Machine learning-assisted neurotoxicity prediction in human midbrain organoids

Monzel, Anna S.; Hemmer, Kathrin; Kaoma, Tony; Smits, Lisa M.; Bolognin, Silvia; Lucarelli, Philippe; Rosety, Isabel; Žagare, Alise; Antony, Paul; Nickels, Sarah Louise; Krüger, Rejko; Azuaje, Francisco; Schwamborn, Jens Christian

doi:10.1016/j.parkreldis.2020.05.011

Cited by 48 publications

(22 citation statements)

References 10 publications

(15 reference statements)

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“…Neuromelanin inclusions characteristic of the human substantia nigra pars compacta have been observed after two months in culture [ 141 ]. MO DA neurons have been shown to be susceptible to neuronal death caused by the known neurotoxins 6-hydroxydopamine (6-OHDA) and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) [ 118 , 123 , 143 ]. MOs derived from iPSCs carrying the known PD risk mutation LRRK2 -G2019S have been shown to demonstrate aberrant accumulation and deposition of α-synuclein, which was successfully ameliorated by LRRK2 kinase inhibitor treatment [ 123 ].…”

Section: 3d Brain Organoids As Models Of Neurodegenerative Diseasementioning

confidence: 99%

Utilising Induced Pluripotent Stem Cells in Neurodegenerative Disease Research: Focus on Glia

Albert

Niskanen

Kälvälä

et al. 2021

IJMS

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Induced pluripotent stem cells (iPSCs) are a self-renewable pool of cells derived from an organism’s somatic cells. These can then be programmed to other cell types, including neurons. Use of iPSCs in research has been two-fold as they have been used for human disease modelling as well as for the possibility to generate new therapies. Particularly in complex human diseases, such as neurodegenerative diseases, iPSCs can give advantages over traditional animal models in that they more accurately represent the human genome. Additionally, patient-derived cells can be modified using gene editing technology and further transplanted to the brain. Glial cells have recently become important avenues of research in the field of neurodegenerative diseases, for example, in Alzheimer’s disease and Parkinson’s disease. This review focuses on using glial cells (astrocytes, microglia, and oligodendrocytes) derived from human iPSCs in order to give a better understanding of how these cells contribute to neurodegenerative disease pathology. Using glia iPSCs in in vitro cell culture, cerebral organoids, and intracranial transplantation may give us future insight into both more accurate models and disease-modifying therapies.

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Section: 3d Brain Organoids As Models Of Neurodegenerative Diseasementioning

confidence: 99%

Utilising Induced Pluripotent Stem Cells in Neurodegenerative Disease Research: Focus on Glia

Albert

Niskanen

Kälvälä

et al. 2021

IJMS

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“…This combined strategy reveals the value of human cell-based assays for predictive toxicology. Similarly, Monzel et al developed a pipeline for a machine learning method, allowing for detailed image-based cell profiling and toxicity prediction in brain organoids treated with the neurotoxic compound 6-hydroxydopamine (6-OHDA) [162].…”

Section: Artificial Intelligence Technologiesmentioning

confidence: 99%

Patient-Derived Induced Pluripotent Stem Cell-Based Models in Parkinson’s Disease for Drug Identification

Kouroupi

Antoniou

Prodromidou

et al. 2020

IJMS

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Parkinson’s disease (PD) is a common progressive neurodegenerative disorder characterized by loss of striatal-projecting dopaminergic neurons of the ventral forebrain, resulting in motor and cognitive deficits. Despite extensive efforts in understanding PD pathogenesis, no disease-modifying drugs exist. Recent advances in cell reprogramming technologies have facilitated the generation of patient-derived models for sporadic or familial PD and the identification of early, potentially triggering, pathological phenotypes while they provide amenable systems for drug discovery. Emerging developments highlight the enhanced potential of using more sophisticated cellular systems, including neuronal and glial co-cultures as well as three-dimensional systems that better simulate the human pathophysiology. In combination with high-throughput high-content screening technologies, these approaches open new perspectives for the identification of disease-modifying compounds. In this review, we discuss current advances and the challenges ahead in the use of patient-derived induced pluripotent stem cells for drug discovery in PD. We address new concepts implicating non-neuronal cells in disease pathogenesis and highlight the necessity for functional assays, such as calcium imaging and multi-electrode array recordings, to predict drug efficacy. Finally, we argue that artificial intelligence technologies will be pivotal for analysis of the large and complex data sets obtained, becoming game-changers in the process of drug discovery.

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“…Deep learning training was done on accepted masks after unbiased stereology using an adaptive segmentation algorithm (ASA) (Mouton et al, 2017) and examined on test data. In one of the recent studies, machine learning was used to predict neurotoxicity in organoids of midbrain dopaminergic neurons in which Parkinson's disease (PD) was induced by 6OHDA neurotoxin (Monzel et al, 2020). Organoids were derived from human iPSC (induced pluripotent stem cell) lines.…”

Section: Ai/deep Learning In Automated Detection and Analysis Of Toxicity In Different Regions Of Brainmentioning

confidence: 99%

Quantitative neurotoxicology: Potential role of artificial intelligence/deep learning approach

Srivastava

Hanig

2020

J of Applied Toxicology

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Neurotoxicity studies are important in the preclinical stages of drug development process, because exposure to certain compounds that may enter the brain across a permeable blood brain barrier damages neurons and other supporting cells such as astrocytes. This could, in turn, lead to various neurological disorders such as Parkinson's or Huntington's disease as well as various dementias. Toxicity assessment is often done by pathologists after these exposures by qualitatively or semiquantitatively grading the severity of neurotoxicity in histopathology slides. Quantification of the extent of neurotoxicity supports qualitative histopathological analysis and provides a better understanding of the global extent of brain damage. Stereological techniques such as the utilization of an optical fractionator provide an unbiased quantification of the neuronal damage; however, the process is time‐consuming. Advent of whole slide imaging (WSI) introduced digital image analysis which made quantification of neurotoxicity automated, faster and with reduced bias, making statistical comparisons possible. Although automated to a certain level, simple digital image analysis requires manual efforts of experts which is time‐consuming and limits analysis of large datasets. Digital image analysis coupled with a deep learning artificial intelligence model provides a good alternative solution to time‐consuming stereological and simple digital analysis. Deep learning models could be trained to identify damaged or dead neurons in an automated fashion. This review has focused on and discusses studies demonstrating the role of deep learning in segmentation of brain regions, toxicity detection and quantification of degenerated neurons as well as the estimation of area/volume of degeneration.

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Machine learning-assisted neurotoxicity prediction in human midbrain organoids

Cited by 48 publications

References 10 publications

Utilising Induced Pluripotent Stem Cells in Neurodegenerative Disease Research: Focus on Glia

Utilising Induced Pluripotent Stem Cells in Neurodegenerative Disease Research: Focus on Glia

Patient-Derived Induced Pluripotent Stem Cell-Based Models in Parkinson’s Disease for Drug Identification

Quantitative neurotoxicology: Potential role of artificial intelligence/deep learning approach

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