Uncovering developmental time and tempo using deep learning

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2023
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
ArXiv-ID
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Hybrid
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
Nature Methods. Springer. 2023, 20(12), pp. 2000-2010. ISSN 1548-7091. eISSN 1548-7105. Available under: doi: 10.1038/s41592-023-02083-8
Zusammenfassung

During animal development, embryos undergo complex morphological changes over time. Differences in developmental tempo between species are emerging as principal drivers of evolutionary novelty, but accurate description of these processes is very challenging. To address this challenge, we present here an automated and unbiased deep learning approach to analyze the similarity between embryos of different timepoints. Calculation of similarities across stages resulted in complex phenotypic fingerprints, which carry characteristic information about developmental time and tempo. Using this approach, we were able to accurately stage embryos, quantitatively determine temperature-dependent developmental tempo, detect naturally occurring and induced changes in the developmental progression of individual embryos, and derive staging atlases for several species de novo in an unsupervised manner. Our approach allows us to quantify developmental time and tempo objectively and provides a standardized way to analyze early embryogenesis.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
570 Biowissenschaften, Biologie
Schlagwörter
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690TOULANY, Nikan, Hernán MORALES-NAVARRETE, Daniel CAPEK, Jannis GRATHWOHL, Murat ÜNALAN, Patrick MÜLLER, 2023. Uncovering developmental time and tempo using deep learning. In: Nature Methods. Springer. 2023, 20(12), pp. 2000-2010. ISSN 1548-7091. eISSN 1548-7105. Available under: doi: 10.1038/s41592-023-02083-8
BibTex
@article{Toulany2023Uncov-69445,
  year={2023},
  doi={10.1038/s41592-023-02083-8},
  title={Uncovering developmental time and tempo using deep learning},
  number={12},
  volume={20},
  issn={1548-7091},
  journal={Nature Methods},
  pages={2000--2010},
  author={Toulany, Nikan and Morales-Navarrete, Hernán and Capek, Daniel and Grathwohl, Jannis and Ünalan, Murat and Müller, Patrick}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/69445">
    <dc:creator>Toulany, Nikan</dc:creator>
    <dc:contributor>Capek, Daniel</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/>
    <dc:contributor>Grathwohl, Jannis</dc:contributor>
    <dc:contributor>Morales-Navarrete, Hernán</dc:contributor>
    <dcterms:title>Uncovering developmental time and tempo using deep learning</dcterms:title>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/>
    <dc:creator>Müller, Patrick</dc:creator>
    <dc:contributor>Müller, Patrick</dc:contributor>
    <dc:creator>Morales-Navarrete, Hernán</dc:creator>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/69445/1/Toulany_2-eqyalyyv6hva9.pdf"/>
    <dc:creator>Ünalan, Murat</dc:creator>
    <dcterms:issued>2023</dcterms:issued>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/69445/1/Toulany_2-eqyalyyv6hva9.pdf"/>
    <dc:language>eng</dc:language>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Capek, Daniel</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/69445"/>
    <dc:contributor>Ünalan, Murat</dc:contributor>
    <dcterms:abstract>During animal development, embryos undergo complex morphological changes over time. Differences in developmental tempo between species are emerging as principal drivers of evolutionary novelty, but accurate description of these processes is very challenging. To address this challenge, we present here an automated and unbiased deep learning approach to analyze the similarity between embryos of different timepoints. Calculation of similarities across stages resulted in complex phenotypic fingerprints, which carry characteristic information about developmental time and tempo. Using this approach, we were able to accurately stage embryos, quantitatively determine temperature-dependent developmental tempo, detect naturally occurring and induced changes in the developmental progression of individual embryos, and derive staging atlases for several species de novo in an unsupervised manner. Our approach allows us to quantify developmental time and tempo objectively and provides a standardized way to analyze early embryogenesis.</dcterms:abstract>
    <dc:rights>Attribution 4.0 International</dc:rights>
    <dc:contributor>Toulany, Nikan</dc:contributor>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2024-03-01T11:50:06Z</dcterms:available>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2024-03-01T11:50:06Z</dc:date>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/>
    <dc:creator>Grathwohl, Jannis</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Ja
Diese Publikation teilen