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At-home, self-sampling of the skin microbiome: development of an unsupervised sampling approach

Year: 2025
Type: article
Abstract: Large-scale skin microbiome studies are often restricted due to the need for participants to visit a research centre to have their skin swabbed by a trained individual. If samples taken by participants at home returned high-quality data, similar to that generated from samples taken by trained experts under controlled conditions, it would provide the potential for studies to have larger cohorts, include participants from multiple locations and facilitate longitudinal sample collection. Here, we describe the development of a novel unsupervised skin microbiome sample collection method and compare the data quality with that of supervised, in-lab sample collection. We enrolled 57 participants to collect skin swabs from their axillae, forearms, cheeks and scalps. Initially, samples were collected in our research centre under strict supervision by a trained expert. Participants then collected swabs from the same body sites 24 h later, unsupervised, at home, which they returned to the research centre within 3-5 days. All samples then underwent bacterial DNA extraction and 16S rRNA gene sequencing. Yield of extracted bacterial DNA was different depending on body site, with the dry swabs from the forearm producing the lowest amount. There were no significant differences in alpha and beta-diversities between supervised and unsupervised sampling methods, regardless of body site. Taxonomic analysis of bacterial genera also did not differ for axilla, cheek or scalp. Our data suggest that self-sampling skin microbiome methods can produce data that are comparable to samples collected under the supervision of a trained expert in lab settings. These findings should encourage the scalability of future research and allow for greater representative population diversity in genomic and microbiome research.
Language: English
Subfield: Dermatology
Field: Medicine
Open Access status: gold
Estimated APC paid: $858
Funder: Innovate UK
OpenAlex ID: W4413242909
FWCI:
Cites: 24
Cited by:
Related to: 10