AT-HOME, SELF-SAMPLING OF THE SKIN MICROBIOME: DEVELOPMENT OF AN UNSUPERVISED SAMPLING APPROACH

preprint OA: closed
Full text JSON View at publisher
Full text 1,964 characters · extracted from oa-doi-fallback · click to expand
Full text loading... 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 data quality with supervised, in lab sample collection. We enrolled 57 participants to collect skin swabs of their axilla, forearm, cheek and scalp. 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 hours 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 identified also did not differ for axilla, cheek or scalp. Our data suggest that self-sampling skin microbiome methods can produce data that is comparable to samples collected under supervision of a trained expert in lab settings. These findings should encourage scalability of future research and allow for greater representative population diversity in genomic and microbiome research. - Received: - Version Posted: Funding - Innovate UK (Award KTP Partnership number 13284) - Principal Award Recipient: Anna Thomas

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00