Cell and Network Disruptions and Associated Pathogenenesis in Tauopathy and Down Syndrome

项目总结

PROJECT SUMMARY Our studies will develop and implement novel artificial intelligence (AI)/ machine learning (ML) technologies to reduce experimental unethical bias in analysis of imaging data of our studies in our parent, NIH-funded grant (AG064579-02) that focuses on identifying mechanisms of neurodegeneration in Alzheimer’s disease and Frontotemporal dementia. We study neurodegeneration in human iPSC-derived neurons (i-neurons) of controls and patients with tauopathies in 3-dimensional (3D) human brain organoids and use robotic microscopy (RM) to monitor changes in morphology and structure of individual i-neurons in large populations of heterogeneous cells over time as an indicator of neurodegeneration. Since the initiation of this grant, we have developed novel approaches to study mechanisms of neurodegeneration with a unique biosensor (Genetically encoded cell death indicator – GEDI) that acutely identifies living neurons at a stage at which they are irreversibly committed to die. Initially, imaging data from these studies involved human curation, which carries some degree of experimental bias that can cause ethical problems in interpretation of data. To reduce experimental bias of our data analysis, we have developed ML and deep neural networks (DNN) and use a subclass of DNN, convolutional neural networks (CNNs) which have mathematical properties particularly adept at Computer Vision. We have developed deep learning (DL) algorithms for detecting neuronal death by constructing a novel quantitative RM pipeline that automatically generates GEDI-curated data to train a CNN without human input. The resulting GEDI-CNN detects neuronal death from images of morphology alone, alleviating the need for any additional use of GEDI in subsequent experiments. Through systematic analysis of a trained GEDI-CNN, we find that it learns to detect death in neurons by locating morphology linked to death, despite receiving no explicit supervision toward these features. Uniquely, it detects cell death as a change in nuclear readouts as well as other cellular features, which human curation can’t easily identify. We also show that this model generalizes to images captured with different parameters or displays of neurons and cell types from different species without additional training. The advances we made in unbiased AI image analysis are not restricted to our benefit but will be applicable to a large range of ML based imaging studies of other investigators because it focuses on improving how the CNN algorithms are trained to analyze data without the need of humans but with super-human accuracy. In this supplemental application, we will further develop this novel ethical AI technology for studies on neurodegeneration of human i-neurons in 3D brain organoids using GEDI-CNN. We will refine the CNN algorithms to optimize and standardize its widespread use in ethical analysis of live imaging analysis and provide the technology to the scientific community for AI-based imaging research.