Evidence map›Paper›PMID 33936064›Full record

ArticleFrontiers in immunology2021

A Partial Picture of the Single-Cell Transcriptomics of Human IgA Nephropathy.

Rong Tang, Ting Meng, Wei Lin, Chanjuan Shen, Joshua D Ooi, Peter J Eggenhuizen, Peng Jin, Xiang Ding, Jinbiao Chen, Yangshuo Tang and 7 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed
7.6field-weighted citation impact, top 2% 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

66 citing papers in PubMed, 87 citations in OpenAlex.

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6 more citing papers are in PubMed but not listed here.

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

17 authors at 3 institutions in 2 countries.

Rong TangDepartment of Nephrology, Xiangya Hospital, Central South University, Changsha, China.
Ting MengDepartment of Nephrology, Xiangya Hospital, Central South University, Changsha, China.
Wei LinDepartment of Pathology, Xiangya Hospital, Central South University, Changsha, China.
Chanjuan ShenDepartment of Hematology, the Affiliated Zhuzhou Hospital Xiangya Medical College, Central South University, Zhuzhou, China.
Joshua D OoiDepartment of Nephrology, Xiangya Hospital, Central South University, Changsha, China.
Peter J EggenhuizenCentre for Inflammatory Diseases, Monash University, Clayton, VIC, Australia.
Peng JinDepartment of Organ Transplantation, Xiangya Hospital, Central South University, Changsha, China.
Xiang DingDepartment of Organ Transplantation, Xiangya Hospital, Central South University, Changsha, China.
Jinbiao ChenDepartment of Medical Records & Information, Xiangya Hospital, Central South University, Changsha, China.
Yangshuo TangDepartment of Ultrasound, Xiangya Hospital, Central South University, Changsha, China.
Zhou XiaoDepartment of Nephrology, Xiangya Hospital, Central South University, Changsha, China.
Xiang AoDepartment of Nephrology, Xiangya Hospital, Central South University, Changsha, China.
Weisheng PengDepartment of Nephrology, Xiangya Hospital, Central South University, Changsha, China.
Qiaoling ZhouDepartment of Nephrology, Xiangya Hospital, Central South University, Changsha, China.
Ping XiaoDepartment of Nephrology, Xiangya Hospital, Central South University, Changsha, China.
Yong ZhongDepartment of Nephrology, Xiangya Hospital, Central South University, Changsha, China.
Xiangcheng XiaoDepartment of Nephrology, Xiangya Hospital, Central South University, Changsha, China.
Central South University · CNXiangya Hospital Central South University · CNMonash University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The molecular mechanisms underlying renal damage of IgA nephropathy (IgAN) remain incompletely defined. Here, single-cell RNA sequencing (scRNA-seq) was applied to kidney biopsies from IgAN and control subjects to define the transcriptomic landscape at single-cell resolution. We presented a comprehensive scRNA-seq analysis of human renal biopsies from IgAN. We showed for the first time that IgAN mesangial cells displayed increased expression of several novel genes including MALAT1, GADD45B, SOX4, and EDIL3, which were related to cell proliferation and matrix accumulation. The overexpressed genes in tubule cells of IgAN were mainly enriched in inflammatory pathways including TNF signaling, IL-17 signaling, and NOD-like receptor signaling. Furthermore, we compared the results of 4 IgAN patients with the published scRNA-Seq data of healthy kidney tissues of three human donors in order to further validate the findings in our study. The results also verified that the overexpressed genes in tubule cells from IgAN patients were mainly enriched in inflammatory pathways including TNF signaling, IL-17 signaling, and NOD-like receptor signaling. The receptor-ligand crosstalk analysis revealed potential interactions between mesangial cells and other cells in IgAN. IgAN patients with overt proteinuria displayed elevated genes participating in several signaling pathways compared with microproteinuria group. It needs to be mentioned that based on number of mesangial cells and other kidney cells analyzed in this study, the results of our study are preliminary and needs to be confirmed on larger number of cells from larger number of patients and controls in future studies. Therefore, these results offer new insight into pathogenesis and identify new therapeutic targets for IgAN.

Indexed as

TranscriptomeCell CommunicationGlomerulonephritis, IGAHumansInterleukin-17ProteinuriaSequence Analysis, RNASignal TransductionSingle-Cell AnalysisInterleukin-17IgA nephropathykidneyNOD-like receptor signalingsingle-cell RNA sequencingTNF and IL-17 signaling

Identifiers

PMID33936064
PMCPMC8085501
OpenAlexW3154787918

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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.