Biology subjects

Bonin, K.

Publications and source records attributed to Bonin, K..

6 recordsLinked to original sources

bioRxiv2026

Low-Density Lipoprotein Modulates Plasma Fibrin Network Architecture and Impairs Fibrinolysis

Low-density lipoprotein (LDL) is a major atherogenic lipoprotein, yet its potential to directly modify the fibrin scaffold of blood clots is incompletely understood. Here, we investigated how LDL alters plasma fibrin network architecture and internal fibrinolysis across defined fibrinogen/thrombin conditions. Pooled normal human plasma was supplemented with LDL and clotted with controlled concentrations of fibrinogen and thrombin. Fibrin architecture was visualized by confocal microscopy and quantified by pore-size analysis; clot formation and lysis were monitored turbidimetrically in the presence of tissue plasminogen activator (tPA). Increasing LDL produced a pronounced reduction in fibrin-network pore size across the tested fibrinogen/thrombin conditions. The LDL dependence of pore diameter was well described by a power-law relationship, D_pore=(6.54 +/- 0.11)[LDL]^(-0.12 +/- 0.02) , (R^2 = 0.90), with a significant negative LDL exponent (p = 4 x 10^5). Increasing LDL also prolonged clot lysis time and altered turbidity kinetics. These findings extend epidemiologic and clinical associations between ApoB-containing lipoproteins and hypofibrinolytic clot phenotypes by demonstrating, in a controlled plasma system, that LDL itself can modify fibrin network architecture and fibrinolytic susceptibility. The results support a structure-function role for LDL within the fibrin biomaterial and motivate direct tests of LDL incorporation, protofibril packing, fibrinolytic-protein binding, and single-fiber mechanics.

biophysics↗

bioRxiv2026

Nonlinear Relationships of Fibrin Network Structure as a Function of Fibrinogen and Thrombin Concentrations for Purified Fibrinogen and Plasma Clots

BackgroundFibrinogen levels are associated with bleeding disorders and thrombotic disease. Thrombin converts fibrinogen to fibrin, producing the load-bearing fibrin scaffold that governs clot mechanics and transport. ObjectiveQuantitatively map how initial fibrinogen and thrombin concentrations, [Fgn]0 and [T[h]r]0, determine fibrin architecture in human plasma and a purified fibrinogen system. MethodsScanning electron microscopy was used to quantify single-fiber morphology--fiber diameter and branch-to-branch segment length from a standardized sample-preparation protocol. Confocal microscopy was used to quantify network architecture--projected fiber density and pore/bubble size. Results and ConclusionsAcross plasma and purified systems, fibrin structural parameters were quantitatively captured by compact multiplicative scaling laws of the form Y = k[Fgn]0[T[h]r]0{beta}. Unlike prior studies, which examined narrower condition ranges without establishing predictive equations across a systematic fibrinogen-thrombin concentration matrix, this framework defines distinct, quantitative roles for fibrinogen and thrombin in fibrin assembly. The magnitudes and signs of the exponents and {beta} indicate that thrombin primarily controls individual fiber growth kinetics, strongly shortening branch-to-branch segment length and modestly thinning fibers, whereas fibrinogen primarily controls space filling, strongly increasing fiber density, reducing pore/bubble size, and thickening fibers. For matched [Fgn]0 and [T[h]r]0, compared to plasma clots, purified fibrinogen formed denser networks with thinner and shorter fibers, suggesting that the plasma biochemical environment partially inhibits thrombin activity. Fiber length analysis further suggests that each thrombin molecule nucleates one fiber segment. Together, these parameterized scaling relations provide a predictive quantitative framework linking clot composition to fibrin microstructure in plasma and purified fibrinogen clots.

biophysics↗

bioRxiv2025

Free energy spectroscopy reveals the mechanistic landscape of chromatin compaction

Eukaryotic genomic DNA is repeatedly wrapped into nucleosome spools: the basic building block of chromatin. This organization regulates the physical accessibility of the genome to gene transcription, replication, and repair regulatory factors. Chromatin compaction is controlled by multivalent weak interactions, resulting in a complicated conformational landscape that remains challenging to characterize. This work reports a method for characterizing chromatin compaction, Free Energy Spectroscopy (FES), which is based on DNA nanotechnology and transmission electron microscopy. This method experimentally determines the chromatin compaction free energy landscape in terms of end-to-end distance and nucleosome stacking interactions. By deconvolving the free energy landscapes of partially and fully compact tetranucleosomes, FES revealed three separate mechanisms by which linker histones reshape the compaction energetics to condense chromatin. This study establishes FES as a method with the potential to help answer a broad range of mechanistic questions about genome and epigenome function.

biophysics↗

bioRxiv2020

Performance of deep learning restoration methods for the extraction of particle dynamics in noisy microscopy image sequences

Image-based particle tracking is an essential tool to answer research questions in cell biology and beyond. A major challenge of particle tracking in living systems is that low light exposure is required to avoid phototoxicity and photobleaching. In addition, high-speed imaging used to fully capture particle motion dictates fast image acquisition rates. Short exposure times come at the expense of tracking accuracy. This is generally true for quantitative microscopy approaches and particularly relevant to single molecule tracking where the number of photons emitted from a single chromophore is limited. Image restoration methods based on deep learning dramatically improve the signal-to-noise ratio in low-exposure datasets. However, it is not clear whether images generated by these methods yield accurate quantitative measurements such as diffusion parameters in (single) particle tracking experiments. Here, we evaluate the performance of two popular deep learning denoising software packages for particle tracking, using synthetic datasets and movies of diffusing chromatin as biological examples. With synthetic data, both supervised and unsupervised deep learning restored particle motions with high accuracy in two-dimensional datasets, whereas artifacts were introduced by the denoisers in 3D datasets. Experimentally, we found that, while both supervised and unsupervised approaches improved the number of trackable particles and tracking accuracy, supervised learning generally outperformed the unsupervised approach, as expected. We also highlight that with extremely noisy image sequences, deep learning algorithms produce deceiving artifacts, which underscores the need to carefully evaluate the results. Finally, we address the challenge of selecting hyper-parameters to train convolutional neural networks by implementing a frugal Bayesian optimizer that rapidly explores multidimensional parameter spaces, identifying networks yielding optional particle tracking accuracy. Our study provides quantitative outcome measures of image restoration using deep learning. We anticipate broad application of the approaches presented here to critically evaluate artificial intelligence solutions for quantitative microscopy.

biophysics↗

bioRxiv2020

Human Mammary Cells in a Mature, Stratified Epithelial Layer Flatten and Stiffen Compared to Confluent and Single Cells

The epithelium forms a protective barrier against external biological, chemical and physical insults. So far, AFM-based, micro-mechanical measurements have only been performed on single cells and confluent cells, but not yet on cells in the physiologically relevant, mature epithelial layer. Using a combination of atomic force, fluorescence and confocal microscopy, we determined the changes in stiffness, morphology and actin distribution of human mammary epithelial cells (HMECs) as they transition from single cells to confluency to a mature epithelial layer. Single cells have a tall, round (planoconvex) morphology, have actin stress fibers at the base, have diffuse cortical actin, and have a stiffness of 1 kPa. Confluent cells become flatter, basal actin stress fibers start to disappear, and actin accumulates laterally where cells abut. Overall stiffness is still 1 kPa with two-fold higher stiffness in the abutting regions. Cells in an epithelial layer are flat on top and seven times stiffer (average, 7 kPa) than single and confluent cells. Epithelial layer cells show strong actin accumulation in the regions where cells adjoin and in the apical regions. Stiffness is significantly enhanced in the regions of adjoining cells, compared to the central regions of cells. Physiologically, this previously unrecognized, drastic stiffness increase may be important to the protective function of the epithelium.

biophysics↗

bioRxiv2017

Structured illumination to spatially map chromatin motions

We describe a simple optical method that creates structured illumination of a photoactivatable probe and apply this method to characterize chromatin motions in the nuclei of live cells. A laser beam coupled to a diffractive optical element at the back focal plane of an excitation objective generates an array of near diffraction-limited beamlets with FWHM of 340{+/-}30 nm, which simultaneously photoactivate a 7x7 matrix pattern of GFP-labeled histones, with spots 1.70 m apart. From the movements of the photoactivated spots, we map chromatin diffusion coefficients at multiple microdomains of the cell nucleus. The results show correlated motions of nearest chromatin microdomain neighbors, whereas chromatin movements are uncorrelated at the global scale of the nucleus. The method also reveals DNA damage-dependent decrease in chromatin diffusion. The DOE instrumentation can easily and cheaply be implemented on commercial inverted fluorescence microscopes to analyze adherent cell culture models. A protocol to measure chromatin motions in non-adherent human hematopoietic stem and progenitor cells is also described. We anticipate that the method will contribute to the identification of the mechanisms regulating chromatin mobility, which influences most genomic processes and may underlie the biogenesis of genomic translocations associated with hematologic malignancies.

cell biology↗