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Subject: human tissue × WGL Institute: IPHT ×

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    A new human adipocyte model with PTEN haploinsufficiency
    (Abingdon : Taylor and Francis Inc., 2020) Kässner F.; Kirstein A.; Händel N.; Schmid G.L.; Landgraf K.; Berthold A.; Tannert A.; Schaefer M.; Wabitsch M.; Kiess W.; Körner A.; Garten A.
    Few human cell strains are suitable and readily available as in vitro adipocyte models. We used resected lipoma tissue from a patient with germline phosphatase and tensin homolog (PTEN) haploinsufficiency to establish a preadipocyte cell strain termed LipPD1 and aimed to characterize cellular functions and signalling pathway alterations in comparison to the established adipocyte model Simpson-Golabi-Behmel-Syndrome (SGBS) and to primary stromal-vascular fraction cells. We found that both cellular life span and the capacity for adipocyte differentiation as well as adipocyte-specific functions were preserved in LipPD1 and comparable to SGBS adipocytes. Basal and growth factor-stimulated activation of the PI3 K/AKT signalling pathway was increased in LipPD1 preadipocytes, corresponding to reduced PTEN levels in comparison to SGBS cells. Altogether, LipPD1 cells are a novel primary cell model with a defined genetic lesion suitable for the study of adipocyte biology. © 2020, © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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    Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning
    (Washington, DC : OSA, 2021) Pradhan, Pranita; Meyer, Tobias; Vieth, Michael; Stallmach, Andreas; Waldner, Maximilian; Schmitt, Michael; Popp, Juergen; Bocklitz, Thomas
    Hematoxylin and Eosin (H&E) staining is the 'gold-standard' method in histopathology. However, standard H&E staining of high-quality tissue sections requires long sample preparation times including sample embedding, which restricts its application for 'real-time' disease diagnosis. Due to this reason, a label-free alternative technique like non-linear multimodal (NLM) imaging, which is the combination of three non-linear optical modalities including coherent anti-Stokes Raman scattering, two-photon excitation fluorescence and second-harmonic generation, is proposed in this work. To correlate the information of the NLM images with H&E images, this work proposes computational staining of NLM images using deep learning models in a supervised and an unsupervised approach. In the supervised and the unsupervised approach, conditional generative adversarial networks (CGANs) and cycle conditional generative adversarial networks (cycle CGANs) are used, respectively. Both CGAN and cycle CGAN models generate pseudo H&E images, which are quantitatively analyzed based on mean squared error, structure similarity index and color shading similarity index. The mean of the three metrics calculated for the computationally generated H&E images indicate significant performance. Thus, utilizing CGAN and cycle CGAN models for computational staining is beneficial for diagnostic applications without performing a laboratory-based staining procedure. To the author's best knowledge, it is the first time that NLM images are computationally stained to H&E images using GANs in an unsupervised manner.