Högskolan i Skövde

his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Artificial intelligence supports automated characterization of differentiated human pluripotent stem cells
Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Skara, Sweden.
University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Takara Bio Europe, Gothenburg, Sweden. (Translationell bioinformatik, Translational Bioinformatics)ORCID iD: 0000-0003-2942-6702
University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. (Translationell Bioinformatik, Translational Bioinformatics)ORCID iD: 0000-0003-4191-8435
Takara Bio Europe, Gothenburg, Sweden.ORCID iD: 0000-0003-4217-9355
Show others and affiliations
2023 (English)In: Stem Cells, ISSN 1066-5099, E-ISSN 1549-4918, Vol. 41, no 9, p. 850-861, article id sxad049Article in journal (Refereed) Published
Abstract [en]

Revolutionary advances in AI and deep learning in recent years have resulted in an upsurge of papers exploring applications within the biomedical field. Within stem cell research, promising results have been reported from analyses of microscopy images to e.g., distinguish between pluripotent stem cells and differentiated cell types derived from stem cells. In this work, we investigated the possibility of using a deep learning model to predict the differentiation stage of pluripotent stem cells undergoing differentiation towards hepatocytes, based on morphological features of cell cultures. We were able to achieve close to perfect classification of images from early and late time points during differentiation, and this aligned very well with the experimental validation of cell identity and function. Our results suggest that deep learning models can distinguish between different cell morphologies, and provide alternative means of semi-automated functional characterization of stem cell cultures.

Place, publisher, year, edition, pages
Oxford University Press, 2023. Vol. 41, no 9, p. 850-861, article id sxad049
Keywords [en]
artificial intelligence, cell differentiation, computer-assisted, hepatocytes, image analysis, pluripotent stem cells, quality control
National Category
Bioinformatics (Computational Biology) Cell and Molecular Biology
Research subject
Bioinformatics
Identifiers
URN: urn:nbn:se:his:diva-23064DOI: 10.1093/stmcls/sxad049ISI: 001025294200001PubMedID: 37357747Scopus ID: 2-s2.0-85171393798OAI: oai:DiVA.org:his-23064DiVA, id: diva2:1784794
Funder
Knowledge Foundation, 20170302Knowledge Foundation, 20200014University of Skövde
Note

CC BY 4.0

Corresponding author: Benjamin Ulfenborg, PhD, Department of Biology and Bioinformatics, School of Bioscience, University of Skövde, SE-541 28, Sweden. Email: benjamin.ulfenborg@his.se

This work was supported by the Swedish Knowledge Foundation (grant numbers 20170302 and 20200014), the Systems Biology Research Center, University of Skövde, Sweden and Takara Bio Europe, Gothenburg, Sweden.

Available from: 2023-07-31 Created: 2023-07-31 Last updated: 2023-12-19Bibliographically approved

Open Access in DiVA

fulltext(20346 kB)82 downloads
File information
File name FULLTEXT02.pdfFile size 20346 kBChecksum SHA-512
f4d8a613b22f5091b61b9600cc41657335a23a18068ed44ff56b7247afd759645a6fb659d6502a887eb51f0b7f0685df61b71a2fe7661db2055b836daf4562d5
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMedScopus

Authority records

Ghosheh, NidalStahlschmidt, Sören RichardKüppers-Munther, BarbaraSynnergren, JaneUlfenborg, Benjamin

Search in DiVA

By author/editor
Ghosheh, NidalStahlschmidt, Sören RichardKüppers-Munther, BarbaraSynnergren, JaneUlfenborg, Benjamin
By organisation
School of BioscienceSystems Biology Research Environment
In the same journal
Stem Cells
Bioinformatics (Computational Biology)Cell and Molecular Biology

Search outside of DiVA

GoogleGoogle Scholar
Total: 101 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 226 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf