Automated Plasma Proteomic Sample Preparation

Importance of the Topic

Proteomic analysis of blood-based samples such as plasma and serum holds great promise for biomarker discovery and clinical research. However, traditional workflows involve time-consuming manual handling, large sample requirements, and are prone to variability. A rapid, robust, and automated pipeline capable of processing minute volumes of plasma with high reproducibility would address major bottlenecks in large-scale proteomic studies.

Study Objectives and Overview

This Application Note demonstrates the integration of PreOmics iST sample preparation technology with the Agilent Bravo automated liquid handling platform. The goal is to establish a two-hour, fully automated protocol that processes 1 µL of plasma per sample in a 96-well format, covering all key steps—denaturation, reduction, alkylation, digestion, peptide cleanup, and desalting—in a single reaction vessel.

Methodology and Instrumentation

The workflow uses the PreOmics in-StageTip (iST) Kit for bottom-up proteomics and the Agilent Bravo Platform for automation.

• Sample preparation steps (iST Kit): denaturation, reduction, alkylation, enzymatic digestion, peptide purification, desalting in one cartridge.
• Automation: Agilent Bravo with 96-channel pipetting head, heated shaker, Peltier station, and custom adapter plates.
• LC/MS/MS: Thermo Fisher EASY-nLC 1000 coupled to Q Exactive HF Orbitrap with 40 cm C18 columns and a 20 min gradient.
• Data analysis: MaxQuant (v1.5.2.105) and Andromeda search engine against human UniProt database; label-free quantification (LFQ).

Main Results and Discussion

Using 96 replicates (48 from a female donor, 48 from a male donor), the protocol yielded an average of 300 quantified proteins per run, covering a dynamic range of nearly six orders of magnitude. Key findings:

• High reproducibility: Pearson correlation coefficients up to 0.98 between workflow replicates.
• Consistent chromatograms: Overlaid total ion chromatograms exhibited near-identical profiles across replicates.
• Biological variation: Differential abundance of sex-related proteins such as SHBG and PZP in the female sample, and immunoglobulins IGHG4/IGHM between donors.

Benefits and Practical Applications

The described approach offers:

• Minimal sample requirement (1 µL plasma) enabling finger-prick sampling.
• Complete automation reduces hands-on time and minimizes contamination.
• High throughput and scalability for hundreds to thousands of samples.
• Reproducible quantitative data suitable for clinical biomarker studies and longitudinal research.

Future Trends and Potential Uses

Advances in automation and MS sensitivity will further accelerate plasma proteomics. Prospective developments include integration with laboratory information management systems (LIMS), multi-omics workflows combining proteomics with metabolomics or lipidomics, and application to large cohort studies for disease stratification, personalized medicine, and real-time health monitoring.

Conclusion

The synergy of PreOmics iST technology and the Agilent Bravo platform creates a fast, robust, and fully automated plasma proteome profiling workflow. By reliably processing 1 µL of plasma in under two hours with high reproducibility, this method overcomes key pre-analytical challenges and unlocks the potential for large-scale clinical proteomic investigations.

Reference

1. Geyer PE, et al. Revisiting biomarker discovery by plasma proteomics. Mol Syst Biol. 2017;13(9):942.
2. Geyer PE, et al. Plasma Proteome Profiling to Assess Human Health and Disease. Cell Syst. 2016;2(3):185–195.
3. Kulak NA, et al. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Nat Methods. 2014.
4. Anderson L. Six decades searching for meaning in the proteome. J Proteomics. 2014;107:24–30.
5. Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26(12):1367–1372.
6. Cox J, et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res. 2011;10(4):1794–1805.
7. Cox J, et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol Cell Proteomics. 2014;13(9):2513–2526.