References of "Schwamborn, Jens Christian 50003060"
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See detailGenetic Architecture of Parkinson's Disease in the Indian Population: Harnessing Genetic Diversity to Address Critical Gaps in Parkinson's Disease Research.
Rajan, Roopa; Divya, K. P.; Kandadai, Rukmini Mridula et al

in Frontiers in neurology (2020), 11

Over the past two decades, our understanding of Parkinson's disease (PD) has been gleaned from the discoveries made in familial and/or sporadic forms of PD in the Caucasian population. The transferability ... [more ▼]

Over the past two decades, our understanding of Parkinson's disease (PD) has been gleaned from the discoveries made in familial and/or sporadic forms of PD in the Caucasian population. The transferability and the clinical utility of genetic discoveries to other ethnically diverse populations are unknown. The Indian population has been under-represented in PD research. The Genetic Architecture of PD in India (GAP-India) project aims to develop one of the largest clinical/genomic bio-bank for PD in India. Specifically, GAP-India project aims to: (1) develop a pan-Indian deeply phenotyped clinical repository of Indian PD patients; (2) perform whole-genome sequencing in 500 PD samples to catalog Indian genetic variability and to develop an Indian PD map for the scientific community; (3) perform a genome-wide association study to identify novel loci for PD and (4) develop a user-friendly web-portal to disseminate results for the scientific community. Our "hub-spoke" model follows an integrative approach to develop a pan-Indian outreach to develop a comprehensive cohort for PD research in India. The alignment of standard operating procedures for recruiting patients and collecting biospecimens with international standards ensures harmonization of data/bio-specimen collection at the beginning and also ensures stringent quality control parameters for sample processing. Data sharing and protection policies follow the guidelines established by local and national authorities.We are currently in the recruitment phase targeting recruitment of 10,200 PD patients and 10,200 healthy volunteers by the end of 2020. GAP-India project after its completion will fill a critical gap that exists in PD research and will contribute a comprehensive genetic catalog of the Indian PD population to identify novel targets for PD. [less ▲]

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See detailSynapse alterations precede neuronal damage and storage pathology in a human cerebral organoid model of CLN3-juvenile neuronal ceroid lipofuscinosis
Gomez Giro, Gemma UL; Arias-Fuenzalida, Jonathan; Jarazo, Javier UL et al

in Acta Neuropathologica Communications (2020)

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See detailMidbrain organoids: A new tool to investigate Parkinson's disease
Smits, Lisa UL; Schwamborn, Jens Christian UL

in Frontiers in Cell and Developmental Biology (2020)

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See detailMachine learning-assisted neurotoxicity prediction in human midbrain organoids
Monzel, Anna Sophia UL; Hemmer, K; Smits, Lisa UL et al

in Parkinsonism and Related Disorders (2020)

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See detailReproducible generation of human midbrain organoids for in vitro modeling of Parkinson’s disease
Nickels, Sarah Louise UL; Modamio Chamarro, Jennifer UL; Mendes-Pinheiro, Barbara et al

in Stem Cell Research (2020)

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

in Cell and Tissue Research (2020)

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See detailPassive controlled flow for Parkinson’s disease neuronal cell culture in 3D microfluidic devices
Kane, Khalid; Jarazo, Javier UL; Lucumi Moreno, Edinson et al

in Organs-on-a-Chip (2020)

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See detailPeptide-Imprinted Poly(hydroxymethyl 3,4-ethylenedioxythiophene) Nanotubes for Detection of Alpha Synuclein in Human Brain Organoids
Lee, Mei-Hwa; Liu, Kuan-Ting; Thomas, James Louis et al

in ACS Applied Nano Materials (2020)

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See detailImpaired Mitochondrial-Endoplasmic Reticulum Interaction and Mitophagy in Miro1-Mutant Neurons in Parkinson’s Disease
Berenguer-Escuder, Clara; Grossmann, Dajana; Antony, Paul UL et al

in Human Molecular Genetics (2020)

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See detailCultivation and characterization of human midbrain organoids in sensor integrated microfluidic chips
Spitz, Sarah; Zanetti, Cristian; Bolognin, Silvia UL et al

E-print/Working paper (2019)

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

in Scientific Reports (2019)

Autophagic processes play a central role in cellular homeostasis. In pathological conditions, the flow of autophagy can be affected at multiple and distinct steps of the pathway. Current analyses tools do ... [more ▼]

Autophagic processes play a central role in cellular homeostasis. In pathological conditions, the flow of autophagy can be affected at multiple and distinct steps of the pathway. Current analyses tools do not deliver the required detail for dissecting pathway intermediates. The development of new tools to analyze autophagic processes qualitatively and quantitatively in a more straightforward manner is required. Defining all autophagy pathway intermediates in a high-throughput manner is technologically challenging and has not been addressed yet. Here, we overcome those requirements and limitations by the developed of stable autophagy and mitophagy reporter-iPSC and the establishment of a novel high-throughput phenotyping platform utilizing automated high-content image analysis to assess autophagy and mitophagy pathway intermediates. [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 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 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 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)

Detailed reference viewed: 171 (22 UL)