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Related Experiment Video

Updated: Jun 23, 2025

3' End Sequencing Library Preparation with A-seq2

3' End Sequencing Library Preparation with A-seq2

Published on: October 10, 2017

Single-Cell Sequencing of 3' RNA Transcripts.

Claire M Bell¹

¹Asymmetric Operations Sector, Applied Biological Sciences, The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA. Claire.Bell@jhuapl.edu.

Methods in Molecular Biology (Clifton, N.J.)

|June 22, 2024

Summary

This summary is machine-generated.

Single-cell RNA sequencing (scRNA-seq) provides insights into cellular states and disease. This new method uses cell encapsulation and 3’ end sequencing for gene identification.

Keywords:

Cell barcode Droplet microfluidics Fluorescence-activated cell sorting Sequencing Single cell Transcriptome Unique molecular identifier

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Area of Science:

Molecular Biology
Genomics
Biotechnology

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
Analyzing RNA expression profiles reveals cell states, subtypes, and responses to stimuli or disease.
Existing methods have limitations in capturing comprehensive gene expression data at the single-cell level.

Purpose of the Study:

To develop an efficient and informative method for single-cell RNA sequencing.
To enable the identification of expressed genes within individual cells.
To facilitate deeper insights into cellular behavior and disease mechanisms.

Main Methods:

Single-cell encapsulation for isolating individual cells.
Capture of polyadenosine tails at the 3' end of mRNA transcripts.
Cell and molecular barcoding for unique cell and transcript identification.
Sequencing of 3' untranslated regions to identify expressed genes.

Main Results:

The described method allows for effective scRNA-seq.
Identification of expressed genes from individual cells is achieved through 3' end sequencing.
The technique provides valuable data for analyzing cellular states and heterogeneity.

Conclusions:

This novel scRNA-seq method enhances the ability to study cellular populations.
It offers a powerful tool for research in developmental biology, immunology, and disease pathology.
The approach contributes to advancing single-cell genomics and transcriptomics.