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Subcellular Fractionation

Subcellular Fractionation

The homogenate obtained after cell lysis contains various membrane-bound organelles that can be further separated into pure fractions by subcellular fractionation. These isolates are used to study specific cellular components, analyze localized protein activity, and are even employed in diagnostics. Fractionation is typically achieved using centrifugation methods, the most common being density-gradient and differential centrifugation.
Differential Centrifugation
Differential centrifugation is...

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Quantifying the distribution of probes between subcellular locations using unsupervised pattern unmixing.

Luis Pedro Coelho¹, Tao Peng, Robert F Murphy

¹Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Bioinformatics (Oxford, England)

|June 10, 2010

Summary

This study introduces unsupervised methods to analyze protein distribution in cells. The new techniques accurately estimate protein fractions across various subcellular locations from mixed patterns, aiding quantitative cell biology.

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Area of Science:

Cell Biology
Quantitative Biology
Microscopy Imaging

Background:

Proteins can localize to multiple organelles, with distributions varying by cell physiology.
Accurate estimation of protein fractions in subcellular locations is crucial for quantitative cell biology and modeling.
Previous methods required known basic subcellular locations; this study addresses unknown distributions.

Purpose of the Study:

To develop unsupervised methods for identifying fundamental subcellular location patterns from mixed images.
To estimate the fractional composition of proteins in various subcellular locations without prior knowledge.
To provide tools for analyzing complex subcellular distributions on a proteome-wide scale.

Main Methods:

Developed two unsupervised approaches: basis pursuit (linear mixture model) and latent Dirichlet allocation (LDA).
Utilized pre-acquired cell images with organelle-specific probes exhibiting similar fluorescent properties to simulate mixed patterns.
Evaluated methods by comparing estimated fractions against known underlying distributions.

Main Results:

The LDA approach achieved a 0.91 correlation between estimated and underlying fractions.
The basis pursuit method showed a 0.80 correlation.
Both methods demonstrated reasonably high accuracy in unmixing complex subcellular protein distributions.

Conclusions:

Unsupervised methods can accurately determine protein fractions in subcellular locations from mixed patterns.
These approaches are valuable for quantitative analysis of protein localization, especially in proteome-wide studies.
The developed software is available for use in analyzing complex subcellular distributions.