Scalability for high-throughput proteomics - Evotip Pure integration with the Biomek i5 liquid handler

Importance of the Topic

Proteomics workflows are increasingly required to process large numbers of samples with high reproducibility and minimal hands-on time. Automated, scalable solutions reduce variability, cost, and sample loss, enabling comprehensive protein profiling for both basic research and clinical applications.

Objectives and Study Overview

This work presents four fully automated sample preparation workflows for high-throughput proteomics. Developed on the Biomek i5 Automated Liquid Handler and integrated with Evosep technology, these workflows address the preparation bottleneck by enabling parallel processing of up to six 96-well plates (576 samples) per run. The workflows include Evotip Sample Loading, PAC1 Digestion, Neat Plasma, and Mag-Net Plasma protocols.

Methodology and Instrumentation

The study used cultured HeLa cells and human plasma following standardized SOPs. Key steps include cell lysis in SDS buffer, protein reduction and alkylation, protein aggregation capture (PAC) digestion on magnetic beads, and automated desalting/loading onto Evotips. Plasma samples were processed either directly (neat plasma) or enriched for extracellular vesicles via Mag-Net SAX beads prior to PAC digestion. Peptides were analyzed on a timsTOF HT mass spectrometer using short-gradient dia-PASEF methods (200 SPD and 500 SPD) and DIA-NN library-free data analysis against the human proteome.

Instrumentation

• Biomek i5 Automated Liquid Handler (300 µl multichannel head)
• BioShake D30-Telm heating and shaking module
• Magnum FLX magnetic module
• Evotip Pure trap columns and Evosep One LC system
• timsTOF HT mass spectrometer

Key Results and Discussion

• Throughput: Six plates processed in parallel with <2 minutes of active time per sample, completing 576 samples in 18 hours.
• Reproducibility: No inter-plate cross-contamination; Pearson correlation coefficients ≥ 0.91 between plates; median protein CV < 20% across workflows.
• Sensitivity: Identification of ~5,500 proteins from 1 µg HeLa input and ~4,900 from 500 ng input using 200 SPD; neat plasma workflow identified ~500 plasma proteins from 1 µl input; Mag-Net enrichment yielded ~2,700 proteins—a >5-fold increase—including 40 vesicle markers with CVs below 20%.
• Digestion Efficiency: Over 80% zero missed cleavages, consistent across replicates and workflows.

Benefits and Practical Applications

• High-throughput processing suitable for large-scale proteomic studies and clinical cohorts.
• Cost efficiency through minimized enzyme and sample waste and near-complete peptide utilization.
• Standardized, end-to-end automation reduces manual handling and variability.
• Flexibility to accommodate different sample types and digestion times.

Future Trends and Opportunities

Advances may include further miniaturization, integration with AI-driven method optimization, expansion to additional sample types (e.g., tissue lysates, biofluids), and combination with multi-omic analyses to enhance biological insights and clinical translation.

Conclusion

The seamless integration of the Biomek i5 Automated Liquid Handler with Evosep technology provides a robust, scalable, and reproducible platform for high-throughput proteomics. These automated workflows deliver deep proteome coverage, high reproducibility, and cost-effective sample handling, meeting the growing demands of routine and large-scale proteomic applications.

Reference

1. Batth TS, Tollenaere M, Rüther P, et al. Protein Aggregation Capture on Microparticles Enables Multipurpose Proteomics Sample Preparation. Mol Cell Proteomics. 2019; mcp.TIR118.001270.
2. Wu CC, Tsantilas KA, Park J, et al. Mag-Net: Rapid enrichment of membrane-bound particles enables high coverage quantitative analysis of the plasma proteome. BioRxiv. 2023; 10.1101/2023.06.10.544439v1.
3. Tuck MK, Chan DW, Chia D, et al. Standard Operating Procedures for Serum and Plasma Collection: Early Detection Research Network Consensus Statement. J Proteome Res. 2010; 10.1021/pr800545q.