Evidence map›Paper›PMID 36248845›Full record

SynthesisFrontiers in immunology2022

Single cell meta-analysis of EndMT and EMT state in COVID-19.

Lanlan Zhang, Chuang Tang, Min Zhang, Xia Tong, Yingying Xie, Ruitong Yan, Xiangjun Wang, Xin Zhang, Dan Liu, Shasha Li

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.0field-weighted citation impact, top 13% 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

12 citing papers in PubMed, 23 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Article
  6. Article
  7. Single cell 'omic profiles of human aortic endothelial cellsbioRxiv : the preprint server for biology · 2024
    Article
  8. Vascular Contribution to Lung Repair and Fibrosis.American journal of respiratory cell and molecular biology · 2023
    Review
  9. Review
  10. Review
  11. Review
  12. 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

10 authors at 6 institutions in 2 countries.

Lanlan ZhangDivision of Pulmonary Diseases, State Key Laboratory of Biotherapy, And Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China.
Chuang TangDepartment of Gastroenterology, West China (Airport) Hospital, Sichuan University, Chengdu, China.
Min ZhangOncology Bussiness Department, Novogene Co., Ltd, Beijing, China.
Xia TongDepartment of Gastroenterology, West China (Airport) Hospital, Sichuan University, Chengdu, China.
Yingying XieDepartment of Nephrology, Seventh Affiliated Hospital Sun Yat-sen University, Shenzhen, China.
Ruitong YanSichuan University, Chengdu, China.
Xiangjun WangFirst Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Xin ZhangDepartment of Gastroenterology, West China (Airport) Hospital, Sichuan University, Chengdu, China.
Dan LiuDivision of Pulmonary Diseases, State Key Laboratory of Biotherapy, And Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China.
Shasha LiAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States.
Sichuan University · CNWest China Hospital of Sichuan University · CNHarvard University · USNovogene Bioinformatics Institute · CNSun Yat-sen University · CNThe First Affiliated Hospital, Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 prognoses suggests that a proportion of patients develop fibrosis, but there is no evidence to indicate whether patients have progression of mesenchymal transition (MT) in the lungs. The role of MT during the COVID-19 pandemic remains poorly understood. Using single-cell RNA sequencing, we profiled the transcriptomes of cells from the lungs of healthy individuals (n = 45), COVID-19 patients (n = 58), and idiopathic pulmonary fibrosis (IPF) patients (n = 64) human lungs to map the entire MT change. This analysis enabled us to map all high-resolution matrix-producing cells and identify distinct subpopulations of endothelial cells (ECs) and epithelial cells as the primary cellular sources of MT clusters during COVID-19. For the first time, we have identied early and late subgroups of endothelial mesenchymal transition (EndMT) and epithelial-mesenchymal transition (EMT) using analysis of public databases for single-cell sequencing. We assessed epithelial subgroups by age, smoking status, and gender, and the data suggest that the proportional changes in EMT in COVID-19 are statistically significant. Further enumeration of early and late EMT suggests a correlation between invasive genes and COVID-19. Finally, EndMT is upregulated in COVID-19 patients and enriched for more inflammatory cytokines. Further, by classifying EndMT as early or late stages, we found that early EndMT was positively correlated with entry factors but this was not true for late EndMT. Exploring the MT state of may help to mitigate the fibrosis impact of SARS-CoV-2 infection.

Indexed as

COVID-19Epithelial-Mesenchymal TransitionCytokinesEndothelial CellsFibrosisHumansPandemicsSARS-CoV-2Signal TransductionCytokinesCOVID-19endothelial cells (ECs)endothelial-mesenchymal transition (EndMT)epithelial-mesenchymal transition (EMT)single cell RNA sequencing

Identifiers

PMID36248845
PMCPMC9558222
OpenAlexW4297997755

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.