ScanningSWATH enables ultra-fast proteomics using high-flow chromatography and minute-scale gradients

Abstract Rapidly emerging applications in data-driven biology and personalised medicine call for the development of fast and reliable proteomic methods. However, the use of fast chromatographic gradients is limited by the mass spectrometers’ sampling rate and signal interferences. Here we present scanningSWATH, a data-independent acquisition method, in which the DIA-typical stepwise windowed acquisition is replaced by continuous scanning with the first quadrupole. ScanningSWATH enables ultra-fast duty cycles as well as to assign precursor masses to the MS2 fragment traces. Furthermore, we have implemented the support for scanningSWATH in DIA-NN, a fully-automated software suite designed to deconvolute fast DIA experiments, which corrects for signal interferences. We show that the combination of scanningSWATH and DIA-NN enables the efficient application of high-flow liquid chromatography (800 μL/min flow rate) to proteomics. High-flow scanningSWATH increases proteomic sample throughput to the minute-scale, while the use of high-flow chromatography hardware improves reliability and robustness. Benchmarking on yeast and human cell lysates, we demonstrate that the proteomic depth achieved with five-minute high-flow scanningSWATH gradients is comparable to that obtained with several times slower nano- and microflow chromatographic gradients, even if compared to most recent studies that used microflow-SWATH or Evosep-DIA methods. The combination of scanningSWATH, advanced data processing, and industry-standard high-flow LC hardware paves a way for a new generation of cheaper and highly consistent proteomic methods. These allow the recording of hundreds of precise quantitative proteomes per day on a single instrument.

[1]  P. Wright,et al.  Comparison of protein and peptide prefractionation methods for the shotgun proteomic analysis of Synechocystis sp. PCC 6803 , 2005, Proteomics.

[2]  John D. Venable,et al.  Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra , 2004, Nature Methods.

[3]  Loïc Dayon,et al.  Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance , 2019, Molecular & Cellular Proteomics.

[4]  Christoph B. Messner,et al.  Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts , 2018, Cell systems.

[5]  Roland Bruderer,et al.  Surpassing 10 000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy. , 2019, Molecular omics.

[6]  Oliver M. Bernhardt,et al.  Extending the Limits of Quantitative Proteome Profiling with Data-Independent Acquisition and Application to Acetaminophen-Treated Three-Dimensional Liver Microtissues* , 2015, Molecular & Cellular Proteomics.

[7]  M. Mann,et al.  Quantitative, high-resolution proteomics for data-driven systems biology. , 2011, Annual review of biochemistry.

[8]  Steven Ray Wilson,et al.  Nano-LC in proteomics: recent advances and approaches. , 2015, Bioanalysis.

[9]  Samuel H Payne,et al.  PECAN: Library Free Peptide Detection for Data-Independent Acquisition Tandem Mass Spectrometry Data , 2017, Nature Methods.

[10]  Christoph B. Messner,et al.  DIA-NN: Neural networks and interference correction enable deep proteome coverage in high throughput , 2019, Nature Methods.

[11]  G. Desmet,et al.  High-resolution separations of tryptic digest mixtures using core-shell particulate columns operated at 1,200 bar. , 2012, Journal of chromatography. A.

[12]  Maximilian T. Strauss,et al.  The proteome landscape of the kingdoms of life , 2020, Nature.

[13]  Michael J MacCoss,et al.  Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics , 2018, Nature Methods.

[14]  Carol H Collins,et al.  Nano-liquid chromatography in pharmaceutical and biomedical research. , 2013, Journal of chromatographic science.

[15]  Qin Fu,et al.  Highly Reproducible Automated Proteomics Sample Preparation Workflow for Quantitative Mass Spectrometry. , 2018, Journal of proteome research.

[16]  Bernhard Hemmer,et al.  Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS , 2020, Nature Communications.

[17]  Christoph B. Messner,et al.  DIA-NN: Neural networks and interference correction enable deep coverage in high-throughput proteomics , 2018 .

[18]  Dawei Li,et al.  High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery , 2020, Frontiers in Genetics.

[19]  Mario Leutert,et al.  R2‐P2 rapid‐robotic phosphoproteomics enables multidimensional cell signaling studies , 2019, Molecular systems biology.

[20]  M. Mann,et al.  Universal sample preparation method for proteome analysis , 2009, Nature Methods.

[21]  Jeroen Krijgsveld,et al.  Automated sample preparation with SP3 for low‐input clinical proteomics , 2020, Molecular systems biology.

[22]  Oliver M. Bernhardt,et al.  Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results* , 2017, Molecular & Cellular Proteomics.

[23]  D. Wishart.  Emerging applications of metabolomics in drug discovery and precision medicine , 2016, Nature Reviews Drug Discovery.

[24]  Alexander Makarov,et al.  A Compact Quadrupole-Orbitrap Mass Spectrometer with FAIMS Interface Improves Proteome Coverage in Short LC Gradients* , 2019, Molecular & Cellular Proteomics.

[25]  Danielle B. Gutierrez,et al.  Micro-Data-Independent Acquisition for High-Throughput Proteomics and Sensitive Peptide Mass Spectrum Identification. , 2018, Analytical chemistry.

[26]  B. Simons,et al.  Performance characteristics of a new hybrid quadrupole time-of-flight tandem mass spectrometer (TripleTOF 5600). , 2011, Analytical chemistry.

[27]  Víctor González-Ruiz,et al.  Core-shell particles lead the way to renewing high-performance liquid chromatography , 2015 .

[28]  Ruedi Aebersold,et al.  Mass-spectrometric exploration of proteome structure and function , 2016, Nature.

[29]  J. Yates,et al.  An automated multidimensional protein identification technology for shotgun proteomics. , 2001, Analytical chemistry.

[30]  Stefan Tenzer,et al.  Biomedical applications of ion mobility-enhanced data-independent acquisition-based label-free quantitative proteomics , 2014, Expert review of proteomics.

[31]  Hanno Steen,et al.  A Cost-Effective High-Throughput Plasma and Serum Proteomics Workflow Enables Mapping of the Molecular Impact of Total Pancreatectomy with Islet Autotransplantation. , 2018, Journal of proteome research.

[32]  Marco Y. Hein,et al.  Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ * , 2014, Molecular & Cellular Proteomics.

[33]  I. Wilson,et al.  UPLC/MS(E); a new approach for generating molecular fragment information for biomarker structure elucidation. , 2006, Rapid communications in mass spectrometry : RCM.

[34]  Matthias Mann,et al.  A Novel LC System Embeds Analytes in Pre-formed Gradients for Rapid, Ultra-robust Proteomics* , 2018, Molecular & Cellular Proteomics.

[35]  M. Mann,et al.  Deep and Highly Sensitive Proteome Coverage by LC-MS/MS Without Prefractionation* , 2011, Molecular & Cellular Proteomics.

[36]  Ludovic C. Gillet,et al.  Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial , 2018, Molecular systems biology.

[37]  John R Yates,et al.  Proteomic analysis of human parotid gland exosomes by multidimensional protein identification technology (MudPIT). , 2009, Journal of proteome research.

[38]  Christoph B. Messner,et al.  Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection , 2020, Cell Systems.

[39]  John R Yates,et al.  Mass spectrometry in high-throughput proteomics: ready for the big time , 2010, Nature Methods.

[40]  T. Duarte,et al.  Personalized Proteomics: The Future of Precision Medicine. , 2016, Proteomes.

[41]  Ludovic C. Gillet,et al.  Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis* , 2012, Molecular & Cellular Proteomics.

[42]  Roland Bruderer,et al.  Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition , 2018, Scientific Reports.

[43]  Loïc Dayon,et al.  A Versatile Workflow for Cerebrospinal Fluid Proteomic Analysis with Mass Spectrometry: A Matter of Choice between Deep Coverage and Sample Throughput. , 2019, Methods in molecular biology.

[44]  Peter Willett,et al.  What is a tutorial , 2013 .