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Makey DM, Ruotolo BT. Liquid-phase separations coupled with ion mobility-mass spectrometry for next-generation biopharmaceutical analysis. Expert Rev Proteomics 2024; 21:259-270. [PMID: 38934922 PMCID: PMC11299228 DOI: 10.1080/14789450.2024.2373707] [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: 03/28/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024]
Abstract
INTRODUCTION The pharmaceutical industry continues to expand its search for innovative biotherapeutics. The comprehensive characterization of such therapeutics requires many analytical techniques to fully evaluate critical quality attributes, making analysis a bottleneck in discovery and development timelines. While thorough characterization is crucial for ensuring the safety and efficacy of biotherapeutics, there is a need to further streamline analytical characterization and expedite the overall timeline from discovery to market. AREAS COVERED This review focuses on recent developments in liquid-phase separations coupled with ion mobility-mass spectrometry (IM-MS) for the development and characterization of biotherapeutics. We cover uses of IM-MS to improve the characterization of monoclonal antibodies, antibody-drug conjugates, host cell proteins, glycans, and nucleic acids. This discussion is based on an extensive literature search using Web of Science, Google Scholar, and SciFinder. EXPERT OPINION IM-MS has the potential to enhance the depth and efficiency of biotherapeutic characterization by providing additional insights into conformational changes, post-translational modifications, and impurity profiles. The rapid timescale of IM-MS positions it well to enhance the information content of existing assays through its facile integration with standard liquid-phase separation techniques that are commonly used for biopharmaceutical analysis.
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Affiliation(s)
- Devin M Makey
- Department of Chemistry, University of Michigan, Ann Arbor, MI, USA
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2
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Spick M, Muazzam A, Pandha H, Michael A, Gethings LA, Hughes CJ, Munjoma N, Plumb RS, Wilson ID, Whetton AD, Townsend PA, Geifman N. Multi-omic diagnostics of prostate cancer in the presence of benign prostatic hyperplasia. Heliyon 2023; 9:e22604. [PMID: 38076065 PMCID: PMC10709398 DOI: 10.1016/j.heliyon.2023.e22604] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/01/2023] [Accepted: 11/15/2023] [Indexed: 09/11/2024] Open
Abstract
There is an unmet need for improved diagnostic testing and risk prediction for cases of prostate cancer (PCa) to improve care and reduce overtreatment of indolent disease. Here we have analysed the serum proteome and lipidome of 262 study participants by liquid chromatography-mass spectrometry, including participants diagnosed with PCa, benign prostatic hyperplasia (BPH), or otherwise healthy volunteers, with the aim of improving biomarker specificity. Although a two-class machine learning model separated PCa from controls with sensitivity of 0.82 and specificity of 0.95, adding BPH resulted in a statistically significant decline in specificity for prostate cancer to 0.76, with half of BPH cases being misclassified by the model as PCa. A small number of biomarkers differentiating between BPH and prostate cancer were identified, including proteins in MAP Kinase pathways, as well as in lipids containing oleic acid; these may offer a route to greater specificity. These results highlight, however, that whilst there are opportunities for machine learning, these will only be achieved by use of appropriate training sets that include confounding comorbidities, especially when calculating the specificity of a test.
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Affiliation(s)
- Matt Spick
- School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7YH, United Kingdom
| | - Ammara Muazzam
- The Hospital for Sick Children (SickKids), 555 University Ave, Toronto, ON M5G 1X8, Canada
- Division of Cancer Sciences, Manchester Cancer Research Center, Manchester Academic Health Sciences Center, University of Manchester, Manchester, M20 4GJ, United Kingdom
| | - Hardev Pandha
- School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom
| | - Agnieszka Michael
- School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom
| | - Lee A. Gethings
- School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7YH, United Kingdom
- Waters Corporation, Wilmslow, Cheshire, SK9 4AX, United Kingdom
- Manchester Institute of Biotechnology, Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, United Kingdom
| | | | | | - Robert S. Plumb
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, Burlington Danes Building, Du Cane Road, London, W12 0NN, United Kingdom
| | - Ian D. Wilson
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, Burlington Danes Building, Du Cane Road, London, W12 0NN, United Kingdom
| | - Anthony D. Whetton
- Veterinary Health Innovation Engine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7YH, United Kingdom
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7YH, United Kingdom
- Division of Cancer Sciences, Manchester Cancer Research Center, Manchester Academic Health Sciences Center, University of Manchester, Manchester, M20 4GJ, United Kingdom
| | - Paul A. Townsend
- School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom
- Division of Cancer Sciences, Manchester Cancer Research Center, Manchester Academic Health Sciences Center, University of Manchester, Manchester, M20 4GJ, United Kingdom
| | - Nophar Geifman
- School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7YH, United Kingdom
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Vankeerberghen B, Op de Beeck J, Desmet G. On-Chip Comparison of the Performance of First- and Second-Generation Micropillar Array Columns. Anal Chem 2023; 95:13822-13828. [PMID: 37677150 DOI: 10.1021/acs.analchem.3c01829] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Because of its dimensions, the recently introduced micropillar array columns are most suited for high-efficiency liquid chromatography separations in proteomics. Unlike the packed bed columns and capillary-based column formats, the micropillar array concept still has significant room to progress in terms of the reduction of its characteristic size (i.e., pillar diameter and interpillar distance) to open the road to even higher-efficiency separations and their applications. We report here on the on-chip comparison between first-generation (Gen 1) and second-generation (Gen 2) micropillar array columns wherein the pillar and interpillar size have been halved. Because of the on-chip measurements, the observed plate heights H represent the fundamental band broadening, devoid of any extra-column band-broadening effects. The observed reduction of H with a factor of 2 around the uopt-velocity and with a factor of 4 in the C-term dominated regime of the van Deemter-curve is in full agreement with the theoretically expected gain. This shows the pillar and interpillar size reduction could be effectuated without affecting the theoretical separation potential of the micropillar arrays. Compared to Gen 1, Gen 2 offers a 4-fold reduction of the required analysis time around the optimal velocity and about a 16-fold reduction in the C-term-dominated range.
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Affiliation(s)
- Bert Vankeerberghen
- Department of Chemical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Jeff Op de Beeck
- Thermo Fisher Scientific, Technologiepark-Zwijnaarde 82, 9052 Gent, Belgium
| | - Gert Desmet
- Department of Chemical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
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Wu Z, Bonneil É, Belford M, Boeser C, Ruiz Cuevas MV, Lemieux S, Dunyach JJ, Thibault P. Proteogenomics and Differential Ion Mobility Enable the Exploration of the Mutational Landscape in Colon Cancer Cells. Anal Chem 2022; 94:12086-12094. [PMID: 35995421 PMCID: PMC9454824 DOI: 10.1021/acs.analchem.2c02056] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/15/2022] [Indexed: 11/29/2022]
Abstract
The sensitivity and depth of proteomic analyses are limited by isobaric ions and interferences that preclude the identification of low abundance peptides. Extensive sample fractionation is often required to extend proteome coverage when sample amount is not a limitation. Ion mobility devices provide a viable alternate approach to resolve confounding ions and improve peak capacity and mass spectrometry (MS) sensitivity. Here, we report the integration of differential ion mobility with segmented ion fractionation (SIFT) to enhance the comprehensiveness of proteomic analyses. The combination of differential ion mobility and SIFT, where narrow windows of ∼m/z 100 are acquired in turn, is found particularly advantageous in the analysis of protein digests and typically provided more than 60% gain in identification compared to conventional single-shot LC-MS/MS. The application of this approach is further demonstrated for the analysis of tryptic digests from different colorectal cancer cell lines where the enhanced sensitivity enabled the identification of single amino acid variants that were correlated with the corresponding transcriptomic data sets.
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Affiliation(s)
- Zhaoguan Wu
- Institute for Research
in Immunology and Cancer (IRIC), Université
de Montréal, Montréal, Quebec 514 343-6910, Canada
- Department of Chemistry, Université
de Montréal, Montréal, Quebec 514 343-6910, Canada
| | - Éric Bonneil
- Institute for Research
in Immunology and Cancer (IRIC), Université
de Montréal, Montréal, Quebec 514 343-6910, Canada
| | - Michael Belford
- ThermoFisher Scientific, San Jose, California 95134, United States
| | - Cornelia Boeser
- ThermoFisher Scientific, San Jose, California 95134, United States
| | - Maria Virginia Ruiz Cuevas
- Institute for Research
in Immunology and Cancer (IRIC), Université
de Montréal, Montréal, Quebec 514 343-6910, Canada
- Department
of Biochemistry, Université de Montréal, Montréal, Quebec 514 343-6910, Canada
| | - Sébastien Lemieux
- Institute for Research
in Immunology and Cancer (IRIC), Université
de Montréal, Montréal, Quebec 514 343-6910, Canada
- Department
of Biochemistry, Université de Montréal, Montréal, Quebec 514 343-6910, Canada
| | | | - Pierre Thibault
- Institute for Research
in Immunology and Cancer (IRIC), Université
de Montréal, Montréal, Quebec 514 343-6910, Canada
- Department of Chemistry, Université
de Montréal, Montréal, Quebec 514 343-6910, Canada
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Hughes CJ, Gethings LA, Wilson ID, Plumb RS. Access to the Phospho-proteome via the Mitigation of Peptide-Metal Interactions. J Chromatogr A 2022; 1673:463024. [DOI: 10.1016/j.chroma.2022.463024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/21/2022] [Accepted: 04/03/2022] [Indexed: 10/18/2022]
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Abstract
INTRODUCTION Due to its excellent sensitivity, nano-flow liquid chromatography tandem mass spectrometry (LC-MS/MS) is the mainstay in proteome research; however, this comes at the expense of limited throughput and robustness. In contrast, micro-flow LC-MS/MS enables high-throughput, robustness, quantitative reproducibility, and precision while retaining a moderate degree of sensitivity. Such features make it an attractive technology for a wide range of proteomic applications. In particular, large-scale projects involving the analysis of hundreds to thousands of samples. AREAS COVERED This review summarizes the history of chromatographic separation in discovery proteomics with a focus on micro-flow LC-MS/MS, discusses the current state-of-the-art, highlights advances in column development and instrumentation, and provides guidance on which LC flow best supports different types of proteomic applications. EXPERT OPINION Micro-flow LC-MS/MS will replace nano-flow LC-MS/MS in many proteomic applications, particularly when sample quantities are not limited and sample cohorts are large. Examples include clinical analyses of body fluids, tissues, drug discovery and chemical biology investigations, plus systems biology projects across all kingdoms of life. When combined with rapid and sensitive MS, intelligent data acquisition, and informatics approaches, it will soon become possible to analyze large cohorts of more than 10,000 samples in a comprehensive and fully quantitative fashion.
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Affiliation(s)
- Yangyang Bian
- The College of Life Science, Northwest University, Xi'an, P.R. China
| | - Chunli Gao
- The College of Life Science, Northwest University, Xi'an, P.R. China
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
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7
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Yang Y, Cheng J, Wang S, Yang H. StatsPro: Systematic integration and evaluation of statistical approaches for detecting differential expression in label-free quantitative proteomics. J Proteomics 2022; 250:104386. [PMID: 34600153 DOI: 10.1016/j.jprot.2021.104386] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/16/2021] [Accepted: 09/21/2021] [Indexed: 02/08/2023]
Abstract
Quantitative label-free mass spectrometry (MS) is an increasingly powerful technology for profiling thousands of proteins from complex biological samples. One of the primary goals of analyses performed on such proteomics data is to detect differentially expressed proteins (DEPs) under different experimental conditions. Many statistical methods have been developed and assessed for DEP detection in various proteomics studies. However, it remains a challenge for many proteomics scientists to choose an appropriate statistical procedure. Therefore, in this study, we organized 12 common testing algorithms and 6 P-value combination methods and further provided Cohen's d effect size for every protein and three evaluation criteria to help proteomics scientists investigate their influence on DEP detection in a systematic manner. To promote the widespread use of these methods, we developed a user-friendly web tool, StatsPro, and presented two case studies involving label-free quantitative proteomics data obtained using data-dependent acquisition and data-independent acquisition to illustrate its practicability. This tool is freely available in our GitHub repository (https://github.com/YanglabWCH/StatsPro/). SIGNIFICANCE: One of the primary goals of analyses performed on liquid chromatography-mass spectrometry (LC-MS) based proteomics data is to detect differentially expressed proteins (DEPs) under different experimental conditions. Despite of many research efforts have been proposed to detect DEPs, to date, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics scientists to choose an appropriate statistical procedure. Herein, we present a new tool, StatsPro, to enable implementation and evaluation of different statistical methods for proteomics scientists. This tool has two significant advances compared to existing software: a) It integrates up to 18 common statistical approaches (12 statistical tests and 6 P-value combination strategies) and performs Cohen's d effect size systematically for users, moreover, it provides a web-based interface and can be quite conveniently operated by users, even those with less profound computational background. b) It supports three performance evaluation criteria (e.g. number of DEPs, correlation coefficient between P-values and effect sizes, Area under the ROC curve) for users to review the final statistical results, which may guide the method selection for DEPs detection.
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Affiliation(s)
- Yin Yang
- Department of Clinical Research Management, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, China; Institutes for Systems Genetics and NHC Key Lab of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Jingqiu Cheng
- Institutes for Systems Genetics and NHC Key Lab of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Shisheng Wang
- Institutes for Systems Genetics and NHC Key Lab of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China; Sichuan Provincial Engineering Laboratory of Pathology in Clinical Application, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Hao Yang
- Institutes for Systems Genetics and NHC Key Lab of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China; Sichuan Provincial Engineering Laboratory of Pathology in Clinical Application, West China Hospital, Sichuan University, Chengdu 610041, China.
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8
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Lill JR, Mathews WR, Rose CM, Schirle M. Proteomics in the pharmaceutical and biotechnology industry: a look to the next decade. Expert Rev Proteomics 2021; 18:503-526. [PMID: 34320887 DOI: 10.1080/14789450.2021.1962300] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
INTRODUCTION Pioneering technologies such as proteomics have helped fuel the biotechnology and pharmaceutical industry with the discovery of novel targets and an intricate understanding of the activity of therapeutics and their various activities in vitro and in vivo. The field of proteomics is undergoing an inflection point, where new sensitive technologies are allowing intricate biological pathways to be better understood, and novel biochemical tools are pivoting us into a new era of chemical proteomics and biomarker discovery. In this review, we describe these areas of innovation, and discuss where the fields are headed in terms of fueling biotechnological and pharmacological research and discuss current gaps in the proteomic technology landscape. AREAS COVERED Single cell sequencing and single molecule sequencing. Chemoproteomics. Biological matrices and clinical samples including biomarkers. Computational tools including instrument control software, data analysis. EXPERT OPINION Proteomics will likely remain a key technology in the coming decade, but will have to evolve with respect to type and granularity of data, cost and throughput of data generation as well as integration with other technologies to fulfill its promise in drug discovery.
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Affiliation(s)
- Jennie R Lill
- Department of Microchemistry, Lipidomics and Next Generation Sequencing, Genentech Inc. DNA Way, South San Francisco, CA, USA
| | - William R Mathews
- OMNI Department, Genentech Inc. 1 DNA Way, South San Francisco, CA, USA
| | - Christopher M Rose
- Department of Microchemistry, Lipidomics and Next Generation Sequencing, Genentech Inc. DNA Way, South San Francisco, CA, USA
| | - Markus Schirle
- Chemical Biology and Therapeutics Department, Novartis Institutes for Biomedical Research, Cambridge, MA, USA
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Morcuende-Ventura V, Hermoso-Durán S, Abian-Franco N, Pazo-Cid R, Ojeda JL, Vega S, Sanchez-Gracia O, Velazquez-Campoy A, Sierra T, Abian O. Fluorescence Liquid Biopsy for Cancer Detection Is Improved by Using Cationic Dendronized Hyperbranched Polymer. Int J Mol Sci 2021; 22:6501. [PMID: 34204408 PMCID: PMC8234380 DOI: 10.3390/ijms22126501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/14/2021] [Accepted: 06/15/2021] [Indexed: 12/19/2022] Open
Abstract
(1) Background: Biophysical techniques applied to serum samples characterization could promote the development of new diagnostic tools. Fluorescence spectroscopy has been previously applied to biological samples from cancer patients and differences from healthy individuals were observed. Dendronized hyperbranched polymers (DHP) based on bis(hydroxymethyl)propionic acid (bis-MPA) were developed in our group and their potential biomedical applications explored. (2) Methods: A total of 94 serum samples from diagnosed cancer patients and healthy individuals were studied (20 pancreatic ductal adenocarcinoma, 25 blood donor, 24 ovarian cancer, and 25 benign ovarian cyst samples). (3) Results: Fluorescence spectra of serum samples (fluorescence liquid biopsy, FLB) in the presence and the absence of DHP-bMPA were recorded and two parameters from the signal curves obtained. A secondary parameter, the fluorescence spectrum score (FSscore), was calculated, and the diagnostic model assessed. For pancreatic ductal adenocarcinoma (PDAC) and ovarian cancer, the classification performance was improved when including DHP-bMPA, achieving high values of statistical sensitivity and specificity (over 85% for both pathologies). (4) Conclusions: We have applied FLB as a quick, simple, and minimally invasive promising technique in cancer diagnosis. The classification performance of the diagnostic method was further improved by using DHP-bMPA, which interacted differentially with serum samples from healthy and diseased subjects. These preliminary results set the basis for a larger study and move FLB closer to its clinical application, providing useful information for the oncologist during patient diagnosis.
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Affiliation(s)
- Violeta Morcuende-Ventura
- Instituto de Nanociencia y Materiales de Aragón (INMA), Química Orgánica, Facultad de Ciencias, CSIC-Universidad de Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, Spain;
- Joint Units IQFR-CSIC-BIFI and GBsC-CSIC-BIFI, Institute of Biocomputation and Physics of Complex Systems (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain; (S.H.-D.), (S.V.), (A.V.-C.)
| | - Sonia Hermoso-Durán
- Joint Units IQFR-CSIC-BIFI and GBsC-CSIC-BIFI, Institute of Biocomputation and Physics of Complex Systems (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain; (S.H.-D.), (S.V.), (A.V.-C.)
- Instituto de Investigación Sanitaria Aragón (IIS Aragón), 50009 Zaragoza, Spain
| | | | - Roberto Pazo-Cid
- Hospital Universitario Miguel Servet (HUMS), Paseo Isabel la Católica, 1-3, 50009 Zaragoza, Spain;
| | - Jorge L. Ojeda
- Department of Statistical Methods, Universidad de Zaragoza, 50009 Zaragoza, Spain;
| | - Sonia Vega
- Joint Units IQFR-CSIC-BIFI and GBsC-CSIC-BIFI, Institute of Biocomputation and Physics of Complex Systems (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain; (S.H.-D.), (S.V.), (A.V.-C.)
| | | | - Adrian Velazquez-Campoy
- Joint Units IQFR-CSIC-BIFI and GBsC-CSIC-BIFI, Institute of Biocomputation and Physics of Complex Systems (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain; (S.H.-D.), (S.V.), (A.V.-C.)
- Instituto de Investigación Sanitaria Aragón (IIS Aragón), 50009 Zaragoza, Spain
- Departamento de Bioquímica y Biología Molecular y Celular, Universidad de Zaragoza, 50009 Zaragoza, Spain
- Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029 Madrid, Spain
- Fundación ARAID, Gobierno de Aragón, 50018 Zaragoza, Spain
| | - Teresa Sierra
- Instituto de Nanociencia y Materiales de Aragón (INMA), Química Orgánica, Facultad de Ciencias, CSIC-Universidad de Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, Spain;
| | - Olga Abian
- Joint Units IQFR-CSIC-BIFI and GBsC-CSIC-BIFI, Institute of Biocomputation and Physics of Complex Systems (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain; (S.H.-D.), (S.V.), (A.V.-C.)
- Instituto de Investigación Sanitaria Aragón (IIS Aragón), 50009 Zaragoza, Spain
- Departamento de Bioquímica y Biología Molecular y Celular, Universidad de Zaragoza, 50009 Zaragoza, Spain
- Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029 Madrid, Spain
- Instituto Aragonés de Ciencias de la Salud (IACS), 50009 Zaragoza, Spain
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