Identification of KLF6/PSGs and NPY-Related USF2/CEACAM Transcriptional Regulatory Networks via Spinal Cord Bulk and Single-Cell RNA-Seq Analysis
Background. To 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. Methods. The mouse single-cell sequencing (scRNA-seq) dataset was download...
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| description | Background. To 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. Methods. The 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. Results. A 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. Conclusions. This 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. |
| doi_str_mv | 10.1155/2021/2826609 |
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| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8648463</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2623076148</sourcerecordid><originalsourceid>FETCH-LOGICAL-c448t-45b77f72f26d28de891b68e5dfda7cdf37ceab8a7b29043349e2d3c5db2ab26e3</originalsourceid><addsrcrecordid>eNp9kU9v0zAYhy0EYmVw44wscUFiof4Xx7lMCtE6JkqZ1u3AyXJip_OWxp2dbOrH2DfGoWUCDpx8eJ_3kX_vD4C3GH3COE2nBBE8JYJwjvJnYIJFliaCU_QcTBDJRIIIQwfgVQg3CGGSs_wlOKBMCJSnfAIez7TpetvYWvXWddA18Ot8xqfny9MAVafh4vxHcmFa1RsNr5YzMi1PirL4Bi-96kLt7WZcUy28MKshUs5v4cL0D87fBnhvFVxu7Dgundfw89De_pIubbdqTVKaNi4uimRp7mARsW2w4TV40ag2mDf79xBczU4uyy_J_PvpWVnMk5ox0ScsrbKsyUhDuCZCG5HjiguT6karrNYNzWqjKqGyiuSIUcpyQzStU10RVRFu6CE43nk3Q7U2uo5n8KqVG2_Xym-lU1b-PenstVy5eyk4E4zTKPiwF3h3N5jQy7UNdYykOuOGIAknFGUcMxHR9_-gN27wMfBIoRynNBcsUkc7qvYuBG-ap89gJMeu5di13Hcd8Xd_BniCf5cbgY874Np2Wj3Y_-t-AheJsVk</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2609153984</pqid></control><display><type>article</type><title>Identification of KLF6/PSGs and NPY-Related USF2/CEACAM Transcriptional Regulatory Networks via Spinal Cord Bulk and Single-Cell RNA-Seq Analysis</title><source>MEDLINE</source><source>Wiley Online Library Open Access</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central</source><source>Alma/SFX Local Collection</source><creator>Liu, Xinbing ; Gao, Wei ; Liu, Wei</creator><contributor>Xu, Yuzhen ; Yuzhen Xu</contributor><creatorcontrib>Liu, Xinbing ; Gao, Wei ; Liu, Wei ; Xu, Yuzhen ; Yuzhen Xu</creatorcontrib><description>Background. To 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. Methods. The 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. Results. A 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. Conclusions. This 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.</description><identifier>ISSN: 0278-0240</identifier><identifier>EISSN: 1875-8630</identifier><identifier>DOI: 10.1155/2021/2826609</identifier><identifier>PMID: 34880956</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>Animals ; Antigens ; Biomarkers ; Biomarkers - metabolism ; Carcinoembryonic antigen ; CD66 antigen ; CEACAM1 protein ; Cell adhesion ; Cell adhesion molecules ; Cell Adhesion Molecules - genetics ; Cluster analysis ; Clusters ; Datasets ; Flow cytometry ; Functional analysis ; Gene Regulatory Networks ; Genes ; Genotypes ; Glycoproteins ; Glycoproteins - genetics ; Heterogeneity ; Interneurons ; Kruppel-Like Factor 6 - genetics ; Markers ; Mice ; Myelin P0 protein ; Neonates ; Nervous system ; Networks ; Neuropeptide Y ; Neuropeptide Y - metabolism ; Pregnancy Proteins - genetics ; Protein interaction ; Protein Interaction Maps ; Proteins ; Quality control ; RNA, Messenger - genetics ; Sequence Analysis, RNA - methods ; Single-Cell Analysis - methods ; Software ; Spinal cord ; Spinal Cord - metabolism ; Trajectory analysis ; Transcription ; Transcription factors ; Transcription, Genetic ; Transcriptomes ; Upstream Stimulatory Factors - genetics ; Variance analysis</subject><ispartof>Disease markers, 2021, Vol.2021, p.2826609-21</ispartof><rights>Copyright © 2021 Xinbing Liu et al.</rights><rights>Copyright © 2021 Xinbing Liu et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2021 Xinbing Liu et al. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c448t-45b77f72f26d28de891b68e5dfda7cdf37ceab8a7b29043349e2d3c5db2ab26e3</citedby><cites>FETCH-LOGICAL-c448t-45b77f72f26d28de891b68e5dfda7cdf37ceab8a7b29043349e2d3c5db2ab26e3</cites><orcidid>0000-0002-0699-0952 ; 0000-0002-9685-2229 ; 0000-0002-6689-9042</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8648463/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8648463/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,4010,27900,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34880956$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Xu, Yuzhen</contributor><contributor>Yuzhen Xu</contributor><creatorcontrib>Liu, Xinbing</creatorcontrib><creatorcontrib>Gao, Wei</creatorcontrib><creatorcontrib>Liu, Wei</creatorcontrib><title>Identification of KLF6/PSGs and NPY-Related USF2/CEACAM Transcriptional Regulatory Networks via Spinal Cord Bulk and Single-Cell RNA-Seq Analysis</title><title>Disease markers</title><addtitle>Dis Markers</addtitle><description>Background. To 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. Methods. The 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. Results. A 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. Conclusions. This 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.</description><subject>Animals</subject><subject>Antigens</subject><subject>Biomarkers</subject><subject>Biomarkers - metabolism</subject><subject>Carcinoembryonic antigen</subject><subject>CD66 antigen</subject><subject>CEACAM1 protein</subject><subject>Cell adhesion</subject><subject>Cell adhesion molecules</subject><subject>Cell Adhesion Molecules - genetics</subject><subject>Cluster analysis</subject><subject>Clusters</subject><subject>Datasets</subject><subject>Flow cytometry</subject><subject>Functional analysis</subject><subject>Gene Regulatory Networks</subject><subject>Genes</subject><subject>Genotypes</subject><subject>Glycoproteins</subject><subject>Glycoproteins - genetics</subject><subject>Heterogeneity</subject><subject>Interneurons</subject><subject>Kruppel-Like Factor 6 - genetics</subject><subject>Markers</subject><subject>Mice</subject><subject>Myelin P0 protein</subject><subject>Neonates</subject><subject>Nervous system</subject><subject>Networks</subject><subject>Neuropeptide Y</subject><subject>Neuropeptide Y - metabolism</subject><subject>Pregnancy Proteins - genetics</subject><subject>Protein interaction</subject><subject>Protein Interaction Maps</subject><subject>Proteins</subject><subject>Quality control</subject><subject>RNA, Messenger - genetics</subject><subject>Sequence Analysis, RNA - methods</subject><subject>Single-Cell Analysis - methods</subject><subject>Software</subject><subject>Spinal cord</subject><subject>Spinal Cord - metabolism</subject><subject>Trajectory analysis</subject><subject>Transcription</subject><subject>Transcription factors</subject><subject>Transcription, Genetic</subject><subject>Transcriptomes</subject><subject>Upstream Stimulatory Factors - genetics</subject><subject>Variance analysis</subject><issn>0278-0240</issn><issn>1875-8630</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><recordid>eNp9kU9v0zAYhy0EYmVw44wscUFiof4Xx7lMCtE6JkqZ1u3AyXJip_OWxp2dbOrH2DfGoWUCDpx8eJ_3kX_vD4C3GH3COE2nBBE8JYJwjvJnYIJFliaCU_QcTBDJRIIIQwfgVQg3CGGSs_wlOKBMCJSnfAIez7TpetvYWvXWddA18Ot8xqfny9MAVafh4vxHcmFa1RsNr5YzMi1PirL4Bi-96kLt7WZcUy28MKshUs5v4cL0D87fBnhvFVxu7Dgundfw89De_pIubbdqTVKaNi4uimRp7mARsW2w4TV40ag2mDf79xBczU4uyy_J_PvpWVnMk5ox0ScsrbKsyUhDuCZCG5HjiguT6karrNYNzWqjKqGyiuSIUcpyQzStU10RVRFu6CE43nk3Q7U2uo5n8KqVG2_Xym-lU1b-PenstVy5eyk4E4zTKPiwF3h3N5jQy7UNdYykOuOGIAknFGUcMxHR9_-gN27wMfBIoRynNBcsUkc7qvYuBG-ap89gJMeu5di13Hcd8Xd_BniCf5cbgY874Np2Wj3Y_-t-AheJsVk</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Liu, Xinbing</creator><creator>Gao, Wei</creator><creator>Liu, Wei</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QL</scope><scope>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-0699-0952</orcidid><orcidid>https://orcid.org/0000-0002-9685-2229</orcidid><orcidid>https://orcid.org/0000-0002-6689-9042</orcidid></search><sort><creationdate>2021</creationdate><title>Identification of KLF6/PSGs and NPY-Related USF2/CEACAM Transcriptional Regulatory Networks via Spinal Cord Bulk and Single-Cell RNA-Seq Analysis</title><author>Liu, Xinbing ; Gao, Wei ; Liu, Wei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c448t-45b77f72f26d28de891b68e5dfda7cdf37ceab8a7b29043349e2d3c5db2ab26e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Animals</topic><topic>Antigens</topic><topic>Biomarkers</topic><topic>Biomarkers - metabolism</topic><topic>Carcinoembryonic antigen</topic><topic>CD66 antigen</topic><topic>CEACAM1 protein</topic><topic>Cell adhesion</topic><topic>Cell adhesion molecules</topic><topic>Cell Adhesion Molecules - genetics</topic><topic>Cluster analysis</topic><topic>Clusters</topic><topic>Datasets</topic><topic>Flow cytometry</topic><topic>Functional analysis</topic><topic>Gene Regulatory Networks</topic><topic>Genes</topic><topic>Genotypes</topic><topic>Glycoproteins</topic><topic>Glycoproteins - genetics</topic><topic>Heterogeneity</topic><topic>Interneurons</topic><topic>Kruppel-Like Factor 6 - genetics</topic><topic>Markers</topic><topic>Mice</topic><topic>Myelin P0 protein</topic><topic>Neonates</topic><topic>Nervous system</topic><topic>Networks</topic><topic>Neuropeptide Y</topic><topic>Neuropeptide Y - metabolism</topic><topic>Pregnancy Proteins - genetics</topic><topic>Protein interaction</topic><topic>Protein Interaction Maps</topic><topic>Proteins</topic><topic>Quality control</topic><topic>RNA, Messenger - genetics</topic><topic>Sequence Analysis, RNA - methods</topic><topic>Single-Cell Analysis - methods</topic><topic>Software</topic><topic>Spinal cord</topic><topic>Spinal Cord - metabolism</topic><topic>Trajectory analysis</topic><topic>Transcription</topic><topic>Transcription factors</topic><topic>Transcription, Genetic</topic><topic>Transcriptomes</topic><topic>Upstream Stimulatory Factors - genetics</topic><topic>Variance analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Xinbing</creatorcontrib><creatorcontrib>Gao, Wei</creatorcontrib><creatorcontrib>Liu, Wei</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Disease markers</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Xinbing</au><au>Gao, Wei</au><au>Liu, Wei</au><au>Xu, Yuzhen</au><au>Yuzhen Xu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of KLF6/PSGs and NPY-Related USF2/CEACAM Transcriptional Regulatory Networks via Spinal Cord Bulk and Single-Cell RNA-Seq Analysis</atitle><jtitle>Disease markers</jtitle><addtitle>Dis Markers</addtitle><date>2021</date><risdate>2021</risdate><volume>2021</volume><spage>2826609</spage><epage>21</epage><pages>2826609-21</pages><issn>0278-0240</issn><eissn>1875-8630</eissn><abstract>Background. To 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. Methods. The 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. Results. A 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. Conclusions. This 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.</abstract><cop>United States</cop><pub>Hindawi</pub><pmid>34880956</pmid><doi>10.1155/2021/2826609</doi><tpages>21</tpages><orcidid>https://orcid.org/0000-0002-0699-0952</orcidid><orcidid>https://orcid.org/0000-0002-9685-2229</orcidid><orcidid>https://orcid.org/0000-0002-6689-9042</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Animals Antigens Biomarkers Biomarkers - metabolism Carcinoembryonic antigen CD66 antigen CEACAM1 protein Cell adhesion Cell adhesion molecules Cell Adhesion Molecules - genetics Cluster analysis Clusters Datasets Flow cytometry Functional analysis Gene Regulatory Networks Genes Genotypes Glycoproteins Glycoproteins - genetics Heterogeneity Interneurons Kruppel-Like Factor 6 - genetics Markers Mice Myelin P0 protein Neonates Nervous system Networks Neuropeptide Y Neuropeptide Y - metabolism Pregnancy Proteins - genetics Protein interaction Protein Interaction Maps Proteins Quality control RNA, Messenger - genetics Sequence Analysis, RNA - methods Single-Cell Analysis - methods Software Spinal cord Spinal Cord - metabolism Trajectory analysis Transcription Transcription factors Transcription, Genetic Transcriptomes Upstream Stimulatory Factors - genetics Variance analysis |
| title | 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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