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  • 1.
    Emami Khoonsari, Payam
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Musunri, Sravani
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - BMC, Analytical Chemistry.
    Herman, Stephanie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Svensson, Camilla I
    Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden..
    Tanum, Lars
    Department of R&D in Mental Health, Akershus University Hospital, Lørenskog, Norway..
    Gordh, Torsten
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Anaesthesiology and Intensive Care.
    Kultima, Kim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Systematic Analysis of the Cerebrospinal Fluid Proteome of Fibromyalgia patients2019In: Journal of Proteomics, ISSN 1874-3919, E-ISSN 1876-7737, p. 35-43Article in journal (Refereed)
    Abstract [en]

    Fibromyalgia (FM) is a syndrome characterized by widespread muscular pain, fatigue and functional symptoms, which is known to be difficult to diagnose as the various symptoms overlap with many other conditions. Currently, there are no biomarkers for FM, and the diagnosis is made subjectively by the clinicians. We have performed shotgun proteomics on cerebrospinal fluid (CSF) from FM patients and non-pain controls to find potential biomarker candidates for this syndrome. Based on our multivariate and univariate analyses, we found that the relative differences in the CSF proteome between FM patients and controls were moderate. Four proteins, important to discriminate FM patients from non-pain controls, were found: Apolipoprotein C-III, Galectin-3-binding protein, Malate dehydrogenase cytoplasmic and the neuropeptide precursor protein ProSAAS. These proteins are involved in lipoprotein lipase (LPL) activity, inflammatory signaling, energy metabolism and neuropeptide signaling.

  • 2.
    Emami Khoonsari, Payam
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Shevchenko, Ganna
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - BMC, Analytical Chemistry.
    Herman, Stephanie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Remnestal, Julia
    KTH Royal Inst Technol, Dept Prot Sci, Div Affin Prote, SciLifeLab, Stockholm, Sweden.
    Giedraitis, Vilmantas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Brundin, RoseMarie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Degerman Gunnarsson, Malin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Kilander, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Zetterberge, Henrik
    Univ Gothenburg, Sahlgrenska Acad, Inst Neurosci & Physiol, Dept Psychiat & Neurochem, Mölndal, Sweden; Sahlgrens Univ Hosp, Clin Neurochem Lab, Molndal, Sweden; UK Dementia Res Inst UCL, London, England; UCL Inst Neurol, Dept Mol Neurosci, Queen Sq, London, England.
    Nilsson, Peter
    KTH Royal Inst Technol, Dept Prot Sci, Div Affin Prote, SciLifeLab, Stockholm, Sweden.
    Lannfelt, Lars
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Ingelsson, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Kultima, Kim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Improved Differential Diagnosis of Alzheimer's Disease by Integrating ELISA and Mass Spectrometry-Based Cerebrospinal Fluid Biomarkers2019In: Journal of Alzheimer's Disease, ISSN 1387-2877, E-ISSN 1875-8908, Vol. 67, no 2, p. 639-651Article in journal (Refereed)
    Abstract [en]

    Background: Alzheimer’s disease (AD) is diagnosed based on a clinical evaluation as well as analyses of classical biomarkers: Aβ42, total tau (t-tau), and phosphorylated tau (p-tau) in cerebrospinal fluid (CSF). Although the sensitivities and specificities of the classical biomarkers are fairly good for detection of AD, there is still a need to develop novel biochemical markers for early detection of AD.

    Objective: We explored if integration of novel proteins with classical biomarkers in CSF can better discriminate AD from non-AD subjects.

    Methods: We applied ELISA, mass spectrometry, and multivariate modeling to investigate classical biomarkers and the CSF proteome in subjects (n = 206) with 76 AD patients, 74 mild cognitive impairment (MCI) patients, 11 frontotemporal dementia (FTD) patients, and 45 non-dementia controls. The MCI patients were followed for 4–9 years and 21 of these converted to AD, whereas 53 remained stable.

    Results: By combining classical CSF biomarkers with twelve novel markers, the area of the ROC curves (AUROCS) of distinguishing AD and MCI/AD converters from non-AD were 93% and 96%, respectively. The FTDs and non-dementia controls were identified versus all other groups with AUROCS of 96% and 87%, respectively.

    Conclusions: Integration of new and classical CSF biomarkers in a model-based approach can improve the identification of AD, FTD, and non-dementia control subjects.

  • 3.
    Herman, Stephanie
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Emami Khoonsari, Payam
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Aftab, Obaid
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Krishnan, Shibu
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Strömbom, Emil
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Larsson, Rolf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Hammerling, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Kultima, Kim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Gustafsson, Mats G
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions.2017In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 13, no 7, article id 79Article in journal (Refereed)
    Abstract [en]

    INTRODUCTION: Mass spectrometry based metabolomics has become a promising complement and alternative to transcriptomics and proteomics in many fields including in vitro systems pharmacology. Despite several merits, metabolomics based on liquid chromatography mass spectrometry (LC-MS) is a developing area that is yet attached to several pitfalls and challenges. To reach a level of high reliability and robustness, these issues need to be tackled by implementation of refined experimental and computational protocols.

    OBJECTIVES: This study illustrates some key pitfalls in LC-MS based metabolomics and introduces an automated computational procedure to compensate for them.

    METHOD: Non-cancerous mammary gland derived cells were exposed to 27 chemicals from four pharmacological classes plus a set of six pesticides. Changes in the metabolome of cell lysates were assessed after 24 h using LC-MS. A data processing pipeline was established and evaluated to handle issues including contaminants, carry over effects, intensity decay and inherent methodology variability and biases. A key component in this pipeline is a latent variable method called OOS-DA (optimal orthonormal system for discriminant analysis), being theoretically more easily motivated than PLS-DA in this context, as it is rooted in pattern classification rather than regression modeling.

    RESULT: The pipeline is shown to reduce experimental variability/biases and is used to confirm that LC-MS spectra hold drug class specific information.

    CONCLUSION: LC-MS based metabolomics is a promising methodology, but comes with pitfalls and challenges. Key difficulties can be largely overcome by means of a computational procedure of the kind introduced and demonstrated here. The pipeline is freely available on www.github.com/stephanieherman/MS-data-processing.

  • 4.
    Herman, Stephanie
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Emami Khoonsari, Payam
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Tolf, Andreas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    Steinmetz, Julia
    Zetterberg, Henrik
    Åkerfeldt, Torbjörn
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Jakobsson, Per-Johan
    Larsson, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Burman, Joachim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    Kultima, Kim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis.2018In: Theranostics, ISSN 1838-7640, E-ISSN 1838-7640, Vol. 8, no 16, p. 4477-4490Article in journal (Refereed)
    Abstract [en]

    Molecular networks in neurological diseases are complex. Despite this fact, contemporary biomarkers are in most cases interpreted in isolation, leading to a significant loss of information and power. We present an analytical approach to scrutinize and combine information from biomarkers originating from multiple sources with the aim of discovering a condensed set of biomarkers that in combination could distinguish the progressive degenerative phenotype of multiple sclerosis (SPMS) from the relapsing-remitting phenotype (RRMS).

    Methods: Clinical and magnetic resonance imaging (MRI) data were integrated with data from protein and metabolite measurements of cerebrospinal fluid, and a method was developed to sift through all the variables to establish a small set of highly informative measurements. This prospective study included 16 SPMS patients, 30 RRMS patients and 10 controls. Protein concentrations were quantitated with multiplexed fluorescent bead-based immunoassays and ELISA. The metabolome was recorded using liquid chromatography-mass spectrometry. Clinical follow-up data of the SPMS patients were used to assess disease progression and development of disability.

    Results: Eleven variables were in combination able to distinguish SPMS from RRMS patients with high confidence superior to any single measurement. The identified variables consisted of three MRI variables: the size of the spinal cord and the third ventricle and the total number of T1 hypointense lesions; six proteins: galectin-9, monocyte chemoattractant protein-1 (MCP-1), transforming growth factor alpha (TGF-α), tumor necrosis factor alpha (TNF-α), soluble CD40L (sCD40L) and platelet-derived growth factor AA (PDGF-AA); and two metabolites: 20β-dihydrocortisol (20β-DHF) and indolepyruvate. The proteins myelin basic protein (MBP) and macrophage-derived chemokine (MDC), as well as the metabolites 20β-DHF and 5,6-dihydroxyprostaglandin F1a (5,6-DH-PGF1), were identified as potential biomarkers of disability progression.

    Conclusion: Our study demonstrates, in a limited but well-defined and data-rich cohort, the importance and value of combining multiple biomarkers to aid diagnostics and track disease progression.

  • 5.
    Herman, Stephanie
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Niemelä, Valter
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    Emami Khoonsari, Payam
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Sundblom, Jimmy
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurosurgery.
    Burman, Joachim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    Landtblom, Anne-Marie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyholm, Dag
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    Kultima, Kim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Alterations in the tyrosine and phenylalanine pathways revealed by biochemical profiling in cerebrospinal fluid of Huntington's disease subjects2019In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 4129Article in journal (Refereed)
    Abstract [en]

    Huntington's disease (HD) is a severe neurological disease leading to psychiatric symptoms, motor impairment and cognitive decline. The disease is caused by a CAG expansion in the huntingtin (HTT) gene, but how this translates into the clinical phenotype of HD remains elusive. Using liquid chromatography mass spectrometry, we analyzed the metabolome of cerebrospinal fluid (CSF) from premanifest and manifest HD subjects as well as control subjects. Inter-group differences revealed that the tyrosine metabolism, including tyrosine, thyroxine, L-DOPA and dopamine, was significantly altered in manifest compared with premanifest HD. These metabolites demonstrated moderate to strong associations to measures of disease severity and symptoms. Thyroxine and dopamine also correlated with the five year risk of onset in premanifest HD subjects. The phenylalanine and the purine metabolisms were also significantly altered, but associated less to disease severity. Decreased levels of lumichrome were commonly found in mutated HTT carriers and the levels correlated with the five year risk of disease onset in premanifest carriers. These biochemical findings demonstrates that the CSF metabolome can be used to characterize molecular pathogenesis occurring in HD, which may be essential for future development of novel HD therapies.

  • 6.
    Herman, Stephanie
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Åkerfeldt, Torbjörn
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Burman, Joachim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    Kultima, Kim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Biochemical Differences in Cerebrospinal Fluid between Secondary Progressive and Relapsing-Remitting Multiple Sclerosis2019In: Cells, ISSN 2073-4409, Vol. 8, no 2, article id 84Article in journal (Refereed)
    Abstract [en]

    To better understand the pathophysiological differences between secondary progressive multiple sclerosis (SPMS) and relapsing-remitting multiple sclerosis (RRMS), and to identify potential biomarkers of disease progression, we applied high-resolution mass spectrometry (HRMS) to investigate the metabolome of cerebrospinal fluid (CSF). The biochemical differences were determined using partial least squares discriminant analysis (PLS-DA) and connected to biochemical pathways as well as associated to clinical and radiological measures. Tryptophan metabolism was significantly altered, with perturbed levels of kynurenate, 5-hydroxytryptophan, 5-hydroxyindoleacetate, and N-acetylserotonin in SPMS patients compared with RRMS and controls. SPMS patients had altered kynurenine compared with RRMS patients, and altered indole-3-acetate compared with controls. Regarding the pyrimidine metabolism, SPMS patients had altered levels of uridine and deoxyuridine compared with RRMS and controls, and altered thymine and glutamine compared with RRMS patients. Metabolites from the pyrimidine metabolism were significantly associated with disability, disease activity and brain atrophy, making them of particular interest for understanding the disease mechanisms and as markers of disease progression. Overall, these findings are of importance for the characterization of the molecular pathogenesis of SPMS and support the hypothesis that the CSF metabolome may be used to explore changes that occur in the transition between the RRMS and SPMS pathologies.

  • 7.
    Novella, Jon Ander
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Emami Khoonsari, Payam
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Herman, Stephanie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Whitenack, Daniel
    Capuccini, Marco
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Burman, Joachim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    Kultima, Kim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Container-based bioinformatics with Pachyderm2019In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 35, p. 839-846Article in journal (Refereed)
  • 8. Peters, Kristian
    et al.
    Bradbury, James
    Bergmann, Sven
    Capuccini, Marco
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Cascante, Marta
    de Atauri, Pedro
    Ebbels, Timothy M. D.
    Foguet, Carles
    Glen, Robert
    Gonzalez-Beltran, Alejandra
    Günther, Ulrich L.
    Handakas, Evangelos
    Hankemeier, Thomas
    Haug, Kenneth
    Herman, Stephanie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Holub, Petr
    Izzo, Massimiliano
    Jacob, Daniel
    Johnson, David
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Informatics and Media.
    Jourdan, Fabien
    Kale, Namrata
    Karaman, Ibrahim
    Khalili, Bita
    Emami Khoonsari, Payam
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Kultima, Kim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Chemistry.
    Lampa, Samuel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Larsson, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Ludwig, Christian
    Moreno, Pablo
    Neumann, Steffen
    Novella, Jon Ander
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    O'Donovan, Claire
    Pearce, Jake T. M.
    Peluso, Alina
    Piras, Marco Enrico
    Pireddu, Luca
    Reed, Michelle A. C.
    Rocca-Serra, Philippe
    Roger, Pierrick
    Rosato, Antonio
    Rueedi, Rico
    Ruttkies, Christoph
    Sadawi, Noureddin
    Salek, Reza M.
    Sansone, Susanna-Assunta
    Selivanov, Vitaly
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Schober, Daniel
    Thévenot, Etienne A.
    Tomasoni, Mattia
    van Rijswijk, Merlijn
    van Vliet, Michael
    Viant, Mark R.
    Weber, Ralf J. M.
    Zanetti, Gianluigi
    Steinbeck, Christoph
    PhenoMeNal: Processing and analysis of metabolomics data in the cloud2019In: GigaScience, ISSN 2047-217X, E-ISSN 2047-217X, Vol. 8, no 2Article in journal (Refereed)
1 - 8 of 8
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