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Refinement of paramagnetic bead-based digestion protocol for automatic sample preparation using an artificial neural

Sergio Ciordia1, Fátima Milhano Santos1, João M L Dias2

  • 1Functional Proteomics Laboratory, Centro Nacional de Biotecnología, CSIC, Calle Darwin 3, Campus de Cantoblanco, 28049, Madrid, Spain.

Talanta
|April 3, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an automated, bead-based sample preparation protocol for mass spectrometry (MS) analysis using the Opentrons OT-2 platform. Optimized with an artificial neural network (ANN), this method enhances peptide identification and reproducibility across diverse biological samples, including challenging fluids like rat bile.

Keywords:
Artificial neural networkAutomationBileOpentronsProteomicsSP3Sample preparation

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Area of Science:

  • Proteomics and Mass Spectrometry
  • Bioanalytical Chemistry
  • Automation and Robotics in Life Sciences

Background:

  • Sample preparation remains a critical bottleneck in mass spectrometry (MS)-based proteomics.
  • Bead-based protein aggregation offers an efficient, reproducible, and high-throughput alternative for protein extraction and digestion.
  • Automation platforms like the Opentrons OT-2 can streamline complex proteomic workflows.

Purpose of the Study:

  • To develop and validate an automated, paramagnetic bead-based digestion protocol for MS sample preparation on the Opentrons OT-2 platform.
  • To optimize digestion conditions using an artificial neural network (ANN) for maximizing peptide yield and minimizing missed cleavages.
  • To assess the performance and reproducibility of the automated protocol across various biological sample types, including challenging matrices.

Main Methods:

  • Application of an artificial neural network (ANN) to optimize parameters including enzyme quantity (trypsin/Lys-C), bead amount, SDS concentration, acetonitrile percentage, and digestion time.
  • Automation of the optimized manual protocol on the Opentrons OT-2 liquid handling platform.
  • Validation of the automated protocol using diverse samples: HeLa extract, human plasma, Arabidopsis thaliana leaves, Escherichia coli cells, mouse tissue cortex, and rat bile.

Main Results:

  • ANN modeling identified optimal conditions for digesting 50 μg HeLa extract, utilizing 2.5% SDS, 300 μg beads, 16h digestion, and specific enzyme ratios (0.15 μg Lys-C, 2.5 μg trypsin).
  • The automated OT-2 protocol demonstrated high reproducibility and low sample-to-sample variability across all tested sample types.
  • Analysis of rat bile revealed 385 unique proteins, showcasing improved proteome coverage for challenging biological fluids compared to existing methods.

Conclusions:

  • The automated paramagnetic bead-based digestion protocol on the Opentrons OT-2 platform provides a versatile, reproducible, and affordable solution for MS sample preparation.
  • ANN-driven optimization significantly enhances peptide identification efficiency.
  • This method effectively addresses challenges in preparing complex biological samples, thereby advancing proteomic research.