Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles.

Machine learning (ML) has increasingly been used to assist and expand current practices in neuropathology. However, generating large imaging datasets with quality labels is challenging in fields which demand high levels of expertise. Further complicating matters is the often seen disagreement between experts in neuropathology-related tasks, both at the case level and at a more granular level. Neurofibrillary tangles (NFTs) are a hallmark pathological feature of Alzheimer disease, and are associated with disease progression which warrants further investigation and granular quantification at a scale not currently accessible in routine human assessment. In this work, we first provide a baseline of annotator/rater agreement for the tasks of Braak NFT staging between experts and NFT detection using both experts and novices in neuropathology. We use a whole-slide-image (WSI) cohort of neuropathology cases from Emory University Hospital immunohistochemically stained for Tau. We develop a workflow for gathering annotations of the early stage formation of NFTs (Pre-NFTs) and mature intracellular (iNFTs) and show ML models can be trained to learn annotator nuances for the task of NFT detection in WSIs. We utilize a model-assisted-labeling approach and demonstrate ML models can be used to aid in labeling large datasets efficiently. We also show these models can be used to extract case-level features, which predict Braak NFT stages comparable to expert human raters, and do so at scale. This study provides a generalizable workflow for various pathology and related fields, and also provides a technique for accomplishing a high-level neuropathology task with limited human annotations.

Survey of Neuroanatomic Sampling and Staining Procedures in Alzheimer Disease Research Center Brain Banks.

The collection of post-mortem brain tissue has been a core function of the Alzheimer Disease Research Center's (ADRCs) network located within the United States since its inception. Individual brain banks and centers follow detailed protocols to record, store, and manage complex datasets that include clinical data, demographics, and when post-mortem tissue is available, a detailed neuropathological assessment. Since each institution often has specific research foci, there can be variability in tissue collection and processing workflows. While published guidelines exist for select diseases, such as those put forth by the National Institute on Aging and Alzheimer Association (NIA-AA), it is of importance to denote the current practices across institutions. To this end a survey was developed and sent to United States based brain bank leaders, collecting data on brain region sampling, including anatomic landmarks used, staining (including antibodies used), as well as whole-slide-image scanning hardware. We distributed this survey to 40 brain banks and obtained a response rate of 95% (38 / 40). Most brain banks followed guidelines defined by the NIA-AA, having H&E staining in all recommended regions and targeted region-based amyloid beta, tau, and alpha-synuclein immunohistochemical staining. However, sampling consistency varied related to key anatomic landmarks/locations in select regions, such as the striatum, periventricular white matter, and parietal cortex. This study highlights the diversity and similarities amongst brain banks and discusses considerations when amalgamating data/samples across multiple centers. This survey aids in establishing benchmarks to enhance dialogues on divergent workflows in a feasible way.

Radiomics Features Predict CIC Mutation Status in Lower Grade Glioma.

MRI in combination with genomic markers are critical in the management of gliomas. Radiomics and radiogenomics analysis facilitate the quantitative assessment of tumor properties which can be used to model both molecular subtype and predict disease progression. In this work, we report on the Drosophila gene capicua (CIC) mutation biomarker effects alongside radiomics features on the predictive ability of CIC mutation status in lower-grade gliomas (LGG). Genomic data of lower grade glioma (LGG) patients from The Cancer Genome Atlas (TCGA) (n = 509) and corresponding MR images from TCIA (n = 120) were utilized. Following tumor segmentation, radiomics features were extracted from T1, T2, T2 Flair, and T1 contrast enhanced (CE) images. Lasso feature reduction was used to obtain the most important MR image features and then logistic regression used to predict CIC mutation status. In our study, CIC mutation rarely occurred in Astrocytoma but has a high probability of occurrence in Oligodendroglioma. The presence of CIC mutation was found to be associated with better survival of glioma patients (p < 1e-4, HR: 0.2445), even with co-occurrence of IDH mutation and 1p/19q co-deletion (p = 0.0362, HR: 0.3674). An eleven-feature model achieved glioma prediction accuracy of 94.2% (95% CI, 94.03-94.38%), a six-feature model achieved oligodendroglioma prediction accuracy of 92.3% (95% CI, 91.70-92.92%). MR imaging and its derived image of gliomas with CIC mutation appears more complex and non-uniform but are associated with lower malignancy. Our study identified CIC as a potential prognostic factor in glioma which has close associations with survival. MRI radiomic features could predict CIC mutation, and reflect less malignant manifestations such as milder necrosis and larger tumor volume in MRI and its derived images that could help clinical judgment.

Validation of machine learning models to detect amyloid pathologies across institutions.

Semi-quantitative scoring schemes like the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) are the most commonly used method in Alzheimer's disease (AD) neuropathology practice. Computational approaches based on machine learning have recently generated quantitative scores for whole slide images (WSIs) that are highly correlated with human derived semi-quantitative scores, such as those of CERAD, for Alzheimer's disease pathology. However, the robustness of such models have yet to be tested in different cohorts. To validate previously published machine learning algorithms using convolutional neural networks (CNNs) and determine if pathological heterogeneity may alter algorithm derived measures, 40 cases from the Goizueta Emory Alzheimer's Disease Center brain bank displaying an array of pathological diagnoses (including AD with and without Lewy body disease (LBD), and / or TDP-43-positive inclusions) and levels of Aß pathologies were evaluated. Furthermore, to provide deeper phenotyping, amyloid burden in gray matter vs whole tissue were compared, and quantitative CNN scores for both correlated significantly to CERAD-like scores. Quantitative scores also show clear stratification based on AD pathologies with or without additional diagnoses (including LBD and TDP-43 inclusions) vs cases with no significant neurodegeneration (control cases) as well as NIA Reagan scoring criteria. Specifically, the concomitant diagnosis group of AD + TDP-43 showed significantly greater CNN-score for cored plaques than the AD group. Finally, we report that whole tissue computational scores correlate better with CERAD-like categories than focusing on computational scores from a field of view with densest pathology, which is the standard of practice in neuropathological assessment per CERAD guidelines. Together these findings validate and expand CNN models to be robust to cohort variations and provide additional proof-of-concept for future studies to incorporate machine learning algorithms into neuropathological practice.

TDP-43 cytoplasmic inclusion formation is disrupted in C9orf72-associated amyotrophic lateral sclerosis/frontotemporal lobar degeneration.

The G4C2 hexanucleotide repeat expansion mutation in the C9orf72 gene is the most common genetic cause underlying both amyotrophic lateral sclerosis and frontotemporal dementia. Pathologically, these two neurodegenerative disorders are linked by the common presence of abnormal phosphorylated TDP-43 neuronal cytoplasmic inclusions. We compared the number and size of phosphorylated TDP-43 inclusions and their morphology in hippocampi from patients dying with sporadic versus C9orf72-related amyotrophic lateral sclerosis with pathologically defined frontotemporal lobar degeneration with phosphorylated TDP-43 inclusions, the pathological substrate of clinical frontotemporal dementia in patients with amyotrophic lateral sclerosis. In sporadic cases, there were numerous consolidated phosphorylated TDP-43 inclusions that were variable in size, whereas inclusions in C9orf72 amyotrophic lateral sclerosis/frontotemporal lobar degeneration were quantitatively smaller than those in sporadic cases. Also, C9orf72 amyotrophic lateral sclerosis/frontotemporal lobar degeneration homogenized brain contained soluble cytoplasmic TDP-43 that was largely absent in sporadic cases. To better understand these pathological differences, we modelled TDP-43 inclusion formation in fibroblasts derived from sporadic or C9orf72-related amyotrophic lateral sclerosis/frontotemporal dementia patients. We found that both sporadic and C9orf72 amyotrophic lateral sclerosis/frontotemporal dementia patient fibroblasts showed impairment in TDP-43 degradation by the proteasome, which may explain increased TDP-43 protein levels found in both sporadic and C9orf72 amyotrophic lateral sclerosis/frontotemporal lobar degeneration frontal cortex and hippocampus. Fibroblasts derived from sporadic patients, but not C9orf72 patients, demonstrated the ability to sequester cytoplasmic TDP-43 into aggresomes via microtubule-dependent mechanisms. TDP-43 aggresomes in vitro and TDP-43 neuronal inclusions in vivo were both tightly localized with autophagy markers and, therefore, were likely to function similarly as sites for autophagic degradation. The inability for C9orf72 fibroblasts to form TDP-43 aggresomes, together with the observations that TDP-43 protein was soluble in the cytoplasm and formed smaller inclusions in the C9orf72 brain compared with sporadic disease, suggests a loss of protein quality control response to sequester and degrade TDP-43 in C9orf72-related diseases.

A multiplexed magnetic tweezer with precision particle tracking and bi-directional force control.

BACKGROUND: In the past two decades, methods have been developed to measure the mechanical properties of single biomolecules. One of these methods, Magnetic tweezers, is amenable to aquisition of data on many single molecules simultaneously, but to take full advantage of this "multiplexing" ability, it is necessary to simultaneously incorprorate many capabilities that ahve been only demonstrated separately. METHODS: Our custom built magnetic tweezer combines high multiplexing, precision bead tracking, and bi-directional force control into a flexible and stable platform for examining single molecule behavior. This was accomplished using electromagnets, which provide high temporal control of force while achieving force levels similar to permanent magnets via large paramagnetic beads. RESULTS: Here we describe the instrument and its ability to apply 2-260 pN of force on up to 120 beads simultaneously, with a maximum spatial precision of 12 nm using a variety of bead sizes and experimental techniques. We also demonstrate a novel method for increasing the precision of force estimations on heterogeneous paramagnetic beads using a combination of density separation and bi-directional force correlation which reduces the coefficient of variation of force from 27% to 6%. We then use the instrument to examine the force dependence of uncoiling and recoiling velocity of type 1 fimbriae from Eschericia coli (E. coli) bacteria, and see similar results to previous studies. CONCLUSION: This platform provides a simple, effective, and flexible method for efficiently gathering single molecule force spectroscopy measurements.