Evidence map›Paper›PMID 37051201›Full record

ArticleFrontiers in endocrinology2023

Single-cell RNA sequencing reveals

Shengran Wang, Jonathan Greenbaum, Chuan Qiu, Yun Gong, Zun Wang, Xu Lin, Yong Liu, Pei He, Xianghe Meng, Qiang Zhang and 6 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 28% of its field
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. The role of CD8Frontiers in immunology · 2026
    Pooled it
  2. Review
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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

16 authors at 6 institutions in 2 countries.

Shengran WangTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA, United States.
Jonathan GreenbaumTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA, United States.
Chuan QiuTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA, United States.
Yun GongTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA, United States.
Zun WangXiangya School of Nursing, Central South University, Changsha, China.
Xu LinDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Yong LiuCenter for System Biology, Data Sciences and Reproductive Health, School of Basic Medical Science, Central South University, Changsha, China.
Pei HeCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, China.
Xianghe MengCenter for System Biology, Data Sciences and Reproductive Health, School of Basic Medical Science, Central South University, Changsha, China.
Qiang ZhangCollege of Public Health, Zhengzhou University, High-Tech Development Zone of States, Zhengzhou, China.
Hui ShenTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA, United States.
Krishna Chandra VemulapalliDepartment of Orthopaedic Surgery, Tulane University School of Medicine, Tulane University, New Orleans, LA, United States.
Fernando L SanchezDepartment of Orthopaedic Surgery, Tulane University School of Medicine, Tulane University, New Orleans, LA, United States.
Martin R SchillerNevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV, United States.
Hongmei XiaoInstitute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, China.
Hongwen DengTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, Tulane University, New Orleans, LA, United States.
Tulane University · USCentral South University · CNSoochow University · CNThird Affiliated Hospital of Southern Medical University · CNUniversity of Nevada, Las Vegas · USZhengzhou University · CN

Funding

Trans-omics Integration of Multi-omics Studies for OsteoporosisU19AG055373 · NIA · TULANE UNIVERSITY OF LOUISIANA · PI Qi Zhao · 2017 to 2026
$24.3M
Intensive Lifestyle Intervention, Metabolomics, and Risk of Frailty Fracture in Overweight or Obese Patients with Type 2 DiabetesR01AG068232 · NIA · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI JOHNSON, KAREN C, ZHAO, QI · 2021 to 2025
$3.1M
Identification of Metabolomic Profiles for Sarcopenia Traits in Older Whites and BlacksR01AG061917 · NIA · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI SHEN, HUI, ZHAO, QI · 2019 to 2023
$3.0M
Decoding Methylation Mediated Epigenomic Contributions to Male OsteoporosisR01AR069055 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2017 to 2021
$2.9M
NIAMS NIH HHS R01 AR069055NIA NIH HHS R01 AG061917NIA NIH HHS R01 AG068232NIA NIH HHS U19 AG055373
6 · The paper itself

Abstract

Background: While osteoimmunology interactions between the immune and skeletal systems are known to play an important role in osteoblast development, differentiation and bone metabolism related disease like osteoporosis, such interactions in either bone microenvironment or peripheral circulation Methods: We explored the osteoimmunology communications between immune cells and osteoblastic lineage cells (OBCs) by performing CellphoneDB and CellChat analyses with single-cell RNA sequencing (scRNA-seq) data from human femoral head. We also explored the osteoimmunology effects of immune cells in peripheral circulation on skeletal phenotypes. We used a scRNA-seq dataset of peripheral blood monocytes (PBMs) to perform deconvolution analysis. Then weighted gene co-expression network analysis (WGCNA) was used to identify monocyte subtype-specific subnetworks. We next used cell-specific network (CSN) and the least absolute shrinkage and selection operator (LASSO) to analyze the correlation of a gene subnetwork identified by WGCNA with bone mineral density (BMD). Results: We constructed immune cell and OBC communication networks and further identified L-R genes, such as JAG1 and NOTCH1/2, with ossification related functions. We also found a Mono4 related subnetwork that may relate to BMD variation in both older males and postmenopausal female subjects. Conclusions: This is the first study to identify numerous ligand-receptor pairs that likely mediate signals between immune cells and osteoblastic lineage cells. This establishes a foundation to reveal advanced and in-depth osteoimmunology interactions to better understand the relationship between local bone microenvironment and immune cells in peripheral blood and the impact on bone phenotypes.

Indexed as

Bone and BonesOsteoporosisBone DensityFemaleGene Expression ProfilingHumansSequence Analysis, RNAcell-specific networkLASSOligand-receptorosteoimmunologysingle-cell RNA sequencing

Identifiers

PMID37051201
PMCPMC10083244
OpenAlexW4360982902

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.