Evidence map›Paper›PMID 41535358›Full record

ArticleScientific reports2026

​Temporal proteomic profiling of iPSC-derived human liver organoids reveals optimal maturation for drug metabolism and toxicology.

Shu Yang, Jon Hao, Masato Ooka, Menghang Xia

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Review
  2. 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.

Shu YangDivision for Pre-Clinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA.
Jon HaoPoochon Scientific, Frederick, MD, USA.
Masato OokaDivision for Pre-Clinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA.
Menghang XiaDivision for Pre-Clinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA. mxia@mail.nih.gov.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liver organoids have emerged as an innovative three-dimensional model system that effectively recapitulates the structural and functional complexities of human liver tissue, addressing limitations of traditional two-dimensional culture systems. This study presents a comprehensive temporal proteomic characterization of iPSC-derived human liver organoids (iHLOs) by profiling 5,736 proteins across their four developmental stages, Days 7, 15, 30, and 45, to identify optimal timepoints for drug metabolism and toxicological applications. The analysis revealed that Day 30 represents the peak of functional maturity, marked by the highest expression of key-metabolizing enzymes, such as cytochrome P450 3A4 (CYP3A4), along with cholangiocyte-specific proteins and hepatic metabolic enzymes. Functional validation of CYP3A4 activity through rifampicin induction and ketoconazole inhibition demonstrated metabolic competence comparable to existing in vitro liver models, including HepaRG cells and primary human hepatocytes. Interestingly, Day 45 organoids exhibited a shift toward a mesenchymal cell profile, with increased markers of hepatic stellate cells, suggesting utility for disease modeling applications. These findings indicate Day 30 of liver organoids as the optimal stage for drug metabolism and toxicological investigations, providing a proteomic framework that enhances their utility of iHLOs as physiologically relevant liver models.

Indexed as

Induced Pluripotent Stem CellsLiverOrganoidsProteomeProteomicsCytochrome P-450 CYP3AHepatocytesHumansCYP3A4 protein, humanCytochrome P-450 CYP3AProteomeCYP3A4iPSC-derived liver organoidsOrganoid developmentProtein expressionProteomic analysis

Identifiers

PMID41535358
PMCPMC12824400

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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.