Evidence map›Paper›PMID 37269061›Full record

ReviewCNS neuroscience & therapeutics2023

Comprehensive analyses of brain cell communications based on multiple scRNA-seq and snRNA-seq datasets for revealing novel mechanism in neurodegenerative diseases.

Chunlong Zhang, Guiyuan Tan, Yuxi Zhang, Xiaoling Zhong, Ziyan Zhao, Yunyi Peng, Qian Cheng, Ke Xue, Yanjun Xu, Xia Li and 2 more

Abstract readReview
In one paragraph

Review in CNS neuroscience & therapeutics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–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

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

12 authors.

Chunlong ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.ORCID 0000-0002-1164-8182
Guiyuan TanCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Yuxi ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Xiaoling ZhongCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Ziyan ZhaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Yunyi PengCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Qian ChengCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Ke XueCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Yanjun XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Xia LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Feng LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Yunpeng ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.ORCID 0000-0002-3709-3656

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsComplex cellular communications between glial cells and neurons are critical for brain normal function and disorders, and single-cell level RNA-sequencing datasets display more advantages for analyzing cell communications. Therefore, it is necessary to systematically explore brain cell communications when considering factors such as sex and brain region.

methodsWe extracted a total of 1,039,459 cells derived from 28 brain single-cell RNA-sequencing (scRNA-seq) or single-nucleus RNA-sequencing (snRNA-seq) datasets from the GEO database, including 12 human and 16 mouse datasets. These datasets were further divided into 71 new sub-datasets when considering disease, sex, and region conditions. In the meanwhile, we integrated four methods to evaluate ligand-receptor interaction score among six major brain cell types (microglia, neuron, astrocyte, oligodendrocyte, OPC, and endothelial cell).

resultsFor Alzheimer's disease (AD), disease-specific ligand-receptor pairs when compared with normal sub-datasets, such as SEMA4A-NRP1, were identified. Furthermore, we explored the sex- and region-specific cell communications and identified that WNT5A-ROR1 among microglia cells displayed close communications in male, and SPP1-ITGAV displayed close communications in the meninges region from microglia to neurons. Furthermore, based on the AD-specific cell communications, we constructed a model for AD early prediction and confirmed the predictive performance using multiple independent datasets. Finally, we developed an online platform for researchers to explore brain condition-specific cell communications.

conclusionThis research provided a comprehensive study to explore brain cell communications, which could reveal novel biological mechanisms involved in normal brain function and neurodegenerative diseases such as AD.

Indexed as

Alzheimer DiseaseNeurodegenerative DiseasesSemaphorinsAnimalsGene Expression ProfilingHumansLigandsMaleMiceRNA, Small NuclearSingle-Cell Gene Expression AnalysisSolitary NucleusLigandsRNA, Small NuclearSEMA4A protein, humanSemaphorinsAlzheimer's diseasebioinformatic platformbraincell communications

Identifiers

PMID37269061
PMCPMC10493674

What OpenQuestion holds

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LicenceCC BY
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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.