Evidence map›Paper›PMID 42454934›Full record

ArticlemSystems2026

MATRIX: rapid quantification of total and active microbial cells with single-cell phenotypes for environmental microbiomes.

Milena Gonzalo, Xipeng Liu, Yann S Dufour, Ashley Shade

Abstract read
In one paragraph

Article in mSystems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Milena GonzaloLEM, UMR 5557, UMR 1418, Université Lyon 1, CNRS, INRAE, Villeurbanne, France.ORCID 0000-0003-0411-9335
Xipeng LiuLEM, UMR 5557, UMR 1418, Université Lyon 1, CNRS, INRAE, Villeurbanne, France.ORCID 0000-0003-3121-715X
Yann S DufourDatInsights, Lyon, France.ORCID 0000-0003-0244-0192
Ashley ShadeLEM, UMR 5557, UMR 1418, Université Lyon 1, CNRS, INRAE, Villeurbanne, France.ORCID 0000-0002-7189-3067

Funding

HORIZON EUROPE European Research Council 101087042U.S. Department of Agriculture 2024-67019-42477
6 · The paper itself

Abstract

Quantifying the abundance and activity of bacteria within populations and communities is fundamental to systems microbiology and microbiome research. Yet direct microscopic cell counting remains low throughput, labor-intensive, and prone to user variability, leading many researchers to rely on indirect proxies such as optical density or multicopy marker-gene quantification. These indirect approaches do not distinguish between active and inactive cells and can obscure ecological interpretation. Here, we introduce IMPORTANCE: Microbiome studies commonly rely on relative abundance data, which cannot distinguish whether compositional shifts reflect true population growth, declines in total community size, or both. Without explicit measurements of population and community sizes, the mechanistic interpretation of microbiome dynamics remains incomplete. Here, we present a rapid, throughput workflow,

Indexed as

BacteriaMicrobiotaSingle-Cell AnalysisBayes TheoremImage Processing, Computer-AssistedPhenotypeReproducibility of ResultsSoil MicrobiologybacteriaBayesian modelingcell countscommunity sizedormancyfluorescence microscopyimage analysispopulation sizeredoxrespirationRSGsingle-cellsoil microbiomeSYBR

Identifiers

PMID42454934
PMCPMC13483317

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.