Evidence map›Paper›PMID 41907184›Full record

ArticleJournal of respiratory biology and translational medicine2026

Genetic Strategies for Labeling AT2 Cells in Murine Lung via Abca3 and Etv5-Driven Reporters.

Xue Liu, Xuexi Zhang, Zekun Li, Vrishika Kulur, Ningshan Liu, Jiurong Liang, Dianhua Jiang

Abstract read
In one paragraph

Article in Journal of respiratory biology and translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

7 authors.

Xue LiuWomen's Guild Lung Institute, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
Xuexi ZhangWomen's Guild Lung Institute, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
Zekun LiWomen's Guild Lung Institute, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
Vrishika KulurWomen's Guild Lung Institute, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
Ningshan LiuWomen's Guild Lung Institute, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
Jiurong LiangWomen's Guild Lung Institute, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
Dianhua JiangWomen's Guild Lung Institute, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.

Funding

ZIP8-dependent Zinc Metabolic Regulation in Alveolar Progenitor Cell Aging and FibrosisR01AG078655 · NIA · CEDARS-SINAI MEDICAL CENTER · PI Carol Jiurong Liang, Paul Wesley Noble · 2022 to 2026
$2.7M
Targeting WNT7A Signaling in Pulmonary FibrosisR01HL172990 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI Dianhua Jiang · 2024 to 2026
$2.3M
NHLBI NIH HHS R01 HL172990NIA NIH HHS R01 AG078655
6 · The paper itself

Abstract

Precise labeling of alveolar type 2 (AT2) cells is essential for elucidating lung development and injury responses. In this study, we evaluated Abca3 and Etv5-based genetic strategies for labeling AT2 cells in murine models. Using targeted genetic approaches, we generated Abca3-rtTA and Etv5-rtTA knock-in mouse lines and crossed them with pTRE-H2BGFP to create inducible reporter models driven by Abca3 or Etv5. Labeling specificity and efficiency were assessed by flow cytometry and co-immunostaining. Our results show that both Abca3 and Etv5 strategies faithfully label AT2 cells across developmental stages and following lung injury. Comprehensive analyses confirmed the high specificity and efficiency of labeling. These Abca3- and Etv5-driven systems offer robust tools for investigating AT2 cell biology and pathology and may serve as effective drivers for tetO-mediated gene knockout or overexpression studies specifically in AT2 cells in mouse models.

Indexed as

Abca3Alveolar type 2 (AT2) cellsBleomycin injuryEtv5Lung development

Identifiers

PMID41907184
PMCPMC13025979

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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.