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Related Experiment Videos

"Per cell" normalization method for mRNA measurement by quantitative PCR and microarrays.

Jun Kanno1, Ken-ichi Aisaki, Katsuhide Igarashi

  • 1Division of Cellular and Molecular Toxicology, National Institute of Health Sciences, 1-18-1, Kamiyoga, Tokyo 158-8501, Japan. kanno@nihs.go.jp

BMC Genomics
|March 31, 2006
PubMed
Summary

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The Percellome method normalizes mRNA expression data to provide absolute copy numbers per cell. This approach ensures accurate comparisons across different samples, platforms, and studies, improving data reproducibility and standardization.

Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Quantitative PCR (Q-PCR) and DNA microarray data are typically normalized to RNA quantity.
  • Variations in tissue cellularity and RNA yield complicate accurate absolute mRNA quantification per cell.
  • Existing methods struggle to provide consistent, absolute mRNA expression levels across diverse samples.

Purpose of the Study:

  • To develop a novel method for normalizing mRNA expression data.
  • To enable absolute mRNA quantification on a per-cell basis.
  • To improve the comparability of gene expression data across different experimental conditions and platforms.

Main Methods:

  • The Percellome method was developed for normalizing mRNA expression.
  • Genomic DNA content was measured to determine cell number per sample.

Related Experiment Videos

  • A dose-graded spike cocktail (GSC) of RNAs was added proportionally to sample DNA content.
  • Spike RNAs served as an internal standard to convert measured signals into absolute mRNA copy numbers per cell.

    • Main Results:

    • The Percellome method provides a "per cell" readout of mRNA copy number.
    • This normalization is applicable to both quantitative PCR (Q-PCR) and DNA microarray analyses.
    • Measurements using the Percellome method on Q-PCR and Affymetrix GeneChip microarrays showed up to 90% concordance.

    Conclusions:

    • Percellome data allows direct comparison of mRNA expression across samples, studies, and platforms.
    • This normalization method eliminates the need for further data adjustments.
    • Percellome normalization establishes a standard for data exchange and comparison in molecular biology research.