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Preservation Methods Differ in Fecal Microbiome Stability, Affecting Suitability for Field Studies.

Se Jin Song1, Amnon Amir2, Jessica L Metcalf1

  • 1Department of Pediatrics, University of California-San Diego, La Jolla, California, USA; Department of Ecology and Evolutionary Biology, University of Colorado-Boulder, Boulder, Colorado, USA.

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Field-friendly methods like 95% ethanol, FTA cards, and OMNIgene Gut kits effectively preserve stool microbiome stability for up to 8 weeks. These cost-effective options enable crucial microbiome research in diverse ecological and anthropological field studies.

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Area of Science:

  • Microbiology
  • Environmental Science
  • Conservation Biology

Background:

  • Immediate freezing is the standard for microbiome preservation but impractical for field studies.
  • Field conditions involve temperature fluctuations and freeze-thaw cycles, challenging sample integrity.

Purpose of the Study:

  • To evaluate five preservation methods for human and dog fecal samples under simulated field conditions.
  • To determine the stability of microbiome data using different storage methods over 8 weeks.

Main Methods:

  • Tested five preservation techniques: 95% ethanol, 70% ethanol, FTA cards, OMNIgene Gut kit, and no fixative.
  • Samples were stored for up to 8 weeks with simulated freeze-thaw cycles and temperature variations.
  • Microbiome composition was analyzed to assess the impact of storage methods.

Main Results:

  • 95% ethanol, FTA cards, and OMNIgene Gut kit maintained microbiome stability comparable to technical replicates at 8 weeks.
  • Even suboptimal methods distinguished between species and individuals after short storage periods.
  • Storage effects with FTA cards were systematic and correctable; 70% ethanol is not recommended.

Conclusions:

  • Cost-effective preservation methods enable robust microbiome analysis in field settings.
  • These findings support broader microbiome research in human and wildlife studies.
  • Standardized methods are recommended to minimize batch effects in meta-analyses.