Your browser doesn't support javascript.
loading
ScoMorphoFISH: A deep learning enabled toolbox for single-cell single-mRNA quantification and correlative (ultra-)morphometry.
Siegerist, Florian; Hay, Eleonora; Dikou, Juan Saydou; Pollheimer, Marion; Büscher, Anja; Oh, Jun; Ribback, Silvia; Zimmermann, Uwe; Bräsen, Jan Hinrich; Lenoir, Olivia; Drenic, Vedran; Eller, Kathrin; Tharaux, Pierre-Louis; Endlich, Nicole.
Afiliación
  • Siegerist F; Institute for Anatomy and Cell Biology, University Medicine Greifswald, Greifswald, Germany.
  • Hay E; Institute for Anatomy and Cell Biology, University Medicine Greifswald, Greifswald, Germany.
  • Dikou JS; Section of Human Anatomy, Department of Mental and Physical Health and Preventive Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy.
  • Pollheimer M; Institute for Anatomy and Cell Biology, University Medicine Greifswald, Greifswald, Germany.
  • Büscher A; Institute of Pathology, Medical University of Graz, Graz, Austria.
  • Oh J; Department of Pediatrics II, University Hospital Essen, Essen, Germany.
  • Ribback S; Department of Pediatrics, University Hamburg-Eppendorf, Hamburg, Germany.
  • Zimmermann U; Department of Pathology, University Medicine Greifswald, Greifswald, Germany.
  • Bräsen JH; Department of Urology, University Medicine Greifswald, Greifswald, Germany.
  • Lenoir O; Nephropathology, Institute of Pathology, Medical School Hannover, Hannover, Germany.
  • Drenic V; PARCC, Paris Cardiovascular Research Centre, Inserm, Université Paris Cité, Paris, France.
  • Eller K; NIPOKA GmbH, Greifswald, Germany.
  • Tharaux PL; Division of Nephrology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.
  • Endlich N; PARCC, Paris Cardiovascular Research Centre, Inserm, Université Paris Cité, Paris, France.
J Cell Mol Med ; 26(12): 3513-3526, 2022 06.
Article en En | MEDLINE | ID: mdl-35593050
Increasing the information depth of single kidney biopsies can improve diagnostic precision, personalized medicine and accelerate basic kidney research. Until now, information on mRNA abundance and morphologic analysis has been obtained from different samples, missing out on the spatial context and single-cell correlation of findings. Herein, we present scoMorphoFISH, a modular toolbox to obtain spatial single-cell single-mRNA expression data from routinely generated kidney biopsies. Deep learning was used to virtually dissect tissue sections in tissue compartments and cell types to which single-cell expression data were assigned. Furthermore, we show correlative and spatial single-cell expression quantification with super-resolved podocyte foot process morphometry. In contrast to bulk analysis methods, this approach will help to identify local transcription changes even in less frequent kidney cell types on a spatial single-cell level with single-mRNA resolution. Using this method, we demonstrate that ACE2 can be locally upregulated in podocytes upon injury. In a patient suffering from COVID-19-associated collapsing FSGS, ACE2 expression levels were correlated with intracellular SARS-CoV-2 abundance. As this method performs well with standard formalin-fixed paraffin-embedded samples and we provide pretrained deep learning networks embedded in a comprehensive image analysis workflow, this method can be applied immediately in a variety of settings.
Asunto(s)
Palabras clave
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 / COVID-19 Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Cell Mol Med Asunto de la revista: BIOLOGIA MOLECULAR Año: 2022 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido
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 / COVID-19 Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Cell Mol Med Asunto de la revista: BIOLOGIA MOLECULAR Año: 2022 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido