Characterisation of premature cell senescence in Alzheimer's disease using single nuclear transcriptomics.

Aging is associated with cell senescence and is the major risk factor for AD. We characterized premature cell senescence in postmortem brains from non-diseased controls (NDC) and donors with Alzheimer's disease (AD) using imaging mass cytometry (IMC) and single nuclear RNA (snRNA) sequencing (> 200,000 nuclei). We found increases in numbers of glia immunostaining for galactosidase beta (> fourfold) and p16INK4A (up to twofold) with AD relative to NDC. Increased glial expression of genes related to senescence was associated with greater ß-amyloid load. Prematurely senescent microglia downregulated phagocytic pathways suggesting reduced capacity for ß-amyloid clearance. Gene set enrichment and pseudo-time trajectories described extensive DNA double-strand breaks (DSBs), mitochondrial dysfunction and ER stress associated with increased ß-amyloid leading to premature senescence in microglia. We replicated these observations with independent AD snRNA-seq datasets. Our results describe a burden of senescent glia with AD that is sufficiently high to contribute to disease progression. These findings support the hypothesis that microglia are a primary target for senolytic treatments in AD.

A single nuclear transcriptomic characterisation of mechanisms responsible for impaired angiogenesis and blood-brain barrier function in Alzheimer's disease.

Brain perfusion and blood-brain barrier (BBB) integrity are reduced early in Alzheimer's disease (AD). We performed single nucleus RNA sequencing of vascular cells isolated from AD and non-diseased control brains to characterise pathological transcriptional signatures responsible for this. We show that endothelial cells (EC) are enriched for expression of genes associated with susceptibility to AD. Increased ß-amyloid is associated with BBB impairment and a dysfunctional angiogenic response related to a failure of increased pro-angiogenic HIF1A to increased VEGFA signalling to EC. This is associated with vascular inflammatory activation, EC senescence and apoptosis. Our genomic dissection of vascular cell risk gene enrichment provides evidence for a role of EC pathology in AD and suggests that reducing vascular inflammatory activation and restoring effective angiogenesis could reduce vascular dysfunction contributing to the genesis or progression of early AD.

Detecting paroxysmal coughing from pertussis cases using voice recognition technology.

BACKGROUND: Pertussis is highly contagious; thus, prompt identification of cases is essential to control outbreaks. Clinicians experienced with the disease can easily identify classic cases, where patients have bursts of rapid coughing followed by gasps, and a characteristic whooping sound. However, many clinicians have never seen a case, and thus may miss initial cases during an outbreak. The purpose of this project was to use voice-recognition software to distinguish pertussis coughs from croup and other coughs. METHODS: We collected a series of recordings representing pertussis, croup and miscellaneous coughing by children. We manually categorized coughs as either pertussis or non-pertussis, and extracted features for each category. We used Mel-frequency cepstral coefficients (MFCC), a sampling rate of 16 KHz, a frame Duration of 25 msec, and a frame rate of 10 msec. The coughs were filtered. Each cough was divided into 3 sections of proportion 3-4-3. The average of the 13 MFCCs for each section was computed and made into a 39-element feature vector used for the classification. We used the following machine learning algorithms: Neural Networks, K-Nearest Neighbor (KNN), and a 200 tree Random Forest (RF). Data were reserved for cross-validation of the KNN and RF. The Neural Network was trained 100 times, and the averaged results are presented. RESULTS: After categorization, we had 16 examples of non-pertussis coughs and 31 examples of pertussis coughs. Over 90% of all pertussis coughs were properly classified as pertussis. The error rates were: Type I errors of 7%, 12%, and 25% and Type II errors of 8%, 0%, and 0%, using the Neural Network, Random Forest, and KNN, respectively. CONCLUSION: Our results suggest that we can build a robust classifier to assist clinicians and the public to help identify pertussis cases in children presenting with typical symptoms.