Biology subjects

Beckett, L.

Publications and source records attributed to Beckett, L..

4 recordsLinked to original sources

bioRxiv2025

Non-invasive peripheral delivery of CDNF fragment protects neurons in models of Parkinsons and ALS

Non-invasive delivery of brain therapeutics is a key challenge for treating neurodegenerative diseases. Here, we discovered a novel carboxy (C)-terminal fragment of cerebral dopamine neurotrophic factor (C-CDNF) that protects dopamine (DA) and motoneurons (MNs) in rodent models of Parkinsons disease (PD) and amyotrophic lateral sclerosis (ALS). C-CDNF retains the same structure as CDNF and similarly to CDNF regulates cell stress pathways but unorthodoxly enters cultured neurons and passes through the blood-brain barrier. In vivo, intracranially or peripherally delivered C-CDNF improves motor deficits, protects DA neurons, and restores motor behavior in a rat model of PD. Subcutaneous C-CDNF also protects MNs and reduces microglial activation in an ALS model. Based on our findings, beginning C-CDNF treatment soon after diagnosis is anticipated to delay progression of PD and ALS, thereby improving treatment outcome. Thus, systemic delivery of C-CDNF should simplify the administration of protein-based therapeutics to patients while reducing treatment risk and financial burden for patients and families.

neuroscience↗

bioRxiv2024

Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations

Accumulation of abnormal tau protein into neurofibrillary tangles (NFTs) is a pathologic hallmark of Alzheimer disease (AD). Accurate detection of NFTs in tissue samples can reveal relationships with clinical, demographic, and genetic features through deep phenotyping. However, expert manual analysis is time-consuming, subject to observer variability, and cannot handle the data amounts generated by modern imaging. We present a scalable, open-source, deep-learning approach to quantify NFT burden in digital whole slide images (WSIs) of post-mortem human brain tissue. To achieve this, we developed a method to generate detailed NFT boundaries directly from single-point-per-NFT annotations. We then trained a semantic segmentation model on 45 annotated 2400{micro}m by 1200{micro}m regions of interest (ROIs) selected from 15 unique temporal cortex WSIs of AD cases from three institutions (University of California (UC)-Davis, UC-San Diego, and Columbia University). Segmenting NFTs at the single-pixel level, the model achieved an area under the receiver operating characteristic of 0.832 and an F1 of 0.527 (196-fold over random) on a held-out test set of 664 NFTs from 20 ROIs (7 WSIs). We compared this to deep object detection, which achieved comparable but coarser-grained performance that was 60% faster. The segmentation and object detection models correlated well with expert semi-quantitative scores at the whole-slide level (Spearmans rho {rho}=0.654 (p=6.50e-5) and {rho}=0.513 (p=3.18e-3), respectively). We openly release this multi-institution deep-learning pipeline to provide detailed NFT spatial distribution and morphology analysis capability at a scale otherwise infeasible by manual assessment.

pathology↗

bioRxiv2024

Spaceflight alters host-gut microbiota interactions

The rodent habitat on the International Space Station has provided crucial insights into the impact of spaceflight on mammals, including observation of symptoms characteristic of liver disease, insulin resistance, osteopenia and myopathy. Although these physiological responses can involve the microbiome when observed on Earth, changes in host-microbiota interactions during spaceflight are still being elucidated. Here, NASA GeneLab multiomic data from the Rodent Research 6 mission are used to determine changes to gut microbiota and murine host colon and liver gene expression after 29 and 56-days of spaceflight. Using hybrid amplicon and whole metagenome sequencing analysis, significant spaceflight-associated alterations to 42 microbiome species were identified. These included relative reductions of bacteria associated with bile acid and butyrate metabolism, such as Extibacter muris and Dysosmobacter welbionis. Functional prediction suggested over-representation of fatty acid and bile acid metabolism, extracellular matrix interactions, and antibiotic resistance genes within the gut microbiome, while host intestinal and hepatic gene expression described corresponding changes to host bile acid and energy metabolism, and immune suppression from spaceflight. Taken together, these changes imply that interactions at the host-gut microbiome interface contribute to spaceflight pathology and highlight how these interactions might critically influence human health and the feasibility of long-duration spaceflight.

genomics↗

bioRxiv2018

Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline

Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and can lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline identifying specific neuropathologies--amyloid plaques and cerebral amyloid angiopathy--in immunohistochemical-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotated >70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieved strong plaque classification (0.993 and 0.744 areas under the receiver operating characteristic and precision recall curve, respectively) on a 10 WSI hold-out set. Prediction confidence maps visualized morphology distributions from the full-WSI level down to 20x magnification. Resulting plaque-burden scores correlated well with established semi-quantitative scores. Finally, saliency mapping demonstrated that networks learned patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologists ability may suggest a route to neuropathologic deep phenotyping.

pathology↗