Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition
Journal article, 2018

Quantitative proteomics is key for basic research, but needs improvements to satisfy an increasing demand for large sample series in diagnostics, academia and industry. A switch from nanoflowrate to microflowrate chromatography can improve throughput and reduce costs. However, concerns about undersampling and coverage have so far hampered its broad application. We used a QTOF mass spectrometer of the penultimate generation (TripleTOF5600), converted a nanoLC system into a microflow platform, and adapted a SWATH regime for large sample series by implementing retention time-A nd batch correction strategies. From 3 μg to 5 μg of unfractionated tryptic digests that are obtained from proteomics-typical amounts of starting material, microLC-SWATH-MS quantifies up to 4000 human or 1750 yeast proteins in an hour or less. In the acquisition of 750 yeast proteomes, retention times varied between 2% and 5%, and quantified the typical peptide with 5-8% signal variation in replicates, and below 20% in samples acquired over a five-months period. Providing precise quantities without being dependent on the latest hardware, our study demonstrates that the combination of microflow chromatography and data-independent acquisition strategies has the potential to overcome current bottlenecks in academia and industry, enabling the cost-effective generation of precise quantitative proteomes in large scale.


Jakob Vowinckel

Biognosys AG

University of Cambridge

Aleksej Zelezniak

University of Cambridge

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Science for Life Laboratory (SciLifeLab)

Roland Bruderer

Biognosys AG

Michael Mülleder

University of Cambridge

The Francis Crick Institute

Lukas Reiter

Biognosys AG

M. Ralser

The Francis Crick Institute

University of Cambridge

Scientific Reports

2045-2322 (ISSN) 20452322 (eISSN)

Vol. 8 1 4346

Subject Categories

Analytical Chemistry

Bioinformatics (Computational Biology)

Computer Systems





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