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Thromb Haemost
DOI: 10.1055/a-2516-1812
New Technologies, Diagnostic Tools and Drugs

Middle-throughput LC-MS-based Platelet Proteomics with Minute Sample Amounts Using Semiautomated Positive Pressure FASP in 384-Well Format

Stefan Loroch    *
1   Medical Proteome-Center (MPC), Medical Faculty, Ruhr-University Bochum, Bochum, Germany
2   Medical Proteome Analysis, Center for Protein Diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany
3   Fa. Dr. Loroch/QC|MS, Biomedizin Zentrum, Dortmund, Dortmund, Germany
4   Department of Bioanalytics, Leibniz-Institute for Analytical Sciences – ISAS – e.V., Dortmund Germany
,
Eleftherios Panagiotidis   *
4   Department of Bioanalytics, Leibniz-Institute for Analytical Sciences – ISAS – e.V., Dortmund Germany
,
Kristoffer Klewe
3   Fa. Dr. Loroch/QC|MS, Biomedizin Zentrum, Dortmund, Dortmund, Germany
,
Frauke Swieringa 
5   Department of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
6   Synapse Research Institute, Platelet Pathophysiology, Maastricht, the Netherlands
,
Johan W.M. Heemskerk 
5   Department of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
6   Synapse Research Institute, Platelet Pathophysiology, Maastricht, the Netherlands
,
Jan-Paul Lerch
7   Fakultät für Mathematik, Universität Bielefeld, Bielefeld, Germany
,
Andreas Greinacher 
8   Institut für Immunologie und Transfusionsmedizin, Universitätsmedizin Greifswald, Greifswald, Germany
,
Ulrich Walter
9   Center for Thrombosis and Hemostasis (CTH), Univeristy Medical Center Mainz, Mainz, Germany
,
Kerstin Jurk
9   Center for Thrombosis and Hemostasis (CTH), Univeristy Medical Center Mainz, Mainz, Germany
,
Tobias John
1   Medical Proteome-Center (MPC), Medical Faculty, Ruhr-University Bochum, Bochum, Germany
2   Medical Proteome Analysis, Center for Protein Diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany
,
Katalin Barkovits
1   Medical Proteome-Center (MPC), Medical Faculty, Ruhr-University Bochum, Bochum, Germany
2   Medical Proteome Analysis, Center for Protein Diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany
,
Thomas Dandekar
10   Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
,
Katrin Marcus
1   Medical Proteome-Center (MPC), Medical Faculty, Ruhr-University Bochum, Bochum, Germany
2   Medical Proteome Analysis, Center for Protein Diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany
,
Johannes Balkenhol
10   Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
› Author Affiliations
Funding This project was funded by the TRR240 (project Z02) of the Deutsche Forschungsgemeinschaft (DFG); the Bundesministerium für Bildung und Forschung (BMBF, grant number 13N16290; MaZurKA), and the Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen (MKW) as well as the Regierender Bürgermeister von Berlin. Further funding was provided by Protein Research Unit Ruhr within Europe (P.U.R.E.) and Center for Protein Diagnostics (ProDi) grants, both from the Ministry of Innovation, Science and Research of North Rhine-Westphalia, Germany. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
› Further Information
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Abstract

Background

Comprehensive characterization of platelets requires various functional assays and analytical techniques, including omics disciplines, each demanding a separate aliquot of the given sample. Consequently, sample material for each assay is often highly limited, necessitating the downscaling of methods to work with just a few micrograms of platelet protein.

Materials and Methods

Here, we present a novel sample preparation platform for proteomics analysis using only 3 μg of purified platelet protein, corresponding to 2 × 106 platelets, which can be obtained from approximately 2 to 8 μL of blood from a healthy individual (1.5 × 105–4.5 × 105 platelets/μL) or approximately 100 μL of blood from a patient with severe thrombocytopenia (<2 × 104 platelets/µL).

Results

Using this platform, we detected a significant fraction of key players in the platelet activation cascade and, most importantly, identified 36 clinically relevant platelet disease markers even with a non-state-of-the art instrument. This makes LC-MS-based proteomics a highly attractive alternative to conventional assays, which often require milliliters of blood. Our platform transitions from our previously established 96-well proteomics workflow (PF96), which has been successfully employed in numerous platelet proteomics studies, into the 384-well format. This transition is accompanied by (1) a more than two-fold increase in sensitivity, (2) improved reproducibility, (3) a four-fold increase in throughput, allowing 1,536 samples to be processed per lab worker per week, and (4) reduced sample preparation costs.

Conclusion

Thus, LC-MS-based platelet proteomics offers a compelling alternative to immunoaffinity assays (which depend on antibody availability and quality), as well as to genomic assays (which can only reveal genotypes). In summary, in conjunction with recent advances in LC-MS instrumentation, our platform represents a highly valuable tool for rapid phenotyping of platelets in research with extraordinary potential for future employment in companion or routine diagnostics.

Keywords

proteomics - platelets - platelet disease markers - platelet signaling - throughput - PF96 - PF384 - 96-well format - 384-well format

Data Availability Statement

Data have been uploaded to the PRIDE data repository[41] and can be accessed via the dataset identifier PXD028316.


Authors' Contribution

Conceptualization: S.L., J.B., E.P., K.J., and U.W. software (R-Code): S.L., J.B., E.P., and T.D. formal analysis: S.L., E.P., T.J., and K.K. resources: A.G., K.B., K.M., and T.D. data curation: S.L., E.P., F.S., J.H., K.J., and U.W. writing—original draft preparation: S.L., J.B., E.P., K.J., U.W., and K.K. visualization: S.L., E.P., T.D., and K.M. supervision: S.L., K.B., J.B., and T.D. project administration: S.L., J.B., and T.D. All authors have read and agreed to the published version of the manuscript.


* These authors share first authorship.


§ These authors shared the last authorship.


Supplementary Material



Publication History

Received: 12 September 2024

Accepted: 13 January 2025

Accepted Manuscript online:
15 January 2025

Article published online:
06 March 2025

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