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Pathogen Gene Expression Profiling During Infection Using a Nanostring nCounter Platform.

Wenjie Xu1, Norma V Solis2, Scott G Filler2

  • 1Department of Biological Sciences, Carnegie Mellon University, 4400 Fifth Avenue, MI 200B, Pittsburgh, PA, 15213, USA.

Methods in Molecular Biology (Clifton, N.J.)
|October 21, 2015
PubMed
Summary
This summary is machine-generated.

The NanoString nCounter platform effectively quantifies pathogen gene expression, even with minimal pathogen RNA present in infected tissues. This method is validated for various fungal pathogens in multiple infection models.

Keywords:
Gene expressionIn vivo profilingInfectionNanoStringPathogenRNA isolation

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Area of Science:

  • Molecular Biology
  • Infectious Diseases
  • Mycology

Background:

  • Accurate quantification of pathogen gene expression is crucial for understanding host-pathogen interactions during infection.
  • Existing methods can be challenged by the low abundance of pathogen RNA within total RNA extracted from infected tissues.
  • The NanoString nCounter platform offers direct multiplexed measurement of gene expression.

Purpose of the Study:

  • To evaluate the suitability of the NanoString nCounter platform for quantifying pathogen gene expression in infection models.
  • To describe a protocol for pathogen gene expression analysis using NanoString nCounter.
  • To demonstrate the application of this protocol across different fungal pathogens and infection types.

Main Methods:

  • Utilized the NanoString nCounter platform with color-coded probe pairs for direct gene expression measurement.
  • Applied a developed protocol to analyze gene expression in various fungal pathogens.
  • Tested the protocol in multiple infection models, including invasive and mucosal infections.

Main Results:

  • The NanoString nCounter platform proved uniquely suitable for quantifying pathogen gene expression, even when pathogen RNA constitutes a small fraction of total RNA.
  • Successfully applied the protocol to analyze gene expression in Candida albicans, Aspergillus fumigatus, and Cryptococcus neoformans.
  • Demonstrated efficacy across diverse infection models, including mucosal and invasive infections.

Conclusions:

  • The NanoString nCounter platform provides a robust method for quantifying pathogen gene expression in complex biological samples.
  • The described protocol is effective for studying gene expression in medically relevant fungal pathogens during infection.
  • This approach facilitates a deeper understanding of pathogen biology and host responses in various infection settings.