Identification of KLF6/PSGs and NPY-Related USF2/CEACAM Transcriptional Regulatory Networks via Spinal Cord Bulk and Single-Cell RNA-Seq Analysis.

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Article in Disease markers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed

0.5field-weighted citation impact, top 37% of its field

1 · What the graph read from it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 6 citations in OpenAlex.

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5 · Who and what money

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3 authors at 1 institution in 1 country.

Xinbing LiuDepartment of Neurosurgery, Changxing People's Hospital, Changxing, Zhejiang Province, China.ORCID https://orcid.org/0000-0002-0699-0952

Wei GaoDepartment of Neurosurgery, Changxing People's Hospital, Changxing, Zhejiang Province, China.ORCID https://orcid.org/0000-0002-6689-9042

Wei LiuDepartment of Neurosurgery, Changxing People's Hospital, Changxing, Zhejiang Province, China.ORCID https://orcid.org/0000-0002-9685-2229

Taixing People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo further understand the development of the spinal cord, an exploration of the patterns and transcriptional features of spinal cord development in newborn mice at the cellular transcriptome level was carried out.

methodsThe mouse single-cell sequencing (scRNA-seq) dataset was downloaded from the GSE108788 dataset. Single-cell RNA-Seq (scRNA-Seq) was conducted on cervical and lumbar spinal V2a interneurons from 2 P0 neonates. Single-cell analysis using the Seurat package was completed, and marker mRNAs were identified for each cluster. Then, pseudotemporal analysis was used to analyze the transcription changes of marker mRNAs in different clusters over time. Finally, the functions of these marker mRNAs were assessed by enrichment analysis and protein-protein interaction (PPI) networks. A transcriptional regulatory network was then constructed using the TRRUST dataset.

resultsA total of 949 cells were screened. Single-cell analysis was conducted based on marker mRNAs of each cluster, which revealed the heterogeneity of neonatal mouse spinal cord neuronal cells. Functional analysis of pseudotemporal trajectory-related marker mRNAs suggested that pregnancy-specific glycoproteins (PSGs) and carcinoembryonic antigen cell adhesion molecules (CEACAMs) were the core mRNAs in cluster 3. GSVA analysis then demonstrated that the different clusters had differences in pathway activity. By constructing a transcriptional regulatory network, USF2 was identified to be a transcriptional regulator of CEACAM1 and CEACAM5, while KLF6 was identified to be a transcriptional regulator of PSG3 and PSG5. This conclusion was then validated using the Genotype-Tissue Expression (GTEx) spinal cord transcriptome dataset.

conclusionsThis study completed an integrated analysis of a single-cell dataset with the utilization of marker mRNAs. USF2/CEACAM1&5 and KLF6/PSG3&5 transcriptional regulatory networks were identified by spinal cord single-cell analysis.

Indexed as

Gene Regulatory NetworksTranscription, GeneticAnimalsBiomarkersCell Adhesion MoleculesGlycoproteinsKruppel-Like Factor 6MiceNeuropeptide YPregnancy ProteinsProtein Interaction MapsRNA, MessengerSequence Analysis, RNASingle-Cell AnalysisSpinal CordUpstream Stimulatory FactorsBiomarkersCell Adhesion MoleculesGlycoproteinsKlf6 protein, mouseKruppel-Like Factor 6Neuropeptide YPregnancy ProteinsRNA, MessengerUpstream Stimulatory FactorsUsf2 protein, mouse

Identifiers

PMID34880956

DOI10.1155/2021/2826609

PMCPMC8648463

OpenAlexW3217454588

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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.