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Related Concept Videos

Proteomics01:33

Proteomics

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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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Single-Cell Proteomics Preparation for Mass Spectrometry Analysis Using Freeze-Heat Lysis and an Isobaric Carrier
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High-Throughput Single-Cell Proteomics of In Vivo Cells.

Shiri Karagach1, Joachim Smollich1, Ofir Atrakchi1

  • 1Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.

Molecular & Cellular Proteomics : MCP
|June 22, 2025
PubMed
Summary
This summary is machine-generated.

We developed a high-throughput single-cell proteomics pipeline for analyzing 1536 cells, identifying over 3000 proteins per cell. This method enhances tumor analysis by revealing differences in tumor macrophages.

Keywords:
1536-well platesautomated sample preparationcancer researchcell fixationhigh-throughputlung metastasismass spectrometrymurine modelsingle-cell proteomicsslice-PASEFtumor macrophages

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Area of Science:

  • Proteomics
  • Biotechnology
  • Cancer Research

Background:

  • Single-cell proteomics (SCP) offers insights into cellular heterogeneity.
  • Current SCP methods have limitations in throughput and focus on cultured cells.
  • There is a need for enhanced SCP for complex biological systems like tumors.

Purpose of the Study:

  • To develop an automated, high-throughput pipeline for single-cell proteomics (SCP).
  • To enhance the depth, accuracy, and throughput of SCP for tumor analysis.
  • To analyze tumor macrophages in a murine lung metastasis model.

Main Methods:

  • Developed an automated, high-throughput pipeline for analyzing 1536 single cells.
  • Integrated low-volume sample preparation, automated purification, and LC-MS with Slice-PASEF.
  • Applied the pipeline to a murine lung metastasis model.

Main Results:

  • Achieved robust identification of over 3000 proteins per cell.
  • Identified over 1700 proteins per cell in tumor macrophages, including key markers.
  • Detected over 500 differentially expressed proteins between tumor and control macrophages.
  • PCA analysis successfully separated macrophage populations, highlighting tumor microenvironment signals.

Conclusions:

  • The developed pipeline is robust, scalable, and reproducible.
  • This approach significantly advances single-cell proteomics capabilities for cancer research.
  • SCP can effectively capture biologically relevant signals within the tumor microenvironment.