@article {70313, title = {BPS2026 {\textendash} Real-time visualization and analysis of fibrin polymerization}, journal = {Biophysical Journal}, volume = {125}, year = {2026}, month = {Jan-02-2026}, pages = {227a - 228a}, abstract = {

To form a blood clot, fibrinogen is converted into fibrin through the action of the enzyme thrombin. Fibrin then polymerizes longitudinally and laterally as it matures into a fiber. Polymerization results in a dense, 3-dimensional branched network. Previous research has shown the relevance of these fibrin gel structures in hemostatic conditions; however, the mechanism by which they form has not been fully resolved. Using light sheet microscopy, 3-dimensional volumes of the fibrin polymerization process were captured. Manual annotation of these microscopy videos revealed that fiber branch points occur through the collision and the binding of diffusing fibers rather than through the splitting of growing fiber termini. However, the density of fibers and amount of data greatly slows manual annotation-based analysis and limits the ability to capture important data, such as growth rates and fiber stiffness. To more quickly process these data, a computational approach was utilized. A custom tracking pipeline, suited to the networks formed by cylindrical fibrin fibers, was developed, beginning with an AI-based classifier. This custom pipeline allowed for the tracking of uniquely labeled fibers over time. Automated merge detection between linking phases further improved accuracy. Additionally, network formation was analyzed through skeletonization techniques to measure the number of branches per junction over time. Combining the skeletonization and tracking methods, single fibers were identified by their lack of branch points and tracked. The addition of branch points to previously tracked objects served as a signal for merge detection. This approach yielded measurements of single fibrin fiber diffusion rates, as well as the first volumetric and length growth rates of fibers throughout polymerization. In addition, the gel point was quantified by analyzing the span of connected objects to characterize the network consolidation over time at the level of single fibers.

}, issn = {00063495}, doi = {10.1016/j.bpj.2025.11.1510}, url = {https://doi.org/10.1016/j.bpj.2025.11.1510}, author = {Miller, Dylan M. and Elangovan, Aravind and Belcher, Heather A. and Lee, Rachel M. and Hobson, Chad M. and Crosley, Key and Hart, David M. and Laverty, Brittany Bannish E. and Guthold, Martin and Hudson, Nathan E.} } @article {69719, title = {Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopyAbstract}, journal = {Nature Communications}, volume = {16}, year = {2025}, month = {Jan-12-2025}, abstract = {

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained {\textquoteleft}de-aberration{\textquoteright} networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in\ C. elegans\ embryos.

}, doi = {10.1038/s41467-024-55267-x}, url = {https://www.nature.com/articles/s41467-024-55267-xhttps://www.nature.com/articles/s41467-024-55267-x.pdfhttps://www.nature.com/articles/s41467-024-55267-x.pdfhttps://www.nature.com/articles/s41467-024-55267-x}, author = {Guo, Min and Wu, Yicong and Hobson, Chad M. and Su, Yijun and Qian, Shuhao and Krueger, Eric and Christensen, Ryan and Kroeschell, Grant and Bui, Johnny and Chaw, Matthew and Zhang, Lixia and Liu, Jiamin and Hou, Xuekai and Han, Xiaofei and Lu, Zhiye and Ma, Xuefei and Zhovmer, Alexander and Combs, Christian and Moyle, Mark and Yemini, Eviatar and Liu, Huafeng and Liu, Zhiyi and Benedetto, Alexandre and La Riviere, Patrick and Col{\'o}n-Ramos, Daniel and Shroff, Hari} } @article {69720, title = {HD2Net: A deep learning framework for simultaneous denoising and deaberration in fluorescence microscopy}, journal = {bioRxiv}, year = {2025}, month = {01/2025}, abstract = {

Fluorescence microscopy is essential for biological research, offering high-contrast imaging of microscopic structures. However, the quality of these images is often compromised by optical aberrations and noise, particularly in low signal-to-noise ratio (SNR) conditions. While adaptive optics (AO) can correct aberrations, it requires costly hardware and slows down imaging; whereas current denoising approaches boost the SNR but leave out the aberration compensation. To address these limitations, we introduce HD2Net, a deep learning framework that enhances image quality by simultaneously denoising and suppressing the effect of aberrations without the need for additional hardware. Building on our previous work, HD2Net incorporates noise estimation and aberration removal modules, effectively restoring images degraded by noise and aberrations. Through comprehensive evaluation of synthetic phantoms and biological data, we demonstrate that HD2Net outperforms existing methods, significantly improving image resolution and contrast. This framework offers a promising solution for enhancing biological imaging, particularly in challenging aberrating and low-light conditions.

}, doi = {10.1101/2025.01.06.631475}, url = {https://www.biorxiv.org/content/early/2025/01/08/2025.01.06.631475}, author = {Hou, Xuekai and Li, Yue and Hobson, Chad M. and Shroff, Hari and Guo, Min and Liu, Huafeng} } @article {69734, title = {HD2Net: A Deep Learning Framework for Simultaneous Denoising and Deaberration in Fluorescence Microscopy}, journal = {bioRxiv}, year = {2025}, month = {2025 Jan 8}, abstract = {

Fluorescence microscopy is essential for biological research, offering high-contrast imaging of microscopic structures. However, the quality of these images is often compromised by optical aberrations and noise, particularly in low signal-to-noise ratio (SNR) conditions. While adaptive optics (AO) can correct aberrations, it requires costly hardware and slows down imaging; whereas current denoising approaches boost the SNR but leave out the aberration compensation. To address these limitations, we introduce HD2Net, a deep learning framework that enhances image quality by simultaneously denoising and suppressing the effect of aberrations without the need for additional hardware. Building on our previous work, HD2Net incorporates noise estimation and aberration removal modules, effectively restoring images degraded by noise and aberrations. Through comprehensive evaluation of synthetic phantoms and biological data, we demonstrate that HD2Net outperforms existing methods, significantly improving image resolution and contrast. This framework offers a promising solution for enhancing biological imaging, particularly in challenging aberrating and low-light conditions.

}, doi = {10.1101/2025.01.06.631475}, url = {https://www.biorxiv.org/content/early/2025/01/08/2025.01.06.631475}, author = {Hou, Xuekai and Li, Yue and Hobson, Chad M. and Shroff, Hari and Guo, Min and Liu, Huafeng} }