Poster

  • P-I-0281

High-throughput LC-MS for plasma proteomics in patients with stroke

Presented in

Clinical Proteomics I

Poster topics

Authors

Teresa Barth (Martinsried / DE), Aileen Preuss (Martinsried / DE), Yasin Eshragi (Munich / DE), Teresa Wölfer (Munich / DE), Walter Viegener (Martinsried / DE), Jürgen Cox (Martinsried / DE), Steffen Tiedt (Munich / DE), Axel Imhof (Martinsried / DE)

Abstract

As part of the CLINSPECT-M consortium (clinical mass spectrometry center Munich), we are focusing on diseases of the nervous system like stroke. Stroke is a leading cause of death and disability world-wide (1), with ischemic stroke as major form. Different etiologies lead to the formation of causative blood clots. Knowing the origin of the thrombus is essential for the strategy of secondary prevention medication. Proteins in blood might serve as biomarkers for stroke pathogenesis and thus allow a quick, low invasive diagnostic test. The PROMISE study (PRecisiOn Medicine In StrokE) includes 502 patients with ischemic stroke with serial high-frequency blood sampling in the acute phase in combination with deep clinical phenotyping. For a high-throughput measurement of a large number of plasma samples, we developed a pipeline to prepare samples in a 96-well format. We make use of automatization on the Agilent Bravo liquid handling system with the autoSP3 protocol (2). Peptides are analyzed on an Evosep One – Exploris 480 setup using data-independent acquisition (DIA). Subsequent data analysis is carried out using MaxDIA (3) and compared to other softwares in an unbiased, library-free manner. Sample throughput per week can be up to almost 280 samples. This pipeline now allows us to run thousands of plasma samples from the PROMISE study.

(1) Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

GBD 2019 Stroke Collaborators, Lancet Neurol. 2021, PMID: 34487721

(2) Automated sample preparation with SP3 for low-input clinical proteomics

Müller et al., Molecular Systems Biology 2020, PMID: 32129943

(3) MaxDIA enables library-based and library-free data-independent acquisition proteomics

Sinitcyn et al., Nat Biotechnol 2021, PMID: 34239088

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