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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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Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE.

Oliver M Crook^1,2,3, Colin T R Davies^4,5,6, Lisa M Breckels^4,5

¹Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, CB2 1GA, Cambridge, UK. oliver.crook@stats.ox.ac.uk.

Nature Communications

|October 10, 2022

Summary

This summary is machine-generated.

We developed BANDLE, a Bayesian method to accurately identify protein differential localization using mass spectrometry data. This approach improves the understanding of protein dynamics and cellular responses to perturbations.

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Area of Science:

Cellular Biology
Proteomics
Bioinformatics

Background:

Protein localization is crucial for cellular function and dynamically changes with environmental perturbations.
Understanding these dynamic changes, known as differential localization, offers mechanistic insights into subcellular protein behavior.
High-throughput mass spectrometry enables mapping of protein localization and re-localization.

Purpose of the Study:

To introduce BANDLE, a Bayesian method for analyzing high-throughput mass spectrometry data to detect differential protein localization.
To evaluate BANDLE's performance against existing methods using extensive simulations.
To apply BANDLE to biological datasets, including cytomegalovirus infection, to uncover host proteome rewiring.

Main Methods:

Developed a principled Bayesian approach named BANDLE.
Utilized high-accuracy, high-throughput mass spectrometry data for protein localization analysis.
Performed extensive simulation studies to compare BANDLE with existing methods.

Main Results:

BANDLE significantly reduces both type I and type II errors in detecting differential localization compared to current methods.
Application of BANDLE successfully identified known protein translocations in various datasets.
Analysis of cytomegalovirus infection data revealed insights into host proteome rewiring.

Conclusions:

BANDLE provides a robust and accurate computational framework for identifying differential protein localization from mass spectrometry data.
The method enhances the mechanistic understanding of subcellular protein dynamics and cellular responses.
Integration with other high-throughput data can provide functional context for observed protein re-localizations.