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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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Updated: Dec 12, 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

Encoding Method of Single-cell Spatial Transcriptomics Sequencing.

Ying Zhou¹, Erteng Jia¹, Min Pan²

¹State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China.

International Journal of Biological Sciences

|August 15, 2020

Summary

This summary is machine-generated.

Spatial transcriptomics reveals gene expression within intact tissues, overcoming limitations of single-cell RNA sequencing. This review compares key technologies for spatial gene expression analysis.

Keywords:

Encoding method In situ sequencing Single-cell RNA sequencing Spatial transcriptomics

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Published on: April 25, 2011

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Last Updated: Dec 12, 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

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) has uncovered significant cellular heterogeneity.
However, scRNA-seq lacks crucial spatial context within tissues.
Understanding spatial gene expression is vital for tissue function and disease research.

Purpose of the Study:

To review and compare the latest single-cell spatial transcriptomics technologies.
To provide a reference for selecting appropriate spatial encoding methods.
To highlight the importance of spatial information in transcriptomics.

Main Methods:

Review of technologies including microwell plates, barcoded bead arrays, microdissection, and in situ hybridization.
Analysis of barcode in situ targeting and mixed separation-based methods.
Comparison of different spatial encoding strategies and their efficiencies.

Main Results:

Various spatial transcriptomics technologies offer different approaches to encoding positional information.
Each method presents unique efficiencies and application scenarios.
The choice of technology depends on specific research needs and experimental design.

Conclusions:

Spatial transcriptomics is essential for a comprehensive understanding of tissue biology.
Accurate spatial encoding is critical for the success of these technologies.
This review offers guidance for selecting optimal spatial transcriptomics methods.