Feb 03, 2025

Public workspaceNuclear senescence scoring

DOI
dx.doi.org/10.17504/protocols.io.8epv52z4jv1b/v1
  • 1Institut Imagine
  • Team Deleidi
Federico Bertoli
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DOI: dx.doi.org/10.17504/protocols.io.8epv52z4jv1b/v1
Protocol Citation: Maria Jose Perez J. 2025. Nuclear senescence scoring. protocols.io https://dx.doi.org/10.17504/protocols.io.8epv52z4jv1b/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: November 26, 2024
Last Modified: February 03, 2025
Protocol Integer ID: 112829
Funders Acknowledgements:
ASAP
Abstract
Nuclear senescence scoring
Immunofluorescence
Immunofluorescence
Stain iPSC-derived cells in monoculture and triculture systems with DAPI.
Perform immunolabeling of cell type-specific markers:
  • MAP2 for neurons,
  • IBA1 for microglia, and
  • GFAP for astrocytes.
Conduct imaging using a Leica TCS SP8 confocal microscope with a 40×/1.4 numerical aperture oil immersion objective.
Analysis of senescence using nuclear morphology (Heckenbach et al., 2022)
Analysis of senescence using nuclear morphology (Heckenbach et al., 2022)
Detection of nuclei in DAPI-stained images using a U-Net segmentation model.
Analysis of nuclear morphometrics, including area, convexity, and aspect ratio.
Resizing and conversion of nuclei to a two-color mask for consistent representation.
Application of an ensemble of deep neural networks trained on human fibroblasts to predict the likelihood of senescence.
Software used
Software used
  • Python 3.9.18,
  • TensorFlow GPU 2.15.0,
  • Keras 2.15.0,
  • Numpy 1.23.5,
  • Pandas 1.2.4.