KIND TO BIOME

In-vivo microbiome studies

Anyone can sequence a microbiome. The value is knowing what it means.

Skin microbiome data is compositional, noisy, and unforgiving of shortcuts — a raw species table is not an answer. We run end-to-end in-vivo studies on real, acne-and-condition-matched volunteers, and turn the sequencing into a defensible story: what your product did to the skin ecosystem, whether it holds up statistically, and which claims it will support.

See how we recruit volunteers
Close-up of a volunteer's neck and décolleté skin during an in-vivo microbiome study

Your data is only as good as the thinking and analysis behind it

Two studies can use the same swabs, the same sequencer and the same reference database, and reach opposite conclusions. The difference is method and interpretation. Microbiome datasets are compositional: they describe relative proportions, not absolute counts, so naïve analysis routinely produces artefacts that look like real biological effects.[1] The choice of normalisation and differential-abundance method materially changes which “significant” findings you get.[2] And skin communities vary enormously between individuals and body sites, which can swamp a genuine product effect if a study isn’t designed and read correctly.[6][8]

This is exactly where we earn our keep. We don’t hand you a data dump; we design the study to detect the effect you care about, analyse it with methods appropriate to compositional data, and tell you, plainly, what is real, what is noise, and what it means for your product.[1][2]

Compositional by nature

Microbiome data describes relative proportions, not absolute counts. Analysed naïvely, it produces artefacts that look like real biology.[1]

High person-to-person variability

Skin communities differ enormously between individuals and body sites, which can swamp a genuine product effect.[6][8]

Interpretation is a skill, not a button.

Normalisation and differential-abundance choices change which findings come out “significant”. Judgement decides what is real.[2]

Resolution that reaches the strain — bacteria and fungi

We sequence full-length 16S rRNA (bacteria) and ITS (fungi) using HiFi long-read sequencing on the PacBio Revio platform. That matters more than it sounds:

PacBio Revio long-read sequencer used for full-length 16S and ITS microbiome profiling
sequencing platform

  • Species- and strain-level resolution, not just genus.

    Short-read 16S typically resolves only to genus; full-length 16S has been shown to deliver taxonomic resolution down to species and strain level.[3] For skin, that is decisive — acne, for example, is driven by the balance of C. acnes strains, not the total amount, so genus- or species-only data can miss the entire effect.[3][7] (See our Acne-Targeting page.)

  • Bacteria and fungi together.

    Adding ITS fungal profiling to 16S gives a fuller picture of the community particularly relevant to some body sites — and different sampling and sequencing choices are known to shift both bacterial and fungal readouts, so getting this right is not optional.[9]

  • A pipeline built for low-biomass skin.

    High-quality DNA extraction, rigorous QC, amplicon library prep, taxonomic profiling, alpha diversity (richness and evenness), beta diversity (weighted and unweighted), and appropriate statistical analysis — the full chain, controlled end to end.

Platform
PacBio Revio HiFi
Targets
full-length 16S + ITS
Readouts
taxonomy, alpha/beta diversity, differential abundance

We help you navigate the trade-off between cost, data and insight

More data is not automatically more insight. A study over-powered on sample count but under-designed on timepoints, or one that sequences deeply but samples the wrong site, wastes budget and still can’t support a claim. We help you find the right point on the curve for your question and your claim, number of volunteers, timepoints, sampling sites, sequencing depth, so you pay for evidence that will actually stand up, not for data you can’t use.

Because we design with the end in mind, the output is built to substantiate claims: under EU cosmetic-claims rules, a claim must rest on “adequate and verifiable evidence” using well-designed, reliable and reproducible methods, which is precisely what a properly designed and interpreted in-vivo study delivers.[10]

Most importantly, the final reports will have a conclusion you can easily understand and translate to a product claim. Most laboratories that offer such studies stop short of that conclusion and merely lay out data, which only a trained DNA-microbiologist together with a statistician could interpret.

Cost, data and insight trade-offA triangle with cost, data and insight at its corners and the optimal study design marked inside.InsightCostDataRight point
Finding the right point on the cost / data / insight curve

End to end, with a scientist beside you the whole way

  1. 1

    Scope & design.

    We translate your product hypothesis and target claim into a study that can actually test it — endpoints, power, timepoints, controls.

  2. 2

    Recruit & match.

    We draw condition-matched, pre-screened volunteers from our panel. (How we do this.)

  3. 3

    In-lab sampling.

    Professionals sample under controlled conditions, so your data is clean and comparable — not corrupted at the source.[9]

  4. 4

    Sequence.

    Full-length 16S + ITS on PacBio Revio HiFi.[3]

  5. 5

    Analyse.

    Compositional-aware statistics: diversity, differential abundance, effect sizes — done properly.[1][2]

  6. 6

    Interpret & report.

    A plain-language readout of what happened, what’s significant, and what you can claim, not a raw table.

Clean data starts before the sequencer

Every result on this page depends on one thing most labs treat as an afterthought: the sample. We recruit from a database of 1,500+ pre-screened volunteers and sample every one of them in-lab, under professional, standardised conditions, because sampling method alone can significantly change the microbiome data you get.[9]

Ethics & data

Studies are conducted with ethics-committee oversight and informed consent, and all volunteer data is handled in compliance with the GDPR.

See how our recruitment platform works

FAQs

What am I really paying you for?

Design and interpretation. Sequencing is a commodity; correct compositional analysis, a study powered to detect a real effect against high inter-individual variability, and a defensible link from data to claim are not.[1][2][6] A cheaper raw dataset you can’t interpret — or can’t substantiate a claim with — is more expensive in the end.

Why full-length 16S and ITS rather than standard short-read 16S?

Full-length 16S reaches species and strain level, where short-read 16S usually stops at genus[3] — and for skin conditions like acne the meaningful signal is at the strain level.[7] Adding ITS captures fungi, for a fuller community picture particularly relevant to some body sites.[9]

How do you make sure a "significant" result is actually real?

By using methods matched to compositional data, reporting effect sizes and diversity properly, and designing the study to separate a product effect from natural person-to-person variation.[1][2][6] We tell you candidly where the evidence is strong and where it isn’t.

Can the results support marketing claims?

Yes — that’s the point. We design studies to produce the “adequate and verifiable evidence” EU claims rules require, and we help frame what the data can and cannot support.[10] (See claims framing on our Acne-Targeting page.)

What our clients say

“KIND TO BIOME provided invaluable insights into understanding the microbiome and assisted us in formulating strategies around effective protocols. We utilized a combination of In Vitro, Ex Vivo, and In Vivo studies to achieve comprehensive results.”
John Milligan
John MilliganVP R&D — The Honey Pot CompanyThe Honey Pot Company logo

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.)