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Combined tissue and fluid proteomics with Tandem Mass Tags to identify low-abundance protein biomarkers of disease in peripheral body fluid: An Alzheimer's Disease case study

Russell, CL; Heslegrave, A; Mitra, V; Zetterberg, H; Pocock, JM; Ward, MA; Pike, I; (2017) Combined tissue and fluid proteomics with Tandem Mass Tags to identify low-abundance protein biomarkers of disease in peripheral body fluid: An Alzheimer's Disease case study. Rapid Communications in Mass Spectrometry , 31 (2) pp. 153-159. 10.1002/rcm.7777. Green open access

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Abstract

RATIONALE: Ideal biomarkers are present in readily accessible samples including plasma and cerebrospinal fluid (CSF), and are directly derived from diseased tissue, therefore likely to be of relatively low abundance. Traditional unbiased proteomic approaches for biomarker discovery have struggled to detect low-abundance markers due to the high dynamic range of proteins, the predominance of hyper-abundant proteins, and the use of data-dependent acquisition mass spectrometry (MS). To overcome these limitations and improve biomarker discovery in peripheral fluids, we have developed TMTcalibrator™; a novel MS workflow combining isobarically labelled diseased tissue digests in parallel with an appropriate set of labelled body fluids to increase the chance of identifying low-abundance, tissue-derived biomarkers. METHODS: A disease relevant cell line was labelled with TMT® in a range of concentrations generating a multi-point calibration curve. Peripheral biofluid samples were labelled with the remaining tags and quantitative analysis was performed using an Orbitrap Fusion Tribrid mass spectrometer with a Top10 CID-HCD MS3 synchronous precursor selection (SPS) method. SPS allowed direct analysis of non-depleted, unfractionated CSF samples with complete profiling of six individual samples requiring only 15 hours of MS time, equivalent to 1.5 h per sample. RESULTS: Using the TMTcalibrator™ workflow allowed the identification of several markers of microglia activation that are differentially quantified in the CSF of patients with Alzheimer's disease (AD). We report peptides from 41 proteins that have not previously been detected in the CSF, that appear to be regulated by at least 60% in AD. CONCLUSIONS: This study has demonstrated the benefits of the new TMTcalibrator™ workflow and the results suggest this is a suitable and efficient method of detecting low-abundance peptides within biological fluids. The use of TMTcalibrator™ in further biomarker discovery studies should be considered to overcome some of the limitations commonly associated with more conventional approaches.

Type: Article
Title: Combined tissue and fluid proteomics with Tandem Mass Tags to identify low-abundance protein biomarkers of disease in peripheral body fluid: An Alzheimer's Disease case study
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/rcm.7777
Publisher version: http://doi.org/10.1002/rcm.7777
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Life Sciences & Biomedicine, Physical Sciences, Technology, Biochemical Research Methods, Chemistry, Analytical, Spectroscopy, Biochemistry & Molecular Biology, Chemistry, CEREBROSPINAL-FLUID, BRAIN INFLAMMATION, MICROGLIA, CELLS, DEPLETION, SYSTEM, MICE
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neuroinflammation
URI: https://discovery.ucl.ac.uk/id/eprint/1529024
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