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Related Experiment Video

Updated: Feb 7, 2026

The Use of Chemostats in Microbial Systems Biology

The Use of Chemostats in Microbial Systems Biology

Published on: October 14, 2013

Single cell protein analysis for systems biology.

Ezra Levy¹, Nikolai Slavov^2,3

¹Department of Biology, Northeastern University, Boston, MA 02115, U.S.A.

Essays in Biochemistry

|August 4, 2018

Summary

Single-cell protein variability influences cell fate decisions. New mass spectrometry methods offer unprecedented coverage for analyzing single-cell proteomes, advancing quantitative systems biology.

Keywords:

antibodies gene expression and regulation mass-spectrometry single cell proteomics

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Microcontact Printing of Proteins for Cell Biology

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Microcontact Printing of Proteins for Cell Biology

Microcontact Printing of Proteins for Cell Biology

Published on: December 5, 2008

Area of Science:

Cellular biology
Proteomics
Systems biology

Background:

Cellular protein abundance varies even in identical cells, impacting cell fate.
Tracking protein levels in single cells has been advanced by antibodies and fluorescent proteins.

Purpose of the Study:

To review the connection between single-cell protein levels and cellular functions.
To discuss the trade-offs of current single-cell protein analysis methods.
To highlight emerging mass spectrometry techniques for proteome analysis.

Main Methods:

Review of existing literature on single-cell protein analysis.
Discussion of antibody and fluorescent protein-based tracking methods.
Evaluation of emerging mass spectrometry techniques for proteomic analysis.

Main Results:

Single-cell protein variability plays regulatory roles in cell fate decisions.
Current methods have limitations in analyzing complex cellular systems.
Emerging mass spectrometry methods promise high coverage and specificity for proteome analysis.

Conclusions:

Understanding single-cell protein dynamics is crucial for deciphering cellular functions.
Advanced proteomic techniques are essential for systems-level analysis.
Integrating proteomic, transcriptomic, and metabolomic data will drive quantitative systems biology.