Evidence map›Paper›PMID 39504047›Full record

ArticleInvestigative ophthalmology & visual science2024

Single-Cell Multiomics Profiling Reveals Heterogeneity of Müller Cells in the Oxygen-Induced Retinopathy Model.

Xueming Yao, Ziqi Li, Yi Lei, Qiangyun Liu, Siyue Chen, Haokun Zhang, Xue Dong, Kai He, Ju Guo, Mulin Jun Li and 2 more

Abstract read
In one paragraph

Article in Investigative ophthalmology & visual science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Zinc in eye health, retinal biology and disease.Progress in retinal and eye research · 2025
    Review
  5. Article
  6. 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

12 authors.

Xueming YaoDepartment of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, China.
Ziqi LiDepartment of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, China.
Yi LeiDepartment of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
Qiangyun LiuDepartment of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, China.
Siyue ChenDepartment of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, China.
Haokun ZhangLaboratory of Molecular Ophthalmology, Tianjin Medical University, Tianjin, China.
Xue DongDepartment of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
Kai HeDepartment of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, China.
Ju GuoDepartment of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, China.
Mulin Jun LiDepartment of Bioinformatics, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
Xiaohong WangDepartment of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
Hua YanDepartment of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Retinal neovascularization poses heightened risks of vision loss and blindness. Despite its clinical significance, the molecular mechanisms underlying the pathogenesis of retinal neovascularization remain elusive. This study utilized single-cell multiomics profiling in an oxygen-induced retinopathy (OIR) model to comprehensively investigate the intricate molecular landscape of retinal neovascularization. Methods: Mice were exposed to hyperoxia to induce the OIR model, and retinas were isolated for nucleus isolation. The cellular landscape of the single-nucleus suspensions was extensively characterized through single-cell multiomics sequencing. Single-cell data were integrated with genome-wide association study (GWAS) data to identify correlations between ocular cell types and diabetic retinopathy. Cell communication analysis among cells was conducted to unravel crucial ligand-receptor signals. Trajectory analysis and dynamic characterization of Müller cells were performed, followed by integration with human retinal data for pathway analysis. Results: The multiomics dataset revealed six major ocular cell classes, with Müller cells/astrocytes showing significant associations with proliferative diabetic retinopathy (PDR). Cell communication analysis highlighted pathways that are associated with vascular proliferation and neurodevelopment, such as Vegfa-Vegfr2, Igf1-Igf1r, Nrxn3-Nlgn1, and Efna5-Epha4. Trajectory analysis identified a subset of Müller cells expressing genes linked to photoreceptor degeneration. Multiomics data integration further unveiled positively regulated genes in OIR Müller cells/astrocytes associated with axon development and neurotransmitter transmission. Conclusions: This study significantly advances our understanding of the intricate cellular and molecular mechanisms underlying retinal neovascularization, emphasizing the pivotal role of Müller cells. The identified pathways provide valuable insights into potential therapeutic targets for PDR, offering promising directions for further research and clinical interventions.

Indexed as

Disease Models, AnimalEpendymoglial CellsMice, Inbred C57BLOxygenRetinal NeovascularizationSingle-Cell AnalysisAnimalsDiabetic RetinopathyGene Expression ProfilingGenome-Wide Association StudyHyperoxiaMiceMultiomicsOxygen

Identifiers

PMID39504047
PMCPMC11547256

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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.