1
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Núñez E, Gómez-Serrano M, Calvo E, Bonzon-Kulichenko E, Trevisan-Herraz M, Rodríguez JM, García-Marqués F, Magni R, Lara-Pezzi E, Martín-Ventura JL, Camafeita E, Vázquez J. A Multiplexed Quantitative Proteomics Approach to the Human Plasma Protein Signature. Biomedicines 2024; 12:2118. [PMID: 39335631 PMCID: PMC11428418 DOI: 10.3390/biomedicines12092118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Revised: 08/30/2024] [Accepted: 09/09/2024] [Indexed: 09/30/2024] Open
Abstract
Despite the plasma proteome being able to provide a unique insight into the health and disease status of individuals, holding singular promise as a source of protein biomarkers that could be pivotal in the context of personalized medicine, only around 100 proteins covering a few human conditions have been approved as biomarkers by the US Food and Drug Administration (FDA) so far. Mass spectrometry (MS) currently has enormous potential for high-throughput analysis in clinical research; however, plasma proteomics remains challenging mainly due to the wide dynamic range of plasma protein abundances and the time-consuming procedures required. We applied a new MS-based multiplexed proteomics workflow to quantitate proteins, encompassing 67 FDA-approved biomarkers, in >1300 human plasma samples from a clinical cohort. Our results indicate that this workflow is suitable for large-scale clinical studies, showing good accuracy and reproducibility (coefficient of variation (CV) < 20 for 90% of the proteins). Furthermore, we identified plasma signature proteins (stable in time on an individual basis), stable proteins (exhibiting low biological variability and high temporal stability), and highly variable proteins (with low temporal stability) that can be used for personalized health monitoring and medicine.
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Affiliation(s)
- Estefanía Núñez
- Centro Nacional de Investigaciones Cardiovasculares Carlos III, 28029 Madrid, Spain; (E.N.); (E.C.); (E.B.-K.); (J.M.R.); (R.M.); (E.L.-P.)
- CIBER de Enfermedades Cardiovasculares (CIBERCV), 28029 Madrid, Spain;
| | - María Gómez-Serrano
- Institute for Tumor Immunology, Center for Tumor Biology and Immunology (ZTI), Philipps University, 35043 Marburg, Germany;
| | - Enrique Calvo
- Centro Nacional de Investigaciones Cardiovasculares Carlos III, 28029 Madrid, Spain; (E.N.); (E.C.); (E.B.-K.); (J.M.R.); (R.M.); (E.L.-P.)
- CIBER de Enfermedades Cardiovasculares (CIBERCV), 28029 Madrid, Spain;
| | - Elena Bonzon-Kulichenko
- Centro Nacional de Investigaciones Cardiovasculares Carlos III, 28029 Madrid, Spain; (E.N.); (E.C.); (E.B.-K.); (J.M.R.); (R.M.); (E.L.-P.)
| | - Marco Trevisan-Herraz
- International Center for Life, Newcastle University, Newcastle upon Tyne NE1 4EP, UK;
| | - José Manuel Rodríguez
- Centro Nacional de Investigaciones Cardiovasculares Carlos III, 28029 Madrid, Spain; (E.N.); (E.C.); (E.B.-K.); (J.M.R.); (R.M.); (E.L.-P.)
| | | | - Ricardo Magni
- Centro Nacional de Investigaciones Cardiovasculares Carlos III, 28029 Madrid, Spain; (E.N.); (E.C.); (E.B.-K.); (J.M.R.); (R.M.); (E.L.-P.)
- CIBER de Enfermedades Cardiovasculares (CIBERCV), 28029 Madrid, Spain;
| | - Enrique Lara-Pezzi
- Centro Nacional de Investigaciones Cardiovasculares Carlos III, 28029 Madrid, Spain; (E.N.); (E.C.); (E.B.-K.); (J.M.R.); (R.M.); (E.L.-P.)
- CIBER de Enfermedades Cardiovasculares (CIBERCV), 28029 Madrid, Spain;
| | - José Luis Martín-Ventura
- CIBER de Enfermedades Cardiovasculares (CIBERCV), 28029 Madrid, Spain;
- IIS-Fundación Jiménez-Díaz, 28015 Madrid, Spain
| | - Emilio Camafeita
- Centro Nacional de Investigaciones Cardiovasculares Carlos III, 28029 Madrid, Spain; (E.N.); (E.C.); (E.B.-K.); (J.M.R.); (R.M.); (E.L.-P.)
- CIBER de Enfermedades Cardiovasculares (CIBERCV), 28029 Madrid, Spain;
| | - Jesús Vázquez
- Centro Nacional de Investigaciones Cardiovasculares Carlos III, 28029 Madrid, Spain; (E.N.); (E.C.); (E.B.-K.); (J.M.R.); (R.M.); (E.L.-P.)
- CIBER de Enfermedades Cardiovasculares (CIBERCV), 28029 Madrid, Spain;
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2
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Wang Y, Sun H, Sheng N, He K, Hou W, Zhao Z, Yang Q, Huang L. ESMSec: Prediction of Secreted Proteins in Human Body Fluids Using Protein Language Models and Attention. Int J Mol Sci 2024; 25:6371. [PMID: 38928078 PMCID: PMC11204320 DOI: 10.3390/ijms25126371] [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: 04/24/2024] [Revised: 06/02/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024] Open
Abstract
The secreted proteins of human body fluid have the potential to be used as biomarkers for diseases. These biomarkers can be used for early diagnosis and risk prediction of diseases, so the study of secreted proteins of human body fluid has great application value. In recent years, the deep-learning-based transformer language model has transferred from the field of natural language processing (NLP) to the field of proteomics, leading to the development of protein language models (PLMs) for protein sequence representation. Here, we propose a deep learning framework called ESM Predict Secreted Proteins (ESMSec) to predict three types of proteins secreted in human body fluid. The ESMSec is based on the ESM2 model and attention architecture. Specifically, the protein sequence data are firstly put into the ESM2 model to extract the feature information from the last hidden layer, and all the input proteins are encoded into a fixed 1000 × 480 matrix. Secondly, multi-head attention with a fully connected neural network is employed as the classifier to perform binary classification according to whether they are secreted into each body fluid. Our experiment utilized three human body fluids that are important and ubiquitous markers. Experimental results show that ESMSec achieved average accuracy of 0.8486, 0.8358, and 0.8325 on the testing datasets for plasma, cerebrospinal fluid (CSF), and seminal fluid, which on average outperform the state-of-the-art (SOTA) methods. The outstanding performance results of ESMSec demonstrate that the ESM can improve the prediction performance of the model and has great potential to screen the secretion information of human body fluid proteins.
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Affiliation(s)
- Yan Wang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (Y.W.); (H.S.); (N.S.); (W.H.); (Z.Z.); (Q.Y.)
| | - Huiting Sun
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (Y.W.); (H.S.); (N.S.); (W.H.); (Z.Z.); (Q.Y.)
| | - Nan Sheng
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (Y.W.); (H.S.); (N.S.); (W.H.); (Z.Z.); (Q.Y.)
| | - Kai He
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48103, USA;
| | - Wenjv Hou
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (Y.W.); (H.S.); (N.S.); (W.H.); (Z.Z.); (Q.Y.)
| | - Ziqi Zhao
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (Y.W.); (H.S.); (N.S.); (W.H.); (Z.Z.); (Q.Y.)
| | - Qixing Yang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (Y.W.); (H.S.); (N.S.); (W.H.); (Z.Z.); (Q.Y.)
| | - Lan Huang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (Y.W.); (H.S.); (N.S.); (W.H.); (Z.Z.); (Q.Y.)
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3
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Perez KA, Deppe DW, Filas A, Singh SA, Aikawa E. Multimodal Analytical Tools to Enhance Mechanistic Understanding of Aortic Valve Calcification. THE AMERICAN JOURNAL OF PATHOLOGY 2024; 194:539-550. [PMID: 37517686 PMCID: PMC10988764 DOI: 10.1016/j.ajpath.2023.06.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 06/14/2023] [Accepted: 06/29/2023] [Indexed: 08/01/2023]
Abstract
This review focuses on technologies at the core of calcific aortic valve disease (CAVD) and drug target research advancement, including transcriptomics, proteomics, and molecular imaging. We examine how bulk RNA sequencing and single-cell RNA sequencing have engendered organismal genomes and transcriptomes, promoting the analysis of tissue gene expression profiles and cell subpopulations, respectively. We bring into focus how the field is also largely influenced by increasingly accessible proteome profiling techniques. In unison, global transcriptional and protein expression analyses allow for increased understanding of cellular behavior and pathogenic pathways under pathologic stimuli including stress, inflammation, low-density lipoprotein accumulation, increased calcium and phosphate levels, and vascular injury. We also look at how direct investigation of protein signatures paves the way for identification of targetable pathways for pharmacologic intervention. Here, we note that imaging techniques, once a clinical diagnostic tool for late-stage CAVD, have since been refined to address a clinical need to identify microcalcifications using positron emission tomography/computed tomography and even detect in vivo cellular events indicative of early stage CAVD and map the expression of identified proteins in animal models. Together, these techniques generate a holistic approach to CAVD investigation, with the potential to identify additional novel regulatory pathways.
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Affiliation(s)
- Katelyn A Perez
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Daniel W Deppe
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Aidan Filas
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Sasha A Singh
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
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4
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Kussmann M. Mass spectrometry as a lens into molecular human nutrition and health. EUROPEAN JOURNAL OF MASS SPECTROMETRY (CHICHESTER, ENGLAND) 2023; 29:370-379. [PMID: 37587732 DOI: 10.1177/14690667231193555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Mass spectrometry (MS) has developed over the last decades into the most informative and versatile analytical technology in molecular and structural biology (). The platform enables discovery, identification, and characterisation of non-volatile biomolecules, such as proteins, peptides, DNA, RNA, nutrients, metabolites, and lipids at both speed and scale and can elucidate their interactions and effects. The versatility, robustness, and throughput have rendered MS a major research and development platform in molecular human health and biomedical science. More recently, MS has also been established as the central tool for 'Molecular Nutrition', enabling comprehensive and rapid identification and characterisation of macro- and micronutrients, bioactives, and other food compounds. 'Molecular Nutrition' thereby helps understand bioaccessibility, bioavailability, and bioefficacy of macro- and micronutrients and related health effects. Hence, MS provides a lens through which the fate of nutrients can be monitored along digestion via absorption to metabolism. This in turn provides the bioanalytical foundation for 'Personalised Nutrition' or 'Precision Nutrition' in which design and development of diets and nutritional products is tailored towards consumer and patient groups sharing similar genetic and environmental predisposition, health/disease conditions and lifestyles, and/or objectives of performance and wellbeing. The next level of integrated nutrition science is now being built as 'Systems Nutrition' where public and personal health data are correlated with life condition and lifestyle factors, to establish directional relationships between nutrition, lifestyle, environment, and health, eventually translating into science-based public and personal heath recommendations and actions. This account provides a condensed summary of the contributions of MS to a precise, quantitative, and comprehensive nutrition and health science and sketches an outlook on its future role in this fascinating and relevant field.
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Affiliation(s)
- Martin Kussmann
- Abteilung Wissenschaft, Kompetenzzentrum für Ernährung (KErn), Germany
- Kussmann Biotech GmbH, Germany
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5
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Zoanni B, Brioschi M, Mallia A, Gianazza E, Eligini S, Carini M, Aldini G, Banfi C. Novel insights about albumin in cardiovascular diseases: Focus on heart failure. MASS SPECTROMETRY REVIEWS 2023; 42:1113-1128. [PMID: 34747521 DOI: 10.1002/mas.21743] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/27/2021] [Accepted: 09/02/2021] [Indexed: 06/07/2023]
Abstract
The Human Plasma Proteome has always been the most investigated compartment in proteomics-based biomarker discovery, and is considered the largest and deepest version of the human proteome, reflecting the state of the body in health and disease. Even if efforts have been always dedicated to the refinement of proteomic approaches to investigate more deeply the plasma proteome, it should not be forgotten that also highly abundant plasma proteins, like human serum albumin (HSA), often neglected in these studies, might provide fundamental physiological functions in plasma, and should be better considered. This review summarizes the important roles of HSA in the context of cardiovascular diseases (CVD), and in particular in heart failure. Notwithstanding much attention has been historically directed toward the association of HSA levels and CVD risk, the advances in the field of mass spectrometry research allow also a better characterization of the effects of oxidative modifications that could alter not only the structure but also the function of HSA.
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Affiliation(s)
| | | | - Alice Mallia
- Centro Cardiologico Monzino, IRCCS, Milano, Italy
| | | | | | - Marina Carini
- Dipartimento di Scienze Farmaceutiche, Università di Milano, Milan, Italy
| | - Giancarlo Aldini
- Dipartimento di Scienze Farmaceutiche, Università di Milano, Milan, Italy
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6
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Torun F, Virreira Winter S, Doll S, Riese FM, Vorobyev A, Mueller-Reif JB, Geyer PE, Strauss MT. Transparent Exploration of Machine Learning for Biomarker Discovery from Proteomics and Omics Data. J Proteome Res 2023; 22:359-367. [PMID: 36426751 PMCID: PMC9903317 DOI: 10.1021/acs.jproteome.2c00473] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Indexed: 11/27/2022]
Abstract
Biomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy; still, they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become a promising tool for this purpose. However, it is sometimes applied in an opaque manner and generally requires specialized knowledge. To enable easy access to ML for biomarker discovery without any programming or bioinformatics skills, we developed "OmicLearn" (http://OmicLearn.org), an open-source browser-based ML tool using the latest advances in the Python ML ecosystem. Data matrices from omics experiments are easily uploaded to an online or a locally installed web server. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental data sets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences.
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Affiliation(s)
| | | | - Sophia Doll
- OmicEra
Diagnostics GmbH, 82152 Planegg, Germany
| | | | | | | | | | - Maximilian T. Strauss
- Novo
Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark
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7
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Monteiro JP, Morine MJ, Ued FV, Kaput J. Identifying and Analyzing Topic Clusters in a Nutri-, Food-, and Diet-Proteomic Corpus Using Machine Reading. Nutrients 2023; 15:nu15020270. [PMID: 36678141 PMCID: PMC9863309 DOI: 10.3390/nu15020270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/27/2022] [Accepted: 12/30/2022] [Indexed: 01/06/2023] Open
Abstract
Nutrition affects the early stages of disease development, but the mechanisms remain poorly understood. High-throughput proteomic methods are being used to generate data and information on the effects of nutrients, foods, and diets on health and disease processes. In this report, a novel machine reading pipeline was used to identify all articles and abstracts on proteomics, diet, food, and nutrition in humans. The resulting proteomic corpus was further analyzed to produce seven clusters of "thematic" content defined as documents that have similar word content. Examples of publications from several of these clusters were then described in a similar way to a typical descriptive review.
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Affiliation(s)
- Jacqueline Pontes Monteiro
- Department of Pediatrics, Ribeirão Preto Medical School, University of São Paulo, Bandeirantes Avenue, 3900, Ribeirão Preto 14049-900, Brazil
- Correspondence:
| | | | - Fabio V. Ued
- Department of Pediatrics, Ribeirão Preto Medical School, University of São Paulo, Bandeirantes Avenue, 3900, Ribeirão Preto 14049-900, Brazil
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King CD, Kapp KL, Arul AB, Choi MJ, Robinson RAS. Advancements in automation for plasma proteomics sample preparation. Mol Omics 2022; 18:828-839. [PMID: 36048090 PMCID: PMC9879274 DOI: 10.1039/d2mo00122e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Automation is necessary to increase sample processing throughput for large-scale clinical analyses. Replacement of manual pipettes with robotic liquid handler systems is especially helpful in processing blood-based samples, such as plasma and serum. These samples are very heterogenous, and protein expression can vary greatly from sample-to-sample, even for healthy controls. Detection of true biological changes requires that variation from sample preparation steps and downstream analytical detection methods, such as mass spectrometry, remains low. In this mini-review, we discuss plasma proteomics protocols and the benefits of automation towards enabling detection of low abundant proteins and providing low sample error and increased sample throughput. This discussion includes considerations for automation of major sample depletion and/or enrichment strategies for plasma toward mass spectrometry detection.
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Affiliation(s)
- Christina D King
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, USA
| | - Kathryn L Kapp
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee 37232, USA
| | - Albert B Arul
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, USA
| | - Min Ji Choi
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, USA
| | - Renã A S Robinson
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee 37232, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA
- Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee 37212, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, Tennessee 37232, USA
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9
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Cominetti O, Núñez Galindo A, Corthésy J, Carayol J, Germain N, Galusca B, Estour B, Hager J, Gheldof N, Dayon L. Proteomics reveals unique plasma signatures in constitutional thinness. Proteomics Clin Appl 2022; 16:e2100114. [PMID: 35579096 PMCID: PMC9787820 DOI: 10.1002/prca.202100114] [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: 11/11/2021] [Revised: 04/14/2022] [Accepted: 05/13/2022] [Indexed: 12/30/2022]
Abstract
PURPOSE Studying the plasma proteome of control versus constitutionally thin (CT) individuals, exposed to overfeeding, may give insights into weight-gain management, providing relevant information to the clinical entity of weight-gain resistant CT, and discovering new markers for the condition. EXPERIMENTAL DESIGN Untargeted protein relative quantification of 63 CT and normal-weight individuals was obtained in blood plasma at baseline, during and after an overfeeding challenge using mass spectrometry-based proteomics. RESULTS The plasma proteome of CT subjects presented limited specificity with respect to controls at baseline. Yet, CT showed lower levels of inflammatory C-reactive protein and larger levels of protective insulin-like growth factor-binding protein 2. Differences were more marked during and after overfeeding. CT plasma proteome showed larger magnitude and significance in response, suggesting enhanced "resilience" and more rapid adaptation to changes. Four proteins behaved similarly between CT and controls, while five were regulated in opposite fashion. Ten proteins were differential during overfeeding in CT only (including increased fatty acid-binding protein and glyceraldehyde-3-phosphate dehydrogenase, and decreased apolipoprotein C-II and transferrin receptor protein 1). CONCLUSIONS AND CLINICAL RELEVANCE This first proteomic profiling of a CT cohort reveals different plasma proteomes between CT subjects and controls in a longitudinal clinical trial. Our molecular observations further support that the resistance to weight gain in CT subjects appears predominantly biological. CLINICALTRIALS gov Identifier: NCT02004821.
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Affiliation(s)
- Ornella Cominetti
- Nestlé Institute of Food Safety & Analytical SciencesNestlé ResearchLausanneSwitzerland
| | - Antonio Núñez Galindo
- Nestlé Institute of Food Safety & Analytical SciencesNestlé ResearchLausanneSwitzerland
| | - John Corthésy
- Nestlé Institute of Food Safety & Analytical SciencesNestlé ResearchLausanneSwitzerland
| | - Jérôme Carayol
- Nestlé Institute of Health SciencesNestlé ResearchLausanneSwitzerland,Present address:
Playtika Switzerland SARue du Port‐Franc 2ALausanne1003Switzerland
| | - Natacha Germain
- Division of EndocrinologyDiabetes, Metabolism and Eating Disorders, CHU St‐EtienneFrance
| | - Bogdan Galusca
- Division of EndocrinologyDiabetes, Metabolism and Eating Disorders, CHU St‐EtienneFrance
| | - Bruno Estour
- Division of EndocrinologyDiabetes, Metabolism and Eating Disorders, CHU St‐EtienneFrance
| | - Jörg Hager
- Nestlé Institute of Health SciencesNestlé ResearchLausanneSwitzerland
| | - Nele Gheldof
- Nestlé Institute of Health SciencesNestlé ResearchLausanneSwitzerland,Present address:
VPA ‐ AVP‐R‐Administration, EPFLBI A2 483, Station 7Lausanne1015Switzerland
| | - Loïc Dayon
- Nestlé Institute of Food Safety & Analytical SciencesNestlé ResearchLausanneSwitzerland,Institut des Sciences et Ingénierie ChimiquesÉcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
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10
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Tognetti M, Sklodowski K, Müller S, Kamber D, Muntel J, Bruderer R, Reiter L. Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area. J Proteome Res 2022; 21:1718-1735. [PMID: 35605973 PMCID: PMC9251764 DOI: 10.1021/acs.jproteome.2c00122] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
![]()
The plasma proteome
has the potential to enable a holistic analysis
of the health state of an individual. However, plasma biomarker discovery
is difficult due to its high dynamic range and variability. Here,
we present a novel automated analytical approach for deep plasma profiling
and applied it to a 180-sample cohort of human plasma from lung, breast,
colorectal, pancreatic, and prostate cancers. Using a controlled quantitative
experiment, we demonstrate a 257% increase in protein identification
and a 263% increase in significantly differentially abundant proteins
over neat plasma. In the cohort, we identified 2732 proteins. Using
machine learning, we discovered biomarker candidates such as STAT3
in colorectal cancer and developed models that classify the diseased
state. For pancreatic cancer, a separation by stage was achieved.
Importantly, biomarker candidates came predominantly from the low
abundance region, demonstrating the necessity to deeply profile because
they would have been missed by shallow profiling.
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Affiliation(s)
| | | | | | | | - Jan Muntel
- Biognosys, Schlieren, Zurich 8952, Switzerland
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11
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Dayon L, Cominetti O, Affolter M. Proteomics of Human Biological Fluids for Biomarker Discoveries: Technical Advances and Recent Applications. Expert Rev Proteomics 2022; 19:131-151. [PMID: 35466824 DOI: 10.1080/14789450.2022.2070477] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Biological fluids are routine samples for diagnostic testing and monitoring. Blood samples are typically measured because of their moderate collection invasiveness and high information content on health and disease. Several body fluids, such as cerebrospinal fluid (CSF), are also studied and suited to specific pathologies. Over the last two decades proteomics has quested to identify protein biomarkers but with limited success. Recent technologies and refined pipelines have accelerated the profiling of human biological fluids. AREAS COVERED We review proteomic technologies for the identification of biomarkers. Those are based on antibodies/aptamers arrays or mass spectrometry (MS), but new ones are emerging. Advances in scalability and throughput have allowed to better design studies and cope with the limited sample size that had until now prevailed due to technological constraints. With these enablers, plasma/serum, CSF, saliva, tears, urine, and milk proteomes have been further profiled; we provide a non-exhaustive picture of some recent highlights (mainly covering literature from last five years in the Scopus database) using MS-based proteomics. EXPERT OPINION While proteomics has been in the shadow of genomics for years, proteomic tools and methodologies have reached a certain maturity. They are better suited to discover innovative and robust biofluid biomarkers.
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Affiliation(s)
- Loïc Dayon
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, CH-1015 Lausanne, Switzerland.,Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Ornella Cominetti
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, CH-1015 Lausanne, Switzerland
| | - Michael Affolter
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, CH-1015 Lausanne, Switzerland
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12
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Metabolomic Analysis of Serum and Tear Samples from Patients with Obesity and Type 2 Diabetes Mellitus. Int J Mol Sci 2022; 23:ijms23094534. [PMID: 35562924 PMCID: PMC9105607 DOI: 10.3390/ijms23094534] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/13/2022] [Accepted: 04/14/2022] [Indexed: 12/14/2022] Open
Abstract
Metabolomics strategies are widely used to examine obesity and type 2 diabetes (T2D). Patients with obesity (n = 31) or T2D (n = 26) and sex- and age-matched controls (n = 28) were recruited, and serum and tear samples were collected. The concentration of 23 amino acids and 10 biogenic amines in serum and tear samples was analyzed. Statistical analysis and Pearson correlation analysis along with network analysis were carried out. Compared to controls, changes in the level of 6 analytes in the obese group and of 10 analytes in the T2D group were statistically significant. For obesity, the energy generation, while for T2D, the involvement of NO synthesis and its relation to insulin signaling and inflammation, were characteristic. We found that BCAA and glutamine metabolism, urea cycle, and beta-oxidation make up crucial parts of the metabolic changes in T2D. According to our data, the retromer-mediated retrograde transport, the ethanolamine metabolism, and, consequently, the endocannabinoid signaling and phospholipid metabolism were characteristic of both conditions and can be relevant pathways to understanding and treating insulin resistance. By providing potential therapeutic targets and new starting points for mechanistic studies, our results emphasize the importance of complex data analysis procedures to better understand the pathomechanism of obesity and diabetes.
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Dubois E, Galindo AN, Dayon L, Cominetti O. Assessing normalization methods in mass spectrometry-based proteome profiling of clinical samples. Biosystems 2022; 215-216:104661. [PMID: 35247480 DOI: 10.1016/j.biosystems.2022.104661] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 02/21/2022] [Accepted: 02/28/2022] [Indexed: 12/31/2022]
Abstract
BACKGROUND Large-scale proteomic studies have to deal with unwanted variability, especially when samples originate from different centers and multiple analytical batches are needed. Such variability is typically added throughout all the steps of a clinical research study, from human biological sample collection and storage, sample preparation, spectral data acquisition, to peptide and protein quantification. In order to remove such diverse and unwanted variability, normalization of the protein data is performed. There have been already several published reviews comparing normalization methods in the -omics field, but reports focusing on proteomic data generated with mass spectrometry (MS) are much fewer. Additionally, most of these reports have only dealt with small datasets. RESULTS As a case study, here we focused on the normalization of a large MS-based proteomic dataset obtained from an overweight and obese pan-European cohort, where different normalization methods were evaluated, namely: center standardize, quantile protein, quantile sample, global standardization, ComBat, median centering, mean centering, single standard and removal of unwanted variation (RUV); some of these are generic normalization methods while others have been specifically created to deal with genomic or metabolomic data. We checked how relationships between proteins and clinical variables (e.g., gender, levels of triglycerides or cholesterol) were improved after normalizing the data with the different methods. CONCLUSIONS Some normalization methods were better adapted for this particular large-scale shotgun proteomic dataset of human plasma samples labeled with isobaric tags and analyzed with liquid chromatography-tandem MS. In particular, quantile sample normalization, RUV, mean and median centering showed very good performance, while quantile protein normalization provided worse results than those obtained with unnormalized data.
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Affiliation(s)
- Etienne Dubois
- Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, EPFL Innovation Park, 1015, Lausanne, Switzerland
| | - Antonio Núñez Galindo
- Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, EPFL Innovation Park, 1015, Lausanne, Switzerland
| | - Loïc Dayon
- Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, EPFL Innovation Park, 1015, Lausanne, Switzerland; Chemistry and Chemical Engineering Section, School of Basic Sciences, Ecole Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland
| | - Ornella Cominetti
- Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, EPFL Innovation Park, 1015, Lausanne, Switzerland.
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Gaither C, Popp R, Zahedi RP, Borchers CH. Multiple reaction monitoring-mass spectrometry enables robust quantitation of plasma proteins regardless of whole blood processing delays that may occur in the clinic. Mol Cell Proteomics 2022; 21:100212. [PMID: 35182769 PMCID: PMC9062485 DOI: 10.1016/j.mcpro.2022.100212] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 01/24/2022] [Accepted: 02/13/2022] [Indexed: 12/23/2022] Open
Abstract
Plasma is an important biofluid for clinical research and diagnostics. In the clinic, unpredictable delays—from minutes to hours—between blood collection and plasma generation are often unavoidable. These delays can potentially lead to protein degradation and modification and might considerably affect intact protein measurement methods such as sandwich enzyme-linked immunosorbent assays that bind proteins on two epitopes to increase specificity, thus requiring largely intact protein structures. Here, we investigated, using multiple reaction monitoring mass spectrometry (MRM-MS), how delays in plasma processing affect peptide-centric “bottom-up” proteomics. We used validated assays for proteotypic peptide surrogates of 270 human proteins to analyze plasma generated after whole blood had been kept at room temperature from 0 to 40 h to mimic delays that occur in the clinic. Moreover, we evaluated the impact of different plasma-thawing conditions on MRM-based plasma protein quantitation. We demonstrate that >90% of protein concentration measurements were unaffected by the thawing procedure and by up to 40-h delayed plasma generation, reflected by relative standard deviations (RSDs) of <30%. Of the 159 MRM assays that yielded quantitative results in 60% of the measured time points, 139 enabled a stable protein quantitation (RSD <20%), 14 showed a slight variation (RSD 20–30%), and 6 appeared unstable/irreproducible (RSD > 30%). These results demonstrate the high robustness and thus the potential for MRM-based plasma-protein quantitation to be used in a clinical setting. In contrast to enzyme-linked immunosorbent assay, peptide-based MRM assays do not require intact three-dimensional protein structures for an accurate and precise quantitation of protein concentrations in the original sample. Delays in whole blood processing often cannot be avoided in the clinic. These delays might affect measurements by intact protein assays such as ELISA. The impact on LC/MRM was evaluated using validated assays to quantify 270 proteins. >95% of the measured concentrations had RSDs <30% between delays of 0 to 40 h. Protein quantitation by LC/MRM-MS is robust against pitfalls in the clinical setting.
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Affiliation(s)
| | - Robert Popp
- MRM Proteomics Inc, Montreal, Quebec, Canada
| | - René P Zahedi
- Segal Cancer Proteomics Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
| | - Christoph H Borchers
- Segal Cancer Proteomics Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, Canada; Gerald Bronfman Department of Oncology, Jewish General Hospital, McGill University, Montreal, Quebec, Canada.
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15
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Chung DJ, Madison GP, Aponte AM, Singh K, Li Y, Pirooznia M, Bleck CKE, Darmani NA, Balaban RS. Metabolic design in a mammalian model of extreme metabolism, the North American least shrew (Cryptotis parva). J Physiol 2022; 600:547-567. [PMID: 34837710 PMCID: PMC10655134 DOI: 10.1113/jp282153] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 11/19/2021] [Indexed: 01/10/2023] Open
Abstract
Mitochondrial adaptations are fundamental to differentiated function and energetic homeostasis in mammalian cells. But the mechanisms that underlie these relationships remain poorly understood. Here, we investigated organ-specific mitochondrial morphology, connectivity and protein composition in a model of extreme mammalian metabolism, the least shrew (Cryptotis parva). This was achieved through a combination of high-resolution 3D focused ion beam electron microscopy imaging and tandem mass tag mass spectrometry proteomics. We demonstrate that liver and kidney mitochondrial content are equivalent to the heart, permitting assessment of mitochondrial adaptations in different organs with similar metabolic demand. Muscle mitochondrial networks (cardiac and skeletal) are extensive, with a high incidence of nanotunnels - which collectively support the metabolism of large muscle cells. Mitochondrial networks were not detected in the liver and kidney as individual mitochondria are localized with sites of ATP consumption. This configuration is not observed in striated muscle, likely due to a homogeneous ATPase distribution and the structural requirements of contraction. These results demonstrate distinct, fundamental mitochondrial structural adaptations for similar metabolic demand that are dependent on the topology of energy utilization process in a mammalian model of extreme metabolism. KEY POINTS: Least shrews were studied to explore the relationship between metabolic function, mitochondrial morphology and protein content in different tissues. Liver and kidney mitochondrial content and enzymatic activity approaches that of the heart, indicating similar metabolic demand among tissues that contribute to basal and maximum metabolism. This allows an examination of mitochondrial structure and composition in tissues with similar maximum metabolic demands. Mitochondrial networks only occur in striated muscle. In contrast, the liver and kidney maintain individual mitochondria with limited reticulation. Muscle mitochondrial reticulation is the result of dense ATPase activity and cell-spanning myofibrils which require networking for adequate metabolic support. In contrast, liver and kidney ATPase activity is localized to the endoplasmic reticulum and basolateral membrane, respectively, generating a locally balanced energy conversion and utilization. Mitochondrial morphology is not driven by maximum metabolic demand, but by the cytosolic distribution of energy-utilizing systems set by the functions of the tissue.
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Affiliation(s)
- Dillon J. Chung
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Grey P. Madison
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Angel M. Aponte
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Komudi Singh
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Yuesheng Li
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Mehdi Pirooznia
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Christopher K. E. Bleck
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Nissar A. Darmani
- Department of Basic Medical Sciences, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, Pomona, California, USA
| | - Robert S. Balaban
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
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16
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Chen X, Sun Y, Zhang T, Shu L, Roepstorff P, Yang F. Quantitative Proteomics Using Isobaric Labeling: A Practical Guide. GENOMICS, PROTEOMICS & BIOINFORMATICS 2021; 19:689-706. [PMID: 35007772 PMCID: PMC9170757 DOI: 10.1016/j.gpb.2021.08.012] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 05/19/2021] [Accepted: 09/27/2021] [Indexed: 01/09/2023]
Abstract
In the past decade, relative proteomic quantification using isobaric labeling technology has developed into a key tool for comparing the expression of proteins in biological samples. Although its multiplexing capacity and flexibility make this a valuable technology for addressing various biological questions, its quantitative accuracy and precision still pose significant challenges to the reliability of its quantification results. Here, we give a detailed overview of the different kinds of isobaric mass tags and the advantages and disadvantages of the isobaric labeling method. We also discuss which precautions should be taken at each step of the isobaric labeling workflow, to obtain reliable quantification results in large-scale quantitative proteomics experiments. In the last section, we discuss the broad applications of the isobaric labeling technology in biological and clinical studies, with an emphasis on thermal proteome profiling and proteogenomics.
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Affiliation(s)
- Xiulan Chen
- Key Laboratory of Protein and Peptide Pharmaceuticals & Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100149, China.
| | - Yaping Sun
- Key Laboratory of Protein and Peptide Pharmaceuticals & Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100149, China
| | - Tingting Zhang
- Key Laboratory of Protein and Peptide Pharmaceuticals & Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100149, China
| | - Lian Shu
- Key Laboratory of Protein and Peptide Pharmaceuticals & Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100149, China
| | - Peter Roepstorff
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark
| | - Fuquan Yang
- Key Laboratory of Protein and Peptide Pharmaceuticals & Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100149, China.
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17
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Zheng R, Govorukhina N, Arrey TN, Pynn C, van der Zee A, Marko-Varga G, Bischoff R, Boychenko A. Online-2D NanoLC-MS for Crude Serum Proteome Profiling: Assessing Sample Preparation Impact on Proteome Composition. Anal Chem 2021; 93:9663-9668. [PMID: 34236853 DOI: 10.1021/acs.analchem.1c01291] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Although current LC-MS technology permits scientists to efficiently screen clinical samples in translational research, e.g., steroids, biogenic amines, and even plasma or serum proteomes, in a daily routine, maintaining the balance between throughput and analytical depth is still a limiting factor. A typical approach to enhance the proteome depth is employing offline two-dimensional (2D) fractionation techniques before reversed-phase nanoLC-MS/MS analysis (1D-nanoLC-MS). These additional sample preparation steps usually require extensive sample manipulation, which could result in sample alteration and sample loss. Here, we present and compare 1D-nanoLC-MS with an automated online-2D high-pH RP × low pH RP separation method for deep proteome profiling using a nanoLC system coupled to a high-resolution accurate-mass mass spectrometer. The proof-of-principle study permitted the identification of ca. 500 proteins with ∼10,000 peptides in 15 enzymatically digested crude serum samples collected from healthy donors in 3 laboratories across Europe. The developed method identified 60% more peptides in comparison with conventional 1D nanoLC-MS/MS analysis with ca. 4 times lower throughput while retaining the quantitative information. Serum sample preparation related changes were revealed by applying unsupervised classification techniques and, therefore, must be taken into account while planning multicentric biomarker discovery and validation studies. Overall, this novel method reduces sample complexity and boosts the number of peptide and protein identifications without the need for extra sample handling procedures for samples equivalent to less than 1 μL of blood, which expands the space for potential biomarker discovery by looking deeper into the composition of biofluids.
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Affiliation(s)
- Runsheng Zheng
- Thermo Fisher Scientific, Dornierstrasse 4, 82110 Germering, Germany
| | - Natalia Govorukhina
- Department of Analytical Biochemistry, University of Groningen, 9713 AV Groningen, The Netherlands
| | - Tabiwang N Arrey
- Thermo Fisher Scientific, Hanna-Kunath-Straße 11, 28199 Bremen, Germany
| | - Christopher Pynn
- Thermo Fisher Scientific, Dornierstrasse 4, 82110 Germering, Germany
| | - Ate van der Zee
- University Medical Centre Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - György Marko-Varga
- Clinical Protein Science and Imaging, Lund University, Box 117, S-22100 Lund, Sweden
| | - Rainer Bischoff
- Department of Analytical Biochemistry, University of Groningen, 9713 AV Groningen, The Netherlands
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18
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Dayon L, Macron C, Lahrichi S, Núñez Galindo A, Affolter M. Proteomics of Human Milk: Definition of a Discovery Workflow for Clinical Research Studies. J Proteome Res 2021; 20:2283-2290. [PMID: 33769819 DOI: 10.1021/acs.jproteome.0c00816] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Milk is a complex biological fluid composed mainly of water, carbohydrates, lipids, proteins, and diverse bioactive factors. Human milk represents a unique tailored source of nutrients that adapts during lactation to the specific needs of the developing infant. Proteins in milk have been studied for decades, and proteomics, peptidomics, and glycoproteomics are the main approaches previously deployed to decipher the proteome of human milk. In the present work, we aimed at implementing a highly automated pipeline for the proteomic analysis of human milk with liquid chromatography mass spectrometry (MS). Commercial human milk samples were used to evaluate and optimize workflows. Centrifugation for defatting milk samples was assessed before and after reduction, alkylation, and enzymatic digestion of proteins, without and with presence of surfactants. Skimmed milk samples were analyzed using isobaric labeling-based quantitative MS on an Orbitrap Tribrid mass spectrometer. Sample fractionation using isoelectric focusing was also evaluated to more deeply profile the human milk proteome. Finally, the most appropriate workflow was transferred to a liquid handling workstation for automated sample preparation. In conclusion, we have defined and describe herein an efficient highly automated proteomic workflow for human milk sample analysis. It is compatible with clinical research, possibly allowing the analysis of sufficiently large cohorts of samples.
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Affiliation(s)
- Loïc Dayon
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, Lausanne 1015, Switzerland.,Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Charlotte Macron
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, Lausanne 1015, Switzerland
| | - Sabine Lahrichi
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, Lausanne 1015, Switzerland
| | - Antonio Núñez Galindo
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, Lausanne 1015, Switzerland
| | - Michael Affolter
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, Lausanne 1015, Switzerland
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19
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Ngo D, Benson MD, Long JZ, Chen ZZ, Wang R, Nath AK, Keyes MJ, Shen D, Sinha S, Kuhn E, Morningstar JE, Shi X, Peterson BD, Chan C, Katz DH, Tahir UA, Farrell LA, Melander O, Mosley JD, Carr SA, Vasan RS, Larson MG, Smith JG, Wang TJ, Yang Q, Gerszten RE. Proteomic profiling reveals biomarkers and pathways in type 2 diabetes risk. JCI Insight 2021; 6:144392. [PMID: 33591955 PMCID: PMC8021115 DOI: 10.1172/jci.insight.144392] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 01/28/2021] [Indexed: 01/14/2023] Open
Abstract
Recent advances in proteomic technologies have made high-throughput profiling of low-abundance proteins in large epidemiological cohorts increasingly feasible. We investigated whether aptamer-based proteomic profiling could identify biomarkers associated with future development of type 2 diabetes (T2DM) beyond known risk factors. We identified dozens of markers with highly significant associations with future T2DM across 2 large longitudinal cohorts (n = 2839) followed for up to 16 years. We leveraged proteomic, metabolomic, genetic, and clinical data from humans to nominate 1 specific candidate to test for potential causal relationships in model systems. Our studies identified functional effects of aminoacylase 1 (ACY1), a top protein association with future T2DM risk, on amino acid metabolism and insulin homeostasis in vitro and in vivo. Furthermore, a loss-of-function variant associated with circulating levels of the biomarker WAP, Kazal, immunoglobulin, Kunitz, and NTR domain-containing protein 2 (WFIKKN2) was, in turn, associated with fasting glucose, hemoglobin A1c, and HOMA-IR measurements in humans. In addition to identifying potentially novel disease markers and pathways in T2DM, we provide publicly available data to be leveraged for insights about gene function and disease pathogenesis in the context of human metabolism.
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Affiliation(s)
- Debby Ngo
- Cardiovascular Institute
- Division of Pulmonary, Critical Care and Sleep Medicine, and
| | - Mark D. Benson
- Cardiovascular Institute
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center (BIDMC), Boston, Massachusetts, USA
| | - Jonathan Z. Long
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Zsu-Zsu Chen
- Cardiovascular Institute
- Division of Endocrinology, Diabetes and Metabolism, BIDMC, Boston, Massachusetts, USA
| | - Ruiqi Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | | | | | | | | | - Eric Kuhn
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | | | | | | | | | - Daniel H. Katz
- Cardiovascular Institute
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center (BIDMC), Boston, Massachusetts, USA
| | - Usman A. Tahir
- Cardiovascular Institute
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center (BIDMC), Boston, Massachusetts, USA
| | | | - Olle Melander
- Department of Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Lund, Sweden
| | - Jonathan D. Mosley
- Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Steven A. Carr
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Ramachandran S. Vasan
- Department of Medicine, Divisions of Preventive Medicine and Cardiology, Boston University School of Medicine, Boston, Massachusetts, USA
- The National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, USA
| | - Martin G. Larson
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
- The National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, USA
| | - J. Gustav Smith
- Department of Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Lund, Sweden
- Wallenberg Center for Molecular Medicine and Diabetes Center, Lund University, Lund, Sweden
- Department of Cardiology and Wallenberg Laboratory, Gothenburg University and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Thomas J. Wang
- Department of Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Qiong Yang
- Division of Endocrinology, Diabetes and Metabolism, BIDMC, Boston, Massachusetts, USA
| | - Robert E. Gerszten
- Cardiovascular Institute
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center (BIDMC), Boston, Massachusetts, USA
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
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20
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Cramer R. High-speed Analysis of Large Sample Sets - How Can This Key Aspect of the Omics Be Achieved? Mol Cell Proteomics 2020; 19:1760-1766. [PMID: 32796012 DOI: 10.1074/mcp.p120.001997] [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: 02/21/2020] [Revised: 07/29/2020] [Indexed: 01/25/2023] Open
Abstract
High-speed analysis of large (prote)omics sample sets at the rate of thousands or millions of samples per day on a single platform has been a challenge since the beginning of proteomics. For many years, ESI-based MS methods have dominated proteomics because of their high sensitivity and great depth in analyzing complex proteomes. However, despite improvements in speed, ESI-based MS methods are fundamentally limited by their sample introduction, which excludes off-line sample preparation/fractionation because of the time required to switch between individual samples/sample fractions, and therefore being dependent on the speed of on-line sample preparation methods such as liquid chromatography. Laser-based ionization methods have the advantage of moving from one sample to the next without these limitations, being mainly restricted by the speed of modern sample stages, i.e. 10 ms or less between samples. This speed matches the data acquisition speed of modern high-performing mass spectrometers whereas the pulse repetition rate of the lasers (>1 kHz) provides a sufficient number of desorption/ionization events for successful ion signal detection from each sample at the above speed of the sample stages. Other advantages of laser-based ionization methods include the generally higher tolerance to sample additives and contamination compared with ESI MS, and the contact-less and pulsed nature of the laser used for desorption, reducing the risk of cross-contamination. Furthermore, new developments in MALDI have expanded its analytical capabilities, now being able to fully exploit high-performing hybrid mass analyzers and their strengths in sensitivity and MS/MS analysis by generating an ESI-like stable yield of multiply charged analyte ions. Thus, these new developments and the intrinsically high speed of laser-based methods now provide a good basis for tackling extreme sample analysis speed in the omics.
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Affiliation(s)
- Rainer Cramer
- Department of Chemistry, University of Reading, Whiteknights, Reading, UK.
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21
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Macron C, Núñez Galindo A, Cominetti O, Dayon L. A Versatile Workflow for Cerebrospinal Fluid Proteomic Analysis with Mass Spectrometry: A Matter of Choice between Deep Coverage and Sample Throughput. Methods Mol Biol 2020; 2044:129-154. [PMID: 31432411 DOI: 10.1007/978-1-4939-9706-0_9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Human cerebrospinal fluid (CSF) is a sample of choice in the study of brain disorders. This biological fluid circulates in the brain and the spinal cord and contains tissue-specific proteins, indicative of health and disease conditions. Despite its potential as a valid source of biological markers, CSF remains largely understudied as compared to blood, in particular due to its more invasive way of sampling.Challenges remain when performing proteomic analysis in clinical research studies. State-of-the-art mass spectrometry (MS) enables deep characterization of the human proteome. But some technical limitations are cardinal to be addressed, such as the capacity to routinely analyze large cohorts of samples. Importantly, a trade-off still needs to be made between the proteome coverage depth and the number of measured samples. In this context, we developed a scalable automated proteomic pipeline for the analysis of CSF. Because of its versatility, this workflow can be adapted to accommodate proteome coverage and/or sample throughput. It allows us to prepare and quantitatively analyze hundreds to thousands of CSF samples; it can also allow identification of more than 3000 proteins in a CSF sample when coupled with isoelectric focusing fractionation.In this chapter, we describe an end-to-end pipeline for the proteomic analysis of CSF. The main steps of the sample preparation comprise spiking of a standard, protein digestion, isobaric labeling, and purification; these are performed in a 96-well plate format enabling automation. Depending on the targeted depth of the CSF proteome, optional analytical steps can be included, such as the removal of abundant proteins and sample pre-fractionation. Liquid chromatography tandem MS as well as data processing and analysis complete the pipeline.
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Affiliation(s)
- Charlotte Macron
- Proteomics, Nestlé Institute of Health Sciences, Nestlé Research, Lausanne, Switzerland
| | - Antonio Núñez Galindo
- Proteomics, Nestlé Institute of Health Sciences, Nestlé Research, Lausanne, Switzerland
| | - Ornella Cominetti
- Proteomics, Nestlé Institute of Health Sciences, Nestlé Research, Lausanne, Switzerland
| | - Loïc Dayon
- Proteomics, Nestlé Institute of Health Sciences, Nestlé Research, Lausanne, Switzerland.
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22
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A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma. PLoS Comput Biol 2020; 16:e1007882. [PMID: 32492067 PMCID: PMC7295243 DOI: 10.1371/journal.pcbi.1007882] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 06/15/2020] [Accepted: 04/16/2020] [Indexed: 11/19/2022] Open
Abstract
Molecular quantitative trait locus (QTL) analyses are increasingly popular to explore the genetic architecture of complex traits, but existing studies do not leverage shared regulatory patterns and suffer from a large multiplicity burden, which hampers the detection of weak signals such as trans associations. Here, we present a fully multivariate proteomic QTL (pQTL) analysis performed with our recently proposed Bayesian method LOCUS on data from two clinical cohorts, with plasma protein levels quantified by mass-spectrometry and aptamer-based assays. Our two-stage study identifies 136 pQTL associations in the first cohort, of which >80% replicate in the second independent cohort and have significant enrichment with functional genomic elements and disease risk loci. Moreover, 78% of the pQTLs whose protein abundance was quantified by both proteomic techniques are confirmed across assays. Our thorough comparisons with standard univariate QTL mapping on (1) these data and (2) synthetic data emulating the real data show how LOCUS borrows strength across correlated protein levels and markers on a genome-wide scale to effectively increase statistical power. Notably, 15% of the pQTLs uncovered by LOCUS would be missed by the univariate approach, including several trans and pleiotropic hits with successful independent validation. Finally, the analysis of extensive clinical data from the two cohorts indicates that the genetically-driven proteins identified by LOCUS are enriched in associations with low-grade inflammation, insulin resistance and dyslipidemia and might therefore act as endophenotypes for metabolic diseases. While considerations on the clinical role of the pQTLs are beyond the scope of our work, these findings generate useful hypotheses to be explored in future research; all results are accessible online from our searchable database. Thanks to its efficient variational Bayes implementation, LOCUS can analyze jointly thousands of traits and millions of markers. Its applicability goes beyond pQTL studies, opening new perspectives for large-scale genome-wide association and QTL analyses. Diet, Obesity and Genes (DiOGenes) trial registration number: NCT00390637. Exploring the functional mechanisms between the genotype and disease endpoints in view of identifying innovative therapeutic targets has prompted molecular quantitative trait locus studies, which assess how genetic variants (single nucleotide polymorphisms, SNPs) affect intermediate gene (eQTL), protein (pQTL) or metabolite (mQTL) levels. However, conventional univariate screening approaches do not account for local dependencies and association structures shared by multiple molecular levels and markers. Conversely, the current joint modelling approaches are restricted to small datasets by computational constraints. We illustrate and exploit the advantages of our recently introduced Bayesian framework LOCUS in a fully multivariate pQTL study, with ≈300K tag SNPs (capturing information from 4M markers) and 100 − 1, 000 plasma protein levels measured by two distinct technologies. LOCUS identifies novel pQTLs that replicate in an independent cohort, confirms signals documented in studies 2 − 18 times larger, and detects more pQTLs than a conventional two-stage univariate analysis of our datasets. Moreover, some of these pQTLs might be of biomedical relevance and would therefore deserve dedicated investigation. Our extensive numerical experiments on these data and on simulated data demonstrate that the increased statistical power of LOCUS over standard approaches is largely attributable to its ability to exploit shared information across outcomes while efficiently accounting for the genetic correlation structures at a genome-wide level.
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Zhang T, Gaffrey MJ, Monroe ME, Thomas DG, Weitz KK, Piehowski PD, Petyuk VA, Moore RJ, Thrall BD, Qian WJ. Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling. J Proteome Res 2020; 19:2863-2872. [PMID: 32407631 DOI: 10.1021/acs.jproteome.0c00310] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Label-free quantitative proteomics has become an increasingly popular tool for profiling global protein abundances. However, one major limitation is the potential performance drift of the LC-MS platform over time, which, in turn, limits its utility for analyzing large-scale sample sets. To address this, we introduce an experimental and data analysis scheme based on a block design with common references within each block for enabling large-scale label-free quantification. In this scheme, a large number of samples (e.g., >100 samples) are analyzed in smaller and more manageable blocks, minimizing instrument drift and variability within individual blocks. Each designated block also contains common reference samples (e.g., controls) for normalization across all blocks. We demonstrated the robustness of this approach by profiling the proteome response of human macrophage THP-1 cells to 11 engineered nanomaterials at two different doses. A total of 116 samples were analyzed in six blocks, yielding an average coverage of 4500 proteins per sample. Following a common reference-based correction, 2537 proteins were quantified with high reproducibility without any imputation of missing values from 116 data sets. The data revealed the consistent quantification of proteins across all six blocks, as illustrated by the highly consistent abundances of house-keeping proteins in all samples and the high levels of correlation among samples from different blocks. The data also demonstrated that label-free quantification is robust and accurate enough to quantify even very subtle abundance changes as well as large fold-changes. Our streamlined workflow is easy to implement and can be readily adapted to other large cohort studies for reproducible label-free proteome quantification.
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Affiliation(s)
- Tong Zhang
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Matthew J Gaffrey
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Matthew E Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Dennis G Thomas
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Karl K Weitz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Paul D Piehowski
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Vladislav A Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Brian D Thrall
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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Alexovič M, Urban PL, Tabani H, Sabo J. Recent advances in robotic protein sample preparation for clinical analysis and other biomedical applications. Clin Chim Acta 2020; 507:104-116. [PMID: 32305536 DOI: 10.1016/j.cca.2020.04.015] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 04/11/2020] [Accepted: 04/13/2020] [Indexed: 02/06/2023]
Abstract
Discovery of new protein biomarker candidates has become a major research goal in the areas of clinical chemistry, analytical chemistry, and biomedicine. These important species constitute the molecular target when it comes to diagnosis, prognosis, and further monitoring of disease. However, their analysis requires powerful, selective and high-throughput sample preparation and product (analyte) characterisation approaches. In general, manual sample processing is tedious, complex and time-consuming, especially when large numbers of samples have to be processed (e.g., in clinical studies). Automation via microtiter-plate platforms involving robotics has brought improvements in high-throughput performance while comparable or even better precisions and repeatability (intra-day, inter-day) were achieved. At the same time, waste production and exposure of laboratory personnel to hazards were reduced. In comprehensive protein analysis workflows (e.g., liquid chromatography-tandem mass spectrometry analysis), sample preparation is an unavoidable step. This review surveys the recent achievements in automation of bottom-up and top-down protein and/or proteomics approaches. Emphasis is put on high-end multi-well plate robotic platforms developed for clinical analysis and other biomedical applications. The literature from 2013 to date has been covered.
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Affiliation(s)
- Michal Alexovič
- Department of Medical and Clinical Biophysics, Faculty of Medicine, University of P.J. Šafárik in Košice, 04011 Košice, Slovakia.
| | - Pawel L Urban
- Department of Chemistry, National Tsing Hua University, 101, Sec 2, Kuang-Fu Rd., Hsinchu 30013, Taiwan
| | - Hadi Tabani
- Department of Environmental Geology, Research Institute of Applied Sciences (ACECR), Shahid Beheshti University, Tehran, Iran
| | - Ján Sabo
- Department of Medical and Clinical Biophysics, Faculty of Medicine, University of P.J. Šafárik in Košice, 04011 Košice, Slovakia
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25
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Yee WLS, Drum CL. Increasing Complexity to Simplify Clinical Care: High Resolution Mass Spectrometry as an Enabler of AI Guided Clinical and Therapeutic Monitoring. ADVANCED THERAPEUTICS 2020. [DOI: 10.1002/adtp.201900163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Wei Loong Sherman Yee
- Yong Loo Lin School of MedicineDepartment of MedicineNational University of Singapore Singapore 119077 Singapore
- Cardiovascular Research Institute (CVRI)National University Health System Singapore 119228 Singapore
| | - Chester Lee Drum
- Yong Loo Lin School of MedicineDepartment of MedicineNational University of Singapore Singapore 119077 Singapore
- Cardiovascular Research Institute (CVRI)National University Health System Singapore 119228 Singapore
- Yong Loo Lin School of MedicineDepartment of BiochemistryNational University of Singapore Singapore 119077 Singapore
- The N.1 Institute for Health (N.1)National University of Singapore Singapore 119077 Singapore
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26
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Dayon L, Affolter M. Progress and pitfalls of using isobaric mass tags for proteome profiling. Expert Rev Proteomics 2020; 17:149-161. [PMID: 32067523 DOI: 10.1080/14789450.2020.1731309] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Introduction: Quantitative proteomics using mass spectrometry is performed via label-free or label-based approaches. Labeling strategies rely on the incorporation of stable heavy isotopes by metabolic, enzymatic, or chemical routes. Isobaric labeling uses chemical labels of identical masses but of different fragmentation behaviors to allow the relative quantitative comparison of peptide/protein abundances between biological samples.Areas covered: We have carried out a systematic review on the use of isobaric mass tags in proteomic research since their inception in 2003. We focused on their quantitative performances, their multiplexing evolution, as well as their broad use for relative quantification of proteins in pre-clinical models and clinical studies. Current limitations, primarily linked to the quantitative ratio distortion, as well as state-of-the-art and emerging solutions to improve their quantitative readouts are discussed.Expert opinion: The isobaric mass tag technology offers a unique opportunity to compare multiple protein samples simultaneously, allowing higher sample throughput and internal relative quantification for improved trueness and precision. Large studies can be performed when shared reference samples are introduced in multiple experiments. The technology is well suited for proteome profiling in the context of proteomic discovery studies.
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Affiliation(s)
- Loïc Dayon
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, Lausanne, Switzerland.,Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Michael Affolter
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, Lausanne, Switzerland
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27
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Kowalczyk T, Ciborowski M, Kisluk J, Kretowski A, Barbas C. Mass spectrometry based proteomics and metabolomics in personalized oncology. Biochim Biophys Acta Mol Basis Dis 2020; 1866:165690. [PMID: 31962175 DOI: 10.1016/j.bbadis.2020.165690] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 12/18/2019] [Accepted: 01/15/2020] [Indexed: 02/06/2023]
Abstract
Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.
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Affiliation(s)
- Tomasz Kowalczyk
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Michal Ciborowski
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Joanna Kisluk
- Department of Clinical Molecular Biology, Medical University of Bialystok, Bialystok, Poland
| | - Adam Kretowski
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland; Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Madrid, Spain.
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Tsypin M, Asmellash S, Meyer K, Touchet B, Roder H. Extending the information content of the MALDI analysis of biological fluids via multi-million shot analysis. PLoS One 2019; 14:e0226012. [PMID: 31815946 PMCID: PMC6901224 DOI: 10.1371/journal.pone.0226012] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 11/18/2019] [Indexed: 12/31/2022] Open
Abstract
INTRODUCTION Reliable measurements of the protein content of biological fluids like serum or plasma can provide valuable input for the development of personalized medicine tests. Standard MALDI analysis typically only shows high abundance proteins, which limits its utility for test development. It also exhibits reproducibility issues with respect to quantitative measurements. In this paper we show how the sensitivity of MALDI profiling of intact proteins in unfractionated human serum can be substantially increased by exposing a sample to many more laser shots than are commonly used. Analytical reproducibility is also improved. METHODS To assess what is theoretically achievable we utilized spectra from the same samples obtained over many years and combined them to generate MALDI spectral averages of up to 100,000,000 shots for a single sample, and up to 8,000,000 shots for a set of 40 different serum samples. Spectral attributes, such as number of peaks and spectral noise of such averaged spectra were investigated together with analytical reproducibility as a function of the number of shots. We confirmed that results were similar on MALDI instruments from different manufacturers. RESULTS We observed an expected decrease of noise, roughly proportional to the square root of the number of shots, over the whole investigated range of the number of shots (5 orders of magnitude), resulting in an increase in the number of reliably detected peaks. The reproducibility of the amplitude of these peaks, measured by CV and concordance analysis also improves with very similar dependence on shot number, reaching median CVs below 2% for shot numbers > 4 million. Measures of analytical information content and association with biological processes increase with increasing number of shots. CONCLUSIONS We demonstrate that substantially increasing the number of laser shots in a MALDI-TOF analysis leads to more informative and reliable data on the protein content of unfractionated serum. This approach has already been used in the development of clinical tests in oncology.
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Affiliation(s)
- Maxim Tsypin
- Biodesix Inc., Boulder, Colorado, United States of America
| | | | - Krista Meyer
- Biodesix Inc., Boulder, Colorado, United States of America
| | | | - Heinrich Roder
- Biodesix Inc., Boulder, Colorado, United States of America
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29
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Ignjatovic V, Geyer PE, Palaniappan KK, Chaaban JE, Omenn GS, Baker MS, Deutsch EW, Schwenk JM. Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data. J Proteome Res 2019; 18:4085-4097. [PMID: 31573204 DOI: 10.1021/acs.jproteome.9b00503] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The proteomic analysis of human blood and blood-derived products (e.g., plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g., interindividual variability) and analysis of biospecimens (e.g., sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing, and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest while enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.
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Affiliation(s)
- Vera Ignjatovic
- Haematology Research , Murdoch Children's Research Institute , Parkville , VIC 3052 , Australia.,Department of Paediatrics , The University of Melbourne , Parkville , VIC 3052 , Australia
| | - Philipp E Geyer
- NNF Center for Protein Research, Faculty of Health Sciences , University of Copenhagen , 2200 Copenhagen , Denmark.,Department of Proteomics and Signal Transduction , Max Planck Institute of Biochemistry , 82152 Martinsried , Germany
| | - Krishnan K Palaniappan
- Freenome , 259 East Grand Avenue , South San Francisco , California 94080 , United States
| | - Jessica E Chaaban
- Haematology Research , Murdoch Children's Research Institute , Parkville , VIC 3052 , Australia
| | - Gilbert S Omenn
- Departments of Computational Medicine & Bioinformatics, Human Genetics, and Internal Medicine and School of Public Health , University of Michigan , 100 Washtenaw Avenue , Ann Arbor , Michigan 48109-2218 , 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 , United States
| | - Jochen M Schwenk
- Affinity Proteomics, SciLifeLab , KTH Royal Institute of Technology , 171 65 Stockholm , Sweden
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30
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Chen Y, Guenther JM, Gin JW, Chan LJG, Costello Z, Ogorzalek TL, Tran HM, Blake-Hedges JM, Keasling JD, Adams PD, García Martín H, Hillson NJ, Petzold CJ. Automated “Cells-To-Peptides” Sample Preparation Workflow for High-Throughput, Quantitative Proteomic Assays of Microbes. J Proteome Res 2019; 18:3752-3761. [DOI: 10.1021/acs.jproteome.9b00455] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
| | - Joel M. Guenther
- Sandia National Laboratories (NTESS), Livermore, California 94551, United States
| | | | | | | | | | - Huu M. Tran
- Sandia National Laboratories (NTESS), Livermore, California 94551, United States
| | | | - Jay D. Keasling
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720-1460, United States
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby 2800, Denmark
- Center for Synthetic Biochemistry, Synthetic Biology Institute, Shenzhen Institutes for Advanced Technologies, Shenzhen 518000, China
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Bruderer R, Muntel J, Müller S, Bernhardt OM, Gandhi T, Cominetti O, Macron C, Carayol J, Rinner O, Astrup A, Saris WHM, Hager J, Valsesia A, Dayon L, Reiter L. Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance. Mol Cell Proteomics 2019; 18:1242-1254. [PMID: 30948622 PMCID: PMC6553938 DOI: 10.1074/mcp.ra118.001288] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 03/26/2019] [Indexed: 12/14/2022] Open
Abstract
Comprehensive, high throughput analysis of the plasma proteome has the potential to enable holistic analysis of the health state of an individual. Based on our own experience and the evaluation of recent large-scale plasma mass spectrometry (MS) based proteomic studies, we identified two outstanding challenges: slow and delicate nano-flow liquid chromatography (LC) and irreproducibility of identification of data-dependent acquisition (DDA). We determined an optimal solution reducing these limitations with robust capillary-flow data-independent acquisition (DIA) MS. This platform can measure 31 plasma proteomes per day. Using this setup, we acquired a large-scale plasma study of the diet, obesity and genes dietary (DiOGenes) comprising 1508 samples. Proving the robustness, the complete acquisition was achieved on a single analytical column. Totally, 565 proteins (459 identified with two or more peptide sequences) were profiled with 74% data set completeness. On average 408 proteins (5246 peptides) were identified per acquisition (319 proteins in 90% of all acquisitions). The workflow reproducibility was assessed using 34 quality control pools acquired at regular intervals, resulting in 92% data set completeness with CVs for protein measurements of 10.9%.The profiles of 20 apolipoproteins could be profiled revealing distinct changes. The weight loss and weight maintenance resulted in sustained effects on low-grade inflammation, as well as steroid hormone and lipid metabolism, indicating beneficial effects. Comparison to other large-scale plasma weight loss studies demonstrated high robustness and quality of biomarker candidates identified. Tracking of nonenzymatic glycation indicated a delayed, slight reduction of glycation in the weight maintenance phase. Using stable-isotope-references, we could directly and absolutely quantify 60 proteins in the DIA.In conclusion, we present herein the first large-scale plasma DIA study and one of the largest clinical research proteomic studies to date. Application of this fast and robust workflow has great potential to advance biomarker discovery in plasma.
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Affiliation(s)
| | - Jan Muntel
- From the ‡Biognosys, 8952 Zurich-Schlieren, Switzerland
| | | | | | - Tejas Gandhi
- From the ‡Biognosys, 8952 Zurich-Schlieren, Switzerland
| | | | - Charlotte Macron
- §Nestlé Institute of Health Sciences, 1015 Lausanne, Switzerland
| | - Jérôme Carayol
- §Nestlé Institute of Health Sciences, 1015 Lausanne, Switzerland
| | - Oliver Rinner
- From the ‡Biognosys, 8952 Zurich-Schlieren, Switzerland
| | - Arne Astrup
- ¶Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Wim H M Saris
- ‖NUTRIM, School for Nutrition, Toxicology and Metabolism, Department of Human Biology, Maastricht University Medical Centre, 6200 MD Maastricht, The Netherlands
| | - Jörg Hager
- §Nestlé Institute of Health Sciences, 1015 Lausanne, Switzerland
| | - Armand Valsesia
- §Nestlé Institute of Health Sciences, 1015 Lausanne, Switzerland
| | - Loïc Dayon
- §Nestlé Institute of Health Sciences, 1015 Lausanne, Switzerland
| | - Lukas Reiter
- From the ‡Biognosys, 8952 Zurich-Schlieren, Switzerland;
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32
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Picó C, Serra F, Rodríguez AM, Keijer J, Palou A. Biomarkers of Nutrition and Health: New Tools for New Approaches. Nutrients 2019; 11:E1092. [PMID: 31100942 PMCID: PMC6567133 DOI: 10.3390/nu11051092] [Citation(s) in RCA: 122] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 05/07/2019] [Accepted: 05/08/2019] [Indexed: 12/18/2022] Open
Abstract
A main challenge in nutritional studies is the valid and reliable assessment of food intake, as well as its effects on the body. Generally, food intake measurement is based on self-reported dietary intake questionnaires, which have inherent limitations. They can be overcome by the use of biomarkers, capable of objectively assessing food consumption without the bias of self-reported dietary assessment. Another major goal is to determine the biological effects of foods and their impact on health. Systems analysis of dynamic responses may help to identify biomarkers indicative of intake and effects on the body at the same time, possibly in relation to individuals' health/disease states. Such biomarkers could be used to quantify intake and validate intake questionnaires, analyse physiological or pathological responses to certain food components or diets, identify persons with specific dietary deficiency, provide information on inter-individual variations or help to formulate personalized dietary recommendations to achieve optimal health for particular phenotypes, currently referred as "precision nutrition." In this regard, holistic approaches using global analysis methods (omics approaches), capable of gathering high amounts of data, appear to be very useful to identify new biomarkers and to enhance our understanding of the role of food in health and disease.
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Affiliation(s)
- Catalina Picó
- Laboratory of Molecular Biology, Nutrition and Biotechnology (Group of Nutrigenomics and Obesity), CIBER de Fisiopatología de la Obesidad y Nutrición (CIBERobn) and Instituto de Investigación Sanitaria Illes Balears (IdISBa), University of the Balearic Islands, ES-07122 Palma de Mallorca, Spain.
| | - Francisca Serra
- Laboratory of Molecular Biology, Nutrition and Biotechnology (Group of Nutrigenomics and Obesity), CIBER de Fisiopatología de la Obesidad y Nutrición (CIBERobn) and Instituto de Investigación Sanitaria Illes Balears (IdISBa), University of the Balearic Islands, ES-07122 Palma de Mallorca, Spain.
| | - Ana María Rodríguez
- Laboratory of Molecular Biology, Nutrition and Biotechnology (Group of Nutrigenomics and Obesity), CIBER de Fisiopatología de la Obesidad y Nutrición (CIBERobn) and Instituto de Investigación Sanitaria Illes Balears (IdISBa), University of the Balearic Islands, ES-07122 Palma de Mallorca, Spain.
| | - Jaap Keijer
- Human and Animal Physiology, Wageningen University, PO Box 338, 6700 AH Wageningen, The Netherlands.
| | - Andreu Palou
- Laboratory of Molecular Biology, Nutrition and Biotechnology (Group of Nutrigenomics and Obesity), CIBER de Fisiopatología de la Obesidad y Nutrición (CIBERobn) and Instituto de Investigación Sanitaria Illes Balears (IdISBa), University of the Balearic Islands, ES-07122 Palma de Mallorca, Spain.
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Lee J, Kim H, Sohn A, Yeo I, Kim Y. Cost-Effective Automated Preparation of Serum Samples for Reproducible Quantitative Clinical Proteomics. J Proteome Res 2019; 18:2337-2345. [PMID: 30985128 DOI: 10.1021/acs.jproteome.9b00023] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Reproducible sample preparation remains a significant challenge in large-scale clinical research using selected reaction monitoring-mass spectrometry (SRM-MS), which enables a highly sensitive multiplexed assay. Although automated liquid-handling platforms have tremendous potential for addressing this issue, the high cost of their consumables is a drawback that renders routine operation expensive. Here we evaluated the performance of a liquid-handling platform in preparing serum samples compared with a standard experiment while reducing the outlay for consumables, such as tips, wasted reagents, and reagent stock plates. A total of 26 multiplex assays were quantified by SRM-MS using four sets of 24 pooled human serum aliquots; the four sets used a fixed number (1, 4, 8, or 24) of tips to dispense digestion reagents. This study demonstrated that the use of 4 or 8 tips is comparable to 24 tips (standard experiment), as evidenced by their coefficients of variation: 13.5% (for 4 and 8 tips) versus 12.0% (24 tips). Thus we can save 37% of the total experimental cost compared with the standard experiment, maintaining nearly equivalent reproducibility. The routine operation of cost-effective liquid-handling platforms can enable researchers to process large-scale samples with high throughput, adding credibility to their findings by minimizing human error.
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Affiliation(s)
| | - Hyunsoo Kim
- Institute of Medical and Biological Engineering, MRC , Seoul National University , Seoul , Korea
| | | | - Injoon Yeo
- Interdisciplinary Program of Bioengineering , Seoul National University College of Engineering , Seoul , Korea
| | - Youngsoo Kim
- Institute of Medical and Biological Engineering, MRC , Seoul National University , Seoul , Korea.,Interdisciplinary Program of Bioengineering , Seoul National University College of Engineering , Seoul , Korea
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Pernemalm M, Sandberg A, Zhu Y, Boekel J, Tamburro D, Schwenk JM, Björk A, Wahren-Herlenius M, Åmark H, Östenson CG, Westgren M, Lehtiö J. In-depth human plasma proteome analysis captures tissue proteins and transfer of protein variants across the placenta. eLife 2019; 8:41608. [PMID: 30958262 PMCID: PMC6519984 DOI: 10.7554/elife.41608] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 04/04/2019] [Indexed: 01/03/2023] Open
Abstract
Here, we present a method for in-depth human plasma proteome analysis based on high-resolution isoelectric focusing HiRIEF LC-MS/MS, demonstrating high proteome coverage, reproducibility and the potential for liquid biopsy protein profiling. By integrating genomic sequence information to the MS-based plasma proteome analysis, we enable detection of single amino acid variants and for the first time demonstrate transfer of multiple protein variants between mother and fetus across the placenta. We further show that our method has the ability to detect both low abundance tissue-annotated proteins and phosphorylated proteins in plasma, as well as quantitate differences in plasma proteomes between the mother and the newborn as well as changes related to pregnancy. Blood cells travel through the blood vessels in a soupy mixture of proteins called plasma. Most of these proteins are plasma-specific, yet small amounts of proteins can leak into the plasma from other body parts and may provide hints about what is going on elsewhere in the body. This could allow doctors to use plasma samples to assess health or detect disease. But so far developing methods to detect these leaked proteins has proved difficult. Plasma passing through the placenta can transfer proteins between a pregnant woman and her baby. Learning more about these protein exchanges may help scientists understand how the mother and baby adapt to each other and what triggers child birth. But, so far, they have been hard to study. Using DNA to help trace the origins of proteins found in mother or baby could make it easier. Now, Pernemalm et al. have used DNA sequencing in combination with protein analysis to identify proteins passed between two pregnant mothers and their babies. Comparing the genetic sequences of each mother and child made it possible to trace the origin of the proteins. For example, if a mother had a version of the protein that matched genes the child inherited from its father, they knew it passed from the baby to the mother. This approach found 24 proteins in plasma from two pregnant mothers that had likely passed through the placenta during pregnancy. Pernemalm et al. also analyzed the plasma of 30 healthy individuals and confirmed that it contained several proteins that had likely leaked from other organs, including the lungs and pancreas. Monitoring protein transfer between pregnant mother and baby may help scientists identify what triggers normal or premature deliveries. One advantage of the technique developed Pernemalm et al. is that it can analyze plasma proteins from large numbers of people, which could enable larger studies. More refinement of the technique may also allow scientists to identify leaked proteins in the plasma that provide an early warning of cancer or other diseases.
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Affiliation(s)
- Maria Pernemalm
- Karolinska Institute, Stockholm, Sweden.,Proteogenomics, Science for Life Laboratory, Sweden
| | | | - Yafeng Zhu
- Karolinska Institute, Stockholm, Sweden.,Proteogenomics, Science for Life Laboratory, Sweden
| | - Jorrit Boekel
- Karolinska Institute, Stockholm, Sweden.,Proteogenomics, Science for Life Laboratory, Sweden
| | | | | | | | | | | | | | | | - Janne Lehtiö
- Karolinska Institute, Stockholm, Sweden.,Proteogenomics, Science for Life Laboratory, Sweden
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Harney DJ, Hutchison AT, Hatchwell L, Humphrey SJ, James DE, Hocking S, Heilbronn LK, Larance M. Proteomic Analysis of Human Plasma during Intermittent Fasting. J Proteome Res 2019; 18:2228-2240. [PMID: 30892045 PMCID: PMC6503536 DOI: 10.1021/acs.jproteome.9b00090] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Intermittent fasting (IF) increases lifespan and decreases metabolic disease phenotypes and cancer risk in model organisms, but the health benefits of IF in humans are less clear. Human plasma derived from clinical trials is one of the most difficult sample sets to analyze using mass spectrometry-based proteomics due to the extensive sample preparation required and the need to process many samples to achieve statistical significance. Here, we describe an optimized and accessible device (Spin96) to accommodate up to 96 StageTips, a widely used sample preparation medium enabling efficient and consistent processing of samples prior to LC-MS/MS. We have applied this device to the analysis of human plasma from a clinical trial of IF. In this longitudinal study employing 8-weeks IF, we identified significant abundance differences induced by the IF intervention, including increased apolipoprotein A4 (APOA4) and decreased apolipoprotein C2 (APOC2) and C3 (APOC3). These changes correlated with a significant decrease in plasma triglycerides after the IF intervention. Given that these proteins have a role in regulating apolipoprotein particle metabolism, we propose that IF had a positive effect on lipid metabolism through modulation of HDL particle size and function. In addition, we applied a novel human protein variant database to detect common protein variants across the participants. We show that consistent detection of clinically relevant peptides derived from both alleles of many proteins is possible, including some that are associated with human metabolic phenotypes. Together, these findings illustrate the power of accessible workflows for proteomics analysis of clinical samples to yield significant biological insight.
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Affiliation(s)
- Dylan J Harney
- Charles Perkins Centre, School of Life and Environmental Sciences , University of Sydney , Sydney , NSW 2006 , Australia
| | - Amy T Hutchison
- Discipline of Medicine , University of Adelaide , Adelaide , SA 5005 , Australia
| | - Luke Hatchwell
- Charles Perkins Centre, School of Life and Environmental Sciences , University of Sydney , Sydney , NSW 2006 , Australia
| | - Sean J Humphrey
- Charles Perkins Centre, School of Life and Environmental Sciences , University of Sydney , Sydney , NSW 2006 , Australia
| | - David E James
- Charles Perkins Centre, School of Life and Environmental Sciences , University of Sydney , Sydney , NSW 2006 , Australia
| | - Samantha Hocking
- Central Clinical School, Faculty of Medicine and Health , University of Sydney , Sydney , NSW 2006 , Australia
| | - Leonie K Heilbronn
- Discipline of Medicine , University of Adelaide , Adelaide , SA 5005 , Australia
| | - Mark Larance
- Charles Perkins Centre, School of Life and Environmental Sciences , University of Sydney , Sydney , NSW 2006 , Australia
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Spodzieja M, Rodziewicz-Motowidło S, Szymanska A. Hyphenated Mass Spectrometry Techniques in the Diagnosis of Amyloidosis. Curr Med Chem 2019; 26:104-120. [DOI: 10.2174/0929867324666171003113019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Revised: 07/25/2016] [Accepted: 09/01/2016] [Indexed: 12/18/2022]
Abstract
Amyloidoses are a group of diseases caused by the extracellular deposition of proteins forming amyloid fibrils. The amyloidosis is classified according to the main protein or peptide that constitutes the amyloid fibrils. The most effective methods for the diagnosis of amyloidosis are based on mass spectrometry. Mass spectrometry enables confirmation of the identity of the protein precursor of amyloid fibrils in biological samples with very high sensitivity and specificity, which is crucial for proper amyloid typing. Due to the fact that biological samples are very complex, mass spectrometry is usually connected with techniques such as liquid chromatography or capillary electrophoresis, which enable the separation of proteins before MS analysis. Therefore mass spectrometry constitutes an important part of the so called “hyphenated techniques” combining, preferentially in-line, different analytical methods to provide comprehensive information about the studied problem. Hyphenated methods are very useful in the discovery of biomarkers in different types of amyloidosis. In systemic forms of amyloidosis, the analysis of aggregated proteins is usually performed based on the tissues obtained during a biopsy of an affected organ or a subcutaneous adipose tissue. In some cases, when the diagnostic biopsy is not possible due to the fact that amyloid fibrils are formed in organs like the brain (Alzheimer’s disease), the study of biomarkers presented in body fluids can be carried out. Currently, large-scale studies are performed to find and validate more effective biomarkers, which can be used in diagnostic procedures. We would like to present the methods connected with mass spectrometry which are used in the diagnosis of amyloidosis based on the analysis of proteins occurring in tissues, blood and cerebrospinal fluid.
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Affiliation(s)
- Marta Spodzieja
- Department of Biomedical Chemistry, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Sylwia Rodziewicz-Motowidło
- Department of Biomedical Chemistry, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Aneta Szymanska
- Department of Biomedical Chemistry, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
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Sahebekhtiari N, Saraswat M, Joenväärä S, Jokinen R, Lovric A, Kaye S, Mardinoglu A, Rissanen A, Kaprio J, Renkonen R, Pietiläinen KH. Plasma Proteomics Analysis Reveals Dysregulation of Complement Proteins and Inflammation in Acquired Obesity-A Study on Rare BMI-Discordant Monozygotic Twin Pairs. Proteomics Clin Appl 2019; 13:e1800173. [PMID: 30688043 DOI: 10.1002/prca.201800173] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 12/27/2018] [Indexed: 01/24/2023]
Abstract
PURPOSE The purpose of this study is to elucidate the effect of excess body weight and liver fat on the plasma proteome without interference from genetic variation. EXPERIMENTAL DESIGN The effect of excess body weight is assessed in young, healthy monozygotic twins from pairs discordant for body mass index (intrapair difference (Δ) in BMI > 3 kg m-2 , n = 26) with untargeted LC-MS proteomics quantification. The effect of liver fat is interrogated via subgroup analysis of the BMI-discordant twin cohort: liver fat discordant pairs (Δliver fat > 2%, n = 12) and liver fat concordant pairs (Δliver fat < 2%, n = 14), measured by magnetic resonance spectroscopy. RESULTS Seventy-five proteins are differentially expressed, with significant enrichment for complement and inflammatory response pathways in the heavier co-twins. The complement dysregulation is found in obesity in both the liver fat subgroups. The complement and inflammatory proteins are significantly associated with adiposity measures, insulin resistance and impaired lipids. CONCLUSIONS AND CLINICAL RELEVANCE The early pathophysiological mechanisms in obesity are incompletely understood. It is shown that aberrant complement regulation in plasma is present in very early stages of clinically healthy obese persons, independently of liver fat and in the absence of genetic variation that typically confounds human studies.
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Affiliation(s)
- Navid Sahebekhtiari
- Obesity Research Unit, Research Programs Unit, Diabetes and Obesity Research Program, University of Helsinki, 00014, Helsinki, Finland
| | - Mayank Saraswat
- Transplantation Laboratory, Faculty of Medicine, University of Helsinki, 00014, Helsinki, Finland.,HUSLAB, Helsinki University Hospital, 00029, Helsinki, Finland
| | - Sakari Joenväärä
- Transplantation Laboratory, Faculty of Medicine, University of Helsinki, 00014, Helsinki, Finland.,HUSLAB, Helsinki University Hospital, 00029, Helsinki, Finland
| | - Riikka Jokinen
- Obesity Research Unit, Research Programs Unit, Diabetes and Obesity Research Program, University of Helsinki, 00014, Helsinki, Finland
| | - Alen Lovric
- Science for Life Laboratory, KTH-Royal Institute of Technology, 17121, Stockholm, Sweden
| | - Sanna Kaye
- Obesity Research Unit, Research Programs Unit, Diabetes and Obesity Research Program, University of Helsinki, 00014, Helsinki, Finland
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH-Royal Institute of Technology, 17121, Stockholm, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, 41296, Gothenburg, Sweden.,Centre for Host-Microbiome Interactions, Dental Institute, King's College London, SE19RT, London, UK
| | - Aila Rissanen
- Obesity Research Unit, Research Programs Unit, Diabetes and Obesity Research Program, University of Helsinki, 00014, Helsinki, Finland
| | - Jaakko Kaprio
- Department of Public Health, Finnish Twin Cohort Study, University of Helsinki, 00014, Helsinki, Finland.,Institute for Molecular Medicine Finland, FIMM, University of Helsinki, 00014, Helsinki, Finland
| | - Risto Renkonen
- Transplantation Laboratory, Faculty of Medicine, University of Helsinki, 00014, Helsinki, Finland.,HUSLAB, Helsinki University Hospital, 00029, Helsinki, Finland
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research Programs Unit, Diabetes and Obesity Research Program, University of Helsinki, 00014, Helsinki, Finland.,Abdominal Center, Endocrinology, Helsinki University Central Hospital and University of Helsinki, 00014, Helsinki, Finland
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Dayon L, Cominetti O, Wojcik J, Galindo AN, Oikonomidi A, Henry H, Migliavacca E, Kussmann M, Bowman GL, Popp J. Proteomes of Paired Human Cerebrospinal Fluid and Plasma: Relation to Blood–Brain Barrier Permeability in Older Adults. J Proteome Res 2019; 18:1162-1174. [DOI: 10.1021/acs.jproteome.8b00809] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Loïc Dayon
- Nestlé Institute of Health Sciences, 1015 Lausanne, Switzerland
| | | | | | | | | | - Hugues Henry
- Department of Laboratories, CHUV, 1011 Lausanne, Switzerland
| | | | - Martin Kussmann
- Nestlé Institute of Health Sciences, 1015 Lausanne, Switzerland
| | - Gene L. Bowman
- Nestlé Institute of Health Sciences, 1015 Lausanne, Switzerland
| | - Julius Popp
- Old Age Psychiatry, Department of Psychiatry, CHUV, 1011 Lausanne, Switzerland
- Geriatric Psychiatry, Department of Mental Health and Psychiatry, HUG, 1226 Geneva, Switzerland
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Analyzing Cerebrospinal Fluid Proteomes to Characterize Central Nervous System Disorders: A Highly Automated Mass Spectrometry-Based Pipeline for Biomarker Discovery. Methods Mol Biol 2019; 1959:89-112. [PMID: 30852817 DOI: 10.1007/978-1-4939-9164-8_6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Over the past decade, liquid chromatography tandem mass spectrometry (LC MS/MS)-based workflows become standard for biomarker discovery in proteomics. These medium- to high-throughput (in terms of protein content) profiling approaches have been applied to clinical research. As a result, human proteomes have been characterized to a greater extent than ever before. However, proteomics in clinical research and biomarker discovery studies has generally been performed with small cohorts of subjects (or pooled samples from larger cohorts). This is problematic, as when aiming to identify novel biomarkers, small studies suffer from inherent and important limitations, as a result of the reduced biological diversity and representativity of human populations. Consequently, larger-scale proteomics will be key to delivering robust biomarker candidates and enabling translation to clinical practice.Cerebrospinal fluid (CSF) is a highly clinically relevant body fluid, and an important source of potential biomarkers for brain-associated damage, such as that induced by traumatic brain injury and stroke, and brain diseases, such as Alzheimer's disease and Parkinson's disease. We have developed a scalable automated proteomic pipeline (ASAP2) for biomarker discovery. This workflow is compatible with larger clinical research studies in terms of sample size, while still allowing several hundred proteins to be measured in CSF by MS. In this chapter, we describe the whole proteomic workflow to analyze human CSF. We further illustrate our protocol with some examples from an analysis of hundreds of human CSF samples, in the specific context of biomarker discovery to characterize central nervous system disorders.
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40
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Dufresne J, Bowden P, Thavarajah T, Florentinus-Mefailoski A, Chen ZZ, Tucholska M, Norzin T, Ho MT, Phan M, Mohamed N, Ravandi A, Stanton E, Slutsky AS, Dos Santos CC, Romaschin A, Marshall JC, Addison C, Malone S, Heyland D, Scheltens P, Killestein J, Teunissen C, Diamandis EP, Siu KWM, Marshall JG. The plasma peptidome. Clin Proteomics 2018; 15:39. [PMID: 30519149 PMCID: PMC6271647 DOI: 10.1186/s12014-018-9211-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Accepted: 10/23/2018] [Indexed: 02/07/2023] Open
Abstract
Background It may be possible to discover new diagnostic or therapeutic peptides or proteins from blood plasma using LC–ESI–MS/MS to identify, with a linear quadrupole ion trap to identify, quantify and compare the statistical distributions of peptides cleaved ex vivo from plasma samples from different clinical populations. Methods A systematic method for the organic fractionation of plasma peptides was applied to identify and quantify the endogenous tryptic peptides from human plasma from multiple institutions by C18 HPLC followed nano electrospray ionization and tandem mass spectrometry (LC–ESI–MS/MS) with a linear quadrupole ion trap. The endogenous tryptic peptides, or tryptic phospho peptides (i.e. without exogenous digestion), were extracted in a mixture of organic solvent and water, dried and collected by preparative C18. The tryptic peptides from 6 institutions with 12 different disease and normal EDTA plasma populations, alongside ice cold controls for pre-analytical variation, were characterized by mass spectrometry. Each patient plasma was precipitated in 90% acetonitrile and the endogenous tryptic peptides extracted by a stepwise gradient of increasing water and then formic acid resulting in 10 sub-fractions. The fractionated peptides were manually collected over preparative C18 and injected for 1508 LC–ESI–MS/MS experiments analyzed in SQL Server R. Results Peptides that were cleaved in human plasma by a tryptic activity ex vivo provided convenient and sensitive access to most human proteins in plasma that show differences in the frequency or intensity of proteins observed across populations that may have clinical significance. Combination of step wise organic extraction of 200 μL of plasma with nano electrospray resulted in the confident identification and quantification ~ 14,000 gene symbols by X!TANDEM that is the largest number of blood proteins identified to date and shows that you can monitor the ex vivo proteolysis of most human proteins, including interleukins, from blood. A total of 15,968,550 MS/MS spectra ≥ E4 intensity counts were correlated by the SEQUEST and X!TANDEM algorithms to a federated library of 157,478 protein sequences that were filtered for best charge state (2+ or 3+) and peptide sequence in SQL Server resulting in 1,916,672 distinct best-fit peptide correlations for analysis with the R statistical system. SEQUEST identified some 140,054 protein accessions, or some ~ 26,000 gene symbols, proteins or loci, with at least 5 independent correlations. The X!TANDEM algorithm made at least 5 best fit correlations to more than 14,000 protein gene symbols with p-values and FDR corrected q-values of ~ 0.001 or less. Log10 peptide intensity values showed a Gaussian distribution from E8 to E4 arbitrary counts by quantile plot, and significant variation in average precursor intensity across the disease and controls treatments by ANOVA with means compared by the Tukey–Kramer test. STRING analysis of the top 2000 gene symbols showed a tight association of cellular proteins that were apparently present in the plasma as protein complexes with related cellular components, molecular functions and biological processes. Conclusions The random and independent sampling of pre-fractionated blood peptides by LC-ESI-MS/MS with SQL Server-R analysis revealed the largest plasma proteome to date and was a practical method to quantify and compare the frequency or log10 intensity of individual proteins cleaved ex vivo across populations of plasma samples from multiple clinical locations to discover treatment-specific variation using classical statistics suitable for clinical science. It was possible to identify and quantify nearly all human proteins from EDTA plasma and compare the results of thousands of LC–ESI–MS/MS experiments from multiple clinical populations using standard database methods in SQL Server and classical statistical strategies in the R data analysis system. Electronic supplementary material The online version of this article (10.1186/s12014-018-9211-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jaimie Dufresne
- 1Ryerson Analytical Biochemistry Laboratory (RABL), Department of Chemistry and Biology, Faculty of Science, Ryerson University, 350 Victoria St, Toronto, ON Canada
| | - Pete Bowden
- 1Ryerson Analytical Biochemistry Laboratory (RABL), Department of Chemistry and Biology, Faculty of Science, Ryerson University, 350 Victoria St, Toronto, ON Canada
| | - Thanusi Thavarajah
- 1Ryerson Analytical Biochemistry Laboratory (RABL), Department of Chemistry and Biology, Faculty of Science, Ryerson University, 350 Victoria St, Toronto, ON Canada
| | - Angelique Florentinus-Mefailoski
- 1Ryerson Analytical Biochemistry Laboratory (RABL), Department of Chemistry and Biology, Faculty of Science, Ryerson University, 350 Victoria St, Toronto, ON Canada
| | - Zhuo Zhen Chen
- 1Ryerson Analytical Biochemistry Laboratory (RABL), Department of Chemistry and Biology, Faculty of Science, Ryerson University, 350 Victoria St, Toronto, ON Canada
| | - Monika Tucholska
- 1Ryerson Analytical Biochemistry Laboratory (RABL), Department of Chemistry and Biology, Faculty of Science, Ryerson University, 350 Victoria St, Toronto, ON Canada
| | - Tenzin Norzin
- 1Ryerson Analytical Biochemistry Laboratory (RABL), Department of Chemistry and Biology, Faculty of Science, Ryerson University, 350 Victoria St, Toronto, ON Canada
| | - Margaret Truc Ho
- 1Ryerson Analytical Biochemistry Laboratory (RABL), Department of Chemistry and Biology, Faculty of Science, Ryerson University, 350 Victoria St, Toronto, ON Canada
| | - Morla Phan
- 1Ryerson Analytical Biochemistry Laboratory (RABL), Department of Chemistry and Biology, Faculty of Science, Ryerson University, 350 Victoria St, Toronto, ON Canada
| | - Nargiz Mohamed
- 1Ryerson Analytical Biochemistry Laboratory (RABL), Department of Chemistry and Biology, Faculty of Science, Ryerson University, 350 Victoria St, Toronto, ON Canada
| | - Amir Ravandi
- 2Institute of Cardiovascular Sciences, St Boniface Hospital Research Center, University of Manitoba, Winnipeg, Canada
| | - Eric Stanton
- 3Division of Cardiology, Department of Medicine, McMaster University, Hamilton, Canada
| | - Arthur S Slutsky
- 4St. Michael's Hospital, Keenan Chair in Medicine, University of Toronto, Toronto, Canada
| | - Claudia C Dos Santos
- 5St. Michael's Hospital, Keenan Research Centre for Biomedical Science, Toronto, Canada
| | - Alexander Romaschin
- 5St. Michael's Hospital, Keenan Research Centre for Biomedical Science, Toronto, Canada
| | - John C Marshall
- 5St. Michael's Hospital, Keenan Research Centre for Biomedical Science, Toronto, Canada
| | - Christina Addison
- 6Program for Cancer Therapeutics, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Shawn Malone
- 6Program for Cancer Therapeutics, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Daren Heyland
- 7Clinical Evaluation Research Unit, Kingston General Hospital, Kingston, Canada
| | - Philip Scheltens
- 8Alzheimer Center, Department of Neurology, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Joep Killestein
- 9MS Center, Department of Neurology, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Charlotte Teunissen
- 10Neurochemistry Lab and Biobank, Department of Clinical Chemistry, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | | | - K W M Siu
- 12University of Windsor, Windsor, Canada
| | - John G Marshall
- 1Ryerson Analytical Biochemistry Laboratory (RABL), Department of Chemistry and Biology, Faculty of Science, Ryerson University, 350 Victoria St, Toronto, ON Canada.,13International Biobank of Luxembourg (IBBL), Luxembourg Institute of Health (formerly CRP Sante Luxembourg), Strassen, Luxembourg
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Cominetti O, Núñez Galindo A, Corthésy J, Valsesia A, Irincheeva I, Kussmann M, Saris WHM, Astrup A, McPherson R, Harper ME, Dent R, Hager J, Dayon L. Obesity shows preserved plasma proteome in large independent clinical cohorts. Sci Rep 2018; 8:16981. [PMID: 30451909 PMCID: PMC6242904 DOI: 10.1038/s41598-018-35321-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 11/02/2018] [Indexed: 12/21/2022] Open
Abstract
Holistic human proteome maps are expected to complement comprehensive profile assessment of health and disease phenotypes. However, methodologies to analyze proteomes in human tissue or body fluid samples at relevant scale and performance are still limited in clinical research. Their deployment and demonstration in large enough human populations are even sparser. In the present study, we have characterized and compared the plasma proteomes of two large independent cohorts of obese and overweight individuals using shotgun mass spectrometry (MS)-based proteomics. Herein, we showed, in both populations from different continents of about 500 individuals each, the concordance of plasma protein MS measurements in terms of variability, gender-specificity, and age-relationship. Additionally, we replicated several known and new associations between proteins, clinical and molecular variables, such as insulin and glucose concentrations. In conclusion, our MS-based analyses of plasma samples from independent human cohorts proved the practical feasibility and efficiency of a large and unified discovery/replication approach in proteomics, which was also recently coined “rectangular” design.
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Affiliation(s)
- Ornella Cominetti
- Proteomics, Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | | | - John Corthésy
- Proteomics, Nestlé Institute of Health Sciences, Lausanne, Switzerland.,Nutrition Analytics, Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Armand Valsesia
- Nutrition and Metabolic Health, Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Irina Irincheeva
- Nutrition and Metabolic Health, Nestlé Institute of Health Sciences, Lausanne, Switzerland.,Clinical Trial Unit, University of Bern, Bern, Switzerland
| | - Martin Kussmann
- Proteomics, Nestlé Institute of Health Sciences, Lausanne, Switzerland.,The Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Wim H M Saris
- NUTRIM, School for Nutrition, Toxicology and Metabolism, Department of Human Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Arne Astrup
- Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Ruth McPherson
- Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada
| | - Mary-Ellen Harper
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Robert Dent
- Ottawa Hospital Weight Management Clinic, The Ottawa Hospital, Ottawa, Canada
| | - Jörg Hager
- Nutrition and Metabolic Health, Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Loïc Dayon
- Proteomics, Nestlé Institute of Health Sciences, Lausanne, Switzerland.
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42
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Titz B, Gadaleta RM, Lo Sasso G, Elamin A, Ekroos K, Ivanov NV, Peitsch MC, Hoeng J. Proteomics and Lipidomics in Inflammatory Bowel Disease Research: From Mechanistic Insights to Biomarker Identification. Int J Mol Sci 2018; 19:ijms19092775. [PMID: 30223557 PMCID: PMC6163330 DOI: 10.3390/ijms19092775] [Citation(s) in RCA: 30] [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: 08/29/2018] [Revised: 09/11/2018] [Accepted: 09/12/2018] [Indexed: 02/06/2023] Open
Abstract
Inflammatory bowel disease (IBD) represents a group of progressive disorders characterized by recurrent chronic inflammation of the gut. Ulcerative colitis and Crohn's disease are the major manifestations of IBD. While our understanding of IBD has progressed in recent years, its etiology is far from being fully understood, resulting in suboptimal treatment options. Complementing other biological endpoints, bioanalytical "omics" methods that quantify many biomolecules simultaneously have great potential in the dissection of the complex pathogenesis of IBD. In this review, we focus on the rapidly evolving proteomics and lipidomics technologies and their broad applicability to IBD studies; these range from investigations of immune-regulatory mechanisms and biomarker discovery to studies dissecting host⁻microbiome interactions and the role of intestinal epithelial cells. Future studies can leverage recent advances, including improved analytical methodologies, additional relevant sample types, and integrative multi-omics analyses. Proteomics and lipidomics could effectively accelerate the development of novel targeted treatments and the discovery of complementary biomarkers, enabling continuous monitoring of the treatment response of individual patients; this may allow further refinement of treatment and, ultimately, facilitate a personalized medicine approach to IBD.
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Affiliation(s)
- Bjoern Titz
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland.
| | - Raffaella M Gadaleta
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland.
| | - Giuseppe Lo Sasso
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland.
| | - Ashraf Elamin
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland.
| | - Kim Ekroos
- Lipidomics Consulting Ltd., Irisviksvägen 31D, 02230 Esbo, Finland.
| | - Nikolai V Ivanov
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland.
| | - Manuel C Peitsch
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland.
| | - Julia Hoeng
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland.
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43
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Masood A, Benabdelkamel H, Alfadda AA. Obesity Proteomics: An Update on the Strategies and Tools Employed in the Study of Human Obesity. High Throughput 2018; 7:ht7030027. [PMID: 30213114 PMCID: PMC6164994 DOI: 10.3390/ht7030027] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 08/30/2018] [Accepted: 09/10/2018] [Indexed: 02/07/2023] Open
Abstract
Proteomics has become one of the most important disciplines for characterizing cellular protein composition, building functional linkages between protein molecules, and providing insight into the mechanisms of biological processes in a high-throughput manner. Mass spectrometry-based proteomic advances have made it possible to study human diseases, including obesity, through the identification and biochemical characterization of alterations in proteins that are associated with it and its comorbidities. A sizeable number of proteomic studies have used the combination of large-scale separation techniques, such as high-resolution two-dimensional gel electrophoresis or liquid chromatography in combination with mass spectrometry, for high-throughput protein identification. These studies have applied proteomics to comprehensive biochemical profiling and comparison studies while using different tissues and biological fluids from patients to demonstrate the physiological or pathological adaptations within their proteomes. Further investigations into these proteome-wide alterations will enable us to not only understand the disease pathophysiology, but also to determine signature proteins that can serve as biomarkers for obesity and related diseases. This review examines the different proteomic techniques used to study human obesity and discusses its successful applications along with its technical limitations.
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Affiliation(s)
- Afshan Masood
- Obesity Research Center, College of Medicine, King Saud University, P.O. Box 2925 (98), Riyadh 11461, Saudi Arabia.
| | - Hicham Benabdelkamel
- Obesity Research Center, College of Medicine, King Saud University, P.O. Box 2925 (98), Riyadh 11461, Saudi Arabia.
| | - Assim A Alfadda
- Obesity Research Center, College of Medicine, King Saud University, P.O. Box 2925 (98), Riyadh 11461, Saudi Arabia.
- Department of Medicine, College of Medicine, King Saud University, P.O. Box 2925 (38), Riyadh 11461, Saudi Arabia.
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Jensen SB, Hindberg K, Solomon T, Smith EN, Lapek JD, Gonzalez DJ, Latysheva N, Frazer KA, Braekkan SK, Hansen JB. Discovery of novel plasma biomarkers for future incident venous thromboembolism by untargeted synchronous precursor selection mass spectrometry proteomics. J Thromb Haemost 2018; 16:1763-1774. [PMID: 29964323 PMCID: PMC6123273 DOI: 10.1111/jth.14220] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Indexed: 01/08/2023]
Abstract
Essentials Discovery of predictive biomarkers of venous thromboembolism (VTE) may aid risk stratification. A case-control study where plasma was sampled before the occurrence of VTE was established. We generated untargeted plasma proteomic profiles of 200 individuals by use of mass spectrometry. Assessment of the biomarker potential of 501 proteins yielded 46 biomarker candidates. ABSTRACT Background Prophylactic anticoagulant treatment may substantially reduce the incidence of venous thromboembolism (VTE) but entails considerable risk of severe bleeding. Identification of individuals at high risk of VTE through the use of predictive biomarkers is desirable in order to achieve a favorable benefit-to-harm ratio. Objective We aimed to identify predictive protein biomarker candidates of VTE. Methods We performed a case-control study of 200 individuals that participated in the Tromsø Study, a population-based cohort, where blood samples were collected before the VTE events occurred. Untargeted tandem mass tag-synchronous precursor selection-mass spectrometry (TMT-SPS-MS3)-based proteomic profiling was used to study the plasma proteomes of each individual. Results Of the 501 proteins detected in a sufficient number of samples to allow multivariate analysis, 46 proteins were associated with VTE case-control status with P-values below the 0.05 significance threshold. The strongest predictive biomarker candidates, assessed by statistical significance, were transthyretin, vitamin K-dependent protein Z and protein/nucleic acid deglycase DJ-1. Conclusions Our untargeted approach of plasma proteome profiling revealed novel predictive biomarker candidates of VTE and confirmed previously reported candidates, thereby providing conceptual support for the validity of the study. A larger nested case-control study will be conducted to validate our findings.
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Affiliation(s)
- S B Jensen
- K. G. Jebsen Thrombosis Research and Expertise Center, Department of Clinical Medicine, UiT - The Arctic University of Norway, Tromsø, Norway
| | - K Hindberg
- K. G. Jebsen Thrombosis Research and Expertise Center, Department of Clinical Medicine, UiT - The Arctic University of Norway, Tromsø, Norway
| | - T Solomon
- Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, California, USA
| | - E N Smith
- K. G. Jebsen Thrombosis Research and Expertise Center, Department of Clinical Medicine, UiT - The Arctic University of Norway, Tromsø, Norway
- Department of Pediatrics and Rady's Children's Hospital, University of California San Diego, La Jolla, California, USA
| | - J D Lapek
- Department of Pharmacology, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA
| | - D J Gonzalez
- Department of Pharmacology, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA
| | - N Latysheva
- K. G. Jebsen Thrombosis Research and Expertise Center, Department of Clinical Medicine, UiT - The Arctic University of Norway, Tromsø, Norway
| | - K A Frazer
- K. G. Jebsen Thrombosis Research and Expertise Center, Department of Clinical Medicine, UiT - The Arctic University of Norway, Tromsø, Norway
- Department of Pediatrics and Rady's Children's Hospital, University of California San Diego, La Jolla, California, USA
- Institute of Genomic Medicine, University of California San Diego, La Jolla, California, USA
| | - S K Braekkan
- K. G. Jebsen Thrombosis Research and Expertise Center, Department of Clinical Medicine, UiT - The Arctic University of Norway, Tromsø, Norway
- Division of Internal Medicine, University Hospital of North Norway, Tromsø, Norway
| | - J-B Hansen
- K. G. Jebsen Thrombosis Research and Expertise Center, Department of Clinical Medicine, UiT - The Arctic University of Norway, Tromsø, Norway
- Division of Internal Medicine, University Hospital of North Norway, Tromsø, Norway
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Moulder R, Bhosale SD, Goodlett DR, Lahesmaa R. Analysis of the plasma proteome using iTRAQ and TMT-based Isobaric labeling. MASS SPECTROMETRY REVIEWS 2018; 37:583-606. [PMID: 29120501 DOI: 10.1002/mas.21550] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Accepted: 09/26/2017] [Indexed: 05/23/2023]
Abstract
Over the past decade, chemical labeling with isobaric tandem mass tags, such as isobaric tags for relative and absolute quantification reagents (iTRAQ) and tandem mass tag (TMT) reagents, has been employed in a wide range of different clinically orientated serum and plasma proteomics studies. In this review the scope of these works is presented with attention to the areas of research, methods employed and performance limitations. These applications have covered a wide range of diseases, disorders and infections, and have implemented a variety of different preparative and mass spectrometric approaches. In contrast to earlier works, which struggled to quantify more than a few hundred proteins, increasingly these studies have provided deeper insight into the plasma proteome extending the numbers of quantified proteins to over a thousand.
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Affiliation(s)
- Robert Moulder
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
| | - Santosh D Bhosale
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
| | | | - Riitta Lahesmaa
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
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Zhang P, Zhu S, Zhao M, Zhao P, Zhao H, Deng J, Li J. Identification of plasma biomarkers for diffuse axonal injury in rats by iTRAQ-coupled LC-MS/MS and bioinformatics analysis. Brain Res Bull 2018; 142:224-232. [PMID: 30077728 DOI: 10.1016/j.brainresbull.2018.07.015] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 07/24/2018] [Accepted: 07/25/2018] [Indexed: 12/30/2022]
Abstract
DAI is a serious and complex brain injury associated with significant morbidity and mortality. The lack of reliable objective diagnostic modalities for DAI delays administration of therapeutic interventions. Hence, identifying reliable biomarkers is urgently needed to enable early DAI diagnosis in the clinic. Herein, we established a rat model of DAI and applied an isobaric tags for a relative and absolute quantification (iTRAQ) coupled with nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS) proteomics approach to screen differentially expressed plasma proteins associated with DAI. A total of 58 proteins were found to be significantly modulated in blood plasma samples of the injury group in at least one time point compared to controls. Bioinformatics analysis of the differentially expressed proteins revealed that the pathogenesis of axonal injury underlying DAI is multi-stage biological process involved. Two significantly changed proteins, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and hemopexin (Hpx), were identified as potential diagnostic biomarkers for DAI, and were successfully confirmed by further western blot analysis. This proteomic profiling study not only identified novel plasma biomarkers that may facilitate the development of clinically diagnostic for DAI, but also provided enhanced understanding of the molecular mechanisms underlying DAI.
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Affiliation(s)
- Peng Zhang
- Department of Forensic Medicine, Hainan Medical University, Haikou 571199, China
| | - Shisheng Zhu
- Faculty of Medical Technology, Chongqing Medical and Pharmaceutical College, Chongqing 401331, China
| | - Minzhu Zhao
- Department of Forensic Medicine, Faculty of Basic Medical Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Peng Zhao
- Faculty of Basic Medical Sciences, Zunyi Medical And Pharmaceutical College, Zunyi 563006, China
| | - Haiyi Zhao
- Genecreate Biological Engineering Co., Ltd., National Bio-Industry Base, Wuhan, 430075, China
| | - Jianqiang Deng
- Department of Forensic Medicine, Hainan Medical University, Haikou 571199, China
| | - Jianbo Li
- Department of Forensic Medicine, Faculty of Basic Medical Sciences, Chongqing Medical University, Chongqing 400016, China.
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Dayon L, Núñez Galindo A, Wojcik J, Cominetti O, Corthésy J, Oikonomidi A, Henry H, Kussmann M, Migliavacca E, Severin I, Bowman GL, Popp J. Alzheimer disease pathology and the cerebrospinal fluid proteome. Alzheimers Res Ther 2018; 10:66. [PMID: 30021611 PMCID: PMC6052524 DOI: 10.1186/s13195-018-0397-4] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 06/11/2018] [Indexed: 12/18/2022]
Abstract
BACKGROUND Altered proteome profiles have been reported in both postmortem brain tissues and body fluids of subjects with Alzheimer disease (AD), but their broad relationships with AD pathology, amyloid pathology, and tau-related neurodegeneration have not yet been fully explored. Using a robust automated MS-based proteomic biomarker discovery workflow, we measured cerebrospinal fluid (CSF) proteomes to explore their association with well-established markers of core AD pathology. METHODS Cross-sectional analysis was performed on CSF collected from 120 older community-dwelling adults with normal (n = 48) or impaired cognition (n = 72). LC-MS quantified hundreds of proteins in the CSF. CSF concentrations of β-amyloid 1-42 (Aβ1-42), tau, and tau phosphorylated at threonine 181 (P-tau181) were determined with immunoassays. First, we explored proteins relevant to biomarker-defined AD. Then, correlation analysis of CSF proteins with CSF markers of amyloid pathology, neuronal injury, and tau hyperphosphorylation (i.e., Aβ1-42, tau, P-tau181) was performed using Pearson's correlation coefficient and Bonferroni correction for multiple comparisons. RESULTS We quantified 790 proteins in CSF samples with MS. Four CSF proteins showed an association with CSF Aβ1-42 levels (p value ≤ 0.05 with correlation coefficient (R) ≥ 0.38). We identified 50 additional CSF proteins associated with CSF tau and 46 proteins associated with CSF P-tau181 (p value ≤ 0.05 with R ≥ 0.37). The majority of those proteins that showed such associations were brain-enriched proteins. Gene Ontology annotation revealed an enrichment for synaptic proteins and proteins originating from reelin-producing cells and the myelin sheath. CONCLUSIONS We used an MS-based proteomic workflow to profile the CSF proteome in relation to cerebral AD pathology. We report strong evidence of previously reported CSF proteins and several novel CSF proteins specifically associated with amyloid pathology or neuronal injury and tau hyperphosphorylation.
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Affiliation(s)
- Loïc Dayon
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | | | | | | | - John Corthésy
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | | | - Hugues Henry
- Department of Laboratories, CHUV, Lausanne, Switzerland
| | - Martin Kussmann
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
- Present address: Liggins Institute, University of Auckland, Auckland, New Zealand
| | | | - India Severin
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Gene L. Bowman
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Julius Popp
- Old Age Psychiatry, Department of Psychiatry, CHUV, Lausanne, Switzerland
- Geriatric Psychiatry, Department of Mental Health and Psychiatry, HUG, Geneva, Switzerland
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Corthésy J, Theofilatos K, Mavroudi S, Macron C, Cominetti O, Remlawi M, Ferraro F, Núñez Galindo A, Kussmann M, Likothanassis S, Dayon L. An Adaptive Pipeline To Maximize Isobaric Tagging Data in Large-Scale MS-Based Proteomics. J Proteome Res 2018; 17:2165-2173. [DOI: 10.1021/acs.jproteome.8b00110] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- John Corthésy
- Nestlé Institute of Health Sciences, Lausanne 1015, Switzerland
| | | | - Seferina Mavroudi
- InSybio, Ltd., Innovations House, 19 Staple Gardens, Winchester SO238SR, United Kingdom
- Department of Social Work, School of Sciences of Health and Care, Technological Educational Institute of Western Greece, Patras 26334, Greece
| | | | | | - Mona Remlawi
- Nestlé Institute of Health Sciences, Lausanne 1015, Switzerland
| | | | | | - Martin Kussmann
- Nestlé Institute of Health Sciences, Lausanne 1015, Switzerland
| | - Spiridon Likothanassis
- InSybio, Ltd., Innovations House, 19 Staple Gardens, Winchester SO238SR, United Kingdom
- Department of Computer Engineering and Informatics, University of Patras, Patras 26500, Greece
| | - Loïc Dayon
- Nestlé Institute of Health Sciences, Lausanne 1015, Switzerland
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Greco V, Piras C, Pieroni L, Urbani A. Direct Assessment of Plasma/Serum Sample Quality for Proteomics Biomarker Investigation. Methods Mol Biol 2018; 1619:3-21. [PMID: 28674873 DOI: 10.1007/978-1-4939-7057-5_1] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Blood proteome analysis for biomarker discovery represents one of the most challenging tasks to be achieved through clinical proteomics due to the sample complexity, such as the extreme heterogeneity of proteins in very dynamic concentrations, and to the observation of proper sampling and storage conditions. Quantitative and qualitative proteomics profiling of plasma and serum could be useful both for the early detection of diseases and for the evaluation of pathological status. Two main sources of variability can affect the precision and accuracy of the quantitative experiments designed for biomarker discovery and validation. These sources are divided into two categories, pre-analytical and analytical, and are often ignored; however, they can contribute to consistent errors and misunderstanding in biomarker research. In this chapter, we review critical pre-analytical and analytical variables that can influence quantitative proteomics. According to guidelines accepted by proteomics community, we propose some recommendations and strategies for a proper proteomics analysis addressed to biomarker studies.
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Affiliation(s)
- Viviana Greco
- Proteomics and metabonomics unit, Fondazione Santa Lucia, IRCCS, Rome, Italy
| | - Cristian Piras
- Department of Veterinary Medicine, University of Milan, Milan, Italy
| | - Luisa Pieroni
- Proteomics and metabonomics unit, Fondazione Santa Lucia, IRCCS, Rome, Italy
| | - Andrea Urbani
- Proteomics and metabonomics unit, Fondazione Santa Lucia, IRCCS, Rome, Italy. .,Institute of Biochemistry and Clinical Biochemistry, Catholic University of Sacred Heart, Rome, Italy.
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Ping L, Duong DM, Yin L, Gearing M, Lah JJ, Levey AI, Seyfried NT. Global quantitative analysis of the human brain proteome in Alzheimer's and Parkinson's Disease. Sci Data 2018; 5:180036. [PMID: 29533394 PMCID: PMC5848788 DOI: 10.1038/sdata.2018.36] [Citation(s) in RCA: 132] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 01/09/2018] [Indexed: 02/07/2023] Open
Abstract
Patients with Alzheimer's disease (AD) and Parkinson's disease (PD) often have overlap in clinical presentation and brain neuropathology suggesting that these two diseases share common underlying mechanisms. Currently, the molecular pathways linking AD and PD are incompletely understood. Utilizing Tandem Mass Tag (TMT) isobaric labeling and synchronous precursor selection-based MS3 (SPS-MS3) mass spectrometry, we performed an unbiased quantitative proteomic analysis of post-mortem human brain tissues (n=80) from four different groups defined as controls, AD, PD, and co-morbid AD/PD cases across two brain regions (frontal cortex and anterior cingulate gyrus). In total, we identified 11 840 protein groups representing 10 230 gene symbols, which map to ~65% of the protein coding genes in brain. The utility of including two reference standards in each TMT 10-plex assay to assess intra- and inter-batch variance is also described. Ultimately, this comprehensive human brain proteomic dataset serves as a valuable resource for various research endeavors including, but not limited to, the identification of disease-specific protein signatures and molecular pathways that are common in AD and PD.
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Affiliation(s)
- Lingyan Ping
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA
- Center for Neurodegenerative Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Duc M. Duong
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA
- Center for Neurodegenerative Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Luming Yin
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA
- Center for Neurodegenerative Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Marla Gearing
- Center for Neurodegenerative Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - James J. Lah
- Center for Neurodegenerative Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Allan I. Levey
- Center for Neurodegenerative Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Nicholas T. Seyfried
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA
- Center for Neurodegenerative Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
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