Zhou, F.-L. | Search bioRxiv

bioRxiv2021

Validating Pore Size Estimates in a Complex Microfibre Environment on a Human MRI System

PurposeRecent advances in diffusion-weighted MRI provide restricted diffusion signal fraction and restricting pore size estimates. Materials based on co-electrospun oriented hollow cylinders have been introduced to provide validation for such methods. This study extends this work, exploring accuracy and repeatability using an extended acquisition on a 300 mT/m gradient human MRI scanner, in substrates closely mimicking tissue, i.e., non-circular cross-sections, intra-voxel fibre crossing, intra-voxel distributions of pore-sizes and smaller pore-sizes overall. MethodsIn a single-blind experiment, diffusion-weighted data were collected from a biomimetic phantom on a 3T Connectom system using multiple gradient directions/diffusion times. Repeated scans established short-term and long-term repeatability. The total scan time (54 minutes) matched similar protocols used in human studies. The number of distinct fibre populations was estimated using spherical deconvolution, and median pore size estimated through the combination of CHARMED and AxCaliber3D framework. Diffusion-based estimates were compared with measurements derived from scanning electron microscopy. ResultsThe phantom contained substrates with different orientations, fibre configurations and pore size distributions. Irrespective of one or two populations within the voxel, the pore-size estimates (~5m) and orientation-estimates showed excellent agreement with the median values of pore-size derived from scanning electron microscope and phantom configuration. Measurement repeatability depended on substrate complexity, with lower values seen in samples containing crossing-fibres. Sample-level repeatability was found to be good. ConclusionWhile no phantom mimics tissue completely, this study takes a step closer to validating diffusion microstructure measurements for use in vivo by demonstrating the ability to quantify microgeometry in relatively complex configurations.

biophysics↗

bioRxiv2018

Modeling circRNAs expression pattern with integrated sequence and epigenetic features identifies H3K79me2 as regulators for circRNAs expression

Circular RNAs (circRNAs) are an abundant class of noncoding RNAs with widespread, cell/tissue specific pattern. Because of their involvement in the pathogenesis of multiple disease, they are receiving increasing attention. Previous work suggested that epigenetic features might be related to circRNA expression. However, current algorithms for circRNAs prediction neglect these features, leading to constant results across different cells.\n\nHere we built a machine learning framework named CIRCScan, to predict expression status and expression levels of circRNAs in various cell lines based on sequence and epigenetic features. Both expression status and expression levels can be accurately predicted by different groups of features. For expression status, the top features were similar in different cells. However, the top features for predicting expression levels were different in different cells. Noteworthy, the importance of H3K79me2 ranked high in predicting both circRNAs expression status and levels across different cells, indicating its important role in regulating circRNAs expression. Further validation experiment in K562 confirmed that knock down of H3K79me2 did result in reduction of circRNA production.\n\nOur study offers new insights into the regulation of circRNAs by incorporating epigenetic features in prediction models in different cellular contexts.

bioinformatics↗