Academic Journal
View in EDS

Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells.

Bibliographic Details
Title: Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells.
Authors: Bhattacharya, Soumyaroop, Myers, Jacquelyn A., Baker, Cameron, Guo, Minzhe, Danopoulos, Soula, Myers, Jason R., Bandyopadhyay, Gautam, Romas, Stephen T., Huyck, Heidie L., Misra, Ravi S., Dutra, Jennifer, Holden-Wiltse, Jeanne, McDavid, Andrew N., Ashton, John M., Al Alam, Denise, Potter, S. Steven, Whitsett, Jeffrey A., Xu, Yan, Pryhuber, Gloria S., Mariani, Thomas J.
Source: Genes; Mar2024, Vol. 15 Issue 3, p298, 20p
Subject Terms: LUNGS, HUMAN phenotype, TRANSCRIPTOMES, LUNG development, GENE mapping, SMOOTH muscle, FROZEN human embryos, KNOWLEDGE gap theory
Abstract: While animal model studies have extensively defined the mechanisms controlling cell diversity in the developing mammalian lung, there exists a significant knowledge gap with regards to late-stage human lung development. The NHLBI Molecular Atlas of Lung Development Program (LungMAP) seeks to fill this gap by creating a structural, cellular and molecular atlas of the human and mouse lung. Transcriptomic profiling at the single-cell level created a cellular atlas of newborn human lungs. Frozen single-cell isolates obtained from two newborn human lungs from the LungMAP Human Tissue Core Biorepository, were captured, and library preparation was completed on the Chromium 10X system. Data was analyzed in Seurat, and cellular annotation was performed using the ToppGene functional analysis tool. Transcriptional interrogation of 5500 newborn human lung cells identified distinct clusters representing multiple populations of epithelial, endothelial, fibroblasts, pericytes, smooth muscle, immune cells and their gene signatures. Computational integration of data from newborn human cells and with 32,000 cells from postnatal days 1 through 10 mouse lungs generated by the LungMAP Cincinnati Research Center facilitated the identification of distinct cellular lineages among all the major cell types. Integration of the newborn human and mouse cellular transcriptomes also demonstrated cell type-specific differences in maturation states of newborn human lung cells. Specifically, newborn human lung matrix fibroblasts could be separated into those representative of younger cells (n = 393), or older cells (n = 158). Cells with each molecular profile were spatially resolved within newborn human lung tissue. This is the first comprehensive molecular map of the cellular landscape of neonatal human lung, including biomarkers for cells at distinct states of maturity. [ABSTRACT FROM AUTHOR]
Copyright of Genes is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:edb&genre=article&issn=20734425&ISBN=&volume=15&issue=3&date=20240301&spage=298&pages=298-317&title=Genes&atitle=Single-Cell%20Transcriptomic%20Profiling%20Identifies%20Molecular%20Phenotypes%20of%20Newborn%20Human%20Lung%20Cells.&aulast=Bhattacharya%2C%20Soumyaroop&id=DOI:10.3390/genes15030298
    Name: Full Text Finder (for New FTF UI) (s8985755)
    Category: fullText
    Text: Find It @ SCU Libraries
    MouseOverText: Find It @ SCU Libraries
Header DbId: edb
DbLabel: Complementary Index
An: 176563536
RelevancyScore: 1007
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1007.03765869141
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Bhattacharya%2C+Soumyaroop%22">Bhattacharya, Soumyaroop</searchLink><br /><searchLink fieldCode="AR" term="%22Myers%2C+Jacquelyn+A%2E%22">Myers, Jacquelyn A.</searchLink><br /><searchLink fieldCode="AR" term="%22Baker%2C+Cameron%22">Baker, Cameron</searchLink><br /><searchLink fieldCode="AR" term="%22Guo%2C+Minzhe%22">Guo, Minzhe</searchLink><br /><searchLink fieldCode="AR" term="%22Danopoulos%2C+Soula%22">Danopoulos, Soula</searchLink><br /><searchLink fieldCode="AR" term="%22Myers%2C+Jason+R%2E%22">Myers, Jason R.</searchLink><br /><searchLink fieldCode="AR" term="%22Bandyopadhyay%2C+Gautam%22">Bandyopadhyay, Gautam</searchLink><br /><searchLink fieldCode="AR" term="%22Romas%2C+Stephen+T%2E%22">Romas, Stephen T.</searchLink><br /><searchLink fieldCode="AR" term="%22Huyck%2C+Heidie+L%2E%22">Huyck, Heidie L.</searchLink><br /><searchLink fieldCode="AR" term="%22Misra%2C+Ravi+S%2E%22">Misra, Ravi S.</searchLink><br /><searchLink fieldCode="AR" term="%22Dutra%2C+Jennifer%22">Dutra, Jennifer</searchLink><br /><searchLink fieldCode="AR" term="%22Holden-Wiltse%2C+Jeanne%22">Holden-Wiltse, Jeanne</searchLink><br /><searchLink fieldCode="AR" term="%22McDavid%2C+Andrew+N%2E%22">McDavid, Andrew N.</searchLink><br /><searchLink fieldCode="AR" term="%22Ashton%2C+John+M%2E%22">Ashton, John M.</searchLink><br /><searchLink fieldCode="AR" term="%22Al+Alam%2C+Denise%22">Al Alam, Denise</searchLink><br /><searchLink fieldCode="AR" term="%22Potter%2C+S%2E+Steven%22">Potter, S. Steven</searchLink><br /><searchLink fieldCode="AR" term="%22Whitsett%2C+Jeffrey+A%2E%22">Whitsett, Jeffrey A.</searchLink><br /><searchLink fieldCode="AR" term="%22Xu%2C+Yan%22">Xu, Yan</searchLink><br /><searchLink fieldCode="AR" term="%22Pryhuber%2C+Gloria+S%2E%22">Pryhuber, Gloria S.</searchLink><br /><searchLink fieldCode="AR" term="%22Mariani%2C+Thomas+J%2E%22">Mariani, Thomas J.</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Genes; Mar2024, Vol. 15 Issue 3, p298, 20p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22LUNGS%22">LUNGS</searchLink><br /><searchLink fieldCode="DE" term="%22HUMAN+phenotype%22">HUMAN phenotype</searchLink><br /><searchLink fieldCode="DE" term="%22TRANSCRIPTOMES%22">TRANSCRIPTOMES</searchLink><br /><searchLink fieldCode="DE" term="%22LUNG+development%22">LUNG development</searchLink><br /><searchLink fieldCode="DE" term="%22GENE+mapping%22">GENE mapping</searchLink><br /><searchLink fieldCode="DE" term="%22SMOOTH+muscle%22">SMOOTH muscle</searchLink><br /><searchLink fieldCode="DE" term="%22FROZEN+human+embryos%22">FROZEN human embryos</searchLink><br /><searchLink fieldCode="DE" term="%22KNOWLEDGE+gap+theory%22">KNOWLEDGE gap theory</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: While animal model studies have extensively defined the mechanisms controlling cell diversity in the developing mammalian lung, there exists a significant knowledge gap with regards to late-stage human lung development. The NHLBI Molecular Atlas of Lung Development Program (LungMAP) seeks to fill this gap by creating a structural, cellular and molecular atlas of the human and mouse lung. Transcriptomic profiling at the single-cell level created a cellular atlas of newborn human lungs. Frozen single-cell isolates obtained from two newborn human lungs from the LungMAP Human Tissue Core Biorepository, were captured, and library preparation was completed on the Chromium 10X system. Data was analyzed in Seurat, and cellular annotation was performed using the ToppGene functional analysis tool. Transcriptional interrogation of 5500 newborn human lung cells identified distinct clusters representing multiple populations of epithelial, endothelial, fibroblasts, pericytes, smooth muscle, immune cells and their gene signatures. Computational integration of data from newborn human cells and with 32,000 cells from postnatal days 1 through 10 mouse lungs generated by the LungMAP Cincinnati Research Center facilitated the identification of distinct cellular lineages among all the major cell types. Integration of the newborn human and mouse cellular transcriptomes also demonstrated cell type-specific differences in maturation states of newborn human lung cells. Specifically, newborn human lung matrix fibroblasts could be separated into those representative of younger cells (n = 393), or older cells (n = 158). Cells with each molecular profile were spatially resolved within newborn human lung tissue. This is the first comprehensive molecular map of the cellular landscape of neonatal human lung, including biomarkers for cells at distinct states of maturity. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Genes is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://login.libproxy.scu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edb&AN=176563536
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/genes15030298
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 20
        StartPage: 298
    Subjects:
      – SubjectFull: LUNGS
        Type: general
      – SubjectFull: HUMAN phenotype
        Type: general
      – SubjectFull: TRANSCRIPTOMES
        Type: general
      – SubjectFull: LUNG development
        Type: general
      – SubjectFull: GENE mapping
        Type: general
      – SubjectFull: SMOOTH muscle
        Type: general
      – SubjectFull: FROZEN human embryos
        Type: general
      – SubjectFull: KNOWLEDGE gap theory
        Type: general
    Titles:
      – TitleFull: Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Bhattacharya, Soumyaroop
      – PersonEntity:
          Name:
            NameFull: Myers, Jacquelyn A.
      – PersonEntity:
          Name:
            NameFull: Baker, Cameron
      – PersonEntity:
          Name:
            NameFull: Guo, Minzhe
      – PersonEntity:
          Name:
            NameFull: Danopoulos, Soula
      – PersonEntity:
          Name:
            NameFull: Myers, Jason R.
      – PersonEntity:
          Name:
            NameFull: Bandyopadhyay, Gautam
      – PersonEntity:
          Name:
            NameFull: Romas, Stephen T.
      – PersonEntity:
          Name:
            NameFull: Huyck, Heidie L.
      – PersonEntity:
          Name:
            NameFull: Misra, Ravi S.
      – PersonEntity:
          Name:
            NameFull: Dutra, Jennifer
      – PersonEntity:
          Name:
            NameFull: Holden-Wiltse, Jeanne
      – PersonEntity:
          Name:
            NameFull: McDavid, Andrew N.
      – PersonEntity:
          Name:
            NameFull: Ashton, John M.
      – PersonEntity:
          Name:
            NameFull: Al Alam, Denise
      – PersonEntity:
          Name:
            NameFull: Potter, S. Steven
      – PersonEntity:
          Name:
            NameFull: Whitsett, Jeffrey A.
      – PersonEntity:
          Name:
            NameFull: Xu, Yan
      – PersonEntity:
          Name:
            NameFull: Pryhuber, Gloria S.
      – PersonEntity:
          Name:
            NameFull: Mariani, Thomas J.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 03
              Text: Mar2024
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 20734425
          Numbering:
            – Type: volume
              Value: 15
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Genes
              Type: main
ResultId 1