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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: Jun 10, 2025

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

Published on: August 8, 2013

Integrating Metabolic RNA Labeling-Based Time-Resolved Single-Cell RNA Sequencing with Spatial Transcriptomics for

Xiaoyong Chen^1,2, Shichao Lin³, Honghai You^1,2

¹Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, P. R. China.

|October 11, 2024

Summary

Integrating time-resolved single-cell RNA sequencing with spatial transcriptomics reveals glioblastoma cell communication driving mesenchymal transition. This spatiotemporal analysis uncovers key pathways in the tumor microenvironment.

Keywords:

EZH2 glioblastoma mesenchymal transition single‐cell spatiotemporal

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Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays

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Last Updated: Jun 10, 2025

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

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Published on: August 8, 2013

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

Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays

Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays

Published on: January 16, 2018

Area of Science:

Molecular Biology
Genomics
Cancer Research

Background:

Single-cell RNA sequencing (scRNA-seq) offers insights into gene expression dynamics but lacks spatial context.
Spatial organization and intercellular communication significantly influence gene regulation within tissues.
Understanding glioblastoma (GBM) progression requires integrating temporal gene expression with spatial cellular interactions.

Purpose of the Study:

To develop and apply an integrated spatiotemporal analysis method for profiling GBM.
To investigate the transcriptional dynamics and intercellular communication in GBM.
To elucidate the mechanisms driving mesenchymal transition in glioblastoma.

Main Methods:

Metabolic RNA labeling-based time-resolved Well-TEMP-seq for transcriptional dynamics.
Integration with spatial transcriptomics to map cellular locations and interactions.
Analysis of gene expression patterns and intercellular communication axes.

Main Results:

Identified two potential EZH2-mediated pathways driving mesenchymal transition in GBM.
Revealed crosstalk between malignant CCL2+ cells and IL10+ tumor-associated macrophages.
Discovered an EZH2-FOSL2-CCL2 axis contributing to GBM mesenchymal transition.

Conclusions:

The integration of time-resolved scRNA-seq and spatial transcriptomics provides a powerful paradigm for spatiotemporal analysis.
This approach elucidates complex gene regulatory mechanisms and cellular processes in disease.
The findings advance the understanding of GBM pathogenesis and potential therapeutic targets.