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Article ; Online: Fast proteomics with dia-PASEF and analytical flow-rate chromatography.

Szyrwiel, Lukasz / Gille, Christoph / Mülleder, Michael / Demichev, Vadim / Ralser, Markus

2023 Volume 24, Issue 1-2, Page(s) e2300100

Abstract: Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs, and facilitate new approaches in systems biology and biomedical research. Here we propose combination of analytical flow rate chromatography ... ...

Abstract	Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs, and facilitate new approaches in systems biology and biomedical research. Here we propose combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition, and data analysis with the DIA-NN software suite, to achieve high-quality proteomic experiments from limited sample amounts, at a throughput of up to 400 samples per day. For instance, when benchmarking our workflow using a 500-μL/min flow rate and 3-min chromatographic gradients, we report the quantification of 5211 proteins from 2 μg of a mammalian cell-line standard at high quantitative accuracy and precision. We further used this platform to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-min chromatographic gradient and alternating column regeneration on a dual pump system. The method delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates.
MeSH term(s)	Animals ; Humans ; Proteomics/methods ; Peptides/analysis ; Proteome/analysis ; Chromatography, Liquid/methods ; COVID-19 ; Mammals/metabolism
Chemical Substances	Peptides ; Proteome
Language	English
Publishing date	2023-06-07
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2032093-0
ISSN	1615-9861 ; 1615-9853
ISSN (online)	1615-9861
ISSN	1615-9853
DOI	10.1002/pmic.202300100
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Article ; Online: Ubiquitinomics: History, methods, and applications in basic research and drug discovery.

Steger, Martin / Karayel, Özge / Demichev, Vadim

Proteomics

2022 Volume 22, Issue 15-16, Page(s) e2200074

Abstract: The ubiquitin-proteasome system (UPS) was discovered about 40 years ago and is known to regulate a multitude of cellular processes including protein homeostasis. Ubiquitylated proteins are recognized by downstream effectors, resulting in alterations of ... ...

Abstract	The ubiquitin-proteasome system (UPS) was discovered about 40 years ago and is known to regulate a multitude of cellular processes including protein homeostasis. Ubiquitylated proteins are recognized by downstream effectors, resulting in alterations of protein abundance, activity, or localization. Not surprisingly, the ubiquitylation machinery is dysregulated in numerous diseases, including cancers and neurodegeneration. Mass spectrometry (MS)-based proteomics has emerged as a transformative technology for characterizing protein ubiquitylation in an unbiased fashion. Here, we provide an overview of the different MS-based approaches for studying protein ubiquitylation. We review various methods for enriching and quantifying ubiquitin modifications at the peptide or protein level, outline MS acquisition, and data processing approaches and discuss key challenges. Finally, we examine how MS-based ubiquitinomics can aid both basic biology and drug discovery research.
MeSH term(s)	Drug Discovery ; Proteasome Endopeptidase Complex/metabolism ; Proteomics/methods ; Ubiquitin/metabolism ; Ubiquitination
Chemical Substances	Ubiquitin ; Proteasome Endopeptidase Complex (EC 3.4.25.1)
Language	English
Publishing date	2022-05-11
Publishing country	Germany
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	2032093-0
ISSN	1615-9861 ; 1615-9853
ISSN (online)	1615-9861
ISSN	1615-9853
DOI	10.1002/pmic.202200074
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Article: Neat plasma proteomics: getting the best out of the worst.

Metatla, Ines / Roger, Kevin / Chhuon, Cerina / Ceccacci, Sara / Chapelle, Manuel / Pierre-Olivier Schmit / Demichev, Vadim / Guerrera, Ida Chiara

Clinical proteomics

2024 Volume 21, Issue 1, Page(s) 22

Abstract: Plasma proteomics holds immense potential for clinical research and biomarker discovery, serving as a non-invasive "liquid biopsy" for tissue sampling. Mass spectrometry (MS)-based proteomics, thanks to improvement in speed and robustness, emerges as an ... ...

Abstract	Plasma proteomics holds immense potential for clinical research and biomarker discovery, serving as a non-invasive "liquid biopsy" for tissue sampling. Mass spectrometry (MS)-based proteomics, thanks to improvement in speed and robustness, emerges as an ideal technology for exploring the plasma proteome for its unbiased and highly specific protein identification and quantification. Despite its potential, plasma proteomics is still a challenge due to the vast dynamic range of protein abundance, hindering the detection of less abundant proteins. Different approaches can help overcome this challenge. Conventional depletion methods face limitations in cost, throughput, accuracy, and off-target depletion. Nanoparticle-based enrichment shows promise in compressing dynamic range, but cost remains a constraint. Enrichment strategies for extracellular vesicles (EVs) can enhance plasma proteome coverage dramatically, but current methods are still too laborious for large series. Neat plasma remains popular for its cost-effectiveness, time efficiency, and low volume requirement. We used a test set of 33 plasma samples for all evaluations. Samples were digested using S-Trap and analyzed on Evosep One and nanoElute coupled to a timsTOF Pro using different elution gradients and ion mobility ranges. Data were mainly analyzed using library-free searches using DIA-NN. This study explores ways to improve proteome coverage in neat plasma both in MS data acquisition and MS data analysis. We demonstrate the value of sampling smaller hydrophilic peptides, increasing chromatographic separation, and using library-free searches. Additionally, we introduce the EV boost approach, that leverages on the extracellular vesicle fraction to enhance protein identification in neat plasma samples. Globally, our optimized analysis workflow allows the quantification of over 1000 proteins in neat plasma with a 24SPD throughput. We believe that these considerations can be of help independently of the LC-MS platform used.
Language	English
Publishing date	2024-03-12
Publishing country	England
Document type	Journal Article
ZDB-ID	2205154-5
ISSN	1542-6416
ISSN	1542-6416
DOI	10.1186/s12014-024-09477-6
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Article ; Online: Speedy-PASEF: Analytical flow rate chromatography and trapped ion mobility for deep high-throughput proteomics

Szyrwiel, Lukasz / Gille, Christoph / Muelleder, Michael / Demichev, Vadim / Ralser, Markus

bioRxiv

Abstract: Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs and facilitate new approaches in systems biology and biomedical research. Here we propose Speedy-PASEF, a combination of analytical flow rate ... ...

Abstract	Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs and facilitate new approaches in systems biology and biomedical research. Here we propose Speedy-PASEF, a combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition and data analysis with the DIA-NN software suite, for conducting fast, high-quality proteomic experiments that require only moderate sample amounts. For instance, using a 500-μl/min flow rate and a 3-minute chromatographic gradient, Speedy-PASEF quantified 5,211 proteins from 2 μg of a mammalian cell-line standard at high quantitative accuracy and precision. We further used Speedy-PASEF to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-minute chromatographic gradient and alternating column regeneration on a dual pump system, for processing 398 samples per day. Speedy-PASEF delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates. Speedy-PASEF thus facilitates acquisition of high-quality proteomes in large numbers.
Keywords	covid19
Language	English
Publishing date	2023-02-19
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2023.02.17.528968
Database	COVID19

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Article ; Online: Speedy-PASEF: Analytical flow rate chromatography and trapped ion mobility for deep high-throughput proteomics

Szyrwiel, Lukasz / Gille, Christoph / Mülleder, Michael / Demichev, Vadim / Ralser, Markus

bioRxiv

Abstract	Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs and facilitate new approaches in systems biology and biomedical research. Here we propose Speedy-PASEF, a combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition and data analysis with the DIA-NN software suite, for conducting fast, high-quality proteomic experiments that require only moderate sample amounts. For instance, using a 500-μl/min flow rate and a 3-minute chromatographic gradient, Speedy-PASEF quantified 5,211 proteins from 2 μg of a mammalian cell-line standard at high quantitative accuracy and precision. We further used Speedy-PASEF to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-minute chromatographic gradient and alternating column regeneration on a dual pump system, for processing 398 samples per day. Speedy-PASEF delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates. Speedy-PASEF thus facilitates acquisition of high-quality proteomes in large numbers.
Keywords	covid19
Language	English
Publishing date	2023-02-19
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2023.02.17.528968
Database	COVID19

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Article ; Online: MSBooster: improving peptide identification rates using deep learning-based features.

Yang, Kevin L / Yu, Fengchao / Teo, Guo Ci / Li, Kai / Demichev, Vadim / Ralser, Markus / Nesvizhskii, Alexey I

Nature communications

2023 Volume 14, Issue 1, Page(s) 4539

Abstract: Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. ...

Abstract	Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.
MeSH term(s)	Chromatography, Liquid/methods ; Tandem Mass Spectrometry/methods ; Deep Learning ; Peptides/chemistry ; Algorithms ; Databases, Protein
Chemical Substances	Peptides
Language	English
Publishing date	2023-07-27
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-40129-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform.

Yu, Fengchao / Teo, Guo Ci / Kong, Andy T / Fröhlich, Klemens / Li, Ginny Xiaohe / Demichev, Vadim / Nesvizhskii, Alexey I

Nature communications

2023 Volume 14, Issue 1, Page(s) 4154

Abstract: Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA ... ...

Abstract	Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.
MeSH term(s)	Proteomics ; Reproducibility of Results ; Tandem Mass Spectrometry ; Chromatography, Liquid ; Databases, Factual
Language	English
Publishing date	2023-07-12
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-39869-5
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Article ; Online: CysQuant: Simultaneous quantification of cysteine oxidation and protein abundance using data dependent or independent acquisition mass spectrometry.

Huang, Jingjing / Staes, An / Impens, Francis / Demichev, Vadim / Van Breusegem, Frank / Gevaert, Kris / Willems, Patrick

Redox biology

2023 Volume 67, Page(s) 102908

Abstract: Protein cysteinyl thiols are susceptible to reduction-oxidation reactions that can influence protein function. Accurate quantification of cysteine oxidation is therefore crucial for decoding protein redox regulation. Here, we present CysQuant, a novel ... ...

Abstract	Protein cysteinyl thiols are susceptible to reduction-oxidation reactions that can influence protein function. Accurate quantification of cysteine oxidation is therefore crucial for decoding protein redox regulation. Here, we present CysQuant, a novel approach for simultaneous quantification of cysteine oxidation degrees and protein abundancies. CysQuant involves light/heavy iodoacetamide isotopologues for differential labeling of reduced and reversibly oxidized cysteines analyzed by data-dependent acquisition (DDA) or data-independent acquisition mass spectrometry (DIA-MS). Using plexDIA with in silico predicted spectral libraries, we quantified an average of 18% cysteine oxidation in Arabidopsis thaliana by DIA-MS, including a subset of highly oxidized cysteines forming disulfide bridges in AlphaFold2 predicted structures. Applying CysQuant to Arabidopsis seedlings exposed to excessive light, we successfully quantified the well-established increased reduction of Calvin-Benson cycle enzymes and discovered yet uncharacterized redox-sensitive disulfides in chloroplastic enzymes. Overall, CysQuant is a highly versatile tool for assessing the cysteine modification status that can be widely applied across various mass spectrometry platforms and organisms.
MeSH term(s)	Cysteine/metabolism ; Proteins/metabolism ; Sulfhydryl Compounds/metabolism ; Mass Spectrometry ; Oxidation-Reduction
Chemical Substances	Cysteine (K848JZ4886) ; Proteins ; Sulfhydryl Compounds
Language	English
Publishing date	2023-09-27
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2701011-9
ISSN	2213-2317 ; 2213-2317
ISSN (online)	2213-2317
ISSN	2213-2317
DOI	10.1016/j.redox.2023.102908
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Article ; Online: Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC.

Kamrad, Stephan / Correia-Melo, Clara / Szyrwiel, Lukasz / Aulakh, Simran Kaur / Bähler, Jürg / Demichev, Vadim / Mülleder, Michael / Ralser, Markus

Nature microbiology

2023 Volume 8, Issue 3, Page(s) 441–454

Abstract: Genetically identical cells are known to differ in many physiological parameters such as growth rate and drug tolerance. Metabolic specialization is believed to be a cause of such phenotypic heterogeneity, but detection of metabolically divergent ... ...

Abstract	Genetically identical cells are known to differ in many physiological parameters such as growth rate and drug tolerance. Metabolic specialization is believed to be a cause of such phenotypic heterogeneity, but detection of metabolically divergent subpopulations remains technically challenging. We developed a proteomics-based technology, termed differential isotope labelling by amino acids (DILAC), that can detect producer and consumer subpopulations of a particular amino acid within an isogenic cell population by monitoring peptides with multiple occurrences of the amino acid. We reveal that young, morphologically undifferentiated yeast colonies contain subpopulations of lysine producers and consumers that emerge due to nutrient gradients. Deconvoluting their proteomes using DILAC, we find evidence for in situ cross-feeding where rapidly growing cells ferment and provide the more slowly growing, respiring cells with ethanol. Finally, by combining DILAC with fluorescence-activated cell sorting, we show that the metabolic subpopulations diverge phenotypically, as exemplified by a different tolerance to the antifungal drug amphotericin B. Overall, DILAC captures previously unnoticed metabolic heterogeneity and provides experimental evidence for the role of metabolic specialization and cross-feeding interactions as a source of phenotypic heterogeneity in isogenic cell populations.
MeSH term(s)	Saccharomyces cerevisiae/metabolism ; Amino Acids/metabolism ; Isotope Labeling
Chemical Substances	Amino Acids
Language	English
Publishing date	2023-02-16
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2058-5276
ISSN (online)	2058-5276
DOI	10.1038/s41564-022-01304-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Fengchao Yu / Guo Ci Teo / Andy T. Kong / Klemens Fröhlich / Ginny Xiaohe Li / Vadim Demichev / Alexey I. Nesvizhskii

Nature Communications, Vol 14, Iss 1, Pp 1-

2023 Volume 14

Abstract: Abstract Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification ... ...

Abstract	Abstract Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.
Keywords	Science ; Q
Language	English
Publishing date	2023-07-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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