Evidence map›Paper›PMID 42096154›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Dual Fluorescent Labeling for Bacterial Aggregation Assays.

Anna M Tingler, Erin C Chard, Kate Psenka, Melinda A Engevik

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Anna M TinglerDepartment of Regenerative Medicine & Cell Biology, Medical University of South Carolina, Charleston, SC, USA.
Erin C ChardDepartment of Regenerative Medicine & Cell Biology, Medical University of South Carolina, Charleston, SC, USA.
Kate PsenkaDepartment of Regenerative Medicine & Cell Biology, Medical University of South Carolina, Charleston, SC, USA.
Melinda A EngevikDepartment of Regenerative Medicine & Cell Biology, Medical University of South Carolina, Charleston, SC, USA. engevik@musc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clostridioides difficile is an anaerobic, spore-forming pathogen that causes severe gastrointestinal disease. During infection, C. difficile interacts with other gut microbes within the intestinal mucus layer, which potentially influences colonization and disease progression. Here, we present a fluorescence-based protocol to examine bacterial adhesion and aggregation between C. difficile and other gut bacteria, such as Fusobacterium nucleatum. In this method, C. difficile is labeled with the green fluorescent dye CFDA-SE (carboxyfluorescein diacetate succinimidyl ester) and F. nucleatum or other bacterial partners are labeled with the red fluorescent dye CellTracker™ Orange CMRA (2,6-Bis-(4-chloro-phenyl)-isonicotinic acid 4-(1-carboxy-3-methyl-butoxy)-phenyl ester.) Following labeling, bacterial cultures are co-incubated in aggregation buffer, and aggregation is assessed by fluorescence microscopy. Alternatively, the labeled bacteria can be applied to cultured epithelial cells to evaluate both aggregation and host adhesion. This approach enables direct visualization and quantification of interspecies interactions, providing valuable insights into how pathobionts exploit interbacterial adhesion to persist and thrive within polymicrobial intestinal communities.

Indexed as

Bacterial AdhesionClostridioides difficileFluorescent DyesFusobacterium nucleatumStaining and LabelingEpithelial CellsHumansMicroscopy, FluorescenceFluorescent DyesAdhesionAggregationClostridioides difficileFluorescence

Identifiers

What OpenQuestion holds

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Registered trials

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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.