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Deep Learning Neural Networks Highly Predict Very Early Onset of Pluripotent Stem Cell Differentiation.
Waisman, Ariel; La Greca, Alejandro; Möbbs, Alan M; Scarafía, María Agustina; Santín Velazque, Natalia L; Neiman, Gabriel; Moro, Lucía N; Luzzani, Carlos; Sevlever, Gustavo E; Guberman, Alejandra S; Miriuka, Santiago G.
Afiliação
  • Waisman A; LIAN-CONICET, FLENI, Ruta 9 Km 52.2 (B1625XAF), Belén de Escobar, Argentina.
  • La Greca A; LIAN-CONICET, FLENI, Ruta 9 Km 52.2 (B1625XAF), Belén de Escobar, Argentina.
  • Möbbs AM; LIAN-CONICET, FLENI, Ruta 9 Km 52.2 (B1625XAF), Belén de Escobar, Argentina.
  • Scarafía MA; LIAN-CONICET, FLENI, Ruta 9 Km 52.2 (B1625XAF), Belén de Escobar, Argentina.
  • Santín Velazque NL; LIAN-CONICET, FLENI, Ruta 9 Km 52.2 (B1625XAF), Belén de Escobar, Argentina.
  • Neiman G; LIAN-CONICET, FLENI, Ruta 9 Km 52.2 (B1625XAF), Belén de Escobar, Argentina.
  • Moro LN; LIAN-CONICET, FLENI, Ruta 9 Km 52.2 (B1625XAF), Belén de Escobar, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
  • Luzzani C; LIAN-CONICET, FLENI, Ruta 9 Km 52.2 (B1625XAF), Belén de Escobar, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
  • Sevlever GE; LIAN-CONICET, FLENI, Ruta 9 Km 52.2 (B1625XAF), Belén de Escobar, Argentina.
  • Guberman AS; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina; Laboratorio de Regulación Génica en Células Madre, Departamento de Química Biológica y Departamento de Fisiología, Biología Molecular y Celular, FCEN, Universidad de Buenos Aires, Argentina, Intendente Gui
  • Miriuka SG; LIAN-CONICET, FLENI, Ruta 9 Km 52.2 (B1625XAF), Belén de Escobar, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina. Electronic address: smiriuka@fleni.org.ar.
Stem Cell Reports ; 12(4): 845-859, 2019 04 09.
Article em En | MEDLINE | ID: mdl-30880077
Deep learning is a significant step forward for developing autonomous tasks. One of its branches, computer vision, allows image recognition with high accuracy thanks to the use of convolutional neural networks (CNNs). Our goal was to train a CNN with transmitted light microscopy images to distinguish pluripotent stem cells from early differentiating cells. We induced differentiation of mouse embryonic stem cells to epiblast-like cells and took images at several time points from the initial stimulus. We found that the networks can be trained to recognize undifferentiated cells from differentiating cells with an accuracy higher than 99%. Successful prediction started just 20 min after the onset of differentiation. Furthermore, CNNs displayed great performance in several similar pluripotent stem cell (PSC) settings, including mesoderm differentiation in human induced PSCs. Accurate cellular morphology recognition in a simple microscopic set up may have a significant impact on how cell assays are performed in the near future.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Diferenciação Celular / Redes Neurais de Computação / Células-Tronco Pluripotentes / Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Stem Cell Reports Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Argentina País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Diferenciação Celular / Redes Neurais de Computação / Células-Tronco Pluripotentes / Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Stem Cell Reports Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Argentina País de publicação: Estados Unidos