A machine-learning approach to classifying neuron type
COLLABORATORS: Dr Akshay Bhinge (College of Medicine and Health), Dr David Richards (Physics), Professor Krasimira Tsaneva-Atanasova (Mathematics, Turing Fellow), Dr Kate Madden (University of Newcastle).
IDSAI Research Fellow: Dr Ravi Pandit
Description: Motor neuron disease (MND) is a devastating neurodegenerative condition characterized by loss of motor neurons. Understanding the molecular events that lead to motor neuron degeneration in MND is paramount in developing therapies to halt or even reverse the degeneration. Monitoring of motor neuron health in vitro is currently performed using immunostaining where cells are chemically fixed and specific intracellular proteins are stained with fluorescent antibodies. However, immunostaining does not allow real-time monitoring of motor neuron health and can lead to artefacts in neuronal structure. Real-time analysis is important because MND motor neurons display progressive deterioration over extended time periods in vitro. What is critically needed is the ability to identify motor neurons in unstained images. At the moment, however, this is simply not possible. In this project, the team aim to rectify this by developing a novel machine learning approach that can identify neuron type based solely on cell shape without the need for any staining.
