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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Related Experiment Video

Updated: Jan 10, 2026

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

Deep Learning-Based Quality Control Using Subcellular RNA Spatial Distribution Patterns for Cell Segmentation in

Renpeng Ding¹, Kerem Celikay¹, Ming Ni²

¹Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, 69120, Heidelberg, Germany.

|November 28, 2025

Summary

This summary is machine-generated.

This study introduces a deep learning method to improve cell segmentation in spatial transcriptomics (sST) data. The AI assesses RNA patterns to identify and correct segmentation errors, enhancing data quality for research.

Keywords:

cell segmentation quality control spatial transcriptomics subcellular RNA spatial distribution

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Last Updated: Jan 10, 2026

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

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Published on: July 6, 2022

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

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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

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Sequencing-based spatial transcriptomics (sST) offers high-resolution transcriptome analysis at the subcellular level.
Accurate cell segmentation remains a challenge in sST data analysis, hindering precise RNA spot assignment.
Existing methods lack robust quality control for cell segmentation in sST.

Purpose of the Study:

To develop a deep learning-based quality control (QC) method for cell segmentation in sST data.
To improve the accuracy and reliability of cell segmentation results.
To enhance the overall analysis of spatial transcriptomics data.

Main Methods:

A deep neural network was designed to analyze subcellular RNA distribution patterns.
The method identifies partially segmented and merged cells based on RNA spatial characteristics.
The QC approach was integrated with a Transformer-based segmentation model, using it to refine training datasets.

Main Results:

The deep learning method effectively assesses the quality of segmented cells in sST data.
Partially segmented and merged cells were accurately identified, addressing common segmentation issues.
Integrating the QC method with Transformer-based segmentation improved overall cell segmentation performance.

Conclusions:

The proposed deep learning method provides a novel approach for quality control and enhancement of cell segmentation in sST.
This technique addresses key challenges in assigning RNA spots to cells, improving data interpretation.
The method demonstrates significant potential for advancing spatial transcriptomics research using real and synthetic datasets.