Background: Recently, there has been tremendous interest in the sinus microbiome and how it relates to disease. However, a lack of a standardized sample collection and DNA extraction methods makes comparison of results across studies nearly impossible. Furthermore, current techniques fail to identify which components of the microbiome are actually alive within the host at the time of sampling.
Objective: To develop and optimize a method to differentiate which bacterial species in the human sinus microbiome are live versus dead.
Methods: Duplicate samples from the middle meatus of patients with healthy sinus tissue and those patients with chronic rhinosinusitis were collected by using brushes (n = 12), swabs (n = 27), and tissue biopsy (n = 8) methods. One sample from each pair was either deoxyribonuclease I- or control-treated before DNA extraction. The relative bacterial versus human composition of each sample was determined. A 16S ribosomal RNA gene analysis was performed on a six-paired sample from patients with healthy sinus tissue.
Results: We found that swabs and brushes collected a higher percentage of bacterial DNA than did tissue biopsy. We also determined that as much as 50% of the bacteria collected in these samples was already dead at the time of collection. The 16S ribosomal RNA gene analysis found significant changes in the relative abundance of taxa identified in the live versus dead bacterial communities of healthy human sinuses.
Conclusions: Our findings indicated that swabs provided the best quality microbiome samples and that a large portion of the bacteria identified in the sinus were deoxyribonuclease I sensitive. These results highlighted the need for improved techniques such as those presented here, which can differentiate between living and dead bacteria in a sample, a potentially critical distinction when examining changes in sinus innate immune function because both components play important, but distinct, functions. Further studies will determine how these living and dead bacterial populations shift in different disease states and after clinical intervention.