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Digital Microfluidics for Automated Proteomic Processing
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Miniaturised interaction proteomics on a microfluidic platform with ultra-low input requirements.

Cristina Furlan1, René A M Dirks1, Peter C Thomas2

  • 1Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, 6525 GA, The Netherlands.

Nature Communications
|April 6, 2019
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Summary

We developed an on-chip affinity purification coupled to mass spectrometry (AP-MS) method to identify protein-protein interactions (PPIs). This automated workflow significantly reduces the required sample amount, enabling PPI analysis with limited cellular material.

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Area of Science:

  • Biochemistry
  • Molecular Biology
  • Proteomics

Background:

  • Protein-protein interactions (PPIs) are fundamental to cellular processes.
  • Affinity purification coupled to mass spectrometry (AP-MS) is a key technique for identifying PPIs.
  • Traditional AP-MS methods require substantial amounts of biological material.

Purpose of the Study:

  • To develop a microfluidic-based AP-MS workflow (on-chip AP-MS) for PPI identification.
  • To enable PPI analysis using significantly reduced input material.
  • To demonstrate the versatility of the on-chip AP-MS platform for both tagged and native proteins.

Main Methods:

  • Implementation of a microfluidic-based automated platform for AP-MS.
  • Purification of protein complexes from minute amounts of cellular lysate (as low as 4 micrograms).
  • Application of the on-chip AP-MS workflow to identify components of human Cohesin, CCC, and Mediator complexes.

Main Results:

  • Achieved a 50-100 fold downscaling of input material compared to conventional methods.
  • Successfully purified tagged baits and native cellular proteins with their interaction partners.
  • Demonstrated the efficacy of the on-chip AP-MS workflow for complex protein mixture analysis.

Conclusions:

  • The on-chip AP-MS workflow offers a highly sensitive method for PPI identification.
  • This technology significantly reduces sample requirements, making PPI analysis more accessible.
  • The platform holds substantial potential for biological and clinical research involving limited sample quantities.