1
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Yang JC, Chen SP, Wang YF, Chang CH, Chang KH, Fuh JL, Chow LH, Han CL, Chen YJ, Wang SJ. Cerebrospinal Fluid Proteome Map Reveals Molecular Signatures of Reversible Cerebral Vasoconstriction Syndrome. Mol Cell Proteomics 2024; 23:100794. [PMID: 38839039 DOI: 10.1016/j.mcpro.2024.100794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 04/08/2024] [Accepted: 05/07/2024] [Indexed: 06/07/2024] Open
Abstract
Reversible cerebral vasoconstriction syndrome (RCVS) is a complex neurovascular disorder characterized by repetitive thunderclap headaches and reversible cerebral vasoconstriction. The pathophysiological mechanism of this mysterious syndrome remains underexplored and there is no clinically available molecular biomarker. To provide insight into the pathogenesis of RCVS, this study reported the first landscape of dysregulated proteome of cerebrospinal fluid (CSF) in patients with RCVS (n = 21) compared to the age- and sex-matched controls (n = 20) using data-independent acquisition mass spectrometry. Protein-protein interaction and functional enrichment analysis were employed to construct functional protein networks using the RCVS proteome. An RCVS-CSF proteome library resource of 1054 proteins was established, which illuminated large groups of upregulated proteins enriched in the brain and blood-brain barrier (BBB). Personalized RCVS-CSF proteomic profiles from 17 RCVS patients and 20 controls reveal proteomic changes involving the complement system, adhesion molecules, and extracellular matrix, which may contribute to the disruption of BBB and dysregulation of neurovascular units. Moreover, an additional validation cohort validated a panel of biomarker candidates and a two-protein signature predicted by machine learning model to discriminate RCVS patients from controls with an area under the curve of 0.997. This study reveals the first RCVS proteome and a potential pathogenetic mechanism of BBB and neurovascular unit dysfunction. It also nominates potential biomarker candidates that are mechanistically plausible for RCVS, which may offer potential diagnostic and therapeutic opportunities beyond the clinical manifestations.
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Affiliation(s)
- Jhih-Ci Yang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan; Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Shih-Pin Chen
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Division of Translational Research, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Yen-Feng Wang
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chan-Hua Chang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan; Department of Chemistry, National Central University, Taoyuan, Taiwan
| | - Kun-Hao Chang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan; Molecular Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan; Department of Chemistry, Institute of Chemistry, Academia Sinica, Naitonal Tsing Hua University, Hsinchu, Taiwan
| | - Jong-Ling Fuh
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Lok-Hi Chow
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Anesthesiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chia-Li Han
- Master Program in Clinical Genomics and Proteomics, College of Pharmacy, Taipei Medical University, Taipei, Taiwan.
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan; Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Chemistry, National Taiwan University, Taipei, Taiwan.
| | - Shuu-Jiun Wang
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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2
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Ha A, Khoo A, Ignatchenko V, Khan S, Waas M, Vesprini D, Liu SK, Nyalwidhe JO, Semmes OJ, Boutros PC, Kislinger T. Comprehensive Prostate Fluid-Based Spectral Libraries for Enhanced Protein Detection in Urine. J Proteome Res 2024; 23:1768-1778. [PMID: 38580319 PMCID: PMC11077481 DOI: 10.1021/acs.jproteome.4c00009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 03/02/2024] [Accepted: 03/06/2024] [Indexed: 04/07/2024]
Abstract
Biofluids contain molecules in circulation and from nearby organs that can be indicative of disease states. Characterizing the proteome of biofluids with DIA-MS is an emerging area of interest for biomarker discovery; yet, there is limited consensus on DIA-MS data analysis approaches for analyzing large numbers of biofluids. To evaluate various DIA-MS workflows, we collected urine from a clinically heterogeneous cohort of prostate cancer patients and acquired data in DDA and DIA scan modes. We then searched the DIA data against urine spectral libraries generated using common library generation approaches or a library-free method. We show that DIA-MS doubles the sample throughput compared to standard DDA-MS with minimal losses to peptide detection. We further demonstrate that using a sample-specific spectral library generated from individual urines maximizes peptide detection compared to a library-free approach, a pan-human library, or libraries generated from pooled, fractionated urines. Adding urine subproteomes, such as the urinary extracellular vesicular proteome, to the urine spectral library further improves the detection of prostate proteins in unfractionated urine. Altogether, we present an optimized DIA-MS workflow and provide several high-quality, comprehensive prostate cancer urine spectral libraries that can streamline future biomarker discovery studies of prostate cancer using DIA-MS.
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Affiliation(s)
- Annie Ha
- Department
of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
- Princess
Margaret Cancer Centre, University Health
Network, Toronto, Ontario M5G 1L7, Canada
| | - Amanda Khoo
- Department
of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
- Princess
Margaret Cancer Centre, University Health
Network, Toronto, Ontario M5G 1L7, Canada
| | - Vladimir Ignatchenko
- Princess
Margaret Cancer Centre, University Health
Network, Toronto, Ontario M5G 1L7, Canada
| | - Shahbaz Khan
- Princess
Margaret Cancer Centre, University Health
Network, Toronto, Ontario M5G 1L7, Canada
| | - Matthew Waas
- Princess
Margaret Cancer Centre, University Health
Network, Toronto, Ontario M5G 1L7, Canada
| | - Danny Vesprini
- Department
of Radiation Oncology, University of Toronto, Toronto, Ontario M5T 1P5, Canada
- Odette
Cancer Research Program, Sunnybrook Research
Institute, Toronto, Ontario M4N 3M5, Canada
| | - Stanley K. Liu
- Department
of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
- Department
of Radiation Oncology, University of Toronto, Toronto, Ontario M5T 1P5, Canada
- Odette
Cancer Research Program, Sunnybrook Research
Institute, Toronto, Ontario M4N 3M5, Canada
| | - Julius O. Nyalwidhe
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23501, United States
- Department
of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23501, United States
| | - Oliver John Semmes
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23501, United States
- Department
of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23501, United States
| | - Paul C. Boutros
- Department
of Human Genetics, University of California,
Los Angeles, Los Angeles, California 90095, United States
- Department
of Urology, University of California, Los
Angeles, Los Angeles, California 90095, United States
- Institute
for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, United States
- Eli
and Edythe Broad Stem Cell Research Center, University of California, Los
Angeles, California 90095, United States
- Broad
Stem Cell Research Center, University of
California, Los Angeles, California 90095, United States
- Jonsson
Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90024, United States
- Department
of Human Genetics, University of California,
Los Angeles, Los Angeles, California 90095, United States
| | - Thomas Kislinger
- Department
of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
- Princess
Margaret Cancer Centre, University Health
Network, Toronto, Ontario M5G 1L7, Canada
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3
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Makepeace KA, Rookyard AW, Das L, Vardarajan BN, Chakrabarty JK, Jain A, Kang MS, Werth EG, Reyes-Dumeyer D, Zerlin-Esteves M, Honig LS, Mayeux R, Brown LM. Data-Independent Acquisition and Label-Free Quantification for Quantitative Proteomics Analysis of Human Cerebrospinal Fluid. Curr Protoc 2024; 4:e1014. [PMID: 38506436 PMCID: PMC11032743 DOI: 10.1002/cpz1.1014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
This article presents a practical guide to mass spectrometry-based data-independent acquisition and label-free quantification for proteomics analysis applied to cerebrospinal fluid, offering a robust and scalable approach to probing the proteomic composition of the central nervous system. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Cerebrospinal fluid sample collection and preparation for mass spectrometry analysis Basic Protocol 2: Mass spectrometry sample analysis with data-independent acquisition Support Protocol: Data-dependent mass spectrometry and spectral library construction Basic Protocol 3: Analysis of mass spectrometry data.
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Affiliation(s)
- Karl A.T. Makepeace
- Department of Biological Sciences, Quantitative Proteomics and Metabolomics Center, Columbia University, New York, NY, USA
| | - Alexander W. Rookyard
- Department of Biological Sciences, Quantitative Proteomics and Metabolomics Center, Columbia University, New York, NY, USA
| | - Lipi Das
- Department of Biological Sciences, Quantitative Proteomics and Metabolomics Center, Columbia University, New York, NY, USA
| | - Badri N. Vardarajan
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Jayanta K. Chakrabarty
- Department of Biological Sciences, Quantitative Proteomics and Metabolomics Center, Columbia University, New York, NY, USA
| | - Anu Jain
- Department of Biological Sciences, Quantitative Proteomics and Metabolomics Center, Columbia University, New York, NY, USA
| | - Min Suk Kang
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Emily G. Werth
- Department of Biological Sciences, Quantitative Proteomics and Metabolomics Center, Columbia University, New York, NY, USA
| | - Dolly Reyes-Dumeyer
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
- The Gertrude H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Marielba Zerlin-Esteves
- The Gertrude H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Lawrence S. Honig
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University and the New York Presbyterian Hospital, New York, NY, USA
- The Gertrude H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Richard Mayeux
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University and the New York Presbyterian Hospital, New York, NY, USA
- The Gertrude H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Lewis M. Brown
- Department of Biological Sciences, Quantitative Proteomics and Metabolomics Center, Columbia University, New York, NY, USA
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4
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Waury K, de Wit R, Verberk IMW, Teunissen CE, Abeln S. Deciphering Protein Secretion from the Brain to Cerebrospinal Fluid for Biomarker Discovery. J Proteome Res 2023; 22:3068-3080. [PMID: 37606934 PMCID: PMC10476268 DOI: 10.1021/acs.jproteome.3c00366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Indexed: 08/23/2023]
Abstract
Cerebrospinal fluid (CSF) is an essential matrix for the discovery of neurological disease biomarkers. However, the high dynamic range of protein concentrations in CSF hinders the detection of the least abundant protein biomarkers by untargeted mass spectrometry. It is thus beneficial to gain a deeper understanding of the secretion processes within the brain. Here, we aim to explore if and how the secretion of brain proteins to the CSF can be predicted. By combining a curated CSF proteome and the brain elevated proteome of the Human Protein Atlas, brain proteins were classified as CSF or non-CSF secreted. A machine learning model was trained on a range of sequence-based features to differentiate between CSF and non-CSF groups and effectively predict the brain origin of proteins. The classification model achieves an area under the curve of 0.89 if using high confidence CSF proteins. The most important prediction features include the subcellular localization, signal peptides, and transmembrane regions. The classifier generalized well to the larger brain detected proteome and is able to correctly predict novel CSF proteins identified by affinity proteomics. In addition to elucidating the underlying mechanisms of protein secretion, the trained classification model can support biomarker candidate selection.
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Affiliation(s)
- Katharina Waury
- Department
of Computer Science, Vrije Universiteit
Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Renske de Wit
- Department
of Computer Science, Vrije Universiteit
Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Inge M. W. Verberk
- Neurochemistry
Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands
| | - Charlotte E. Teunissen
- Neurochemistry
Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands
| | - Sanne Abeln
- Department
of Computer Science, Vrije Universiteit
Amsterdam, 1081 HV Amsterdam, The Netherlands
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5
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Quality Control—A Stepchild in Quantitative Proteomics: A Case Study for the Human CSF Proteome. Biomolecules 2023; 13:biom13030491. [PMID: 36979426 PMCID: PMC10046854 DOI: 10.3390/biom13030491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/08/2023] [Accepted: 03/01/2023] [Indexed: 03/09/2023] Open
Abstract
Proteomic studies using mass spectrometry (MS)-based quantification are a main approach to the discovery of new biomarkers. However, a number of analytical conditions in front and during MS data acquisition can affect the accuracy of the obtained outcome. Therefore, comprehensive quality assessment of the acquired data plays a central role in quantitative proteomics, though, due to the immense complexity of MS data, it is often neglected. Here, we address practically the quality assessment of quantitative MS data, describing key steps for the evaluation, including the levels of raw data, identification and quantification. With this, four independent datasets from cerebrospinal fluid, an important biofluid for neurodegenerative disease biomarker studies, were assessed, demonstrating that sample processing-based differences are already reflected at all three levels but with varying impacts on the quality of the quantitative data. Specifically, we provide guidance to critically interpret the quality of MS data for quantitative proteomics. Moreover, we provide the free and open source quality control tool MaCProQC, enabling systematic, rapid and uncomplicated data comparison of raw data, identification and feature detection levels through defined quality metrics and a step-by-step quality control workflow.
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6
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Mun DG, Budhraja R, Bhat FA, Zenka RM, Johnson KL, Moghekar A, Pandey A. Four-dimensional proteomics analysis of human cerebrospinal fluid with trapped ion mobility spectrometry using PASEF. Proteomics 2023; 23:e2200507. [PMID: 36752121 DOI: 10.1002/pmic.202200507] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/02/2023] [Accepted: 02/03/2023] [Indexed: 02/09/2023]
Abstract
A quadrupole time-of-flight mass spectrometer coupled with a trapped ion mobility spectrometry (timsTOF) operated in parallel accumulation-serial fragmentation (PASEF) mode has recently emerged as a platform capable of providing four-dimensional (4D) features comprising of elution time, collision cross section (CCS), mass-to-charge ratio, and intensity of peptides. The PASEF mode provides ∼100% ion sampling efficiency both in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes without sacrificing sensitivity. In addition, targeted measurements using PASEF integrated parallel reaction monitoring (PRM) mode have also been described. However, only limited number of studies have used timsTOF for analysis of clinical samples. Although Orbitrap mass spectrometers have been used for biomarker discovery from cerebrospinal fluid (CSF) in a variety of neurological diseases, these Orbitrap-derived datasets cannot readily be applied for driving experiments on timsTOF mass spectrometers. We generated a catalog of peptides and proteins in human CSF in DDA mode on a timsTOF mass spectrometer and used these data to build a spectral library. This strategy allowed us to use elution times and ion mobility values from the spectral library to design PRM experiments for quantifying previously discovered biomarkers from CSF samples in Alzheimer's disease. When the same samples were analyzed using a DIA approach combined with a spectral library search, a higher number of proteins were identified than in a library-free approach. Overall, we have established a spectral library of CSF as a resource and demonstrated its utility for PRM and DIA studies, which should facilitate studies of neurological disorders.
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Affiliation(s)
- Dong-Gi Mun
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Rohit Budhraja
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Firdous A Bhat
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Roman M Zenka
- Proteomics Core, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA.,Manipal Academy of Higher Education, Manipal, Karnataka, India.,Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
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7
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Karayel O, Virreira Winter S, Padmanabhan S, Kuras YI, Vu DT, Tuncali I, Merchant K, Wills AM, Scherzer CR, Mann M. Proteome profiling of cerebrospinal fluid reveals biomarker candidates for Parkinson's disease. Cell Rep Med 2022; 3:100661. [PMID: 35732154 PMCID: PMC9245058 DOI: 10.1016/j.xcrm.2022.100661] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 10/29/2021] [Accepted: 05/23/2022] [Indexed: 11/16/2022]
Abstract
Parkinson's disease (PD) is a growing burden worldwide, and there is no reliable biomarker used in clinical routines to date. Cerebrospinal fluid (CSF) is routinely collected in patients with neurological symptoms and should closely reflect alterations in PD patients' brains. Here, we describe a scalable and sensitive mass spectrometry (MS)-based proteomics workflow for CSF proteome profiling. From two independent cohorts with over 200 individuals, our workflow reproducibly quantifies over 1,700 proteins from minimal CSF amounts. Machine learning determines OMD, CD44, VGF, PRL, and MAN2B1 to be altered in PD patients or to significantly correlate with clinical scores. We also uncover signatures of enhanced neuroinflammation in LRRK2 G2019S carriers, as indicated by increased levels of CTSS, PLD4, and HLA proteins. A comparison with our previously acquired urinary proteomes reveals a large overlap in PD-associated changes, including lysosomal proteins, opening up new avenues to improve our understanding of PD pathogenesis.
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Affiliation(s)
- Ozge Karayel
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Sebastian Virreira Winter
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
| | | | - Yuliya I Kuras
- APDA Center for Advanced Parkinson Research, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA; Precision Neurology Program, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Duc Tung Vu
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Idil Tuncali
- APDA Center for Advanced Parkinson Research, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA; Precision Neurology Program, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Kalpana Merchant
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Anne-Marie Wills
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Clemens R Scherzer
- APDA Center for Advanced Parkinson Research, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA; Precision Neurology Program, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Matthias Mann
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.
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8
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Greco F, Anastasi F, Pardini LF, Dilillo M, Vannini E, Baroncelli L, Caleo M, McDonnell LA. Longitudinal Bottom-Up Proteomics of Serum, Serum Extracellular Vesicles, and Cerebrospinal Fluid Reveals Candidate Biomarkers for Early Detection of Glioblastoma in a Murine Model. Molecules 2021; 26:5992. [PMID: 34641541 PMCID: PMC8512455 DOI: 10.3390/molecules26195992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/24/2021] [Accepted: 09/30/2021] [Indexed: 12/04/2022] Open
Abstract
Glioblastoma Multiforme (GBM) is a brain tumor with a poor prognosis and low survival rates. GBM is diagnosed at an advanced stage, so little information is available on the early stage of the disease and few improvements have been made for earlier diagnosis. Longitudinal murine models are a promising platform for biomarker discovery as they allow access to the early stages of the disease. Nevertheless, their use in proteomics has been limited owing to the low sample amount that can be collected at each longitudinal time point. Here we used optimized microproteomics workflows to investigate longitudinal changes in the protein profile of serum, serum small extracellular vesicles (sEVs), and cerebrospinal fluid (CSF) in a GBM murine model. Baseline, pre-symptomatic, and symptomatic tumor stages were determined using non-invasive motor tests. Forty-four proteins displayed significant differences in signal intensities during GBM progression. Dysregulated proteins are involved in cell motility, cell growth, and angiogenesis. Most of the dysregulated proteins already exhibited a difference from baseline at the pre-symptomatic stage of the disease, suggesting that early effects of GBM might be detectable before symptom onset.
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Affiliation(s)
- Francesco Greco
- Institute of Life Sciences, Sant’Anna School of Advanced Studies, 56127 Pisa, Italy;
- Fondazione Pisana per la Scienza ONLUS, 56017 San Giuliano Terme, Italy; (F.A.); (L.F.P.); (M.D.)
| | - Federica Anastasi
- Fondazione Pisana per la Scienza ONLUS, 56017 San Giuliano Terme, Italy; (F.A.); (L.F.P.); (M.D.)
- NEST Laboratories, Scuola Normale Superiore, 56127 Pisa, Italy
| | - Luca Fidia Pardini
- Fondazione Pisana per la Scienza ONLUS, 56017 San Giuliano Terme, Italy; (F.A.); (L.F.P.); (M.D.)
- Department of Chemistry and Industrial Chemistry, University of Pisa, 56124 Pisa, Italy
| | - Marialaura Dilillo
- Fondazione Pisana per la Scienza ONLUS, 56017 San Giuliano Terme, Italy; (F.A.); (L.F.P.); (M.D.)
| | - Eleonora Vannini
- CNR, Neuroscience Institute, 56124 Pisa, Italy; (E.V.); (L.B.); (M.C.)
- Fondazione Umberto Veronesi, 20122 Milano, Italy
| | - Laura Baroncelli
- CNR, Neuroscience Institute, 56124 Pisa, Italy; (E.V.); (L.B.); (M.C.)
- IRCCS Fondazione Stella Maris, 56018 Calambrone, Italy
| | - Matteo Caleo
- CNR, Neuroscience Institute, 56124 Pisa, Italy; (E.V.); (L.B.); (M.C.)
- Dipartimento di Scienze Biomediche, Università di Padova, 35131 Padova, Italy
| | - Liam A. McDonnell
- Fondazione Pisana per la Scienza ONLUS, 56017 San Giuliano Terme, Italy; (F.A.); (L.F.P.); (M.D.)
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9
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Abbasi DA, Nguyen TTA, Hall DA, Robertson-Dick E, Berry-Kravis E, Cologna SM. Characterization of the Cerebrospinal Fluid Proteome in Patients with Fragile X-Associated Tremor/Ataxia Syndrome. THE CEREBELLUM 2021; 21:86-98. [PMID: 34046842 DOI: 10.1007/s12311-021-01262-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/16/2021] [Indexed: 01/11/2023]
Abstract
Fragile X-associated tremor/ataxia syndrome (FXTAS), first described in 2001, is a neurodegenerative and movement disorder, caused by a premutation in the fragile X mental retardation 1 (FMR1) gene. To date, the biological mechanisms causing this condition are still not well understood, as not all premutation carriers develop FXTAS. To further understand this syndrome, we quantitatively compared the cerebrospinal fluid (CSF) proteome of FXTAS patients with age-matched controls using mass spectrometry. We identified 415 proteins of which 97 were altered in FXTAS patients. These proteins suggest changes in acute phase response signaling, liver X receptor/ retinoid X receptor (LXR/RXR) activation, and farnesoid X receptor (FXR)/RXR activation, which are the main pathways found to be affected. Additionally, we detected changes in many other proteins including amyloid-like protein 2, contactin-1, afamin, cell adhesion molecule 4, NPC intracellular cholesterol transporter 2, and cathepsin B, that had been previously noted to hold important roles in other movement disorders. Specific to RXR pathways, several apolipoproteins (APOA1, APOA2, APOA4, APOC2, and APOD) showed significant changes in the CSF of FXTAS patients. Lastly, CSF parameters were analyzed to investigate abnormalities in blood brain barrier function. Correlations were observed between patient albumin quotient values, a measure of permeability, and CGG repeat length as well as FXTAS rating scale scores.
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Affiliation(s)
- Diana A Abbasi
- Department of Pediatrics and Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Thu T A Nguyen
- Department of Chemistry, University of Illinois At Chicago, Chicago, IL, USA
| | - Deborah A Hall
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Erin Robertson-Dick
- Department of Communication Sciences and Disorders, Northwestern University, Chicago, IL, USA
| | - Elizabeth Berry-Kravis
- Department of Pediatrics and Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Stephanie M Cologna
- Department of Chemistry, University of Illinois At Chicago, Chicago, IL, USA.
- Laboratory of Integrated Neuroscience, University of Illinois At Chicago, 845 W Taylor Street, Room 4500, Chicago, IL, 60607, USA.
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10
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Barkovits K, Chen W, Kohl M, Bracht T. Targeted Protein Quantification Using Parallel Reaction Monitoring (PRM). Methods Mol Biol 2021; 2228:145-157. [PMID: 33950489 DOI: 10.1007/978-1-0716-1024-4_11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Targeted proteomics represents an efficient method to quantify proteins of interest with high sensitivity and accuracy. Targeted approaches were first established for triple quadrupole instruments, but the emergence of hybrid instruments allowing for high-resolution and accurate-mass measurements of MS/MS fragment ions enabled the development of parallel reaction monitoring (PRM). In PRM analysis, specific peptides are measured as representatives of proteins in complex samples, with the full product ion spectra being acquired, allowing for identification and quantification of the peptides. Ideally, corresponding stable isotope-labeled peptides are spiked into the analyzed samples to account for technical variation and enhance the precision. Here, we describe the development of a PRM assay including the selection of appropriate peptides that fulfill the criteria to serve as unique surrogates of the targeted proteins. We depict the sequential steps of method development and the generation of calibration curves. Furthermore, we present the open-access tool CalibraCurve for the determination of the linear concentration ranges and limits of quantification (LOQ).
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Affiliation(s)
- Katalin Barkovits
- Medizinisches Proteom-Center (MPC), Ruhr-Universität Bochum, Bochum, Germany.,Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
| | - Weiqiang Chen
- Medizinisches Proteom-Center (MPC), Ruhr-Universität Bochum, Bochum, Germany.,Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
| | - Michael Kohl
- Medizinisches Proteom-Center (MPC), Ruhr-Universität Bochum, Bochum, Germany.,Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
| | - Thilo Bracht
- Medizinisches Proteom-Center (MPC), Ruhr-Universität Bochum, Bochum, Germany. .,Medical Proteome Analysis, Center for Proteindiagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany. .,Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany.
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11
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McKetney J, Panyard DJ, Johnson SC, Carlsson CM, Engelman CD, Coon JJ. Pilot proteomic analysis of cerebrospinal fluid in Alzheimer's disease. Proteomics Clin Appl 2021; 15:e2000072. [PMID: 33682374 PMCID: PMC8197734 DOI: 10.1002/prca.202000072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 02/24/2021] [Accepted: 03/01/2021] [Indexed: 01/10/2023]
Abstract
Proteomic analysis of cerebrospinal fluid (CSF) holds great promise in understanding the progression of neurodegenerative diseases, including Alzheimer's disease (AD). As one of the primary reservoirs of neuronal biomolecules, CSF provides a window into the biochemical and cellular aspects of the neurological environment. CSF can be drawn from living participants allowing the potential alignment of clinical changes with these biochemical markers. Using cutting-edge mass spectrometry technologies, we perform a streamlined proteomic analysis of CSF. We quantify greater than 700 proteins across 10 pairs of age- and sex-matched participants in approximately one hour of analysis time each. Using the paired participant study structure, we identify a small group of biologically relevant proteins that show substantial changes in abundance between cognitive normal and AD participants, which were then analyzed at the peptide level using parallel reaction monitoring experiments. Our findings suggest the utility of fractionating a single sample and using matching to increase proteomic depth in cerebrospinal fluid, as well as the potential power of an expanded study.
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Affiliation(s)
- Justin McKetney
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI
- National Center for Quantitative Biology of Complex Systems, Madison, WI
| | - Daniel J. Panyard
- Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI
| | - Sterling C. Johnson
- Geriatric Research Education and Clinical Center, Middleton Memorial Veterans Hospital, Madison, WI
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine, Madison, WI
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine, Madison, WI
| | - Cynthia M. Carlsson
- Geriatric Research Education and Clinical Center, Middleton Memorial Veterans Hospital, Madison, WI
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine, Madison, WI
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine, Madison, WI
| | - Corinne D. Engelman
- Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine, Madison, WI
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine, Madison, WI
| | - Joshua J. Coon
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI
- National Center for Quantitative Biology of Complex Systems, Madison, WI
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI
- Morgridge Institute for Research, Madison, WI
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12
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Schilde LM, Steinbach S, Serschnitzki B, Maass F, Bähr M, Lingor P, Marcus K, May C. Human cerebrospinal fluid data for use as spectral library, for biomarker research. Data Brief 2020; 32:106048. [PMID: 32775566 PMCID: PMC7399102 DOI: 10.1016/j.dib.2020.106048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 07/15/2020] [Indexed: 11/17/2022] Open
Abstract
Spectral libraries generated by data dependent acquisition (DDA) are a useful tool for the analysis of data created by data independent acquisition (DIA) in mass spectrometry. The quality of DIA analysis is dependent on the quality of the spectral library. We used cerebrospinal fluid (CSF) of patients with Parkinson's disease and healthy controls to create a spectral library of human CSF proteome. To this date, there is no validated CSF biomarker for Parkinson's disease. This data set may therefore be valuable for the future analysis of CSF proteins. Part of the samples consisted of fractions that were separated by gel electrophoresis. After tryptic digestion, all samples were spiked with indexed retention time (iRT) peptides and were measured using a DDA mass spectrometry approach. The here provided data set can be used as a CSF-specific spectral library. Data files generated from the described workflow are hosted in the public repository ProteomeXchange under the identifier PXD013487.
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Affiliation(s)
- Lukas M. Schilde
- Ruhr-University Bochum, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Germany
- Ruhr-University Bochum, Medical Faculty, Medizinisches Proteom-Center, Germany
| | - Simone Steinbach
- Ruhr-University Bochum, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Germany
- Ruhr-University Bochum, Medical Faculty, Medizinisches Proteom-Center, Germany
| | - Bettina Serschnitzki
- Ruhr-University Bochum, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Germany
- Ruhr-University Bochum, Medical Faculty, Medizinisches Proteom-Center, Germany
| | - Fabian Maass
- Department of Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Mathias Bähr
- Department of Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Paul Lingor
- Department of Neurology, University Medical Center Göttingen, Göttingen, Germany
- Center for Biostructural Imaging of Neurodegeneration (BIN), University of Göttingen Medical Center, Göttingen, Germany
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Neurology, München, Germany
| | - Katrin Marcus
- Ruhr-University Bochum, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Germany
- Ruhr-University Bochum, Medical Faculty, Medizinisches Proteom-Center, Germany
- Corresponding authors.
| | - Caroline May
- Ruhr-University Bochum, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Germany
- Ruhr-University Bochum, Medical Faculty, Medizinisches Proteom-Center, Germany
- Corresponding authors.
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13
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Mellinger AL, Griffith EH, Bereman MS. Peptide variability and signatures associated with disease progression in CSF collected longitudinally from ALS patients. Anal Bioanal Chem 2020; 412:5465-5475. [PMID: 32591871 DOI: 10.1007/s00216-020-02765-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 05/20/2020] [Accepted: 06/09/2020] [Indexed: 01/06/2023]
Abstract
We employ shotgun proteomics and data-independent acquisition (DIA) mass spectrometry to analyze cerebrospinal fluid longitudinally collected from 14 amyotrophic lateral sclerosis (ALS) patients (8 males and 6 females). We perform three main analyses of these data: (1) examine the intra- and inter-patient protein variability in CSF; (2) explore the association of inflammation with rate of disease progression; and (3) develop a mixed-effects model to best explain the decrease in ALS-Functional Rating Scale (ALS-FRS) score. Overall, the CSF protein abundances are tightly regulated with the intra-individual variability contributing just 4% to the overall variance. In four patients, a moderately significant correlation (p < 0.1) was observed between inflammation and rate of disease progression. Using a least absolute shrinkage and selection operator (LASSO) variable selection, we selected 55 viable peptides for mathematical modeling via a linear mixed-effects regression. We then employed forward selection to generate a final model by minimizing Akaike's information criterion (AIC). The final model utilized changes in abundance from 28 peptides as fixed effects to model progression of the disease in these patients. These peptides were from proteins involved in stress response and innate immunity. Graphical abstract.
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Affiliation(s)
- Allyson L Mellinger
- Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA
| | - Emily H Griffith
- Department of Statistics, North Carolina State University, Raleigh, NC, 27695, USA
| | - Michael S Bereman
- Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA. .,Department of Biological Sciences, North Carolina State University, Raleigh, NC, 27695, USA. .,Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, 27695, USA.
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14
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Borgmann-Winter KE, Wang K, Bandyopadhyay S, Torshizi AD, Hahn CG, Hahn CG. The proteome and its dynamics: A missing piece for integrative multi-omics in schizophrenia. Schizophr Res 2020; 217:148-161. [PMID: 31416743 PMCID: PMC7500806 DOI: 10.1016/j.schres.2019.07.025] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 07/10/2019] [Accepted: 07/13/2019] [Indexed: 01/08/2023]
Abstract
The complex and heterogeneous pathophysiology of schizophrenia can be deconstructed by integration of large-scale datasets encompassing genes through behavioral phenotypes. Genome-wide datasets are now available for genetic, epigenetic and transcriptomic variations in schizophrenia, which are then analyzed by newly devised systems biology algorithms. A missing piece, however, is the inclusion of information on the proteome and its dynamics in schizophrenia. Proteomics has lagged behind omics of the genome, transcriptome and epigenome since analytic platforms were relatively less robust for proteins. There has been remarkable progress, however, in the instrumentation of liquid chromatography (LC) and mass spectrometry (MS) (LCMS), experimental paradigms and bioinformatics of the proteome. Here, we present a summary of methodological innovations of recent years in MS based proteomics and the power of new generation proteomics, review proteomics studies that have been conducted in schizophrenia to date, and propose how such data can be analyzed and integrated with other omics results. The function of a protein is determined by multiple molecular properties, i.e., subcellular localization, posttranslational modification (PTMs) and protein-protein interactions (PPIs). Incorporation of these properties poses additional challenges in proteomics and their integration with other omics; yet is a critical next step to close the loop of multi-omics integration. In sum, the recent advent of high-throughput proteome characterization technologies and novel mathematical approaches enable us to incorporate functional properties of the proteome to offer a comprehensive multi-omics based understanding of schizophrenia pathophysiology.
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Affiliation(s)
- Karin E. Borgmann-Winter
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104-3403,Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children’s Hospital of Philadelphia, Philadelphia, PA 19104
| | - Kai Wang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104,Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104
| | | | - Abolfazl Doostparast Torshizi
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104
| | - Chang-Gyu Hahn
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104-3403, United States of America.
| | - Chang-Gyu Hahn
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104-3403, United States of America.
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15
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Barkovits K, Kruse N, Linden A, Tönges L, Pfeiffer K, Mollenhauer B, Marcus K. Blood Contamination in CSF and Its Impact on Quantitative Analysis of Alpha-Synuclein. Cells 2020; 9:cells9020370. [PMID: 32033488 PMCID: PMC7072133 DOI: 10.3390/cells9020370] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 01/24/2020] [Accepted: 01/28/2020] [Indexed: 11/28/2022] Open
Abstract
Analysis of cerebrospinal fluid (CSF) is important for diagnosis of neurological diseases. Especially for neurodegenerative diseases, abnormal protein abundance in CSF is an important biomarker. However, the quality of CSF is a key factor for the analytic outcome. Any external contamination has tremendous impact on the analysis and the reliability of the results. In this study, we evaluated the effect of blood contamination in CSF with respect to protein biomarker identification. We compared three distinct measures: Combur10-Test® strips, a specific hemoglobin ELISA, and bottom-up mass spectrometry (MS)-based proteomics for the determination of the general blood contamination level. In parallel, we studied the impact of blood contamination on the detectability of alpha-synuclein (aSyn), a highly abundant protein in blood/erythrocytes and a potential biomarker for Parkinson’s disease. Comparable results were achieved, with all three approaches enabling detection of blood levels in CSF down to 0.001%. We found higher aSyn levels with increasing blood contamination, highlighting the difficulty of authentic quantification of this protein in CSF. Based on our results, we identified other markers for blood contamination beyond hemoglobin and defined a grading system for blood levels in CSF samples, including a lower limit of tolerable blood contamination for MS-based biomarker studies.
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Affiliation(s)
- Katalin Barkovits
- Faculty of Medicine, Medizinisches Proteom-Center, Ruhr-University, 44801 Bochum, Germany; (K.B.); (A.L.); (K.P.)
| | - Niels Kruse
- Institute of Neuropathology, University Medical Center Goettingen, 37075Goettingen, Germany;
| | - Andreas Linden
- Faculty of Medicine, Medizinisches Proteom-Center, Ruhr-University, 44801 Bochum, Germany; (K.B.); (A.L.); (K.P.)
| | - Lars Tönges
- Department of Neurology, Ruhr-University Bochum at St Josef-Hospital, 44791 Bochum, Germany;
| | - Kathy Pfeiffer
- Faculty of Medicine, Medizinisches Proteom-Center, Ruhr-University, 44801 Bochum, Germany; (K.B.); (A.L.); (K.P.)
| | - Brit Mollenhauer
- Paracelsus-Elena Klinik, 34128 Kassel, Germany;
- Department of Neurology, University Medical Center Goettingen, 37075 Goettingen, Germany
| | - Katrin Marcus
- Faculty of Medicine, Medizinisches Proteom-Center, Ruhr-University, 44801 Bochum, Germany; (K.B.); (A.L.); (K.P.)
- Correspondence: ; Tel.: +49-234-3218106
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16
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Teclemariam ET, Pergande MR, Cologna SM. Considerations for mass spectrometry-based multi-omic analysis of clinical samples. Expert Rev Proteomics 2020; 17:99-107. [PMID: 31996049 DOI: 10.1080/14789450.2020.1724540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Introduction: The role of mass spectrometry in biomolecule analysis has become paramount over the last several decades ranging in the analysis across model systems and human specimens. Accordingly, the presence of mass spectrometers in clinical laboratories has also expanded alongside the number of researchers investigating the protein, lipid, and metabolite composition of an array of biospecimens. With this increase in the number of omic investigations, it is important to consider the entire experimental strategy from sample collection and storage, data collection and analysis.Areas covered: In this short review, we outline considerations for working with clinical (e.g. human) specimens including blood, urine, and cerebrospinal fluid, with emphasis on sample handling, profiling composition, targeted measurements and relevance to disease. Discussions of integrated genomic or transcriptomic datasets are not included. A brief commentary is also provided regarding new technologies with clinical relevance.Expert opinion: The role of mass spectrometry to investigate clinically related specimens is on the rise and the ability to integrate multiple omics datasets from mass spectrometry measurements will be crucial to further understanding human health and disease.
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Affiliation(s)
- Esei T Teclemariam
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, USA
| | - Melissa R Pergande
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, USA
| | - Stephanie M Cologna
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, USA.,Laboratory of Integrated Neuroscience, University of Illinois at Chicago, Chicago, IL, USA
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17
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Barkovits K, Pacharra S, Pfeiffer K, Steinbach S, Eisenacher M, Marcus K, Uszkoreit J. Reproducibility, Specificity and Accuracy of Relative Quantification Using Spectral Library-based Data-independent Acquisition. Mol Cell Proteomics 2020; 19:181-197. [PMID: 31699904 PMCID: PMC6944235 DOI: 10.1074/mcp.ra119.001714] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 10/17/2019] [Indexed: 12/14/2022] Open
Abstract
Currently data-dependent acquisition (DDA) is the method of choice for mass spectrometry-based proteomics discovery experiments, but data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirements to perform a DIA analysis is the availability of suitable spectral libraries for peptide identification and quantification. Several studies were performed addressing the evaluation of spectral library performance for protein identification in DIA measurements. But so far only few experiments estimate the effect of these libraries on the quantitative level.In this work we created a gold standard spike-in sample set with known contents and ratios of proteins in a complex protein matrix that allowed a detailed comparison of DIA quantification data obtained with different spectral library approaches. We used in-house generated sample-specific spectral libraries created using varying sample preparation approaches and repeated DDA measurement. In addition, two different search engines were tested for protein identification from DDA data and subsequent library generation. In total, eight different spectral libraries were generated, and the quantification results compared with a library free method, as well as a default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding DIA analysis results was inspected, but also the number of expected and identified differentially abundant protein groups and their ratios.We found, that while libraries of prefractionated samples were generally larger, there was no significant increase in DIA identifications compared with repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantification is strongly dependent on the applied spectral library and whether the quantification is based on peptide or protein level. Overall, the reproducibility and accuracy of DIA quantification is superior to DDA in all applied approaches.Data has been deposited to the ProteomeXchange repository with identifiers PXD012986, PXD012987, PXD012988 and PXD014956.
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Affiliation(s)
- Katalin Barkovits
- Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany
| | - Sandra Pacharra
- Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany
| | - Kathy Pfeiffer
- Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany
| | - Simone Steinbach
- Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany
| | - Martin Eisenacher
- Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany
| | - Katrin Marcus
- Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany.
| | - Julian Uszkoreit
- Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Bochum, Germany.
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18
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Lehmann S, Hirtz C, Vialaret J, Ory M, Combes GG, Corre ML, Badiou S, Cristol JP, Hanon O, Cornillot E, Bauchet L, Gabelle A, Colinge J. In Vivo Large-Scale Mapping of Protein Turnover in Human Cerebrospinal Fluid. Anal Chem 2019; 91:15500-15508. [PMID: 31730336 DOI: 10.1021/acs.analchem.9b03328] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The extraction of accurate physiological parameters from clinical samples provides a unique perspective to understand disease etiology and evolution, including under therapy. We introduce a new methodologic framework to map patient proteome dynamics in vivo, either proteome-wide or in large targeted panels. We applied it to ventricular cerebrospinal fluid (CSF) and could determine the turnover parameters of almost 200 proteins, whereas a handful were known previously. We covered a large number of neuron biology- and immune system-related proteins, including many biomarkers and drug targets. This first large data set unraveled a significant relationship between turnover and protein origin that relates to our ability to investigate organ physiology with protein-labeling strategy specifics. Our data constitute the first draft of CSF proteome dynamics as well as a repertoire of peptides for the community to design new analyses. The disclosed methods apply to other fluids or tissues provided sequential sample collection can be performed. We show that the proposed mathematical modeling applies to other analytical methods in the field.
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Affiliation(s)
- Sylvain Lehmann
- CHU de Montpellier , 34295 Montpellier , France.,IRMB, INSERM, Laboratoire de Biochimie Protéomique Clinique , 34295 Montpellier , France.,Université de Montpellier , 34090 Montpellier , France
| | - Christophe Hirtz
- CHU de Montpellier , 34295 Montpellier , France.,IRMB, INSERM, Laboratoire de Biochimie Protéomique Clinique , 34295 Montpellier , France.,Université de Montpellier , 34090 Montpellier , France
| | - Jérôme Vialaret
- CHU de Montpellier , 34295 Montpellier , France.,IRMB, INSERM, Laboratoire de Biochimie Protéomique Clinique , 34295 Montpellier , France
| | - Maxence Ory
- Institut de Recherche en Cancérologie de Montpellier, INSERM , 34298 Montpellier , France
| | - Guillaume Gras Combes
- Université de Montpellier , 34090 Montpellier , France.,Hôpital Gui de Chauliac, Service de Neurochirurgie , CHU de Montpellier , 34295 Montpellier , France.,INSERM U1051 , 34295 Montpellier , France
| | - Marine Le Corre
- Université de Montpellier , 34090 Montpellier , France.,Hôpital Gui de Chauliac, Service de Neurochirurgie , CHU de Montpellier , 34295 Montpellier , France.,INSERM U1051 , 34295 Montpellier , France
| | - Stéphanie Badiou
- Université de Montpellier , 34090 Montpellier , France.,Département de Biochimie et Hormonologie , CHU de Montpellier , 34295 Montpellier , France.,PhyMedExp , Université de Montpellier, INSERM, CNRS , 34090 Montpellier , France
| | - Jean-Paul Cristol
- Université de Montpellier , 34090 Montpellier , France.,Département de Biochimie et Hormonologie , CHU de Montpellier , 34295 Montpellier , France.,PhyMedExp , Université de Montpellier, INSERM, CNRS , 34090 Montpellier , France
| | - Olivier Hanon
- Service de Gériatrie , Hôpital Broca (AP-HP) , 75013 Paris , France.,Université Paris Descartes, Sorbonne Paris Cité , 75006 Paris , France
| | - Emmanuel Cornillot
- Université de Montpellier , 34090 Montpellier , France.,Institut de Recherche en Cancérologie de Montpellier, INSERM , 34298 Montpellier , France
| | - Luc Bauchet
- Université de Montpellier , 34090 Montpellier , France.,Hôpital Gui de Chauliac, Service de Neurochirurgie , CHU de Montpellier , 34295 Montpellier , France.,INSERM U1051 , 34295 Montpellier , France
| | - Audrey Gabelle
- Université de Montpellier , 34090 Montpellier , France.,Centre Mémoire de Ressources et de Recherche Languedoc-Roussillon , 34295 Montpellier , France.,Hôpital Gui de Chauliac , CHU de Montpellier , 34295 Montpellier , France
| | - Jacques Colinge
- Université de Montpellier , 34090 Montpellier , France.,Institut de Recherche en Cancérologie de Montpellier, INSERM , 34298 Montpellier , France.,Institut Régional du Cancer de Montpellier , 34298 Montpellier , France
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19
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Omenn GS, Lane L, Overall CM, Corrales FJ, Schwenk JM, Paik YK, Van Eyk JE, Liu S, Pennington S, Snyder MP, Baker MS, Deutsch EW. Progress on Identifying and Characterizing the Human Proteome: 2019 Metrics from the HUPO Human Proteome Project. J Proteome Res 2019; 18:4098-4107. [PMID: 31430157 PMCID: PMC6898754 DOI: 10.1021/acs.jproteome.9b00434] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The Human Proteome Project (HPP) annually reports on progress made throughout the field in credibly identifying and characterizing the complete human protein parts list and making proteomics an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2019-01-11 contains 17 694 proteins with strong protein-level evidence (PE1), compliant with HPP Guidelines for Interpretation of MS Data v2.1; these represent 89% of all 19 823 neXtProt predicted coding genes (all PE1,2,3,4 proteins), up from 17 470 one year earlier. Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), has been reduced from 2949 to 2129 since 2016 through efforts throughout the community, including the chromosome-centric HPP. PeptideAtlas is the source of uniformly reanalyzed raw mass spectrometry data for neXtProt; PeptideAtlas added 495 canonical proteins between 2018 and 2019, especially from studies designed to detect hard-to-identify proteins. Meanwhile, the Human Protein Atlas has released version 18.1 with immunohistochemical evidence of expression of 17 000 proteins and survival plots as part of the Pathology Atlas. Many investigators apply multiplexed SRM-targeted proteomics for quantitation of organ-specific popular proteins in studies of various human diseases. The 19 teams of the Biology and Disease-driven B/D-HPP published a total of 160 publications in 2018, bringing proteomics to a broad array of biomedical research.
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Affiliation(s)
- Gilbert S. Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, Michigan 48109-2218, United States
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109-5263, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics and Department of Microbiology and Molecular Medicine, Faculty of Medicine, University of Geneva, CMU, Michel-Servet 1, 1211 Geneva 4, Switzerland
| | - Christopher M. Overall
- Life Sciences Institute, Faculty of Dentistry, University of British Columbia, 2350 Health Sciences Mall, Room 4.401, Vancouver, British Columbia V6T 1Z3, Canada
| | | | - Jochen M. Schwenk
- Science for Life Laboratory, KTH Royal Institute of Technology, Tomtebodavägen 23A, 17165 Solna, Sweden
| | - Young-Ki Paik
- Yonsei Proteome Research Center, Yonsei University, Room 425, Building #114, 50 Yonsei-ro, Seodaemoon-ku, Seoul 120-749, South Korea
| | - Jennifer E. Van Eyk
- Advanced Clinical BioSystems Research Institute, Cedars Sinai Precision Biomarker Laboratories, Barbra Streisand Women’s Heart Center, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Siqi Liu
- BGI Group-Shenzhen, Yantian District, Shenzhen 518083, China
| | - Stephen Pennington
- School of Medicine, University College Dublin, Conway Institute Belfield, Dublin 4, Ireland
| | - Michael P. Snyder
- Department of Genetics, Stanford University, Alway Building, 300 Pasteur Drive and 3165 Porter Drive, Palo Alto, California 94304, United States
| | - Mark S. Baker
- Department of Biomedical Sciences, Faculty of Medicine & Health Sciences, Macquarie University, 75 Talavera Road, North Ryde, NSW 2109, Australia
| | - Eric W. Deutsch
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109-5263, United States
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20
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Essential Features and Use Cases of the Cerebrospinal Fluid Proteome Resource (CSF-PR). Methods Mol Biol 2019. [PMID: 31432427 DOI: 10.1007/978-1-4939-9706-0_25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Every year, a large number of published studies present biomarkers for various neurological disorders. Many of these studies are based on mass spectrometry proteomics data and describe comparison of the abundance of proteins in cerebrospinal fluid between two or more disease groups. As the number of such studies is growing, it is no longer straightforward to obtain an overview of which specific proteins are increased or decreased between the numerous relevant diseases and their many subcategories, or to see the larger picture or trends between related diseases. To alleviate this situation, we therefore mined the literature for mass spectrometry-based proteomics studies including quantitative protein data from cerebrospinal fluid of patients with multiple sclerosis, Alzheimer's disease, and Parkinson's disease and organized the extracted data in the Cerebrospinal Fluid Proteome Resource (CSF-PR). CSF-PR is freely available online at http://probe.uib.no/csf-pr , is highly interactive, and allows for easy navigation, visualization, and export of the published scientific data. This chapter will guide the user through some of the most important features of the tool and show examples of the suggested use cases.
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21
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Mun DG, Nam D, Kim H, Pandey A, Lee SW. Accurate Precursor Mass Assignment Improves Peptide Identification in Data-Independent Acquisition Mass Spectrometry. Anal Chem 2019; 91:8453-8460. [DOI: 10.1021/acs.analchem.9b01474] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Dong-Gi Mun
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Dowoon Nam
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Hokeun Kim
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55902, United States
- Manipal Academy of Higher Education (MAHE), Manipal, 576104 Karnataka, India
| | - Sang-Won Lee
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
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22
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Steinbach S, Serschnitzki B, Gerlach M, Marcus K, May C. Spiked human substantia nigra proteome data set for use as a spectral library for protein modelling and protein mapping. Data Brief 2019; 23:103711. [PMID: 31372383 PMCID: PMC6660433 DOI: 10.1016/j.dib.2019.103711] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 01/18/2019] [Accepted: 01/23/2019] [Indexed: 12/04/2022] Open
Abstract
This article describes a mass spectrometric data set generated from human substantia nigra tissue that was spiked with iRT peptides. The data set can be used as a spectral library for analysis of the human brain; especially for analysis of human substantia nigra, for example, in the context of Parkinson’s disease. Obtaining a sufficient amount of high-quality substantia nigra tissue is the key limiting factor for establishing a brain region-specific spectral library. Hence, combining existing spectral libraries for data-independent acquisition analysis (DIA) can overcome this major limitation. Moreover, these data can be used to map brain region-specific proteins and to model brain region-specific pathways. Both can improve our understanding of the functioning of the brain in greater depth. In addition, these data can also be used to determine the optimal settings for measuring proteins and peptides of interest. To create the substantia nigra-specific spectral library, the tissue was first homogenized and then fractionated via different types of SDS gel electrophoresis, resulting in 18 fractions. These fractions were analysed in triplicate by nanoHPLC-ESI-MS/MS, resulting in 54 data files. The data files generated from the described workflow are hosted in the public repository ProteomeXchange with the identifier PXD011076.
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Affiliation(s)
- Simone Steinbach
- Medizinisches Proteom-Center, Ruhr-Universität Bochum, Bochum, Germany
| | | | - Manfred Gerlach
- Center of Mental Health, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Würzburg, University of Würzburg, Würzburg, Germany
| | - Katrin Marcus
- Medizinisches Proteom-Center, Ruhr-Universität Bochum, Bochum, Germany
| | - Caroline May
- Medizinisches Proteom-Center, Ruhr-Universität Bochum, Bochum, Germany
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23
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Human tear fluid proteome dataset for usage as a spectral library and for protein modeling. Data Brief 2019; 23:103742. [PMID: 31372408 PMCID: PMC6660621 DOI: 10.1016/j.dib.2019.103742] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 01/31/2019] [Accepted: 02/01/2019] [Indexed: 11/20/2022] Open
Abstract
This article provides a detailed dataset of human tear fluid proteins. Samples were fractionated by sodium dodecyl sulfate (SDS) gel electrophoresis resulting in 48 fractions that were spiked with an indexed retention time (iRT) peptide standard. These data are based on a data-dependent acquisition (DDA) mass spectrometric approach and can be used for example as a spectral library for tear fluid proteome analysis by data-independent acquisition (DIA). Moreover, the provided data set can be used with optimized HPLC and mass spectrometric settings for proteins/peptides of interest. Besides these aspects, this dataset can serve as a protein overview for gene ontology enrichment analysis and for modeling and benchmarking of multiple signaling pathways associated with the ocular surface in healthy or disease stages. The mass spectrometry proteomics data from the described workflow have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD011075.
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Lachén-Montes M, González-Morales A, Fernández-Irigoyen J, Santamaría E. Determination of Cerebrospinal Fluid Proteome Variations by Isobaric Labeling Coupled with Strong Cation-Exchange Chromatography and Tandem Mass Spectrometry. Methods Mol Biol 2019; 2044:155-168. [PMID: 31432412 DOI: 10.1007/978-1-4939-9706-0_10] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Cerebrospinal fluid (CSF) is in direct contact with the brain and represents a valuable source of mediators that reflect metabolic processes occurring in the central nervous system (CNS). In this sense, mass spectrometry (MS) methods have proven to be sensitive in quantifying the proteomic profiles of CSF, therefore being able to detect biomarker candidates for neurological disorders. In particular, a key development has been the use of multiplexing technologies to easily identify and quantify complex protein mixtures. This chapter describes a workflow suitable for the analysis of CSF proteome using isobaric labeling coupled to strong cation-exchange chromatography fractionation for its potential use as a biomarker discovery platform. In this case, the isobaric tags for relative and absolute quantitation (iTRAQ) label all proteins in a sample via free amines at the N-terminus and on the side chain of lysine residues. Then, the labeled samples are pooled and chromatographically fractionated. These fractions with the pooled samples are afterward analyzed by tandem mass spectrometry (MS/MS), and proteins are quantified by the relative intensities of the reporter ions in the MS/MS spectra, simultaneously obtaining the amino acid sequence. This method complements the neuroproteomic toolbox to identify new protein biomarkers not only for the early clinical diagnosis and disease staging of CNS-related disorders but also to elucidate the molecular mechanisms related to the pathophysiology of these symptoms.
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Affiliation(s)
- Mercedes Lachén-Montes
- Proteomics Unit, Clinical Neuroproteomics Laboratory, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Proteored-ISCIII, Pamplona, Spain
| | - Andrea González-Morales
- Proteomics Unit, Clinical Neuroproteomics Laboratory, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Proteored-ISCIII, Pamplona, Spain
| | - Joaquín Fernández-Irigoyen
- Proteomics Unit, Clinical Neuroproteomics Laboratory, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Proteored-ISCIII, Pamplona, Spain
| | - Enrique Santamaría
- Proteomics Unit, Clinical Neuroproteomics Laboratory, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Proteored-ISCIII, Pamplona, Spain.
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