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Cell Specific Gene Expression

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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: Aug 25, 2025

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

SPASCER: spatial transcriptomics annotation at single-cell resolution.

Zhiwei Fan^1,2, Yangyang Luo³, Huifen Lu³

¹West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China.

Nucleic Acids Research

|October 16, 2022

Summary

This summary is machine-generated.

SPASCER is a new spatial transcriptomics database that reveals tissue heterogeneity and cell interactions. This resource aids in understanding complex biological mechanisms in health and disease.

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Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Spatial transcriptomics advances tissue architecture characterization by preserving spatial information.
Existing databases offer limited analytical depth for spatial gene and cell heterogeneity.
Understanding spatial cell-cell communication and microenvironment composition is crucial for biological insights.

Purpose of the Study:

Introduce SPASCER, a novel spatial transcriptomics database.
Facilitate the analysis of tissue heterogeneity, microenvironment, and intercellular interactions.
Provide a comprehensive resource for studying biological mechanisms in healthy and diseased tissues.

Main Methods:

Integrated 43 studies with 1082 sub-datasets across 16 organ types and four species.
Utilized single-cell RNA sequencing (scRNA-seq) for deconvolution and mapping.
Performed spatial cell-cell interaction, gene pattern, and pathway enrichment analyses.

Main Results:

SPASCER offers extensive datasets for multi-level analysis of tissue organization.
Integrated analyses reveal spatial cell-cell interactions and gene regulatory networks.
The database supports deconvolution of spatial transcriptomics data.

Conclusions:

SPASCER enhances the understanding of tissue architecture and spatial biology.
Provides a foundation for mechanistic studies in various biological contexts.
Enables deeper insights into tumorigenesis and embryonic differentiation mechanisms.