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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
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Updated: Jun 25, 2026

Single-Cell Resolution Three-Dimensional Imaging of Intact Organoids

Single-Cell Resolution Three-Dimensional Imaging of Intact Organoids

Published on: June 5, 2020

OrgaSegment: deep-learning based organoid segmentation to quantify CFTR dependent fluid secretion.

Juliet W Lefferts^1,2, Suzanne Kroes^1,2, Matthew B Smith^1,2,3

¹Department of Pediatric Respiratory Medicine, Wilhelmina Children's Hospital, University Medical Center, Utrecht University, 3584 EA, Utrecht, The Netherlands.

Communications Biology

|March 14, 2024

Summary

This summary is machine-generated.

A new deep-learning tool, OrgaSegment, accurately segments and quantifies organoid swelling. This method aids in studying cystic fibrosis (CF) and evaluating CF transmembrane conductance regulator (CFTR) modulating drugs.

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Area of Science:

Biomedical Engineering
Cell Biology
Genetics

Background:

Patient-derived organoids (PDOs) are crucial for preclinical research, drug development, and precision medicine.
Epithelial fluid transport in PDOs is assessed by measuring lumen size changes, vital for studying diseases like cystic fibrosis (CF).
CF is caused by mutations in the CF transmembrane conductance regulator (CFTR) gene, affecting ion channel function.

Purpose of the Study:

To introduce OrgaSegment, a deep-learning model for segmenting intestinal PDOs from bright-field images.
To enable accurate quantification of organoid swelling in response to various stimuli.
To facilitate the study of CFTR function and drug responses in PDOs.

Main Methods:

Developed OrgaSegment, a MASK-RCNN-based deep-learning model.
Applied OrgaSegment to segment individual intestinal PDO structures in bright-field microscopy images.
Utilized OrgaSegment for quantifying organoid swelling in drug-induced swelling assays.

Main Results:

OrgaSegment successfully segmented both spherical and irregularly shaped (hallmark of CF) organoids.
The tool enabled straightforward quantification of organoid swelling.
OrgaSegment effectively discriminated between organoids with different CFTR mutations and measured responses to CFTR modulating drugs.

Conclusions:

OrgaSegment is an effective, label-free tool for analyzing organoid swelling.
This method can advance the study of CFTR-mediated fluid secretion and other epithelial transport mechanisms in organoids.
OrgaSegment supports drug development and precision medicine applications for CF and related disorders.