1
|
Targeted Selected Reaction Monitoring Verifies Histology Specific Peptide Signatures in Epithelial Ovarian Cancer. Cancers (Basel) 2021; 13:cancers13225713. [PMID: 34830868 PMCID: PMC8616310 DOI: 10.3390/cancers13225713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 12/05/2022] Open
Abstract
Simple Summary Ovarian cancer is a lethal disease due to its late phase discovery. Any steps towards improving early diagnostics will dramatically increase survival rates. To identify new ovarian cancer biomarker panels, we need to focus on early-stage disease and all histologic subtypes. In this study we have, based on prior discoveries, constructed a multiplexed targeted selected-reaction-monitoring assay to detect peptides from 177 proteins in only 20 µL of plasma. The assay was evaluated in patients with a focus on early-stages and all ovarian cancer histologies in separate groups. With multivariate analysis, we found the highest predictive value in the benign vs. low-grade serous (Q2 = 0.615) and mucinous (Q2 = 0.611) early stage compared to all malignant (Q2 = 0.226) or late stage (Q2 = 0.43) ovarian cancers. The results show that each ovarian cancer histology subgroup can be identified by a unique panel of proteins. Abstract Epithelial ovarian cancer (OC) is a disease with high mortality due to vague early clinical symptoms. Benign ovarian cysts are common and accurate diagnosis remains a challenge because of the molecular heterogeneity of OC. We set out to investigate whether the disease diversity seen in ovarian cyst fluids and tumor tissue could be detected in plasma. Using existing mass spectrometry (MS)-based proteomics data, we constructed a selected reaction monitoring (SRM) assay targeting peptides from 177 cancer-related and classical proteins associated with OC. Plasma from benign, borderline, and malignant ovarian tumors were used to verify expression (n = 74). Unsupervised and supervised multivariate analyses were used for comparisons. The peptide signatures revealed by the supervised multivariate analysis contained 55 to 77 peptides each. The predictive (Q2) values were higher for benign vs. low-grade serous Q2 = 0.615, mucinous Q2 = 0.611, endometrioid Q2 = 0.428 and high-grade serous Q2 = 0.375 (stage I–II Q2 = 0.515; stage III Q2 = 0.43) OC compared to benign vs. all malignant Q2 = 0.226. With targeted SRM MS we constructed a multiplexed assay for simultaneous detection and relative quantification of 185 peptides from 177 proteins in only 20 µL of plasma. With the approach of histology-specific peptide patterns, derived from pre-selected proteins, we may be able to detect not only high-grade serous OC but also the less common OC subtypes.
Collapse
|
2
|
Zohora FT, Rahman MZ, Tran NH, Xin L, Shan B, Li M. Deep neural network for detecting arbitrary precision peptide features through attention based segmentation. Sci Rep 2021; 11:18249. [PMID: 34521906 PMCID: PMC8440683 DOI: 10.1038/s41598-021-97669-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 08/27/2021] [Indexed: 11/09/2022] Open
Abstract
A promising technique of discovering disease biomarkers is to measure the relative protein abundance in multiple biofluid samples through liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics. The key step involves peptide feature detection in the LC-MS map, along with its charge and intensity. Existing heuristic algorithms suffer from inaccurate parameters and human errors. As a solution, we propose PointIso, the first point cloud based arbitrary-precision deep learning network to address this problem. It consists of attention based scanning step for segmenting the multi-isotopic pattern of 3D peptide features along with the charge, and a sequence classification step for grouping those isotopes into potential peptide features. PointIso achieves 98% detection of high-quality MS/MS identified peptide features in a benchmark dataset. Next, the model is adapted for handling the additional 'ion mobility' dimension and achieves 4% higher detection than existing algorithms on the human proteome dataset. Besides contributing to the proteomics study, our novel segmentation technique should serve the general object detection domain as well.
Collapse
Affiliation(s)
- Fatema Tuz Zohora
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
| | - M Ziaur Rahman
- Bioinformatics Solutions Inc., Waterloo, ON, N2L 6J2, Canada
| | - Ngoc Hieu Tran
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
| | - Lei Xin
- Bioinformatics Solutions Inc., Waterloo, ON, N2L 6J2, Canada
| | - Baozhen Shan
- Bioinformatics Solutions Inc., Waterloo, ON, N2L 6J2, Canada
| | - Ming Li
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
| |
Collapse
|
3
|
Zohora FT, Rahman MZ, Tran NH, Xin L, Shan B, Li M. DeepIso: A Deep Learning Model for Peptide Feature Detection from LC-MS map. Sci Rep 2019; 9:17168. [PMID: 31748623 PMCID: PMC6868186 DOI: 10.1038/s41598-019-52954-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 10/21/2019] [Indexed: 11/09/2022] Open
Abstract
Liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics provides the relative different protein abundance in healthy and disease-afflicted patients, which offers the information for molecular interactions, signaling pathways, and biomarker identification to serve the drug discovery and clinical research. Typical analysis workflow begins with the peptide feature detection and intensity calculation from LC-MS map. We are the first to propose a deep learning based model, DeepIso, that combines recent advances in Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to detect peptide features of different charge states, as well as, estimate their intensity. Existing tools are designed with limited engineered features and domain-specific parameters, which are hardly updated despite a huge amount of new coming proteomic data. On the other hand, DeepIso consisting of two separate deep learning based modules, learns multiple levels of representation of high dimensional data itself through many layers of neurons, and adaptable to newly acquired data. The peptide feature list reported by our model matches with 97.43% of high quality MS/MS identifications in a benchmark dataset, which is higher than the matching produced by several widely used tools. Our results demonstrate that novel deep learning tools are desirable to advance the state-of-the-art in protein identification and quantification.
Collapse
Affiliation(s)
- Fatema Tuz Zohora
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
| | - M Ziaur Rahman
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
- Bioinformatics Solutions Inc., Waterloo, ON, N2L 6J2, Canada
| | - Ngoc Hieu Tran
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
| | - Lei Xin
- Bioinformatics Solutions Inc., Waterloo, ON, N2L 6J2, Canada
| | - Baozhen Shan
- Bioinformatics Solutions Inc., Waterloo, ON, N2L 6J2, Canada
| | - Ming Li
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
| |
Collapse
|
4
|
Manes NP, Nita-Lazar A. Application of targeted mass spectrometry in bottom-up proteomics for systems biology research. J Proteomics 2018; 189:75-90. [PMID: 29452276 DOI: 10.1016/j.jprot.2018.02.008] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 01/25/2018] [Accepted: 02/07/2018] [Indexed: 02/08/2023]
Abstract
The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).
Collapse
Affiliation(s)
- Nathan P Manes
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Aleksandra Nita-Lazar
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
| |
Collapse
|
5
|
Bhosale SD, Moulder R, Kouvonen P, Lahesmaa R, Goodlett DR. Mass Spectrometry-Based Serum Proteomics for Biomarker Discovery and Validation. Methods Mol Biol 2017; 1619:451-466. [PMID: 28674903 DOI: 10.1007/978-1-4939-7057-5_31] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Blood protein measurements are used frequently in the clinic in the assessment of patient health. Nevertheless, there remains the need for new biomarkers with better diagnostic specificities. With the advent of improved technology for bioanalysis and the growth of biobanks including collections from specific disease risk cohorts, the plasma proteome has remained a target of proteomics research toward the characterization of disease-related biomarkers. The following protocol presents a workflow for serum/plasma proteomics including details of sample preparation both with and without immunoaffinity depletion of the most abundant plasma proteins and methodology for selected reaction monitoring mass spectrometry validation.
Collapse
Affiliation(s)
| | - Robert Moulder
- Turku Centre for Biotechnology, University of Turku, Turku, Finland
| | - Petri Kouvonen
- Turku Centre for Biotechnology, University of Turku, Turku, Finland
| | - Riitta Lahesmaa
- Turku Centre for Biotechnology, University of Turku, Turku, Finland
| | - David R Goodlett
- Turku Centre for Biotechnology, University of Turku, Turku, Finland. .,Department of Pharmaceutical Science, University of Maryland, 20 North Pine Street, Baltimore, MD, 21201, USA.
| |
Collapse
|
6
|
Quantitative proteomics analysis of cartilage response to mechanical injury and cytokine treatment. Matrix Biol 2016; 63:11-22. [PMID: 27988350 DOI: 10.1016/j.matbio.2016.12.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Revised: 12/09/2016] [Accepted: 12/09/2016] [Indexed: 01/15/2023]
Abstract
Mechanical damage at the time of joint injury and the ensuing inflammatory response associated with elevated levels of pro-inflammatory cytokines in the synovial fluid, are reported to contribute to the progression to osteoarthritis after injury. In this exploratory study, we used a targeted proteomics approach to follow the progression of matrix degradation in response to mechanical damage and cytokine treatment of human knee cartilage explants, and thereby to study potential molecular biomarkers. This proteomics approach allowed us to unambiguously identify and quantify multiple peptides and proteins in the cartilage medium and explants upon treatment with ±injurious compression ±cytokines, treatments that mimic the earliest events in post-traumatic OA. We followed degradation of different protein domains, e.g., G1/G2/G3 of aggrecan, by measuring representative peptides of matrix proteins released into the medium at 7 time points throughout the 21-day culture period. COMP neo-epitopes, which were previously identified in the synovial fluid of knee injury/OA patients, were also released by these human cartilage explants treated with cyt and cyt+inj. The absence of collagen pro-peptides and elevated levels of specific COMP and COL3A1 neo-epitopes after human knee trauma may be relevant as potential biomarkers for post-traumatic OA. This model system thereby enables study of the kinetics of cartilage degradation and the identification of biomarkers within cartilage explants and those released to culture medium. Discovery proteomics revealed that candidate proteases were identified after specific treatment conditions, including MMP1, MMP-3, MMP-10 and MMP-13.
Collapse
|
7
|
Röst HL, Liu Y, D'Agostino G, Zanella M, Navarro P, Rosenberger G, Collins BC, Gillet L, Testa G, Malmström L, Aebersold R. TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics. Nat Methods 2016; 13:777-83. [PMID: 27479329 PMCID: PMC5008461 DOI: 10.1038/nmeth.3954] [Citation(s) in RCA: 122] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Accepted: 06/14/2016] [Indexed: 12/16/2022]
Abstract
Large scale, quantitative proteomic studies have become essential for the analysis of clinical cohorts, large perturbation experiments and systems biology studies. While next-generation mass spectrometric techniques such as SWATH-MS have substantially increased throughput and reproducibility, ensuring consistent quantification of thousands of peptide analytes across multiple LC-MS/MS runs remains a challenging and laborious manual process. To produce highly consistent and quantitatively accurate proteomics data matrices in an automated fashion, we have developed the TRIC software which utilizes fragment ion data to perform cross-run alignment, consistent peak-picking and quantification for high throughput targeted proteomics. TRIC uses a graph-based alignment strategy based on non-linear retention time correction to integrate peak elution information from all LC-MS/MS runs acquired in a study. When compared to state-of-the-art SWATH-MS data analysis, the algorithm was able to reduce the identification error by more than 3-fold at constant recall, while correcting for highly non-linear chromatographic effects. On a pulsed-SILAC experiment performed on human induced pluripotent stem (iPS) cells, TRIC was able to automatically align and quantify thousands of light and heavy isotopic peak groups and substantially increased the quantitative completeness and biological information in the data, providing insights into protein dynamics of iPS cells. Overall, this study demonstrates the importance of consistent quantification in highly challenging experimental setups, and proposes an algorithm to automate this task, constituting the last missing piece in a pipeline for automated analysis of massively parallel targeted proteomics datasets.
Collapse
Affiliation(s)
- Hannes L Röst
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Department of Genetics, Stanford University, Stanford, California, USA
| | - Yansheng Liu
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Giuseppe D'Agostino
- Department of Experimental Oncology, European Institute of Oncology, Milan, Italy
| | - Matteo Zanella
- Department of Experimental Oncology, European Institute of Oncology, Milan, Italy
| | - Pedro Navarro
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Institute for Immunology, University Medical Center of the Johannes Gutenberg University of Mainz, Mainz, Germany
| | - George Rosenberger
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Ben C Collins
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Ludovic Gillet
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Giuseppe Testa
- Department of Experimental Oncology, European Institute of Oncology, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Lars Malmström
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,S3IT, University of Zurich, Zurich, Switzerland
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Faculty of Science, University of Zurich, Zurich, Switzerland
| |
Collapse
|
8
|
Waldemarson S, Kurbasic E, Krogh M, Cifani P, Berggård T, Borg Å, James P. Proteomic analysis of breast tumors confirms the mRNA intrinsic molecular subtypes using different classifiers: a large-scale analysis of fresh frozen tissue samples. Breast Cancer Res 2016; 18:69. [PMID: 27357824 PMCID: PMC4928264 DOI: 10.1186/s13058-016-0732-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Accepted: 06/19/2016] [Indexed: 12/11/2022] Open
Abstract
Background Breast cancer is a complex and heterogeneous disease that is usually characterized by histological parameters such as tumor size, cellular arrangements/rearrangments, necrosis, nuclear grade and the mitotic index, leading to a set of around twenty subtypes. Together with clinical markers such as hormone receptor status, this classification has considerable prognostic value but there is a large variation in patient response to therapy. Gene expression profiling has provided molecular profiles characteristic of distinct subtypes of breast cancer that reflect the divergent cellular origins and degree of progression. Methods Here we present a large-scale proteomic and transcriptomic profiling study of 477 sporadic and hereditary breast cancer tumors with matching mRNA expression analysis. Unsupervised hierarchal clustering was performed and selected proteins from large-scale tandem mass spectrometry (MS/MS) analysis were transferred into a highly multiplexed targeted selected reaction monitoring assay to classify tumors using a hierarchal cluster and support vector machine with leave one out cross-validation. Results The subgroups formed upon unsupervised clustering agree very well with groups found at transcriptional level; however, the classifiers (genes or their respective protein products) differ almost entirely between the two datasets. In-depth analysis shows clear differences in pathways unique to each type, which may lie behind their different clinical outcomes. Targeted mass spectrometry analysis and supervised clustering correlate very well with subgroups determined by RNA classification and show convincing agreement with clinical parameters. Conclusions This work demonstrates the merits of protein expression profiling for breast cancer stratification. These findings have important implications for the use of genomics and expression analysis for the prediction of protein expression, such as receptor status and drug target expression. The highly multiplexed MS assay is easily implemented in standard clinical chemistry practice, allowing rapid and cheap characterization of tumor tissue suitable for directing the choice of treatment. Electronic supplementary material The online version of this article (doi:10.1186/s13058-016-0732-2) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Sofia Waldemarson
- Department of Immunotechnology, Lund University, Medicon Village, 223 81, Lund, Sweden
| | - Emila Kurbasic
- Department of Immunotechnology, Lund University, Medicon Village, 223 81, Lund, Sweden
| | - Morten Krogh
- Amber Biosciences AB, Skrivarevägen 9, 226 57, Lund, Sweden
| | - Paolo Cifani
- Department of Immunotechnology, Lund University, Medicon Village, 223 81, Lund, Sweden
| | | | - Åke Borg
- Department of Oncology, Lund University, Medicon Village, 223 81, Lund, Sweden
| | - Peter James
- Department of Immunotechnology, Lund University, Medicon Village, 223 81, Lund, Sweden. .,Turku Centre for Biotechnology, Åbo Akademi University, University of Turku Biocity, Tykistokatu 6, 20520, Turku, Finland.
| |
Collapse
|
9
|
Stenemo M, Teleman J, Sjöström M, Grubb G, Malmström E, Malmström J, Niméus E. Cancer associated proteins in blood plasma: Determining normal variation. Proteomics 2016; 16:1928-37. [PMID: 27121749 DOI: 10.1002/pmic.201500204] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Revised: 03/12/2016] [Accepted: 04/15/2016] [Indexed: 11/07/2022]
Abstract
Protein biomarkers have the potential to improve diagnosis, stratification of patients into treatment cohorts, follow disease progression and treatment response. One distinct group of potential biomarkers comprises proteins which have been linked to cancer, known as cancer associated proteins (CAPs). We determined the normal variation of 86 CAPs in 72 individual plasma samples collected from ten individuals using SRM mass spectrometry. Samples were collected weekly during 5 weeks from ten volunteers and over one day at nine fixed time points from three volunteers. We determined the degree of the normal variation depending on interpersonal variation, variation due to time of day, and variation over weeks and observed that the variation dependent on the time of day appeared to be the most important. Subdivision of the proteins resulted in two predominant protein groups containing 21 proteins with relatively high variation in all three factors (day, week and individual), and 22 proteins with relatively low variation in all factors. We present a strategy for prioritizing biomarker candidates for future studies based on stratification over their normal variation and have made all data publicly available. Our findings can be used to improve selection of biomarker candidates in future studies and to determine which proteins are most suitable depending on study design.
Collapse
Affiliation(s)
- Markus Stenemo
- Department of Clinical Sciences Lund, Division of Infection Medicine, Lund University, Lund, Sweden.,Department of Clinical Sciences Lund, Oncology and Pathology, Lund University, Lund, Sweden
| | - Johan Teleman
- Department of Clinical Sciences Lund, Division of Infection Medicine, Lund University, Lund, Sweden
| | - Martin Sjöström
- Department of Clinical Sciences Lund, Oncology and Pathology, Lund University, Lund, Sweden
| | - Gabriel Grubb
- Department of Clinical Sciences Lund, Oncology and Pathology, Lund University, Lund, Sweden
| | - Erik Malmström
- Department of Clinical Sciences Lund, Division of Infection Medicine, Lund University, Lund, Sweden
| | - Johan Malmström
- Department of Clinical Sciences Lund, Division of Infection Medicine, Lund University, Lund, Sweden
| | - Emma Niméus
- Department of Clinical Sciences Lund, Oncology and Pathology, Lund University, Lund, Sweden.,Skåne University Hospital, Department of Surgery, Lund, Sweden
| |
Collapse
|
10
|
Wang H, Shi T, Qian WJ, Liu T, Kagan J, Srivastava S, Smith RD, Rodland KD, Camp DG. The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification. Expert Rev Proteomics 2015; 13:99-114. [PMID: 26581546 DOI: 10.1586/14789450.2016.1122529] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Mass spectrometry (MS) -based proteomics has become an indispensable tool with broad applications in systems biology and biomedical research. With recent advances in liquid chromatography (LC) and MS instrumentation, LC-MS is making increasingly significant contributions to clinical applications, especially in the area of cancer biomarker discovery and verification. To overcome challenges associated with analyses of clinical samples (for example, a wide dynamic range of protein concentrations in bodily fluids and the need to perform high throughput and accurate quantification of candidate biomarker proteins), significant efforts have been devoted to improve the overall performance of LC-MS-based clinical proteomics platforms. Reviewed here are the recent advances in LC-MS and its applications in cancer biomarker discovery and quantification, along with the potentials, limitations and future perspectives.
Collapse
Affiliation(s)
- Hui Wang
- a Biological Sciences Division , Pacific Northwest National Laboratory , Richland , WA , USA
| | - Tujin Shi
- a Biological Sciences Division , Pacific Northwest National Laboratory , Richland , WA , USA
| | - Wei-Jun Qian
- a Biological Sciences Division , Pacific Northwest National Laboratory , Richland , WA , USA
| | - Tao Liu
- a Biological Sciences Division , Pacific Northwest National Laboratory , Richland , WA , USA
| | - Jacob Kagan
- b Division of Cancer Prevention , National Cancer Institute (NCI) , Rockville , MD , USA
| | - Sudhir Srivastava
- b Division of Cancer Prevention , National Cancer Institute (NCI) , Rockville , MD , USA
| | - Richard D Smith
- a Biological Sciences Division , Pacific Northwest National Laboratory , Richland , WA , USA
| | - Karin D Rodland
- a Biological Sciences Division , Pacific Northwest National Laboratory , Richland , WA , USA
| | - David G Camp
- a Biological Sciences Division , Pacific Northwest National Laboratory , Richland , WA , USA
| |
Collapse
|
11
|
Bilbao A, Zhang Y, Varesio E, Luban J, Strambio-De-Castillia C, Lisacek F, Hopfgartner G. Ranking Fragment Ions Based on Outlier Detection for Improved Label-Free Quantification in Data-Independent Acquisition LC-MS/MS. J Proteome Res 2015; 14:4581-93. [PMID: 26412574 DOI: 10.1021/acs.jproteome.5b00394] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Data-independent acquisition LC-MS/MS techniques complement supervised methods for peptide quantification. However, due to the wide precursor isolation windows, these techniques are prone to interference at the fragment ion level, which, in turn, is detrimental for accurate quantification. The nonoutlier fragment ion (NOFI) ranking algorithm has been developed to assign low priority to fragment ions affected by interference. By using the optimal subset of high-priority fragment ions, these interfered fragment ions are effectively excluded from quantification. NOFI represents each fragment ion as a vector of four dimensions related to chromatographic and MS fragmentation attributes and applies multivariate outlier detection techniques. Benchmarking conducted on a well-defined quantitative data set (i.e., the SWATH Gold Standard) indicates that NOFI on average is able to accurately quantify 11-25% more peptides than the commonly used Top-N library intensity ranking method. The sum of the area of the Top3-5 NOFIs produces similar coefficients of variation as compared to that with the library intensity method but with more accurate quantification results. On a biologically relevant human dendritic cell digest data set, NOFI properly assigns low-priority ranks to 85% of annotated interferences, resulting in sensitivity values between 0.92 and 0.80, against 0.76 for the Spectronaut interference detection algorithm.
Collapse
Affiliation(s)
- Aivett Bilbao
- Life Sciences Mass Spectrometry, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne , CH-1211 Geneva 4, Switzerland.,Proteome Informatics Group, SIB Swiss Institute of Bioinformatics , CH-1211 Geneva 4, Switzerland
| | - Ying Zhang
- Life Sciences Mass Spectrometry, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne , CH-1211 Geneva 4, Switzerland
| | - Emmanuel Varesio
- Life Sciences Mass Spectrometry, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne , CH-1211 Geneva 4, Switzerland
| | - Jeremy Luban
- Program in Molecular Medicine, University of Massachusetts Medical School , Worcester, Massachusetts 01605, United States
| | - Caterina Strambio-De-Castillia
- Program in Molecular Medicine, University of Massachusetts Medical School , Worcester, Massachusetts 01605, United States
| | - Frédérique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics , CH-1211 Geneva 4, Switzerland.,Faculty of Sciences, University of Geneva , CH-1211 Geneva 4, Switzerland
| | - Gérard Hopfgartner
- Life Sciences Mass Spectrometry, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne , CH-1211 Geneva 4, Switzerland
| |
Collapse
|
12
|
Pathway-centric analysis of the DNA damage response to chemotherapeutic agents in two breast cell lines. EUPA OPEN PROTEOMICS 2015. [DOI: 10.1016/j.euprot.2015.05.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
13
|
Sjöström M, Ossola R, Breslin T, Rinner O, Malmström L, Schmidt A, Aebersold R, Malmström J, Niméus E. A Combined Shotgun and Targeted Mass Spectrometry Strategy for Breast Cancer Biomarker Discovery. J Proteome Res 2015; 14:2807-18. [DOI: 10.1021/acs.jproteome.5b00315] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | | | | | | | | | | | - Ruedi Aebersold
- Department
of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule, 8092 Zurich, Switzerland
| | | | - Emma Niméus
- Division
of Surgery, Skåne University Hospital, 221 85 Lund, Sweden
| |
Collapse
|
14
|
Qi D, Lawless C, Teleman J, Levander F, Holman SW, Hubbard S, Jones AR. Representation of selected-reaction monitoring data in the mzQuantML data standard. Proteomics 2015; 15:2592-6. [PMID: 25884107 PMCID: PMC4692094 DOI: 10.1002/pmic.201400281] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Revised: 03/02/2015] [Accepted: 04/14/2015] [Indexed: 11/06/2022]
Abstract
The mzQuantML data standard was designed to capture the output of quantitative software in proteomics, to support submissions to public repositories, development of visualization software and pipeline/modular approaches. The standard is designed around a common core that can be extended to support particular types of technique through the release of semantic rules that are checked by validation software. The first release of mzQuantML supported four quantitative proteomics techniques via four sets of semantic rules: (i) intensity-based (MS(1) ) label free, (ii) MS(1) label-based (such as SILAC or N(15) ), (iii) MS(2) tag-based (iTRAQ or tandem mass tags), and (iv) spectral counting. We present an update to mzQuantML for supporting SRM techniques. The update includes representing the quantitative measurements, and associated meta-data, for SRM transitions, the mechanism for inferring peptide-level or protein-level quantitative values, and support for both label-based or label-free SRM protocols, through the creation of semantic rules and controlled vocabulary terms. We have updated the specification document for mzQuantML (version 1.0.1) and the mzQuantML validator to ensure that consistent files are produced by different exporters. We also report the capabilities for production of mzQuantML files from popular SRM software packages, such as Skyline and Anubis.
Collapse
Affiliation(s)
- Da Qi
- Institute of Integrative Biology, University of Liverpool, Liverpool, UK
| | - Craig Lawless
- The Faculty of Life Sciences, University of Manchester, Manchester, UK
| | - Johan Teleman
- Department of Immunotechnology, Lund University, Lund, Sweden
| | - Fredrik Levander
- Department of Immunotechnology, Lund University, Lund, Sweden.,Bioinformatics Infrastructure for Life Sciences (BILS), Lund University, Lund, Sweden
| | - Stephen W Holman
- Institute of Integrative Biology, University of Liverpool, Liverpool, UK
| | - Simon Hubbard
- The Faculty of Life Sciences, University of Manchester, Manchester, UK
| | - Andrew R Jones
- Institute of Integrative Biology, University of Liverpool, Liverpool, UK
| |
Collapse
|
15
|
Stella R, Biancotto G, Arrigoni G, Barrucci F, Angeletti R, James P. Proteomics for the detection of indirect markers of steroids treatment in bovine muscle. Proteomics 2015; 15:2332-41. [DOI: 10.1002/pmic.201400468] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2014] [Revised: 12/24/2014] [Accepted: 03/04/2015] [Indexed: 11/08/2022]
Affiliation(s)
- Roberto Stella
- Department of Chemistry; Istituto Zooprofilattico Sperimentale delle Venezie; Legnaro (PD) Italy
| | - Giancarlo Biancotto
- Department of Chemistry; Istituto Zooprofilattico Sperimentale delle Venezie; Legnaro (PD) Italy
| | - Giorgio Arrigoni
- Department of Biomedical Sciences; Padova University; Padova Italy
- Proteomics Center of Padova University; Padova Italy
| | - Federica Barrucci
- Department of Public Health and Risk Analysis; Istituto Zooprofilattico Sperimentale delle Venezie; Legnaro (PD) Italy
| | - Roberto Angeletti
- Department of Chemistry; Istituto Zooprofilattico Sperimentale delle Venezie; Legnaro (PD) Italy
| | - Peter James
- Department of Immunotechnology; Medicon Village, Lund University; Lund Sweden
| |
Collapse
|
16
|
Chawade A, Sandin M, Teleman J, Malmström J, Levander F. Data Processing Has Major Impact on the Outcome of Quantitative Label-Free LC-MS Analysis. J Proteome Res 2014; 14:676-87. [DOI: 10.1021/pr500665j] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Aakash Chawade
- Department
of Immunotechnology, Medicon Village, Lund University, Scheelevägen
2, S-223 63 Lund, Sweden
| | - Marianne Sandin
- Department
of Immunotechnology, Medicon Village, Lund University, Scheelevägen
2, S-223 63 Lund, Sweden
| | - Johan Teleman
- Department
of Immunotechnology, Medicon Village, Lund University, Scheelevägen
2, S-223 63 Lund, Sweden
- Department
of Clinical Sciences, Faculty of Medicine, Lund University, SE-221
84 Lund, Sweden
| | - Johan Malmström
- Department
of Clinical Sciences, Faculty of Medicine, Lund University, SE-221
84 Lund, Sweden
| | - Fredrik Levander
- Department
of Immunotechnology, Medicon Village, Lund University, Scheelevägen
2, S-223 63 Lund, Sweden
- Bioinformatics
Infrastructure for Life Sciences (BILS), Lund University, P.O. Box 117, 221 00 Lund, Sweden
| |
Collapse
|
17
|
Teleman J, Röst HL, Rosenberger G, Schmitt U, Malmström L, Malmström J, Levander F. DIANA--algorithmic improvements for analysis of data-independent acquisition MS data. ACTA ACUST UNITED AC 2014; 31:555-62. [PMID: 25348213 DOI: 10.1093/bioinformatics/btu686] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
MOTIVATION Data independent acquisition mass spectrometry has emerged as a reproducible and sensitive alternative in quantitative proteomics, where parsing the highly complex tandem mass spectra requires dedicated algorithms. Recently, targeted data extraction was proposed as a novel analysis strategy for this type of data, but it is important to further develop these concepts to provide quality-controlled, interference-adjusted and sensitive peptide quantification. RESULTS We here present the algorithm DIANA and the classifier PyProphet, which are based on new probabilistic sub-scores to classify the chromatographic peaks in targeted data-independent acquisition data analysis. The algorithm is capable of providing accurate quantitative values and increased recall at a controlled false discovery rate, in a complex gold standard dataset. Importantly, we further demonstrate increased confidence gained by the use of two complementary data-independent acquisition targeted analysis algorithms, as well as increased numbers of quantified peptide precursors in complex biological samples. AVAILABILITY AND IMPLEMENTATION DIANA is implemented in scala and python and available as open source (Apache 2.0 license) or pre-compiled binaries from http://quantitativeproteomics.org/diana. PyProphet can be installed from PyPi (https://pypi.python.org/pypi/pyprophet). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Johan Teleman
- Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Hannes L Röst
- Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - George Rosenberger
- Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Uwe Schmitt
- Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Lars Malmström
- Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Johan Malmström
- Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Fredrik Levander
- Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| |
Collapse
|
18
|
A targeted proteomics toolkit for high-throughput absolute quantification of Escherichia coli proteins. Metab Eng 2014; 26:48-56. [PMID: 25205128 DOI: 10.1016/j.ymben.2014.08.004] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Revised: 08/06/2014] [Accepted: 08/28/2014] [Indexed: 11/20/2022]
Abstract
Transformation of engineered Escherichia coli into a robust microbial factory is contingent on precise control of metabolism. Yet, the throughput of omics technologies used to characterize cell components has lagged far behind our ability to engineer novel strains. To expand the utility of quantitative proteomics for metabolic engineering, we validated and optimized targeted proteomics methods for over 400 proteins from more than 20 major pathways in E. coli metabolism. Complementing these methods, we constructed a series of synthetic genes to produce concatenated peptides (QconCAT) for absolute quantification of the proteins and made them available through the Addgene plasmid repository (www.addgene.org). To facilitate high sample throughput, we developed a fast, analytical-flow chromatography method using a 5.5-min gradient (10 min total run time). Overall this toolkit provides an invaluable resource for metabolic engineering by increasing sample throughput, minimizing development time and providing peptide standards for absolute quantification of E. coli proteins.
Collapse
|
19
|
Quantitative proteomics at different depths in human articular cartilage reveals unique patterns of protein distribution. Matrix Biol 2014; 40:34-45. [PMID: 25193283 DOI: 10.1016/j.matbio.2014.08.013] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2014] [Revised: 08/20/2014] [Accepted: 08/22/2014] [Indexed: 11/23/2022]
Abstract
The articular cartilage of synovial joints ensures friction-free mobility and attenuates mechanical impact on the joint during movement. These functions are mediated by the complex network of extracellular molecules characteristic for articular cartilage. Zonal differences in the extracellular matrix (ECM) are well recognized. However, knowledge about the precise molecular composition in the different zones remains limited. In the present study, we investigated the distribution of ECM molecules along the surface-to-bone axis, using quantitative non-targeted as well as targeted proteomics.\ In a discovery approach, iTRAQ mass spectrometry was used to identify all extractable ECM proteins in the different layers of a human lateral tibial plateau full thickness cartilage sample. A targeted MRM mass spectrometry approach was then applied to verify these findings and to extend the analysis to four medial tibial plateau samples. In the lateral tibial plateau sample, the unique distribution patterns of 70 ECM proteins were identified, revealing groups of proteins with a preferential distribution to the superficial, intermediate or deep regions of articular cartilage. The detailed analysis of selected 29 proteins confirmed these findings and revealed similar distribution patterns in the four medial tibial plateau samples. The results of this study allow, for the first time, an overview of the zonal distribution of a broad range of cartilage ECM proteins and open up further investigations of the functional roles of matrix proteins in the different zones of articular cartilage in health and disease.
Collapse
|
20
|
Chung LM, Colangelo CM, Zhao H. Data Pre-Processing for Label-Free Multiple Reaction Monitoring (MRM) Experiments. BIOLOGY 2014; 3:383-402. [PMID: 24905083 PMCID: PMC4085614 DOI: 10.3390/biology3020383] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2014] [Revised: 04/16/2014] [Accepted: 05/10/2014] [Indexed: 12/02/2022]
Abstract
Multiple Reaction Monitoring (MRM) conducted on a triple quadrupole mass spectrometer allows researchers to quantify the expression levels of a set of target proteins. Each protein is often characterized by several unique peptides that can be detected by monitoring predetermined fragment ions, called transitions, for each peptide. Concatenating large numbers of MRM transitions into a single assay enables simultaneous quantification of hundreds of peptides and proteins. In recognition of the important role that MRM can play in hypothesis-driven research and its increasing impact on clinical proteomics, targeted proteomics such as MRM was recently selected as the Nature Method of the Year. However, there are many challenges in MRM applications, especially data pre‑processing where many steps still rely on manual inspection of each observation in practice. In this paper, we discuss an analysis pipeline to automate MRM data pre‑processing. This pipeline includes data quality assessment across replicated samples, outlier detection, identification of inaccurate transitions, and data normalization. We demonstrate the utility of our pipeline through its applications to several real MRM data sets.
Collapse
Affiliation(s)
- Lisa M Chung
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA.
| | - Christopher M Colangelo
- Keck Foundation Biotechnology Resource Laboratory, Yale School of Medicine, New Haven, CT 06510, USA.
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA.
| |
Collapse
|
21
|
Teleman J, Dowsey AW, Gonzalez-Galarza FF, Perkins S, Pratt B, Röst HL, Malmström L, Malmström J, Jones AR, Deutsch EW, Levander F. Numerical compression schemes for proteomics mass spectrometry data. Mol Cell Proteomics 2014; 13:1537-42. [PMID: 24677029 DOI: 10.1074/mcp.o114.037879] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The open XML format mzML, used for representation of MS data, is pivotal for the development of platform-independent MS analysis software. Although conversion from vendor formats to mzML must take place on a platform on which the vendor libraries are available (i.e. Windows), once mzML files have been generated, they can be used on any platform. However, the mzML format has turned out to be less efficient than vendor formats. In many cases, the naïve mzML representation is fourfold or even up to 18-fold larger compared with the original vendor file. In disk I/O limited setups, a larger data file also leads to longer processing times, which is a problem given the data production rates of modern mass spectrometers. In an attempt to reduce this problem, we here present a family of numerical compression algorithms called MS-Numpress, intended for efficient compression of MS data. To facilitate ease of adoption, the algorithms target the binary data in the mzML standard, and support in main proteomics tools is already available. Using a test set of 10 representative MS data files we demonstrate typical file size decreases of 90% when combined with traditional compression, as well as read time decreases of up to 50%. It is envisaged that these improvements will be beneficial for data handling within the MS community.
Collapse
Affiliation(s)
- Johan Teleman
- From the ‡Department of Immunotechnology, Lund University, Medicon Village building 406, 223 81 Lund Sweden
| | - Andrew W Dowsey
- §Institute of Human Development, Faculty of Medical and Human Sciences, University of Manchester, United Kingdom; ¶Centre for Advanced Discovery and Experimental Therapeutics (CADET), University of Manchester and Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Sciences Centre, Oxford Road, Manchester M13 9WL, United Kingdom
| | | | - Simon Perkins
- ‖Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, United Kingdom
| | - Brian Pratt
- **Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington, 98195, USA
| | - Hannes L Röst
- ‡‡Department of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule Zürich, Wolfgang-Pauli Strasse 16, 8093 Zurich, Switzerland
| | - Lars Malmström
- ‡‡Department of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule Zürich, Wolfgang-Pauli Strasse 16, 8093 Zurich, Switzerland
| | - Johan Malmström
- §§Department of Clinical Sciences, Faculty of Medicine, Lund University, SE-221 84 Lund, Sweden
| | - Andrew R Jones
- ‖Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, United Kingdom
| | - Eric W Deutsch
- ¶¶Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109, USA;
| | - Fredrik Levander
- From the ‡Department of Immunotechnology, Lund University, Medicon Village building 406, 223 81 Lund Sweden; ‖‖Bioinformatics Infrastructure for Life Sciences, Lund University, Sweden
| |
Collapse
|
22
|
Proteome-wide selected reaction monitoring assays for the human pathogen Streptococcus pyogenes. Nat Commun 2013; 3:1301. [PMID: 23250431 PMCID: PMC3535367 DOI: 10.1038/ncomms2297] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2012] [Accepted: 11/15/2012] [Indexed: 01/07/2023] Open
Abstract
Selected reaction monitoring mass spectrometry (SRM-MS) is a targeted proteomics technology used to identify and quantify proteins with high sensitivity, specificity and high reproducibility. Execution of SRM-MS relies on protein-specific SRM assays, a set of experimental parameters that requires considerable effort to develop. Here we present a proteome-wide SRM assay repository for the gram-positive human pathogen group A Streptococcus. Using a multi-layered approach we generated SRM assays for 10,412 distinct group A Streptococcus peptides followed by extensive testing of the selected reaction monitoring assays in >200 different group A Streptococcus protein pools. Based on the number of SRM assay observations we created a rule-based selected reaction monitoring assay-scoring model to select the most suitable assays per protein for a given cellular compartment and bacterial state. The resource described here represents an important tool for deciphering the group A Streptococcus proteome using selected reaction monitoring and we anticipate that concepts described here can be extended to other pathogens. Selected reaction monitoring mass spectrometry (SRM-MS) can quantify dynamic changes in protein expression with high sensitivity. Karlsson et al. define optimal detection parameters for 10,412 distinct group A Streptococcus pyogenes peptides, which facilitates proteome-wide SRM-MS studies in this bacterium.
Collapse
|
23
|
Teleman J, Waldemarson S, Malmström J, Levander F. Automated quality control system for LC-SRM setups. J Proteomics 2013; 95:77-83. [PMID: 23584149 DOI: 10.1016/j.jprot.2013.03.029] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2012] [Revised: 03/22/2013] [Accepted: 03/25/2013] [Indexed: 10/26/2022]
Abstract
UNLABELLED Selected reaction monitoring (SRM) is emerging as a standard tool for high-throughput protein quantification. For reliable and reproducible SRM protein quantification it is essential that system performance is stable. We present here a quality control workflow that is based on repeated analysis of a standard sample to allow insight into the stability of the key properties of a SRM setup. This is supported by automated software to monitor system performance and display information like signal intensities and retention time stability over time, and alert upon deviations from expected metrics. Utilising the software to evaluate 407 repeated injections of a standard sample during half a year, outliers in relative peptide signal intensities and relative peptide fragment ratios are identified, indicating the need for instrument maintenance. We therefore believe that the software could be a vital and powerful tool for any lab regularly performing SRM, increasing the reliability and quality of the SRM platform. BIOLOGICAL SIGNIFICANCE Selected reaction monitoring (SRM) mass spectrometry is becoming established as a standard technique for accurate protein quantification. However, to achieve the required quantification reproducibility of the liquid chromatography (LC)-SRM setup, system performance needs to be monitored over time. Here we introduce a workflow with associated software to enable automated monitoring of LC-SRM setups. We believe that usage of the presented concepts will further strengthen the role of SRM as a reliable tool for protein quantification. This article is part of a Special Issue entitled: Standardization and Quality Control in Proteomics.
Collapse
Affiliation(s)
- Johan Teleman
- Department of Immunotechnology, Lund University, BMC D13, 22184 Lund, Sweden
| | | | | | | |
Collapse
|
24
|
Sandin M, Teleman J, Malmström J, Levander F. Data processing methods and quality control strategies for label-free LC-MS protein quantification. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2013; 1844:29-41. [PMID: 23567904 DOI: 10.1016/j.bbapap.2013.03.026] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2012] [Revised: 01/18/2013] [Accepted: 03/08/2013] [Indexed: 12/20/2022]
Abstract
Protein quantification using different LC-MS techniques is becoming a standard practice. However, with a multitude of experimental setups to choose from, as well as a wide array of software solutions for subsequent data processing, it is non-trivial to select the most appropriate workflow for a given biological question. In this review, we highlight different issues that need to be addressed by software for quantitative LC-MS experiments and describe different approaches that are available. With focus on label-free quantification, examples are discussed both for LC-MS/MS and LC-SRM data processing. We further elaborate on current quality control methodology for performing accurate protein quantification experiments. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.
Collapse
Affiliation(s)
- Marianne Sandin
- Department of Immunotechnology, Lund University, BMC D13, 22184 Lund, Sweden
| | | | | | | |
Collapse
|
25
|
Sandin M, Ali A, Hansson K, Månsson O, Andreasson E, Resjö S, Levander F. An adaptive alignment algorithm for quality-controlled label-free LC-MS. Mol Cell Proteomics 2013; 12:1407-20. [PMID: 23306530 DOI: 10.1074/mcp.o112.021907] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Label-free quantification using precursor-based intensities is a versatile workflow for large-scale proteomics studies. The method however requires extensive computational analysis and is therefore in need of robust quality control during the data mining stage. We present a new label-free data analysis workflow integrated into a multiuser software platform. A novel adaptive alignment algorithm has been developed to minimize the possible systematic bias introduced into the analysis. Parameters are estimated on the fly from the data at hand, producing a user-friendly analysis suite. Quality metrics are output in every step of the analysis as well as actively incorporated into the parameter estimation. We furthermore show the improvement of this system by comprehensive comparison to classical label-free analysis methodology as well as current state-of-the-art software.
Collapse
Affiliation(s)
- Marianne Sandin
- Department of Immunotechnology, Lund University, BMC D13, 22184 Lund, Sweden
| | | | | | | | | | | | | |
Collapse
|
26
|
Deutsch EW. File formats commonly used in mass spectrometry proteomics. Mol Cell Proteomics 2012; 11:1612-21. [PMID: 22956731 PMCID: PMC3518119 DOI: 10.1074/mcp.r112.019695] [Citation(s) in RCA: 69] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2012] [Revised: 08/06/2012] [Indexed: 11/06/2022] Open
Abstract
The application of mass spectrometry (MS) to the analysis of proteomes has enabled the high-throughput identification and abundance measurement of hundreds to thousands of proteins per experiment. However, the formidable informatics challenge associated with analyzing MS data has required a wide variety of data file formats to encode the complex data types associated with MS workflows. These formats encompass the encoding of input instruction for instruments, output products of the instruments, and several levels of information and results used by and produced by the informatics analysis tools. A brief overview of the most common file formats in use today is presented here, along with a discussion of related topics.
Collapse
|