Novel artificial intelligence-based approaches to understand the pathological and genetic drivers of primary tauopathies

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ABSTRACT Tau normally regulates microtubules in neurons and glia, however during diseases pathogenesis, several post- translational modifications cause hyperphosphorylation of this protein which consequently is toxic to the cell. Primary age-related tauopathy (PART), a common pathology associated with human aging is estimated to effect 1-7 % of the of the population and patients with the disorder can be cognitively normal or exhibit a range of symptomology including mild cognitive impairment or dementia. Neuropathologically, those with PART have var- ying degrees neurofibrillary tangles in the medial temporal lobe, and an absence of amyloid plaques throughout the brain. Our goal is to deploy three independent approaches to understand how PART has convergent and divergent features from other primary and secondary tauopathies. The objective is to use novel high-throughput genetic and transcriptomic technologies combined with innovative computational methods including computer vision and AI to better characterize drivers of tau phosphorylation in PART. Our hypothesis is that machine learning classifiers (supervised and unsupervised) combined with single cell analysis will be able to accurately identify and quantify transcriptomic, genomic, clinical, and morphological features in PART to further understand the underlying amyloid independent mechanisms of tauopathy. Our rationale is that understanding the genetic transcriptomic and clinical architecture of PART will assistant in understand disease staging, diagnosis, and progression. We plan to test our hypothesis by pursing the following significant aims: (1) Quantify neurofibrillary tangle burden using supervised machine learning models and integrate this data in genetic and clinicopatholog- ical association studies (2) Model the sequential progression of neurofibrillary tangle degeneration in PART with unsupervised deep generative approaches. (3) Identify transcriptional alterations associated with neurofibrillary tangles in PART using single cell RNA sequencing. The proposed research is innovative as it applies novel transcriptomic and machine learning techniques to identify in an understudied group of elderly subjects with tauopathy lacking amyloidosis. This proposed research is significant as it addresses a critical unmet need to develop algorithms which can assist neuropathologists in their post-mortem diagnosis and provide better quan- titative phenotypic data which can aid in facilitating better neuroprotective strategies. The proposal builds upon the candidate's established interest in age-related tauopathy and his prior training in biomedical engineering and translational basic science research. The candidate’s primary mentor, Dr. John Crary, is an experienced neuro- pathologist and tau neuroscientist and will be supplemented by mentoring team consisting of Dr. Bin Zhang with specific expertise in computational genetics and transcriptomics and Dr. Thomas Fuchs, a prominent scientist in the field of computational pathology with a specific expertise in machine learning classifiers, AI, and computer vision. They will assure that the proposed research and training prepare the applicant to be an independent investigator in experimental computational neuropathology.