BEST PRACTICES IN SAMPLE COLLECTION FOR ACCURATE 16S RRNA SEQUENCING RESULTS

Accurate microbiome data starts way before the DNA is sent to the sequencer. However, many researchers make errors in processing, storage, or transportation. This may cause systematic bias that cannot be fixed by any downstream bioinformatics pipeline. 

Designing a study that involves 16S rRNA sequencing is very sensitive. You must control variability when collecting data to guarantee reproducible results. 

Read on as we discuss the best practices below.

Control pre-analytical variability at the source

The greatest source of bias is usually when collecting samples. Microbial communities change quickly when they are taken out of their natural location. Relative abundance profiles can be affected by:

  • Oxygen exposure
  • Temperature variations 
  • Time delays.

You should:

  • Apply standard collection kits to all cohorts.
  • Mark specific site of collection, e.g., stool homogenization protocol or a specific location on the skin.
  • Write explicit guidelines on multi-site or participant-led sampling.

Sampling depth or location inconsistencies also cause compositional variation. It may be mistaken for a biological signal. 

Make sure that the samples are taken at the same time. In addition, under the same physiological conditions. This will minimize temporal variability.

Reduce contamination risks

Background contamination is particularly possible in low-biomass samples, such as:

  • Environmental swabs
  • Airway samples
  • Tissue biopsies. 

Best practices include:

  • Working with sterile and DNA-free consumables.
  • Performing negative controls and processing of biological samples.
  • Adopting clean-room or laminar flow processes of low-input material

Contaminant taxa might be mistaken for valid results in 16S rRNA sequencing datasets. Unless appropriate controls are in place. 

As a researcher, you can add extraction blanks and sequencing controls that will help you detect and remove background noise computationally.

Maintain microbial composition as soon as possible

Unless samples are stabilized, the microbial DNA integrity and community structure deteriorate rapidly. Post-collection microbial growth or die-off is possible.

To prevent this:

  • Freeze samples at -20 °C. Better still, at -80 °C.
  • When it is not possible to freeze immediately, use tested stabilization buffers.
  • Avoid repetitive freeze-thaw.

It is wrong to store samples at room temperature without stabilization. This may cause major changes in the apparent microbial diversity. 

Standardize transport conditions

There is an additional layer of variability brought about by transport. Shipment temperature variations may lead to:

  • Selective overgrowth of aerotolerant microorganisms
  • A decline of anaerobes.

You should:

  • Transport samples on dry ice where feasible.
  • Apply temperature-monitoring technology for clinical research.
  • Specify maximum transit times during studies.

Poor delivery and stabilization can affect the quality of the DNA. This directly affects the accuracy and reproducibility of 16S rRNA gene sequencing.

Maintain standardized DNA extraction procedures

There are different lysis procedures for different microbial communities. Let’s take the example of gram-positive bacteria. These need a strong mechanical disruption to obtain DNA efficiently.

Use:

  • Tried and tested extraction kits that best suit your sample.
  • Mechanical lysis, e.g., bead beating, when profiling complex communities.
  • One extraction protocol for all samples of a study.

Avoid changing kits or altering protocols halfway through the study. This presents batch effects that can mask biological variation.

Record and validate the workflow

Lastly, consider your sampling protocol as an experimental variable. Do pilot-test studies before committing to larger studies. Also, record metrics like:

  • Collection date
  • Length of storage
  • Thaw-freeze cycles. 

Such details enable you to interpret sudden variation.

Back to blog