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Comparative study of label and label-free techniques using shotgun proteomics for relative protein quantification
Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - BMC, Analytical Chemistry.
Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - BMC, Analytical Chemistry.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - BMC, Analytical Chemistry.
2013 (English)In: Journal of chromatography. B, ISSN 1570-0232, E-ISSN 1873-376X, Vol. 928, 83-92 p.Article in journal (Refereed) Published
Abstract [en]

The analytical performance of three different strategies, iTRAQ (isobaric tag for relative and absolute quantitation), dimethyl labeling (DML) and label free (LF) for relative protein quantification using shotgun proteomics have been evaluated. The methods have been explored using samples containing (i) Bovine proteins in known ratios and (ii) Bovine proteins in known ratios spiked into E.Coli. The latter case mimics the actual conditions in a typical biological sample with a few differentially expressed proteins and a bulk of proteins with unchanged ratios. Additionally, the evaluation was performed on both Q-TOF and LTQ-FTICR mass spectrometers. LF LTQ-FTICR was found to have the highest proteome coverage (94 %) while the highest accuracy based on the artificially regulated proteins was found for DML LTQ-FTICR (54%). A good linearity (r2: 0.61-0.96) was shown for all methods within selected dynamic ranges. All methods were found to consistently underestimate bovine protein ratios when matrix proteins were added. However LF LTQ-FTICR was more tolerant towards a compression effect.  A single peptide was demonstrated to be sufficient for a reliable quantification using iTRAQ. A ranking system utilizing several parameters important for quantitative proteomics demonstrated that the overall performance of the five different methods were; DML LTQ-FTICR > iTRAQ QTOF > LF LTQ-FTICR > DML Q-TOF > LF Q-TOF.

Place, publisher, year, edition, pages
2013. Vol. 928, 83-92 p.
Keyword [en]
Relative quantification, Proteomics, Mass spectrometry, Stable isotope labeling, Label free
National Category
Analytical Chemistry
Research subject
Analytical Chemistry
Identifiers
URN: urn:nbn:se:uu:diva-180105DOI: 10.1016/j.jchromb.2013.03.027ISI: 000319236700011OAI: oai:DiVA.org:uu-180105DiVA: diva2:548154
Note

De två (2) sista författarna delar sistaförfattarskapet.

Available from: 2012-08-29 Created: 2012-08-29 Last updated: 2017-12-07Bibliographically approved
In thesis
1. Advances for Biomarker Discovery in Neuroproteomics using Mass Spectrometry: From Method Development to Clinical Application
Open this publication in new window or tab >>Advances for Biomarker Discovery in Neuroproteomics using Mass Spectrometry: From Method Development to Clinical Application
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Proteins offer a prominent group of compounds which may be ubiquitously affected in disease and used as biomarkers for early diagnosis, assessing treatment or drug development. Clinical proteomics aim to screen for protein biomarkers by a comprehensive analysis of all proteins expressed in a biological matrix during a certain pathology. Characterization of thousands of proteins in a complex biological matrix is from an analytical point of view a challenging task. Hence, sophisticated methods that are sensitive, specific and robust in a high-throughput manner are required. Mass spectrometry (MS) is able to perform this to a wide extent is.

A prominent source for finding protein biomarkers related to neurological diseases is the central nervous system (CNS) due to close proximity of the pathogenesis. Neuroproteomic analysis of CNS tissue samples is thus likely to reveal novel biomarkers. Cerebrospinal fluid (CSF) bathes the entire CNS and offers a good balance between clinical implementation and usefulness. Both matrices put further requirements on the methodology due to a high dynamic range, low protein concentration and limited sample amount.

The central objective of this thesis was to develop, assess and utilize analytical methods to be used in combination with MS to enable protein biomarker discovery in the CNS. The use of hexapeptide ligand libraries was exemplified on CSF from patients with traumatic brain injury and demonstrated the ability to compress the dynamic range to enable protein profiling in the order of mg/mL to pg/mL. Further, a method based on cloud-point extraction was developed for simultaneous enrichment and fractionation of hydrophobic/hydrophilic proteins in brain tissue. Comparison between label and label-free MS based strategies were carried out, mimicking the true conditions with a few differentially expressed proteins and a bulk of proteins occurring in unchanged ratio. Finally, a clinical application was carried out to explore the molecular mechanism underlying the analgesic effect of spinal cord stimulation (SCS) in patients with neuropathic pain. The CSF concentration of Lynx1 was found to increase upon SCS. Lynx1, acting as a specific modulator of the cholinergic system in the CNS, may act as a potential important molecular explanation of SCS-induced analgesia.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2012. 64 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 966
Keyword
Mass Spectrometry, Biomarker, Proteomics, Central Nervous System, Cerebrospinal Fluid, Traumatic Brain Injury, Cloud-Point Extraction, Neuroproteomics, Relative Quantification, Spinal Cord Stimulation, Neuropathic Pain
National Category
Analytical Chemistry
Research subject
Analytical Chemistry
Identifiers
urn:nbn:se:uu:diva-180109 (URN)978-91-554-8457-6 (ISBN)
Public defence
2012-10-18, Biomedicinskt Centrum, B42, Husargatan 3, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2012-09-26 Created: 2012-08-29 Last updated: 2013-01-23Bibliographically approved

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Sjödin, Marcus O.D.Wetterhall, MagnusKultima, KimArtemenko, Konstantin

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