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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: Jun 4, 2025

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

Protocol for capturing a full transcriptome from single preimplantation embryos using So-Smart-seq.

Chunyao Wei¹, Jeannie T Lee¹

¹Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.

|January 5, 2025

Summary

This summary is machine-generated.

Strand-optimized Smart-seq (So-Smart-seq) provides comprehensive transcriptome analysis from limited samples. This method captures diverse RNA types, including non-polyadenylated and repetitive RNAs, while preserving strand information.

Keywords:

Bioinformatics Developmental biology Genetics Genomics Molecular Biology Systems biology

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Last Updated: Jun 4, 2025

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

Author Spotlight: A Pipeline to Analyze Lineage-Specific Mutant Embryos at Single-Cell Resolution

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Cell-Specific Paired Interrogation of the Mouse Ovarian Epigenome and Transcriptome

Cell-Specific Paired Interrogation of the Mouse Ovarian Epigenome and Transcriptome

Published on: February 24, 2023

Area of Science:

Molecular Biology
Genomics
Transcriptomics

Background:

Low-input samples present challenges for comprehensive RNA analysis.
Existing methods may not capture the full spectrum of RNA molecules or may introduce bias.

Purpose of the Study:

To present a detailed protocol for Strand-optimized Smart-seq (So-Smart-seq) for analyzing single mouse preimplantation embryos.
To highlight the capability of So-Smart-seq in capturing a comprehensive transcriptome from low-input samples.

Main Methods:

Single mouse preimplantation embryo isolation.
Library preparation using So-Smart-seq, including ribosomal cDNA depletion.
Initial data processing steps for transcriptome analysis.

Main Results:

So-Smart-seq effectively captures both polyadenylated and non-polyadenylated RNAs.
The technique includes repetitive RNAs and excludes abundant ribosomal RNAs.
Strand information is preserved, and 5' to 3' coverage bias is minimized.

Conclusions:

So-Smart-seq is a robust method for comprehensive transcriptome profiling of low-input samples.
The protocol is adaptable for other low-input biological materials and small RNA detection (<200 nt).