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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: Jul 4, 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

SPANN: annotating single-cell resolution spatial transcriptome data with scRNA-seq data.

Musu Yuan¹, Hui Wan², Zihao Wang³

¹Center for Quantitative Biology, Peking University, Yiheyuan Road, 100871, Beijing, China.

Briefings in Bioinformatics

|January 27, 2024

Summary

This summary is machine-generated.

SPANN accurately annotates known cell types and discovers novel cells in spatial transcriptomics data by aligning single-cell RNA sequencing data, addressing limitations in current computational tools.

Keywords:

cell-type annotation optimal transport (OT)single-cell transcriptome spatial transcriptome variational autoencoder (VAE)

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Last Updated: Jul 4, 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

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Spatial transcriptomics technologies provide single-cell resolution data but lack tailored computational annotation tools.
Existing integration frameworks for spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) overlook cell type mapping and novel cell detection.

Purpose of the Study:

To develop an advanced annotation method for spatial transcriptome data.
To enable accurate cell type mapping and the discovery of novel cell types from spatial data.

Main Methods:

SPANN transfers cell-type labels from scRNA-seq data to spatial transcriptome data.
SPANN identifies novel cells and cell states within complex tissue contexts.
The method aligns spatial transcriptome data with RNA data prototypes for cell-type-level analysis.

Main Results:

SPANN achieves high annotation accuracy for known cell types.
SPANN successfully detects novel cells and previously unseen cell types.
Experiments across various spatial platforms validate SPANN's effectiveness.

Conclusions:

SPANN enhances the analysis of spatial transcriptome data by improving annotation and enabling novel cell discovery.
The developed method addresses critical limitations in existing computational frameworks for spatial transcriptomics.