Identifying regulators of aged fibroblast activation in 3D tissue models.

Hui Liu, Luezhen Yuan, G V Shivashankar

Abstract read

In one paragraph

Article in Scientific reports, 2025. 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

3 authors.

Hui LiuDivision of Biology and Chemistry, Paul Scherrer Institut, 5232, Villigen, Switzerland.

Luezhen YuanDivision of Biology and Chemistry, Paul Scherrer Institut, 5232, Villigen, Switzerland.

G V ShivashankarDivision of Biology and Chemistry, Paul Scherrer Institut, 5232, Villigen, Switzerland. gshivasha@ethz.ch.

Funding

China Scholarship Council 202008440471Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 310030_208046

6 · The paper itself

Abstract

Robust 3D tissue culture models to activate aged fibroblasts for cell-based therapies and identify regulators of such activation are still missing. In our previous study, we showed that aged fibroblasts can be activated simply through applying compressive force, without the need for exogenous factors, leading to increased migration. In this study, we develop a pipeline to evaluate the role of specific pharmacological inhibitors for transcription factor inference and cell migration involved in aged fibroblast activation. By integrating RNA-seq data with bioinformatic tools (prize collecting Steiner tree method and iRegulon) we inferred 15 candidates. In addition, we used cell migration and heterochromatin content as readouts for validating these candidates. Furthermore, we identified three potential master regulators of fibroblast activation and rejuvenation: FOXO1, STAT3, and PDK1. These findings offer valuable insights for future drug discovery, disease modeling, and regenerative medicine.

Indexed as

Cellular SenescenceFibroblastsAnimalsCell Culture Techniques, Three DimensionalCell MovementComputational BiologyForkhead Box Protein O1HumansSTAT3 Transcription FactorForkhead Box Protein O1STAT3 protein, humanSTAT3 Transcription FactorDrug screeningHuman dermal fibroblastsMechanical rejuvenationTranscript factor

Identifiers

PMID41298573

DOI10.1038/s41598-025-25997-z

PMCPMC12657520

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LicenceCC BY

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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.