Free Quantification LC-MS and its applicatio...