Machine learning can use images of patient-derived stem cells to accurately predict subtypes of Parkinson’s disease, paving the way for personalised medicine and targeted drug therapy, British researchers have reported.
Researchers at the Francis Crick Institute and UCL Queen Square Institute of Neurology, London, UK, worked with technology company Faculty AI to establish that computer models can accurately classify four subtypes of Parkinson’s disease, with one reaching an accuracy of 95%.
Until now there has not been a way to accurately differentiate subtypes of the disease, which results in people being given non-specific diagnoses and do not always have access to targeted treatments, support or care.
The research team generated stem cells from patients’ own cells and chemically created four different subtypes of Parkinson’s disease, two involving pathways leading to toxic build-up of α-synuclein and two involving pathways leading to defunct mitochondria.
They imaged the disease models in microscopic detail and labelled key cell components, including lysosomes, which help to break down worn-out parts of the cell, and went on to train a computer program to recognise each subtype.
The mitochondria and lysosomes were the most important features in predicting the correct subtype, but other areas of the cell, including the nucleus, were also important.
James Evans, PhD student at the Crick and UCL, and co-first author with Karishma D’Sa and Gurvir Virdi, said: “Now that we use more advanced image techniques, we generate vast quantities of data, much of which is discarded when we manually select a few features of interest.
“Using AI in this study enabled us to evaluate a larger number of cell features and assess the importance of these features in discerning disease subtype. Using deep learning, we were able to extract much more information from our images than with conventional image analysis. We now hope to expand this approach to understand how these cellular mechanisms contribute to other subtypes of Parkinson’s.”
Sonia Gandhi, assistant research director and group leader of the Neurodegeneration Biology Laboratory at the Crick, added: “Using a model of the patient’s own neurons, and combining this with large numbers of images, we generated an algorithm to classify certain subtypes – a powerful approach that could open the door to identifying disease subtypes in life.
“Taking this one step further, our platform would allow us to first test drugs in stem cell models and predict whether a patient’s brain cells would be likely to respond to a drug, before enrolling into clinical trials. The hope is that one day this could lead to fundamental changes in how we deliver personalised medicine.”
The team hopes to go on to understand disease subtypes in people with other genetic mutations, and to work out whether sporadic cases of Parkinson’s disease can be classified in a similar way.
D’Sa K, Evans JR, Virdi GS et al. Prediction of mechanistic subtypes of Parkinson’s using patient derived stem cell models. Nature Machine Intelligence 10 August 2023; doi:10.1038/s42256-023-00702-9
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