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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: Jul 18, 2025

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

A standard for sharing spatial transcriptomics data.

Kayla C Jackson^1,2, Lior Pachter^2,3

¹Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

|August 21, 2023

Summary

This summary is machine-generated.

This study introduces a data sharing model for spatial transcriptomics. It aims to standardize primary data and metadata for reproducible analyses and computational methods development in gene and cell function research.

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Last Updated: Jul 18, 2025

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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:

Genomics
Bioinformatics
Cell Biology

Background:

Spatial transcriptomics offers insights into gene and cell function within their spatial context.
Reproducibility and computational method development are crucial for advancing spatial transcriptomics analysis.

Discussion:

A standardized data sharing model is proposed for spatial transcriptomics data.
This model emphasizes essential primary data and metadata for analysis replication.
Facilitating computational methods development is a key objective.

Key Insights:

The proposed model ensures data necessary for reproducing spatial transcriptomics analyses.
Standardized metadata promotes interoperability and comparison across studies.
This framework supports the development of novel computational tools for spatial data.

Outlook:

Wider adoption of this model can accelerate discoveries in spatial biology.
Enhanced data sharing will foster collaborative research in transcriptomics.
Future work may involve extending the model to other high-throughput biological data types.