Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Computational Identification of Ligand-Receptor Pairs that Drive Human Astrocyte Development

AJ Voss, SN Lanjewar, MM Sampson, A King, E Hill, A Sing, C Sojka, SA Sloan
doi: https://doi.org/10.1101/2022.05.31.491513
AJ Voss
Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
SN Lanjewar
Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
MM Sampson
Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
A King
Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
E Hill
Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
A Sing
Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
C Sojka
Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
SA Sloan
Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: sasloan@emory.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

SUMMARY

Extrinsic signaling between diverse cell types is crucial to nervous system development. Ligand binding is a key driver of developmental processes, but it remains a significant challenge to disentangle how collections of these signals act cooperatively to affect changes in recipient cells. In the developing human brain, cortical progenitors transition from neurogenesis to gliogenesis in a stereotyped progression that is influenced by extrinsic ligands. Therefore, we sought to use the wealth of published genomic data in the developing human brain to identify and then test novel ligand combinations that act synergistically to drive gliogenesis. Using computational tools, we identified ligand-receptor pairs that are expressed at appropriate developmental stages, in relevant cell types, and whose activation is predicted to cooperatively stimulate complimentary astrocyte gene signatures. We then tested a group of five neuronally-secreted ligands and validated their synergistic contributions to astrocyte development within both human cortical organoids and primary fetal tissue. We confirm cooperative capabilities of these ligands far greater than their individual capacities and discovered that their combinatorial effects converge on AKT/mTOR signaling to drive transcriptomic and morphological features of astrocyte development. This platform provides a powerful agnostic framework to identify and test how extrinsic signals work in concert to drive developmental processes.

HIGHLIGHTS

  • Computational prediction of active ligand-receptor pairs in the developing brain

  • Synergistic contributions of predicted ligands drive astrocyte development

  • Ligands induce transcriptomic and morphological features of mature astrocytes

  • Cooperative ligand activity converges on AKT/mTOR signaling

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted June 01, 2022.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Computational Identification of Ligand-Receptor Pairs that Drive Human Astrocyte Development
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Computational Identification of Ligand-Receptor Pairs that Drive Human Astrocyte Development
AJ Voss, SN Lanjewar, MM Sampson, A King, E Hill, A Sing, C Sojka, SA Sloan
bioRxiv 2022.05.31.491513; doi: https://doi.org/10.1101/2022.05.31.491513
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Computational Identification of Ligand-Receptor Pairs that Drive Human Astrocyte Development
AJ Voss, SN Lanjewar, MM Sampson, A King, E Hill, A Sing, C Sojka, SA Sloan
bioRxiv 2022.05.31.491513; doi: https://doi.org/10.1101/2022.05.31.491513

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4085)
  • Biochemistry (8755)
  • Bioengineering (6477)
  • Bioinformatics (23331)
  • Biophysics (11740)
  • Cancer Biology (9144)
  • Cell Biology (13237)
  • Clinical Trials (138)
  • Developmental Biology (7410)
  • Ecology (11364)
  • Epidemiology (2066)
  • Evolutionary Biology (15084)
  • Genetics (10397)
  • Genomics (14006)
  • Immunology (9115)
  • Microbiology (22036)
  • Molecular Biology (8777)
  • Neuroscience (47345)
  • Paleontology (350)
  • Pathology (1420)
  • Pharmacology and Toxicology (2480)
  • Physiology (3703)
  • Plant Biology (8045)
  • Scientific Communication and Education (1431)
  • Synthetic Biology (2207)
  • Systems Biology (6014)
  • Zoology (1249)