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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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Updated: May 7, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

UCS: A Unified Approach to Cell Segmentation for Subcellular Spatial Transcriptomics.

Yuheng Chen¹, Xin Xu¹, Xiaomeng Wan¹

¹Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, 999077, China.

|January 7, 2025

Summary

This summary is machine-generated.

A new unified cell segmentation approach (UCS) accurately maps gene expression in subcellular spatial transcriptomics (SST) data. This method improves transcript assignment and enables deeper understanding of tissue architecture and function across diverse SST platforms.

Keywords:

cell segmentation deep learning subcellular spatial transcriptomics

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Last Updated: May 7, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

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Published on: March 29, 2024

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

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Subcellular Spatial Transcriptomics (SST) allows gene expression analysis at the subcellular level.
Accurate cell segmentation is vital for attributing transcripts to cells in SST data.
Current cell segmentation methods struggle with diverse SST technologies.

Purpose of the Study:

To develop a unified cell segmentation approach (UCS) for diverse Subcellular Spatial Transcriptomics datasets.
To enhance the accuracy of transcript assignment to individual cells.
To provide computational advantages for large-scale SST data analysis.

Main Methods:

Developed a deep learning-based unified cell segmentation approach (UCS).
Integrated nuclei segmentation from staining and transcript data.
Applied UCS to diverse SST platforms: 10X Xenium, NanoString CosMx, MERSCOPE, and Stereo-seq.

Main Results:

UCS achieves high accuracy in cell segmentation across multiple SST platforms.
Demonstrated more precise transcript assignment to individual cells compared to existing methods.
UCS offers computational advantages for analyzing large-scale SST data.

Conclusions:

UCS provides a robust solution for cell segmentation in diverse Subcellular Spatial Transcriptomics data.
The method facilitates accurate subcellular gene classification and missing cell detection.
UCS enhances the characterization of gene expression at cellular and subcellular levels, advancing tissue architecture and function studies.