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13 results on '"Schwarz, Martin Karl"'

8. Whole-brain 3D mapping of human neural transplant innervation

Author

Doerr, Jonas, primary, Schwarz, Martin Karl, additional, Wiedermann, Dirk, additional, Leinhaas, Anke, additional, Jakobs, Alina, additional, Schloen, Florian, additional, Schwarz, Inna, additional, Diedenhofen, Michael, additional, Braun, Nils Christian, additional, Koch, Philipp, additional, Peterson, Daniel A., additional, Kubitscheck, Ulrich, additional, Hoehn, Mathias, additional, and Brüstle, Oliver, additional

Published
2017
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9. Glutamatergic synaptic integration of locomotion speed via septoentorhinal projections

Author

Justus, Daniel, primary, Dalügge, Dennis, additional, Bothe, Stefanie, additional, Fuhrmann, Falko, additional, Hannes, Christian, additional, Kaneko, Hiroshi, additional, Friedrichs, Detlef, additional, Sosulina, Liudmila, additional, Schwarz, Inna, additional, Elliott, David Anthony, additional, Schoch, Susanne, additional, Bradke, Frank, additional, Schwarz, Martin Karl, additional, and Remy, Stefan, additional

Published
2016
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11. Glutamatergic synaptic integration of locomotion speed via septoentorhinal projections

Author

Justus, Daniel, Dalügge, Dennis, Bothe, Stefanie, Fuhrmann, Falko, Hannes, Christian, Kaneko, Hiroshi, Friedrichs, Detlef, Sosulina, Liudmila, Schwarz, Inna, Elliott, David Anthony, Schoch, Susanne, Bradke, Frank, Schwarz, Martin Karl, and Remy, Stefan

Abstract

The medial septum and diagonal band of Broca (MSDB) send glutamatergic axons to medial entorhinal cortex (MEC). We found that this pathway provides speed-correlated input to several MEC cell-types in layer 2/3. The speed signal is integrated most effectively by pyramidal cells but also excites stellate cells and interneurons. Thus, the MSDB conveys speed information that can be used by MEC neurons for spatial representation of self-location.

Published
2017
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12. Human stem cell-based models for studying autism spectrum disorder-related neuronal dysfunction

Author

Cheffer, Arquimedes, Flitsch, Lea Jessica, Krutenko, Tamara, Röderer, Pascal, Sokhranyaeva, Liubov, Iefremova, Vira, Hajo, Mohamad, Peitz, Michael, Schwarz, Martin Karl, and Brüstle, Oliver

Subjects
Neurons, In vitro differentiation, Autism Spectrum Disorder, Induced Pluripotent Stem Cells, Cell reprogramming, Review, Cellular Reprogramming, Neuronal connectivity, Models, Biological, lcsh:RC346-429, Organoids, Brain organoids, Animals, Humans, lcsh:Neurology. Diseases of the nervous system
Abstract

The controlled differentiation of pluripotent stem cells (PSCs) into neurons and glia offers a unique opportunity to study early stages of human central nervous system development under controlled conditions in vitro. With the advent of cell reprogramming and the possibility to generate induced pluripotent stem cells (iPSCs) from any individual in a scalable manner, these studies can be extended to a disease- and patient-specific level. Autism spectrum disorder (ASD) is considered a neurodevelopmental disorder, with substantial evidence pointing to early alterations in neurogenesis and network formation as key pathogenic drivers. For that reason, ASD represents an ideal candidate for stem cell-based disease modeling. Here, we provide a concise review on recent advances in the field of human iPSC-based modeling of syndromic and non-syndromic forms of ASD, with a particular focus on studies addressing neuronal dysfunction and altered connectivity. We further discuss recent efforts to translate stem cell-based disease modeling to 3D via brain organoid and cell transplantation approaches, which enable the investigation of disease mechanisms in a tissue-like context. Finally, we describe advanced tools facilitating the assessment of altered neuronal function, comment on the relevance of iPSC-based models for the assessment of pharmaceutical therapies and outline potential future routes in stem cell-based ASD research.

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13. Airy beam light sheet microscopy boosted by deep learning deconvolution.

Author

Stockhausen A, Rodriguez-Gatica JE, Schweihoff J, Schwarz MK, and Kubitscheck U

Abstract

Common light sheet microscopy comes with a trade-off between light sheet width defining the optical sectioning and the usable field of view arising from the divergence of the illuminating Gaussian beam. To overcome this, low-diverging Airy beams have been introduced. Airy beams, however, exhibit side lobes degrading image contrast. Here, we constructed an Airy beam light sheet microscope, and developed a deep learning image deconvolution to remove the effects of the side lobes without knowledge of the point spread function. Using a generative adversarial network and high-quality training data, we significantly enhanced image contrast and improved the performance of a bicubic upscaling. We evaluated the performance with fluorescently labeled neurons in mouse brain tissue samples. We found that deep learning-based deconvolution was about 20-fold faster than the standard approach. The combination of Airy beam light sheet microscopy and deep learning deconvolution allows imaging large volumes rapidly and with high quality.

Published
2023
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