The creation of this phenotypic profiling platform involves automated cell culture, medical imaging, cell marking and the latest artificial intelligence technologies. In the future, this platform could be used for complex disease modeling and drug screening applications.
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Parkinson’s disease, a common neurodegenerative disease
Parkinson’s disease is the second most common neurodegenerative disease, affecting 2% to 3% of people over the age of 65. These are more than 120,000 people concerned in France and more than 8,000 new cases each year.
This pathology is characterized by a slow and progressive self-destruction of the nerve cells of the dark matter of the brain, that is to say the dopaminergic neurons. The progressive destruction of these cells leads to a deficit in dopamine in the patient, a neurotransmitter which ensures the passage of nerve impulses at the level of the synapses.
At first glance, the symptoms of this disease can resemble aging and it is only after a slow evolution over several years that more specific symptoms of the disease appear.
The motor symptoms of Parkinson disease consist of abnormal localized tremors in the hands and arms, even when the person is at rest. Tremors are not systematic since they only concern 70% of cases. These tremors are accompanied by akinesia, ie slowness of movement and non-swinging of the arms when the person walks. The patient is also affected by hypertension which is characterized by a progressive stiffening of the muscles and a curvature of the back.
The non-motor symptoms of Parkinson’s disease are essentially cognitive disorders that affect memory, comprehension and mood. The disease is also accompanied by balance disorders and sleep problems.
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Marked skin cells “coupled” with artificial intelligence
The establishment of biobanks allows researchers to have access to a vast collection of cells from patients suffering from Parkinson’s disease. For their work, the scientists used cells from 91 sick patients, but also from healthy controls.
The researchers cultured fibroblasts from biopsies taken from these patients and then labeled them with a fluorescent molecule called propidium iodide which binds to the bases of the DNA of the cells allowing demonstration by fluorescence microscopy, confocal microscopy or even flow cytometry. In this case, the researchers established a high-resolution optical microscopy image bank.
All of these images were fed into an artificial intelligence-based image analysis pipeline to highlight particular phenotypic characteristics of diseased patient fibroblasts in order to distinguish them from disease-free controls.
This cell staining method associated with artificial intelligence makes it possible to highlight specific characteristics of fibroblasts, otherwise undetectable, in patients with Parkinson’s disease. The algorithms used therefore allow the discovery of new signatures of the disease. These new signatures are of considerable interest, in particular for diagnosing the disease, discovering new drugs and even determining subtypes of Parkinson’s within the sick population itself.
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New applications in drug research
These new signatures of Parkinson’s disease will now allow researchers to carry out “screenings” of potentially therapeutic molecules on the cells of patients affected by the disease. This type of screening is a test method for discovering potentially interesting molecules that can be used as drugs to treat and slow down the progression of the disease as much as possible.
This research on Parkinson’s disease has yielded a large amount of data that scientists leave accessible to the research community around the world.
Their analysis platform is not only intended for research on Parkinson’s disease. It can therefore be used for research on other cell types such as induced pluripotent stem cells. The induced pluripotent stem cells or iPSCs come from somatic adult cells returned to the state of pluripotency, that is to say capable of differentiating into embryonic layers or germ cells by the effect of growth factors and transcription factors.
Thanks to this tool, researchers hope to be able to model many diseases and open up new therapeutic avenues when traditional drug research methods prove unsuccessful. The implications of this technology combining cell culture and artificial intelligence are important for precision medicine and for the development of drug treatments for pathologies that are difficult to treat.
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Parkinson’s: artificial intelligence allows the discovery of new signatures of the disease
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