Evidence map›Paper›PMID 31896769›Full record

ArticleScientific data2020

Single-cell RNA sequencing of human kidney.

Jinling Liao, Zhenyuan Yu, Yang Chen, Mengying Bao, Chunlin Zou, Haiying Zhang, Deyun Liu, Tianyu Li, Qingyun Zhang, Jiaping Li and 2 more

Open access · goldAbstract readDataset
In one paragraph

Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 163 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
163citing papers in PubMed, 3 pooled it
16.4field-weighted citation impact, top 1% 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

163 citing papers in PubMed, 3 syntheses or guidelines pooled it, 298 citations in OpenAlex.

  1. Expanding the Phenotypic Spectrum of Kenny-Caffey Syndrome.The Journal of clinical endocrinology and metabolism · 2023
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103 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

12 authors at 4 institutions in 1 country.

Jinling Liao *Center for Genomic and Personalized Medicine, Guangxi Medical University, 530021, Nanning, Guangxi, China.
Zhenyuan Yu *Center for Genomic and Personalized Medicine, Guangxi Medical University, 530021, Nanning, Guangxi, China.
Yang Chen *Center for Genomic and Personalized Medicine, Guangxi Medical University, 530021, Nanning, Guangxi, China.
Mengying BaoCenter for Genomic and Personalized Medicine, Guangxi Medical University, 530021, Nanning, Guangxi, China.
Chunlin ZouKey Laboratory of Longevity and Aging-related Diseases(Guangxi Medical University), Ministry of Education, Nanning, Guangxi, 530021, P.R. China.
Haiying ZhangCenter for Genomic and Personalized Medicine, Guangxi Medical University, 530021, Nanning, Guangxi, China.
Deyun LiuInstitute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, 530021, Nanning, Guangxi, China.
Tianyu LiInstitute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, 530021, Nanning, Guangxi, China.
Qingyun ZhangCenter for Genomic and Personalized Medicine, Guangxi Medical University, 530021, Nanning, Guangxi, China.
Jiaping LiDepartment of Cardiology, The First Affiliated Hospital of Guangxi Medical University, 530021, Nanning, Guangxi, China.
Jiwen ChengCenter for Genomic and Personalized Medicine, Guangxi Medical University, 530021, Nanning, Guangxi, China. chengjiwen1977@foxmail.com.
Zengnan MoCenter for Genomic and Personalized Medicine, Guangxi Medical University, 530021, Nanning, Guangxi, China. mozengnan@gxmu.edu.cn.
Guangxi Medical University · CNFirst Affiliated Hospital of GuangXi Medical University · CNGuangxi University · CNTumor Hospital of Guangxi Medical University · CN

Funding

National Natural Science Foundation of China (National Science Foundation of China) 81770759
6 · The paper itself

Abstract

A comprehensive cellular anatomy of normal human kidney is crucial to address the cellular origins of renal disease and renal cancer. Some kidney diseases may be cell type-specific, especially renal tubular cells. To investigate the classification and transcriptomic information of the human kidney, we rapidly obtained a single-cell suspension of the kidney and conducted single-cell RNA sequencing (scRNA-seq). Here, we present the scRNA-seq data of 23,366 high-quality cells from the kidneys of three human donors. In this dataset, we show 10 clusters of normal human renal cells. Due to the high quality of single-cell transcriptomic information, proximal tubule (PT) cells were classified into three subtypes and collecting ducts cells into two subtypes. Collectively, our data provide a reliable reference for studies on renal cell biology and kidney disease.

Indexed as

RNA-SeqSingle-Cell AnalysisHumansKidneyKidney Tubules, CollectingKidney Tubules, ProximalTranscriptome

Identifiers

PMID31896769
PMCPMC6940381
OpenAlexW2997954936

What OpenQuestion 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 OpenQuestion graph.