Best Practices For Microbiome Experiment Study Design
Preclinical experiments using animal models are an essential part of the drug discovery process. However, their utility to inform further work is conditional on robust design and reproducibility. One factor which can significantly impact animal model reproducibility is the animal model microbiome. In fact, studies have shown that changes to animal microbiome can lead to changes in, or even loss of, phenotype.
Study design for microbiome experiments
Minimising unwanted variation in an experiment is dependent on good experimental design. The principles for good experimental design in a microbiome study are the same as for any other experiment. The design should focus on reproducibility, appropriate controls, control for confounders and randomisation.
Variables which can influence a lab animal’s microbiome include:
- Biological factors (Age & gender)
- Diet (Food formulation, shelf-life, manufacturer, sterilisation)
- Husbandry (Housing, bedding, water, density)
- Source (Vendor, collaborator)
- Mode of Delivery (Whether the animal model was born naturally or via caesarean-section)
- Iatrogenic Effects (Therapeutic, experimental)
How to minimise variation in animal model microbiomes:
- Control what you can (For example, by freezing all food needed for an experiment from a single batch)
- Standardise microbiomes (Where you have animal models from different batches you can cross-breed these and use the f2 generation for the experiment)
- Document all potential sources of variation (This information can then be used to test for associations with your variable of interest)
- Bank fecal samples (These can then be retrospectively profiled if you suspect a shift in the microbiome)
- Blocked experimental design (To account for nuisance variables)
Why are microbiome studies becoming more popular?
Since 2010 there has been exponential growth in the number of microbiome-related publications due to growing interest in the role of the microbiome in human health, particularly in regards to immune function, host-pathogen interactions, and metabolic disease. Progress has been driven by advances in sequencing technology. It is now possible to rapidly and economically perform 16s sequencing or shotgun metagenomics to get a high-resolution insight into the organisms and functional pathways that are present in a given microbiome.
Microbiome and bioinformatics
Fios Genomics can assist you in the early stages of experimental design to discuss how best to set up an experiment to derive robust data. Our bioinformaticians can then work with your raw sequencing data, performing full quality control and quantifying the relevant abundance of taxa on an individual sample level. Our team perform appropriate statistical tests to uncover differentially abundant taxa between your treatment groups of interest and can also identify function pathways that may differ between your treatment groups of interest.
Your results will be presented in a rich HTML fully-interactive report like this Microbiome Analysis Sample Report.
If you have a microbiome project that you would like to discuss, please contact us.
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Access our Bioinformatics Analysis Reports
Fill in the form below to access the data analysis report our team created. Here, the analysis of 16S rRNA sequencing data helped to compare the microbiomes of celiac disease patients (CeD) to those of first-degree relatives of celiac patients (FDR)
