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Related Concept Videos

Proteomics01:33

Proteomics

7.8K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Related Experiment Video

Updated: Sep 5, 2025

Single-Cell Proteomics Preparation for Mass Spectrometry Analysis Using Freeze-Heat Lysis and an Isobaric Carrier
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Single-Cell Proteomics Preparation for Mass Spectrometry Analysis Using Freeze-Heat Lysis and an Isobaric Carrier

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Optimized data-independent acquisition approach for proteomic analysis at single-cell level.

Yuefan Wang1, Tung-Shing Mamie Lih1, Lijun Chen1

  • 1Department of Pathology, Johns Hopkins University, Baltimore, MD, 21287, USA.

Clinical Proteomics
|July 9, 2022
PubMed
Summary

This study optimized data-independent acquisition (DIA)-MS for single-cell proteomics, enabling reliable quantification of low-input samples. The new workflow successfully identified thousands of protein groups and post-translational modifications from single cells.

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Cell Biology

Background:

  • Single-cell proteomic analysis offers insights into cellular heterogeneity and microenvironments, surpassing bulk analysis limitations.
  • Current single-cell proteomic studies often use data-dependent acquisition (DDA)-MS with TMT labeling, but imbalanced signals compromise quantification.
  • Data-independent acquisition (DIA)-MS presents a promising alternative for reproducible single-cell proteomic quantification, yet optimal workflows are underexplored.

Purpose of the Study:

  • To establish and optimize a data-independent acquisition (DIA)-MS workflow for single-cell proteomic analysis.
  • To evaluate the workflow's performance using breast cancer cell lines and drug-resistant polyaneuploid cancer cells (PACCs).

Main Methods:

  • Utilized an Orbitrap Lumos Tribrid instrument for optimized DIA-MS.
  • Employed a short liquid chromatography (LC) gradient suitable for low-input samples (<2 ng).
  • Determined the critical role of co-searching peptide precursors for identification at nano- and sub-nano-gram levels.

Main Results:

  • Identified up to 1500 protein groups from a single PACC (0.2 ng peptides) using the optimized workflow.
  • Detected approximately 200 post-translationally modified peptides (phosphorylation, acetylation, ubiquitination) from 20 ng of cisplatin-resistant PACCs.
  • Successfully compared whole proteomes of parental and drug-resistant MDA-MB-231 cells at the single-cell level, revealing protein expression changes and copy numbers.

Conclusions:

  • The optimized DIA pipeline provides a reliable quantitative method for single-cell and sub-nanogram proteomic analysis.
  • This workflow enhances the characterization of cellular heterogeneity and drug resistance mechanisms at the proteome level.