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Related Experiment Video

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Detection of Copy Number Alterations Using Single Cell Sequencing
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Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells.

Nils A Kulak1, Garwin Pichler1, Igor Paron1

  • 1Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.

Nature Methods
|February 4, 2014
PubMed
Summary
This summary is machine-generated.

A new in-StageTip method streamlines mass spectrometry (MS)-based proteomics sample preparation, minimizing contamination and loss. This robust technique enhances proteome coverage and quantitative reproducibility for biological and clinical applications.

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Biochemistry

Background:

  • Traditional mass spectrometry (MS)-based proteomics involves multi-step sample preparation.
  • These conventional workflows are prone to sample contamination and loss, impacting data accuracy.
  • There is a need for more robust and efficient sample processing methods in proteomics.

Purpose of the Study:

  • To develop and validate a novel, integrated sample processing method for MS-based proteomics.
  • To minimize sample loss and contamination during proteomic sample preparation.
  • To enhance proteome coverage and quantitative reproducibility in proteomic analyses.

Main Methods:

  • An "in-StageTip" method was developed for integrated sample processing, from cell lysis to peptide elution.
  • The method confines sample processing within a single, enclosed volume.
  • Peptides were eluted in single or multiple fractions for proteome analysis.

Main Results:

  • The in-StageTip method significantly reduced sample contamination and loss.
  • Unprecedented proteome coverage was achieved for yeast species in a single day.
  • High quantitative reproducibility (R² = 0.97) and extensive human protein quantification (9,667 proteins) were demonstrated in a human cell line.

Conclusions:

  • The in-StageTip method offers a robust, scalable, and straightforward approach for MS-based proteomics.
  • This technique largely eliminates contamination and loss, improving data reliability.
  • It is broadly applicable to biological and clinical research, enhancing proteomic analysis efficiency and scope.