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Updated: May 30, 2025

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

Optimizing mouse metatranscriptome profiling by selective removal of redundant nucleic acid sequences.

Morgan Roos¹, Samuel Bunga¹, Asako Tan¹

¹Illumina, Inc., San Diego, CA, USA.

Biorxiv : the Preprint Server for Biology

|January 27, 2025

Summary

Removing abundant rRNA transcripts is crucial for microbiome metatranscriptome (MetaT) sequencing. New, targeted probes improve efficiency and consistency for mouse cecal samples, enhancing MetaT analysis.

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Chromatin Immunoprecipitation of Murine Brown Adipose Tissue

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Last Updated: May 30, 2025

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Chromatin Immunoprecipitation of Murine Brown Adipose Tissue

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Area of Science:

Microbiology
Genomics
Bioinformatics

Background:

Metatranscriptome (MetaT) sequencing profiles microbiome metabolic functions and gene expression.
High rRNA abundance (up to 99%) hinders accurate mRNA analysis in microbiome samples.
Existing human-gut rRNA depletion probes are less effective for mouse cecal samples.

Purpose of the Study:

To develop an efficient and consistent rRNA depletion method for mouse cecal samples.
To refine a taxonomically-neutral probe design for improved MetaT analysis.
To reduce the cost and bias associated with rRNA removal.

Main Methods:

Designed and adapted rRNA depletion probes specifically for mouse cecal content.
Utilized a taxonomically-neutral probe design strategy.
Evaluated probe efficiency and consistency for MetaT analysis.

Main Results:

Human-based rRNA depletion probes showed reduced effectiveness in mouse cecal samples.
Adapted probes provided greater efficiency and consistency for mouse MetaT analysis.
The refined method increased the number of mRNA-rich sequencing reads.

Conclusions:

Targeted rRNA depletion probes are essential for accurate mouse microbiome MetaT analysis.
The developed method offers an efficient, consistent, and cost-effective solution for rRNA removal.
This advancement improves the functional profiling of mouse gut microbiomes.