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Virtual tissue microstructure reconstruction across species using generative deep learning.
Bettancourt, Nicolás; Pérez-Gallardo, Cristian; Candia, Valeria; Guevara, Pamela; Kalaidzidis, Yannis; Zerial, Marino; Segovia-Miranda, Fabián; Morales-Navarrete, Hernán.
Afiliación
  • Bettancourt N; Faculty of Biological Sciences, Department of Cell Biology, Universidad de Concepción, Concepción, Chile.
  • Pérez-Gallardo C; Faculty of Biological Sciences, Grupo de Procesos en Biología del Desarrollo (GDeP), Universidad de Concepción, Concepción, Chile.
  • Candia V; Faculty of Engineering, Department of Electrical Engineering, Universidad de Concepción, Concepción, Chile.
  • Guevara P; Faculty of Biological Sciences, Department of Cell Biology, Universidad de Concepción, Concepción, Chile.
  • Kalaidzidis Y; Faculty of Biological Sciences, Grupo de Procesos en Biología del Desarrollo (GDeP), Universidad de Concepción, Concepción, Chile.
  • Zerial M; Faculty of Biological Sciences, Department of Cell Biology, Universidad de Concepción, Concepción, Chile.
  • Segovia-Miranda F; Faculty of Biological Sciences, Grupo de Procesos en Biología del Desarrollo (GDeP), Universidad de Concepción, Concepción, Chile.
  • Morales-Navarrete H; Faculty of Engineering, Department of Electrical Engineering, Universidad de Concepción, Concepción, Chile.
PLoS One ; 19(7): e0306073, 2024.
Article en En | MEDLINE | ID: mdl-38995963
ABSTRACT
Analyzing tissue microstructure is essential for understanding complex biological systems in different species. Tissue functions largely depend on their intrinsic tissue architecture. Therefore, studying the three-dimensional (3D) microstructure of tissues, such as the liver, is particularly fascinating due to its conserved essential roles in metabolic processes and detoxification. Here, we present TiMiGNet, a novel deep learning approach for virtual 3D tissue microstructure reconstruction using Generative Adversarial Networks and fluorescence microscopy. TiMiGNet overcomes challenges such as poor antibody penetration and time-intensive procedures by generating accurate, high-resolution predictions of tissue components across large volumes without the need of paired images as input. We applied TiMiGNet to analyze tissue microstructure in mouse and human liver tissue. TiMiGNet shows high performance in predicting structures like bile canaliculi, sinusoids, and Kupffer cell shapes from actin meshwork images. Remarkably, using TiMiGNet we were able to computationally reconstruct tissue structures that cannot be directly imaged due experimental limitations in deep dense tissues, a significant advancement in deep tissue imaging. Our open-source virtual prediction tool facilitates accessible and efficient multi-species tissue microstructure analysis, accommodating researchers with varying expertise levels. Overall, our method represents a powerful approach for studying tissue microstructure, with far-reaching applications in diverse biological contexts and species.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / Hígado Límite: Animals / Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: Chile Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / Hígado Límite: Animals / Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: Chile Pais de publicación: Estados Unidos