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Counting Proteins in Single Cells with Addressable Droplet Microarrays
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SPIN: Inkjet-Driven Nanowell Workflow for Scalable and Sensitive Single-Cell Proteomics.

Eric Cheng1, Shuxin Chi2, Huan Zhong2

  • 1Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada.

Analytical Chemistry
|March 13, 2026
PubMed
Summary
This summary is machine-generated.

We developed an automated single-cell printer and nanowell chip for single-cell proteomics. This system improves protein recovery and reproducibility, enabling scalable single-cell proteomic analysis.

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Area of Science:

  • Biochemistry
  • Proteomics
  • Cell Biology

Background:

  • Single-cell proteomics is crucial for understanding cellular heterogeneity.
  • Current sample processing methods face challenges with limited input material and lack of protein amplification.
  • Efficient and reproducible sample preparation is essential for advancing single-cell proteomics.

Purpose of the Study:

  • To present a novel protocol for single-cell proteomics using an integrated inkjet printer and nanowell chip.
  • To overcome limitations in sample processing, including material loss, low throughput, and poor reproducibility.
  • To enable reliable and scalable single-cell proteomic analyses.

Main Methods:

  • Development of an image-guided, machine-learning-driven inkjet single-cell printer.
  • Integration with a dew-point-controlled nanowell chip for precise cell dispensing and environmental regulation.
  • Utilizing a high-thermal-conductivity aluminum substrate for enhanced thermal stability.
  • Minimizing sample loss through sealable nanoliter wells and reduced surface contact.

Main Results:

  • Achieved single-cell dispensing at a rate greater than 1 Hz.
  • Demonstrated significantly higher protein and peptide recovery compared to commercial dispensers.
  • Observed uniform deep proteome coverage without bias towards high-abundance proteins.
  • Confirmed nearly 100% protein completeness for Krebs cycle enzymes in A549 cells and astrocytes, indicating high reproducibility.

Conclusions:

  • The presented platform effectively addresses core bottlenecks in single-cell proteomics sample processing.
  • The system enables reliable, scalable, and highly reproducible single-cell proteomic analyses.
  • This advancement facilitates deeper insights into cellular heterogeneity through enhanced proteomic resolution.