1
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Bilbao A, Munoz N, Kim J, Orton DJ, Gao Y, Poorey K, Pomraning KR, Weitz K, Burnet M, Nicora CD, Wilton R, Deng S, Dai Z, Oksen E, Gee A, Fasani RA, Tsalenko A, Tanjore D, Gardner J, Smith RD, Michener JK, Gladden JM, Baker ES, Petzold CJ, Kim YM, Apffel A, Magnuson JK, Burnum-Johnson KE. PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements. Nat Commun 2023; 14:2461. [PMID: 37117207 PMCID: PMC10147702 DOI: 10.1038/s41467-023-37031-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 02/24/2023] [Indexed: 04/30/2023] Open
Abstract
Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.
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Affiliation(s)
- Aivett Bilbao
- Pacific Northwest National Laboratory, Richland, WA, USA.
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA.
| | - Nathalie Munoz
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Joonhoon Kim
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Daniel J Orton
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Yuqian Gao
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | | | - Kyle R Pomraning
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Karl Weitz
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Meagan Burnet
- Pacific Northwest National Laboratory, Richland, WA, USA
| | | | - Rosemarie Wilton
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Argonne National Laboratory, Lemont, IL, USA
| | - Shuang Deng
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Ziyu Dai
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Ethan Oksen
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Aaron Gee
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Rick A Fasani
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Anya Tsalenko
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Deepti Tanjore
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - James Gardner
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | | | - Joshua K Michener
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - John M Gladden
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Sandia National Laboratory, Livermore, CA, USA
| | - Erin S Baker
- Department of Chemistry, University of North Carolina, Chapel Hill, NC, USA
| | - Christopher J Petzold
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Young-Mo Kim
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Alex Apffel
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Jon K Magnuson
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Kristin E Burnum-Johnson
- Pacific Northwest National Laboratory, Richland, WA, USA.
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA.
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2
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Fang Y, Li QR, Zhang ZH, Wu JR, Zheng HR, Zeng R. FIGS: Featured Ion-Guided Stoichiometry for Data-Independent Proteomics through Dynamic Deconvolution. J Proteome Res 2021; 20:4131-4138. [PMID: 34310138 DOI: 10.1021/acs.jproteome.1c00438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Data-independent acquisition (DIA) has significant advantages for mass spectrometry (MS)-based peptide quantification, while mixed spectra remain challenging for precise stoichiometry. We here choose to analyze the library spectra in specific sets preferentially and locally. Accordingly, the featured ions are defined as the fragment ions uniquely assigned to corresponding precursors in a given spectrum set, which are generated by dynamic deconvolution of the mixed mass spectra. Then, we present featured ion-guided stoichiometry (FIGS), a universal method for accurate and robust peptide quantification for the DIA-MS data. We validate the high performance on the quantification sensitivity, accuracy, and efficiency of FIGS. Notably, our FIGS dramatically improves the quantification accuracy for the full dynamic range, especially for low-abundance peptides.
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Affiliation(s)
- Yan Fang
- School of Computer Science and Technology, University of Science and Technology of China, 96 JinZhai Road, Hefei 230026, China
| | - Qing-Run Li
- CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Sciences, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, 320 YueYang Road, Shanghai 200031, China
| | - Zhen-Hang Zhang
- School of Computer Science and Technology, University of Science and Technology of China, 96 JinZhai Road, Hefei 230026, China
| | - Jia-Rui Wu
- CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Sciences, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, 320 YueYang Road, Shanghai 200031, China
| | - Hao-Ran Zheng
- School of Computer Science and Technology, University of Science and Technology of China, 96 JinZhai Road, Hefei 230026, China.,Anhui Key Laboratory of Software Engineering in Computing and Communication, University of Science and Technology of China, 96 JinZhai Road, Hefei 230026, China.,Department of Systems Biology, University of Science and Technology of China, 96 JinZhai Road, Hefei 230026, China
| | - Rong Zeng
- CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Sciences, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, 320 YueYang Road, Shanghai 200031, China
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Tognetti M, Gabor A, Yang M, Cappelletti V, Windhager J, Rueda OM, Charmpi K, Esmaeilishirazifard E, Bruna A, de Souza N, Caldas C, Beyer A, Picotti P, Saez-Rodriguez J, Bodenmiller B. Deciphering the signaling network of breast cancer improves drug sensitivity prediction. Cell Syst 2021; 12:401-418.e12. [PMID: 33932331 DOI: 10.1016/j.cels.2021.04.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 12/16/2020] [Accepted: 04/07/2021] [Indexed: 02/06/2023]
Abstract
One goal of precision medicine is to tailor effective treatments to patients' specific molecular markers of disease. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data-on more than 80 million single cells from 4,000 conditions-were used to fit mechanistic signaling network models that provide insight into how cancer cells process information. Our dynamic single-cell-based models accurately predicted drug sensitivity and identified genomic features associated with drug sensitivity, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. We observed similar trends in genotype-drug sensitivity associations in patient-derived xenograft mouse models. This work provides proof of principle that patient-specific single-cell measurements and modeling could inform effective precision medicine strategies.
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Affiliation(s)
- Marco Tognetti
- Department of Quantitative Biomedicine, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Life Sciences, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Systems Biology, ETH Zürich, 8093 Zurich, Switzerland; Molecular Life Science PhD Program, Life Science Zürich Graduate School, ETH Zürich and University of Zürich, 8057 Zurich, Switzerland
| | - Attila Gabor
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University, 69117 Heidelberg, Germany; Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany
| | - Mi Yang
- Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany; Faculty of Biosciences, Heidelberg University, 69117 Heidelberg, Germany
| | | | - Jonas Windhager
- Department of Quantitative Biomedicine, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Life Sciences, University of Zürich, 8057 Zurich, Switzerland; Systems Biology PhD Program, Life Science Zürich Graduate School, ETH Zürich and University of Zürich, 8093 Zürich, Switzerland
| | - Oscar M Rueda
- Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | - Konstantina Charmpi
- Cologne Excellence Cluster Cellular Stress Response in Aging-Associated Diseases (CECAD), Medical Faculty and Faculty of Mathematics and Natural Sciences, University of Cologne, 50923 Cologne, Germany
| | - Elham Esmaeilishirazifard
- Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, UK; Bioscience, R&D Oncology, Astra Zeneca, Cancer Research UK Cambridge Institute, Cambridge CB2 0RE, UK
| | - Alejandra Bruna
- Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | - Natalie de Souza
- Department of Quantitative Biomedicine, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Systems Biology, ETH Zürich, 8093 Zurich, Switzerland
| | - Carlos Caldas
- Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, UK; Cambridge Breast Unit, NIHR Cambridge Biomedical Research Centre and Cambridge Experimental Cancer Medicine Centre at Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - Andreas Beyer
- Cologne Excellence Cluster Cellular Stress Response in Aging-Associated Diseases (CECAD), Medical Faculty and Faculty of Mathematics and Natural Sciences, University of Cologne, 50923 Cologne, Germany; Center for Molecular Medicine (CMMC), University of Cologne, 50923 Cologne, Germany; Institute for Genetics, Faculty of Mathematics and Natural Sciences, University of Cologne, 50923 Cologne, Germany
| | - Paola Picotti
- Institute of Molecular Systems Biology, ETH Zürich, 8093 Zurich, Switzerland
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University, 69117 Heidelberg, Germany; Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Life Sciences, University of Zürich, 8057 Zurich, Switzerland.
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Raetz M, Bonner R, Hopfgartner G. SWATH-MS for metabolomics and lipidomics: critical aspects of qualitative and quantitative analysis. Metabolomics 2020; 16:71. [PMID: 32504120 DOI: 10.1007/s11306-020-01692-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 05/29/2020] [Indexed: 12/19/2022]
Abstract
INTRODUCTION While liquid chromatography coupled to mass spectrometric detection in the selected reaction monitoring detection mode offers the best quantification sensitivity for omics, the number of target analytes is limited, must be predefined and specific methods developed. Data independent acquisition (DIA), including SWATH using quadrupole time of flight or orbitrap mass spectrometers and generic acquisition methods, has emerged as a powerful alternative technique for quantitative and qualitative analyses since it can cover a wide range of analytes without predefinition. OBJECTIVES Here we review the current state of DIA, SWATH-MS and highlight novel acquisition strategies for metabolomics and lipidomics and opportunities for data analysis tools. METHOD Different databases were searched for papers that report developments and applications of DIA and in particular SWATH-MS in metabolomics and lipidomics. RESULTS DIA methods generate digital sample records that can be mined retrospectively as further knowledge is gained and, with standardized acquisition schemes, used in multiple studies. The different chemical spaces of metabolites and lipids require different specificities, hence different acquisition and data processing approaches must be considered for their analysis. CONCLUSIONS Although the hardware and acquisition modes are well defined for SWATH-MS, a major challenge for routine use remains the lack of appropriate software tools capable of handling large datasets and large numbers of analytes.
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Affiliation(s)
- Michel Raetz
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, 24 Quai Ernest Ansermet, CH-1211, Geneva, Switzerland
| | - Ron Bonner
- Ron Bonner Consulting, Newmarket, ON, L3Y 3C7, Canada
| | - Gérard Hopfgartner
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, 24 Quai Ernest Ansermet, CH-1211, Geneva, Switzerland.
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5
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Pino LK, Searle BC, Bollinger JG, Nunn B, MacLean B, MacCoss MJ. The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics. MASS SPECTROMETRY REVIEWS 2020; 39:229-244. [PMID: 28691345 PMCID: PMC5799042 DOI: 10.1002/mas.21540] [Citation(s) in RCA: 371] [Impact Index Per Article: 92.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 06/01/2017] [Indexed: 05/03/2023]
Abstract
Skyline is a freely available, open-source Windows client application for accelerating targeted proteomics experimentation, with an emphasis on the proteomics and mass spectrometry community as users and as contributors. This review covers the informatics encompassed by the Skyline ecosystem, from computationally assisted targeted mass spectrometry method development, to raw acquisition file data processing, and quantitative analysis and results sharing.
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Affiliation(s)
- Lindsay K Pino
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Brian C Searle
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - James G Bollinger
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Brook Nunn
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Brendan MacLean
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
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6
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Huang T, Bruderer R, Muntel J, Xuan Y, Vitek O, Reiter L. Combining Precursor and Fragment Information for Improved Detection of Differential Abundance in Data Independent Acquisition. Mol Cell Proteomics 2020; 19:421-430. [PMID: 31888964 PMCID: PMC7000113 DOI: 10.1074/mcp.ra119.001705] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 12/16/2019] [Indexed: 11/16/2022] Open
Abstract
In bottom-up, label-free discovery proteomics, biological samples are acquired in a data-dependent (DDA) or data-independent (DIA) manner, with peptide signals recorded in an intact (MS1) and fragmented (MS2) form. While DDA has only the MS1 space for quantification, DIA contains both MS1 and MS2 at high quantitative quality. DIA profiles of complex biological matrices such as tissues or cells can contain quantitative interferences, and the interferences at the MS1 and the MS2 signals are often independent. When comparing biological conditions, the interferences can compromise the detection of differential peptide or protein abundance and lead to false positive or false negative conclusions.We hypothesized that the combined use of MS1 and MS2 quantitative signals could improve our ability to detect differentially abundant proteins. Therefore, we developed a statistical procedure incorporating both MS1 and MS2 quantitative information of DIA. We benchmarked the performance of the MS1-MS2-combined method to the individual use of MS1 or MS2 in DIA using four previously published controlled mixtures, as well as in two previously unpublished controlled mixtures. In the majority of the comparisons, the combined method outperformed the individual use of MS1 or MS2. This was particularly true for comparisons with low fold changes, few replicates, and situations where MS1 and MS2 were of similar quality. When applied to a previously unpublished investigation of lung cancer, the MS1-MS2-combined method increased the coverage of known activated pathways.Since recent technological developments continue to increase the quality of MS1 signals (e.g. using the BoxCar scan mode for Orbitrap instruments), the combination of the MS1 and MS2 information has a high potential for future statistical analysis of DIA data.
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Affiliation(s)
- Ting Huang
- Northeastern University, Boston MA 02115
| | | | - Jan Muntel
- Biognosys, Wagistrasse 21, 8952 Schlieren, Switzerland
| | - Yue Xuan
- Thermo Fisher Scientific, 28199 Bremen, Germany
| | - Olga Vitek
- Northeastern University, Boston MA 02115.
| | - Lukas Reiter
- Biognosys, Wagistrasse 21, 8952 Schlieren, Switzerland.
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Detection of Functional Overreaching in Endurance Athletes Using Proteomics. Proteomes 2018; 6:proteomes6030033. [PMID: 30200480 PMCID: PMC6161275 DOI: 10.3390/proteomes6030033] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 08/21/2018] [Accepted: 08/29/2018] [Indexed: 01/17/2023] Open
Abstract
No reliable biomarkers exist to identify athletes in various training states including functional overreaching (FOR), non-functional overreaching (NFOR), and overtraining syndrome (OTS). Participants (N = 10, age 38.3 ± 3.4 years) served as their own controls and in random, counterbalanced order either ran/cycled 2.5 h (70.0 ± 3.7% VO2max) three days in a row (FOR) or sat in the lab (rest) (separated by three weeks; 7:00–9:30 am, overnight fasted state). Participants provided fingerprick samples for dried blood spot samples (DBS) pre- and post-exercise/rest, and then during two recovery days. DBS proteins were measured with nanoLC-MS in data-independent acquisition (DIA) mode, and 593 proteins were identified and quantified. Proteins were considered for the FOR cluster if they were elevated during one of the two recovery days but not more than one of the exercise days (compared to rest). The generalized estimating equation (GEE) was used to identify proteins linked to FOR. A total of 13 proteins was linked to FOR and most were associated with the acute phase response and innate immune system activation. This study used a system-wide proteomics approach to define a targeted panel of blood proteins related to FOR that could form the basis of future NFOR- and OTS-based studies.
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Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics. Nat Methods 2018; 15:371-378. [PMID: 29608554 PMCID: PMC5924490 DOI: 10.1038/nmeth.4643] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 02/23/2018] [Indexed: 01/11/2023]
Abstract
Mass spectrometry with data-independent acquisition (DIA) has emerged as a promising method to greatly improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory systematically measuring all peptide precursors within a biological sample. Despite the technical maturity of DIA, the analytical challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms and alternative site localizations in phosphoproteomics data. We have developed Specter, an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly in terms of a spectral library, circumventing the problems associated with typical fragment correlation-based approaches. We validate the sensitivity of Specter and its performance relative to other methods by means of several complex datasets, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.
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9
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Bilbao A, Gibbons BC, Slysz GW, Crowell KL, Monroe ME, Ibrahim YM, Smith RD, Payne SH, Baker ES. An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies. INTERNATIONAL JOURNAL OF MASS SPECTROMETRY 2018; 427:91-99. [PMID: 29706793 PMCID: PMC5920534 DOI: 10.1016/j.ijms.2017.11.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The mass accuracy and peak intensity of ions detected by mass spectrometry (MS) measurements are essential to facilitate compound identification and quantitation. However, high concentration species can yield erroneous results if their ion intensities reach beyond the limits of the detection system, leading to distorted and non-ideal detector response (e.g. saturation), and largely precluding the calculation of accurate m/z and intensity values. Here we present an open source computational method to correct peaks above a defined intensity (saturated) threshold determined by the MS instrumentation such as the analog-to-digital converters or time-to-digital converters used in conjunction with time-of-flight MS. In this method, the isotopic envelope for each observed ion above the saturation threshold is compared to its expected theoretical isotopic distribution. The most intense isotopic peak for which saturation does not occur is then utilized to re-calculate the precursor m/z and correct the intensity, resulting in both higher mass accuracy and greater dynamic range. The benefits of this approach were evaluated with proteomic and lipidomic datasets of varying complexities. After correcting the high concentration species, reduced mass errors and enhanced dynamic range were observed for both simple and complex omic samples. Specifically, the mass error dropped by more than 50% in most cases for highly saturated species and dynamic range increased by 1-2 orders of magnitude for peptides in a blood serum sample.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Erin S Baker
- Corresponding author at: 902 Battelle Blvd., P.O. Box 999, MSIN K8-98, Richland,WA 99352, USA. (E.S. Baker)
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10
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Fenaille F, Barbier Saint-Hilaire P, Rousseau K, Junot C. Data acquisition workflows in liquid chromatography coupled to high resolution mass spectrometry-based metabolomics: Where do we stand? J Chromatogr A 2017; 1526:1-12. [PMID: 29074071 DOI: 10.1016/j.chroma.2017.10.043] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 10/15/2017] [Accepted: 10/16/2017] [Indexed: 01/08/2023]
Abstract
Typical mass spectrometry (MS) based untargeted metabolomics protocols are tedious as well as time- and sample-consuming. In particular, they often rely on "full-scan-only" analyses using liquid chromatography (LC) coupled to high resolution mass spectrometry (HRMS) from which metabolites of interest are first highlighted, and then tentatively identified by using targeted MS/MS experiments. However, this situation is evolving with the emergence of integrated HRMS based-data acquisition protocols able to perform multi-event acquisitions. Most of these protocols, referring to as data dependent and data independent acquisition (DDA and DIA, respectively), have been initially developed for proteomic applications and have recently demonstrated their applicability to biomedical studies. In this context, the aim of this article is to take stock of the progress made in the field of DDA- and DIA-based protocols, and evaluate their ability to change conventional metabolomic and lipidomic data acquisition workflows, through a review of HRMS instrumentation, DDA and DIA workflows, and also associated informatics tools.
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Affiliation(s)
- François Fenaille
- Service de Pharmacologie et Immuno-Analyse (SPI), Laboratoire d'Etude du Métabolisme des Médicaments, CEA, INRA, Université Paris Saclay, MetaboHUB, F-91191 Gif-sur-Yvette, France
| | - Pierre Barbier Saint-Hilaire
- Service de Pharmacologie et Immuno-Analyse (SPI), Laboratoire d'Etude du Métabolisme des Médicaments, CEA, INRA, Université Paris Saclay, MetaboHUB, F-91191 Gif-sur-Yvette, France
| | - Kathleen Rousseau
- Service de Pharmacologie et Immuno-Analyse (SPI), Laboratoire d'Etude du Métabolisme des Médicaments, CEA, INRA, Université Paris Saclay, MetaboHUB, F-91191 Gif-sur-Yvette, France
| | - Christophe Junot
- Service de Pharmacologie et Immuno-Analyse (SPI), CEA, INRA, Université Paris Saclay, MetaboHUB, F-91191 Gif-sur-Yvette, France.
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11
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Teleman J, Hauri S, Malmström J. Improvements in Mass Spectrometry Assay Library Generation for Targeted Proteomics. J Proteome Res 2017; 16:2384-2392. [PMID: 28516777 DOI: 10.1021/acs.jproteome.6b00928] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In data-independent acquisition mass spectrometry (DIA-MS), targeted extraction of peptide signals in silico using mass spectrometry assay libraries is a successful method for the identification and quantification of proteins. However, it remains unclear if high quality assay libraries with more accurate peptide ion coordinates can improve peptide target identification rates in DIA analysis. In this study, we systematically improved and evaluated the common algorithmic steps for assay library generation and demonstrate that increased assay quality results in substantially higher identification rates of peptide targets from mouse organ protein lysates measured by DIA-MS. The introduced changes are (1) a new spectrum interpretation algorithm, (2) reapplication of segmented retention time normalization, (3) a ppm fragment mass error matching threshold, (4) usage of internal peptide fragments, and (5) a multilevel false discovery rate calculation. Taken together, these changes yielded 14-36% more identified peptide targets at 1% assay false discovery rate and are implemented in three new open source tools, Fraggle, Tramler, and Franklin, available at https://github.com/fickludd/eviltools . The improved algorithms provide ways to better utilize discovery MS data, translating to substantially increased DIA performance and ultimately better foundations for drawing biological conclusions in DIA-based experiments.
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Affiliation(s)
- Johan Teleman
- Department of Clinical Sciences, Lund University , BMC D13, 221 84 Lund, Sweden.,Department of Immunotechnology, Lund University , Medicon Village (Building 406), 223 81 Lund, Sweden
| | - Simon Hauri
- Department of Clinical Sciences, Lund University , BMC D13, 221 84 Lund, Sweden
| | - Johan Malmström
- Department of Clinical Sciences, Lund University , BMC D13, 221 84 Lund, Sweden
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12
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Anjo SI, Santa C, Manadas B. SWATH-MS as a tool for biomarker discovery: From basic research to clinical applications. Proteomics 2017; 17. [DOI: 10.1002/pmic.201600278] [Citation(s) in RCA: 102] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 01/05/2017] [Accepted: 01/23/2017] [Indexed: 12/16/2022]
Affiliation(s)
- Sandra Isabel Anjo
- CNC - Center for Neuroscience and Cell Biology; University of Coimbra; Coimbra Portugal
- Faculty of Sciences and Technology; University of Coimbra; Coimbra Portugal
| | - Cátia Santa
- CNC - Center for Neuroscience and Cell Biology; University of Coimbra; Coimbra Portugal
- Institute for Interdisciplinary Research (III); University of Coimbra; Coimbra Portugal
| | - Bruno Manadas
- CNC - Center for Neuroscience and Cell Biology; University of Coimbra; Coimbra Portugal
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Effect of peptide assay library size and composition in targeted data-independent acquisition-MS analyses. Proteomics 2016; 16:2221-37. [DOI: 10.1002/pmic.201600007] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Revised: 04/28/2016] [Accepted: 05/19/2016] [Indexed: 11/07/2022]
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