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Surfactant-assisted one-pot sample preparation for label-free single-cell proteomics.

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  • 1Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.

Communications Biology
|March 2, 2021
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Summary

This study introduces SOP-MS, a novel method for single-cell proteomics that minimizes sample loss using a surfactant. This technique enables sensitive, label-free protein quantification from individual cells and rare cell populations.

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Area of Science:

  • Biochemistry
  • Proteomics
  • Cell Biology

Background:

  • Quantitative global proteome profiling typically requires large cell numbers, limiting analysis of rare cells and obscuring cell heterogeneity.
  • Surface adsorption during sample processing leads to significant sample loss in traditional bulk measurements.

Purpose of the Study:

  • To develop a sensitive, label-free method for global single-cell proteomics.
  • To overcome limitations of bulk measurements and enable proteomic profiling of rare cell populations like circulating tumor cells (CTCs).

Main Methods:

  • Development of a surfactant-assisted one-pot sample preparation coupled with mass spectrometry (MS) method named SOP-MS.
  • Utilizing a MS-compatible nonionic surfactant (n-Dodecyl-β-D-maltoside) and hydrophobic low-bind surfaces for 'all-in-one' sample preparation.
  • Elimination of sample transfer steps to minimize surface adsorption losses.

Main Results:

  • SOP-MS enables label-free quantification of hundreds of proteins from single human cells.
  • The method can profile approximately 1200 proteins from small tissue sections (~20 cells).
  • Application to a patient CTC-derived xenograft (PCDX) model revealed distinct protein signatures between primary and metastatic cells.

Conclusions:

  • SOP-MS significantly improves detection sensitivity for single-cell proteomics by reducing sample loss.
  • The approach facilitates precise, quantitative proteomic analysis of individual cells and rare cell populations.
  • This method paves the way for routine quantitative single-cell proteomics, aiding in understanding cell heterogeneity and disease mechanisms.