Evidence map›Paper›PMID 42734747›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2027

A Defined Strategy for Multi-Lineage Differentiation of Gastric Organoids.

Nanshan Zhong, Fan Hong, Yiran Luo, Ye-Guang Chen

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2027. 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.

Nanshan ZhongThe MOE Basic Research and Innovation Center for the Targeted Therapeutics of Solid Tumors, School of Basic Medical Sciences, Jiangxi Medical College, Nanchang University, Nanchang, 330031, China.
Fan HongGuangzhou National Laboratory, Guangzhou, 510005, China.
Yiran LuoThe MOE Basic Research and Innovation Center for the Targeted Therapeutics of Solid Tumors, School of Basic Medical Sciences, Jiangxi Medical College, Nanchang University, Nanchang, 330031, China.
Ye-Guang ChenThe MOE Basic Research and Innovation Center for the Targeted Therapeutics of Solid Tumors, School of Basic Medical Sciences, Jiangxi Medical College, Nanchang University, Nanchang, 330031, China. ygchen@tsinghua.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the gastric glands, adult stem cells differentiate into multiple epithelial lineages, including pit mucous cells, acid-secreting parietal cells, and enzyme-producing chief cells. Efficient and controllable lineage specification from adult gastric stem cells is essential for modeling gastric homeostasis and disease in vitro. Organoids are a good model for investigating cell differentiation and organ formation. However, methods to achieve appropriate differentiation of multiple gastric lineages are inefficient. Here, we provide a detailed stepwise protocol for the differentiation of functional cells in human or mouse gastric organoids through targeted modulation of key signaling pathways. The workflow enables reproducible generation of distinct gastric epithelial cell types and provides a tractable platform to dissect signaling requirements. This platform facilitates downstream mechanistic exploration and enhances the translational utility of gastric organoids for disease modeling and therapeutic development.

Indexed as

Cell DifferentiationCell LineageGastric MucosaOrganoidsStomachAdult Stem CellsAnimalsCell Culture TechniquesEpithelial CellsHumansMiceSignal TransductionAdult stem cellsCell lineageEpithelial differentiationGastric organoidsSignaling pathways

Identifiers

PMID42734747

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.