Biology subjects

Hayes, D. J.

Publications and source records attributed to Hayes, D. J..

4 recordsLinked to original sources

bioRxiv2025

Hepatic isomiR landscaping reveals new biological insights into metabolic dysfunction in steatotic liver disease

Post-transcriptionally modified microRNA (miRNA), called isomiRs, expand the repertoire of transcripts that can leveraged for therapeutic targets and biological insights. However, the expression of isomiRs has not been characterized in metabolic dysfunction-associated steatotic liver disease (MASLD). Therefore, we assessed the isomiR expression profile in liver biopsies from 79 patients with MASLD and modeled their potential role in disease biology. MiRNAs represented 75% of the sequencing reads and over 65% of them were attributed to isomiRs, demonstrating their higher expression and diversity compared to canonically annotated miRNAs. Differential expression and machine-learning analyses were used to identify 173 isomiRs associated to MASLD severity and 58 isomiRs associated to fibrosis score. Candidate target mRNAs were identified for each isomiR based on sequence complementarity. Using matched mRNA sequencing data, and supported by data from an independent study, we proposed key dysregulated mRNA targets involved in a selection of 33 disease-associated pathways. Importantly, isomiRs offered novel and unique mRNA targets compared to the canonical miRNA, e.g. isomiR-122 targeting INSIG1 (insulin and cholesterol metabolism), and isomiR-21 targeting HMGCS2 and PPARA (PARR and TGF-beta signaling). Our work advances knowledge regarding the role of isomiRs in MASLD and lays a foundation for therapeutic targets identification. HighlightsOur results provide a comprehensive analysis of microRNA (isomiRs) in liver tissue. Machine learning identified isomiRs whose expression is associated with MASLD. Multi-omic analysis uncovered novel isomiR regulatory mechanisms involved in MASLD.

bioinformatics↗

bioRxiv2024

High-Throughput Bioprinting of Spheroids for Scalable Tissue Fabrication

Tissue biofabrication that replicates an organ-specific architecture and function requires physiologically-relevant cell densities. Bioprinting using spheroids has the potential to create constructs with native cell densities, but its application is limited due to the lack of practical, scalable techniques. This study presents HITS-Bio (High-throughput Integrated Tissue Fabrication System for Bioprinting), a novel multiarray spheroid bioprinting technology enabling scalable tissue fabrication by rapidly positioning a number of spheroids simultaneously using a digitally-controlled nozzle array (DCNA) platform. HITS-Bio achieves an unprecedented speed, an order of magnitude faster compared to existing techniques while maintaining high cell viability (>90%). The platforms ability to pattern multiple spheroids simultaneously enhances fabrication rates proportionally to the size of DCNA used. The utility of HITS-Bio was exemplified in multiple applications, including intraoperative bioprinting with microRNA transfected spheroids for calvarial bone regeneration ([~]30 mm3) in a rat model achieving a near-complete defect closure ([~]91% in 3 weeks and [~]96% in 6 weeks). Additionally, the successful fabrication of scalable cartilage constructs (1 cm3) containing [~]600 chondrogenic spheroids highlights its high-throughput efficiency (under 40 min per construct) and potential for repairing volumetric tissue defects.

bioengineering↗

bioRxiv2019

Diffusion Weighted Image Co-registration: Investigation of Best Practices

1.The registration or alignment of diffusion weighted images (DWI) with other imaging modalities is a critical step in neuroimaging analysis. Within-subject T1-DWI co-registration is particularly instrumental. DWI-derived scalar images are commonly used as intermediates for T1-DWI co-registration, and the resulting registration transforms are applied to all other scalar images for analysis. The ideal registration intermediate should register well to T1 and other multimodal images and be practically easy to obtain. It is however, currently unclear which DWI-derived scalar image serves as the best intermediate. We aim to determine the best, practical, intermediate for image co-registration. T1 and DWI images were acquired from 20 healthy subjects. DWIs were acquired with 60 directions. Six DWI-derived scalar images were compared including: 1) fractional anisotropy (FA); 2) generalized FA (GFA); 3) B0 images; 4) mean DWIs with the B0 image (MDWI); 5) anisotropic power (AP) images. AP showed the smallest variability in registration improvements across all the tested DWI derived scalar images, and show the highest average percent changes with CC registration cost function (CC=1.2%, MI=15%). In contrast, the FA and GFA transforms resulted in significantly poorer registration across DWI types. The AP image was the DWI-derived scalar image that provided the most consistent registration to all other images. Practically, it is generated easily and so could be implemented in basic and clinical research pipelines currently using other intermediates. Given these findings, it is recommended that AP images be used for T1-DWI co-registration, and that FA and GFA images in particular be avoided.

neuroscience↗

bioRxiv2019

Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Forest Inventories of New England

Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters between LiDAR datasets, such as pulse density. Here we develop a regional model using a three-dimensional convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud as well as coincident satellite data, identifying features useful for predicting forest attributes, and then making a series of predictions. We compare this to the standard modeling approach for making forest predictions from LiDAR data, and find that the CNN outperformed the standard approach by a large margin in many cases. We then apply our model to publicly available data over New England, generating maps of fourteen forest attributes at a 10 m resolution over 85 % of the region. Our estimates of attributes that quantified tree size were most successful. In assessing aboveground biomass for example, we achieved a root mean square error of 36 Mg/ha (44 %). Our county-level mapped estimates of biomass were in good agreement with federal estimates. Estimates of attributes quantifying stem density and percent conifer were moderately successful, with a tendency to underestimate of extreme values and banding in low density LiDAR acquisitions. Estimate of attributes quantifying detailed species groupings were less successful. Ultimately we believe these maps will be useful to forest managers, wildlife ecologists, and climate modelers in the region.

ecology↗