KIND TO BIOME

Volunteer recruitment & sampling

Your study is only as good as the samples it starts with

We recruit from a database of 1,500+ pre-screened volunteers and sample every participant in-lab, under professional, standardised conditions. No home kits, no guesswork, because the way a sample is taken can change the result as much as the product being tested.[9]

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Gloved professional assessing a volunteer's shoulder skin with a dermatoscope
In-lab, professionally supervised skin assessment

A pre-screened panel, built for microbiome studies

Recruiting the right volunteers is usually the slowest, riskiest part of an in-vivo study. Our platform removes that bottleneck: a database of 1,500+ individuals, pre-screened and profiled by skin type, condition (e.g. acne-prone), demographics, medical history and skincare habits. That means we can assemble a cohort that genuinely matches your study criteria — quickly, and with the confounders already mapped.

1,500+
pre-screened volunteers
profiled by skin type, condition & history
Volunteer profiling
in-lab sampling only
Sampling policy

How recruitment works

  1. 1

    Define study criteria.

    We work with you to set precise inclusion and exclusion criteria: skin type, condition, age, sex, product-use history and anything else your endpoint demands.

  2. 2

    Database search & filtering.

    We filter the 1,500+ panel against those criteria to build a candidate shortlist that fits the study on paper.

  3. 3

    Pre-interview & profiling.

    We review detailed profiles — medical history, skincare habits, lifestyle factors — and pre-interview candidates, so the people who reach your study are a true match, not just a demographic tick-box.

  4. 4

    Final selection & invitation.

    We select a diverse, criteria-matched cohort and invite them in for controlled, in-lab sampling.

We sample in-lab, by professionals — and never by post

This is a deliberate quality decision, not a logistical one. In a head-to-head comparison in acne patients, three sampling approaches produced significantly different bacterial and fungal microbiome results from the same subjects — the method itself moved the data.[9] Skin is also a low-biomass surface, so casual or inconsistent sampling readily introduces contamination and variation that can masquerade as a product effect.

Controlling sampling in-lab removes those variables: consistent site, technique, timing and subject preparation, performed by trained professionals, every time. It is one of the biggest reasons methodological standardisation is repeatedly cited as the key to making skin-microbiome data trustworthy and comparable[4][9] — and it is why we don’t compromise on it.

Scientist in a KIND TO BIOME lab coat preparing a controlled sampling session

What controlled in-lab sampling protects

  • Comparability — every sample taken the same way, so differences reflect your product, not the swabbing.

  • Low contamination — professional technique on a low-biomass surface.

  • Clean confounder control — standardised prep and timing across the cohort.

Why self-sampling puts your whole study at risk

Home self-sampling is cheaper and faster — and it quietly undermines the data you’re paying to generate. Untrained volunteers vary in technique, timing, pressure, site and preparation; each of those is a known lever on microbiome results.[9] On a low-biomass surface like skin, that variability and contamination can produce or erase apparent effects, so a study built on self-sampled data may not be reproducible — and may not survive the scrutiny that a claim invites. When the sample is compromised, no amount of downstream sequencing or statistics can recover the truth, meaning that you might have to remove the volunteer from the study entirely and hence compromise your sample size. We remove that risk at the source.

FAQs

How big is your volunteer database?

1,500+ pre-screened individuals, profiled by skin type, condition, demographics, medical history and skincare habits.

Why does professional in-lab sampling matter so much?

Because sampling method is not neutral — different methods yield significantly different microbiome data from the same person.[9] In-lab sampling standardises site, technique, timing and preparation, so your results reflect the product, not the procedure.[4]

Isn't self-sampling good enough if the sequencing is high quality?

No. Sequencing quality can’t rescue a compromised sample. Variability and contamination introduced at collection propagate through the whole analysis, threatening reproducibility and claim substantiation.[9][10]

How fast can you recruit?

Because the panel is pre-screened and profiled, we move quickly from criteria to a matched cohort.

Can you match specific conditions, like acne-prone skin?

Yes — condition-matched recruitment (e.g. acne-prone volunteers) is a core capability and feeds directly into our acne and quorum-quenching study designs. The same applies for other conditions like rosacea, dandruff, atopic dermatitis etc. (See Acne-Targeting.)

Recruit for my study

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Why brands choose us

Interpretation, not just data.

Compositional-aware analysis and honest, plain-language readouts turn sequencing into decisions and defensible claims.[1][2][10]

A recruitment platform that de-risks the study.

1,500+ pre-screened, profiled volunteers, matched fast to your criteria.

Controlled in-lab sampling.

The single biggest guard against the variability and contamination that sink microbiome studies.[4][9]

Strain-level resolution, bacteria + fungi.

Full-length 16S + ITS on PacBio Revio HiFi — resolution that sees the effects genus-level data misses.[3]

Consultative study design.

The right balance of cost, data and insight, built to substantiate a claim.[10]

End-to-end delivery.

One partner from hypothesis to claim, cross-linked to acne and quorum-quenching capabilities.

References

  1. 1.Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome datasets are compositional: and this is not optional. Front Microbiol. 2017;8:2224. doi:10.3389/fmicb.2017.02224 (PMID: 29187837).
  2. 2.Weiss S, Xu ZZ, Peddada S, et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome. 2017;5(1):27. doi:10.1186/s40168-017-0237-y (PMID: 28253908).
  3. 3.Johnson JS, Spakowicz DJ, Hong BY, et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat Commun. 2019;10:5029. doi:10.1038/s41467-019-13036-1 (PMID: 31695033).
  4. 4.Elbeshbishy RSIA. The skin microbiome: the overlooked axis in modern dermatology. Ann Med Surg. 2026. PMID: 42254217.
  5. 5.Callahan BJ, Wong J, Heiner C, et al. High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution. Nucleic Acids Res. 2019;47(18):e103. doi:10.1093/nar/gkz569 (PMID: 31269198).
  6. 6.Byrd AL, Belkaid Y, Segre JA. The human skin microbiome. Nat Rev Microbiol. 2018;16(3):143–155. doi:10.1038/nrmicro.2017.157 (PMID: 29332945).
  7. 7.Fitz-Gibbon S, Tomida S, Chiu BH, et al. Propionibacterium acnes strain populations in the human skin microbiome associated with acne. J Invest Dermatol. 2013;133(9):2152–2160. doi:10.1038/jid.2013.21 (PMID: 23337890).
  8. 8.(Reserved — inter-individual variability is cited to Byrd 2018 [6]; no separate citation.)
  9. 9.Xu DT, et al. Microbiome data variation depending on sampling methods in acne vulgaris. Front Immunol. 2025;16:1566786. doi:10.3389/fimmu.2025.1566786 (PMID: 40552289).
  10. 10.Commission Regulation (EU) No 655/2013 laying down common criteria for the justification of claims used in relation to cosmetic products. Official Journal of the European Union, 2013. (Claims must rest on “adequate and verifiable evidence” from valid, reliable, reproducible methods.)