References of "Schwamborn, Jens Christian 50003060"
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See detailFrom tech to bench: Deep Learning pipeline for image segmentation of high-throughput high-content microscopy data
Garcia Santa Cruz, Beatriz UL; Jarazo, Javier UL; Saraiva, Claudia UL et al

Poster (2019, November 29)

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards ... [more ▼]

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards precision medicine. Such data acquisition is feasible in the context of high-throughput screening in which the quality of the results relays on the accuracy of image analysis. Although the state-of-the-art solutions for image segmentation employ deep learning approaches, the high cost of manual data curation is hampering the real use in current biomedical research laboratories. Here, we propose a pipeline that employs deep learning not only to conduct accurate segmentation but also to assist with the creation of high-quality datasets in a less time-consuming solution for the experts. Weakly-labelled datasets are becoming a common alternative as a starting point to develop real-world solutions. Traditional approaches based on classical multimedia signal processing were employed to generate a pipeline specifically optimized for the high-throughput screening images of iPSC fused with rosella biosensor. Such pipeline produced good segmentation results but with several inaccuracies. We employed the weakly-labelled masks produced in this pipeline to train a multiclass semantic segmentation CNN solution based on U-net architecture. Since a strong class imbalance was detected between the classes, we employed a class sensitive cost function: Dice coe!cient. Next, we evaluated the accuracy between the weakly-labelled data and the trained network segmentation using double-blind tests conducted by experts in cell biology with experience in this type of images; as well as traditional metrics to evaluate the quality of the segmentation using manually curated segmentations by cell biology experts. In all the evaluations the prediction of the neural network overcomes the weakly-labelled data quality segmentation. Another big handicap that complicates the use of deep learning solutions in wet lab environments is the lack of user-friendly tools for non-computational experts such as biologists. To complete our solution, we integrated the trained network on a GUI built on MATLAB environment with non-programming requirements for the user. This integration allows conducting semantic segmentation of microscopy images in a few seconds. In addition, thanks to the patch-based approach it can be employed in images with different sizes. Finally, the human-experts can correct the potential inaccuracies of the prediction in a simple interactive way which can be easily stored and employed to re-train the network to improve its accuracy. In conclusion, our solution focuses on two important bottlenecks to translate leading-edge technologies in computer vision to biomedical research: On one hand, the effortless obtention of high-quality datasets with expertise supervision taking advantage of the proven ability of our CNN solution to generalize from weakly-labelled inaccuracies. On the other hand, the ease of use provided by the GUI integration of our solution to both segment images and interact with the predicted output. Overall this approach looks promising for fast adaptability to new scenarios. [less ▲]

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See detailDeep Learning Quality Control for High-Throughput High-Content Screening Microscopy Images
Garcia Santa Cruz, Beatriz UL; Jarazo, Javier UL; Schwamborn, Jens Christian UL et al

Poster (2019, October 10)

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards ... [more ▼]

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards precision medicine. Such data acquisition is feasible in the context of high-throughput high-content screening (HTHCS) in which the quality of the results relays on the accuracy of image analysis. Deep learning (DL) yields great performance in image analysis tasks especially with big amounts of data such as the produced in HTHCS contexts. Such DL and HTHCS strength is also their biggest weakness since DL solutions are highly sensitive to bad quality datasets. Hence, accurate Quality Control (QC) for microscopy HTHCS becomes an essential step to obtain reliable pipelines for HTHCS analysis. Usually, artifacts found on these platforms are the consequence of out-of-focus and undesirable density variations. The importance of accurate outlier detection becomes essential for both the training process of generic ML solutions (i.e. segmentation or classification) and the QC of the input data such solution will predict on. Moreover, during the QC of the input dataset, we aim not only to discard unsuitable images but to report the user on the quality of its dataset giving the user the choice to keep or discard the bad images. To build the QC solution we employed fluorescent microscopy images of rosella biosensor generated in the HTHCS platform. A total of 15 planes ranging from -6z to +7z steps to the two optimum planes. We evaluated 27 known focus measure operators and concluded that they have low sensitivity in noisy conditions. We propose a CNN solution which predicts the focus error based on the distance to the optimal plane, outperforming the evaluated focus operators. This QC allows for better results in cell segmentation models based on U-Net architecture as well as promising improvements in image classification tasks. [less ▲]

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See detailSingle-cell transcriptomics reveals multiple neuronal cell types in human midbrain-specific organoids
Smits, Lisa UL; Magni, Stefano UL; Grzyb, Kamil UL et al

E-print/Working paper (2019)

Human stem cell-derived organoids have great potential for modelling physiological and pathological processes. They recapitulate in vitro the organisation and function of a respective organ or part of an ... [more ▼]

Human stem cell-derived organoids have great potential for modelling physiological and pathological processes. They recapitulate in vitro the organisation and function of a respective organ or part of an organ. Human midbrain organoids (hMOs) have been described to contain midbrain-specific dopaminergic neurons that release the neurotransmitter dopamine. However, the human midbrain contains also additional neuronal cell types, which are functionally interacting with each other. Here, we analysed hMOs at high-resolution by means of single-cell RNA-sequencing (scRNA-seq), imaging and electrophysiology to unravel cell heterogeneity. Our findings demonstrate that hMOs show essential neuronal functional properties as spontaneous electrophysiological activity of different neuronal subtypes, including dopaminergic, GABAergic, and glutamatergic neurons. Recapitulating these in vivo features makes hMOs an excellent tool for in vitro disease phenotyping and drug discovery. [less ▲]

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See detailNon-proteolytic ubiquitination of OTULIN regulates NF-κB signaling pathway
Zhao, Mengmeng; Song, Kun; Hao, Wenzhuo et al

in Journal of Molecular Cell Biology (2019)

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See detailModeling Parkinson’s disease in midbrain-like organoids
Smits, Lisa UL; Reinhardt, Lydia; Reinhardt, Peter et al

in NPJ Parkinson's Disease (2019)

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See detailImpaired serine metabolism complements LRRK2-G2019S pathogenicity in PD patients
Nickels, Sarah UL; Walter, Jonas; Bolognin, Silvia UL et al

in Parkinsonism and Related Disorders (2019)

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See detailAutomated high-throughput highcontent autophagy and mitophagy analysis platform
Arias-Fuenzalida, Jonathan; Jarazo, Javier UL; Walter, Jonas et al

in Scientific Reports (2019)

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See detailAutomated microfluidic cell culture of stem cell derived dopaminergic neurons
Kane, Khalid; Lucumi Moreno, Edinson; Hachi, Siham et al

in Scientific Reports (2019)

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See detailSuccesses and Hurdles in Stem Cells Application and Production for Brain Transplantation
Henriques, Daniel; Moreira, Ricardo; Schwamborn, Jens Christian UL et al

in Frontiers in Neuroscience (2019)

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See detailAbsence of TRIM32 Leads to Reduced GABAergic Interneuron Generation and Autism-like Behaviors in Mice via Suppressing mTOR Signaling
Zhu, Jian-Wei; Zou, Ming-Ming; Li, Yi-Fei et al

in Cerebral Cortex (2019)

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See detail3D Cultures of Parkinson's Disease‐Specific Dopaminergic Neurons for High Content Phenotyping and Drug Testing
Bolognin, Silvia UL; Fossépré, Marie; Qing, Xiaobing et al

in Advanced Science (2018)

Parkinson's disease (PD)‐specific neurons, grown in standard 2D cultures, typically only display weak endophenotypes. The cultivation of PD patient‐specific neurons, derived from induced pluripotent stem ... [more ▼]

Parkinson's disease (PD)‐specific neurons, grown in standard 2D cultures, typically only display weak endophenotypes. The cultivation of PD patient‐specific neurons, derived from induced pluripotent stem cells carrying the LRRK2‐G2019S mutation, is optimized in 3D microfluidics. The automated image analysis algorithms are implemented to enable pharmacophenomics in disease‐relevant conditions. In contrast to 2D cultures, this 3D approach reveals robust endophenotypes. High‐content imaging data show decreased dopaminergic differentiation and branching complexity, altered mitochondrial morphology, and increased cell death in LRRK2‐G2019S neurons compared to isogenic lines without using stressor agents. Treatment with the LRRK2 inhibitor 2 (Inh2) rescues LRRK2‐G2019S‐dependent dopaminergic phenotypes. Strikingly, a holistic analysis of all studied features shows that the genetic background of the PD patients, and not the LRRK2‐G2019S mutation, constitutes the strongest contribution to the phenotypes. These data support the use of advanced in vitro models for future patient stratification and personalized drug development. [less ▲]

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See detailAdvanced Good Cell Culture Practice for human primary, stem cell-derived and organoid models as well as microphysiological systems
Pamies, David; Bal-Price, Anna; Chesné, Christophe et al

in ALTEX : Alternativen zu Tierexperimenten (2018)

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See detailMillifluidic culture improves human midbrain organoid vitality and differentiation
Berger, Emanuel UL; Magliaro, Chiara; Paczia, Nicole UL et al

in Lab on a Chip - Miniaturisation for Chemistry and Biology (2018)

Detailed reference viewed: 226 (44 UL)
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See detailNuclear localization and phosphorylation modulate pathological effects of Alpha-Synuclein
Pinho, Raquel; Paiva, Isabel; Jerčić, Kristina Gotovac et al

in Human Molecular Genetics (2018)

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See detailActivity of translation regulator eukaryotic elongation factor-2 kinase is increased in Parkinson disease brain and its inhibition reduces alpha synuclein toxicity
Jan, Asad; Jansonius, Brandon; Delaidelli, Alberto et al

in Acta Neuropathologica Communications (2018)

Detailed reference viewed: 85 (4 UL)