Evidence map›Paper›PMID 40038838›Full record

ArticleMicrobiome2025

Absolute quantification of the living skin microbiome overcomes relic-DNA bias and reveals specific patterns across volunteers.

Deepan Thiruppathy, Oriane Moyne, Clarisse Marotz, Michael Williams, Perris Navarro, Livia Zaramela, Karsten Zengler

Abstract read
In one paragraph

Article in Microbiome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Altering our (soils') future.EMBO reports · 2026
    Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Single-cell quantification of the microbiota by flow cytometry: MicFLY.bioRxiv : the preprint server for biology · 2025
    Article
  9. Article
  10. Article
  11. SkinDuoInternational journal of molecular sciences · 2025
    Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Deepan Thiruppathy *Department of Bioengineering, University of California San Diego, La Jolla, San Diego, CA, 92093, USA.
Oriane Moyne *Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, San Diego, CA, 92093, USA.
Clarisse MarotzDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, San Diego, CA, 92093, USA.
Michael WilliamsDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, San Diego, CA, 92093, USA.
Perris NavarroDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, San Diego, CA, 92093, USA.
Livia ZaramelaDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, San Diego, CA, 92093, USA.
Karsten ZenglerDepartment of Bioengineering, University of California San Diego, La Jolla, San Diego, CA, 92093, USA. kzengler@ucsd.edu.

Funding

Illumina NovaSeq 6000 Sequencing SystemS10OD026929 · OD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JEPSEN, KRISTEN LYNN · 2019 to 2019
$600k
NIH HHS S10 OD026929
6 · The paper itself

Abstract

backgroundAs the first line of defense against external pathogens, the skin and its resident microbiota are responsible for protection and eubiosis. Innovations in DNA sequencing have significantly increased our knowledge of the skin microbiome. However, current characterizations do not discriminate between DNA from live cells and remnant DNA from dead organisms (relic DNA), resulting in a combined readout of all microorganisms that were and are currently present on the skin rather than the actual living population of the microbiome. Additionally, most methods lack the capability for absolute quantification of the microbial load on the skin, complicating the extrapolation of clinically relevant information.

resultsHere, we integrated relic-DNA depletion with shotgun metagenomics and bacterial load determination to quantify live bacterial cell abundances across different skin sites. Though we discovered up to 90% of microbial DNA from the skin to be relic DNA, we saw no significant effect of this on the relative abundances of taxa determined by shotgun sequencing. Relic-DNA depletion prior to sequencing strengthened underlying patterns between microbiomes across volunteers and reduced intraindividual similarity. We determined the absolute abundance and the fraction of population alive for several common skin taxa across body sites and found taxa-specific differential abundance of live bacteria across regions to be different from estimates generated by total DNA (live + dead) sequencing.

conclusionsOur results reveal the significant bias relic DNA has on the quantification of low biomass samples like the skin. The reduced intraindividual similarity across samples following relic-DNA depletion highlights the bias introduced by traditional (total DNA) sequencing in diversity comparisons across samples. The divergent levels of cell viability measured across different skin sites, along with the inconsistencies in taxa differential abundance determined by total vs live cell DNA sequencing, suggest an important hypothesis for certain sites being susceptible to pathogen infection. Overall, our study demonstrates a characterization of the skin microbiome that overcomes relic-DNA bias to provide a baseline for live microbiota that will further improve mechanistic studies of infection, disease progression, and the design of therapies for the skin. Video Abstract.

Indexed as

BacteriaDNA, BacterialMetagenomicsMicrobiotaSkinAdultBacterial LoadFemaleHealthy VolunteersHumansMaleSequence Analysis, DNASkin MicrobiomeDNA, BacterialAbsolute abundanceFlow cytometryMetagenomicsRelic DNASkin microbiome

Identifiers

PMID40038838
PMCPMC11877739

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.