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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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Related Experiment Video

Updated: Jun 29, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Bento: a toolkit for subcellular analysis of spatial transcriptomics data.

Clarence K Mah^1,2,3, Noorsher Ahmed^2,3, Nicole A Lopez²

¹Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA.

|April 2, 2024

Summary

This summary is machine-generated.

Bento is a new Python toolkit for subcellular spatial analysis. It uses single-molecule data to define cellular domains, localization patterns, and gene colocalization for deeper biological insights.

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Area of Science:

Molecular Biology
Bioinformatics
Cell Biology

Background:

Cellular functions depend on the spatial organization of molecules.
Existing spatial analysis methods are limited to the multicellular scale.
Subcellular spatial analysis is crucial for understanding molecular functions.

Purpose of the Study:

To introduce Bento, a Python toolkit for subcellular spatial analysis.
To enable spatial analysis at the subcellular level using single-molecule information.
To integrate with existing single-cell analysis tools.

Main Methods:

Bento ingests molecular coordinates and segmentation boundaries.
It performs analyses including defining subcellular domains, annotating localization patterns, and quantifying gene-gene colocalization.
The toolkit is demonstrated on MERFISH, seqFISH+, Molecular Cartography, and Xenium datasets.

Main Results:

Bento enables spatial analysis at the subcellular scale.
It provides tools for defining subcellular domains and localization patterns.
Quantification of gene-gene colocalization within subcellular regions is achieved.

Conclusions:

Bento facilitates high-resolution spatial analysis within cells.
The toolkit enhances understanding of molecular organization and function at the subcellular level.
As part of the Scverse ecosystem, Bento promotes integrated single-cell data analysis.