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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: Aug 6, 2025

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Published on: April 7, 2023

Spatial transcriptomics using multiplexed deterministic barcoding in tissue.

Johannes Wirth¹, Nina Huber¹, Kelvin Yin¹

¹Helmholtz Pioneer Campus, Helmholtz Munich, Munich, Germany.

Nature Communications

|March 19, 2023

Summary

This summary is machine-generated.

Multiplexed Deterministic Barcoding in Tissue (xDBiT) enables parallel spatial transcriptomics on nine tissue sections. This method enhances gene and read counts, providing detailed molecular insights into tissue architecture.

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Last Updated: Aug 6, 2025

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

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Genetic Barcoding with Fluorescent Proteins for Multiplexed Applications

Genetic Barcoding with Fluorescent Proteins for Multiplexed Applications

Published on: April 14, 2015

Area of Science:

Biomedical Research
Genomics
Molecular Biology

Background:

Spatially resolved transcriptomics is crucial for understanding tissue function.
Existing methods have limitations in throughput and resolution.

Purpose of the Study:

To introduce Multiplexed Deterministic Barcoding in Tissue (xDBiT) for high-throughput spatial transcriptomics.
To enhance data acquisition efficiency and accuracy for tissue analysis.

Main Methods:

Development of novel microfluidic chips for mRNA spatial encoding.
Optimization of biochemical protocols to increase read and gene counts.
Integration of alignment markers for image and data registration.
Creation of an open-source computational pipeline for data processing.

Main Results:

Parallel acquisition of spatially resolved transcriptomes from nine tissue sections.
Achieved 50 µm resolution over a 1.17 cm² tissue area.
Increased read and gene counts per spot by one order of magnitude.
Successfully generated 16 datasets from five murine organs (cerebellum, liver, kidney, spleen, heart).
Enabled in-depth characterization of the murine kidney via factor analysis and deconvolution.

Conclusions:

xDBiT significantly advances spatial transcriptomics capabilities.
The technology facilitates comprehensive molecular profiling of complex tissues.
Open-source tools support broad adoption and further research in spatial biology.