Biophysical journal最新文献

Fluorescence microscopy, which employs fluorescent tags to label and observe cellular structures and their dynamics, is a powerful tool for life sciences. However, due to the spectral overlap between different dyes, a limited number of structures can be separately labeled and imaged for live-cell applications. In addition, the conventional sequential channel imaging procedure is quite time consuming, as it needs to switch either different lasers or filters. Here, we propose a novel double-structure network (DBSN) that consists of multiple connected models, which can extract six distinct subcellular structures from three raw images with only two separate fluorescent labels. DBSN combines the intensity-balance model to compensate for uneven fluorescent labels for different structures and the structure-separation model to extract multiple different structures with the same fluorescent labels. Therefore, DBSN breaks the bottleneck of the existing technologies and holds immense potential applications in the field of cell biology.

Lipid compositional asymmetry across the leaflets of the plasma membrane is a ubiquitous feature in eukaryotic cells. How this asymmetry is maintained is thought to be primarily controlled by active transport of lipids between leaflets. This strategy is facilitated by the fact that long tail phospholipids and sphingolipids diffuse through the lipid bilayer slowly - taking many hours or days. However, a lipid like cholesterol - which is the most abundant lipid in the plasma membrane of animal cells - has been harder to pin-point in terms of its favored side. In the present work we show that when a saturated lipid is added to a mix of the unsaturated lipid palmitoyl-oleoyl-phosphatidylcholine (POPC) and cholesterol, both cholesterol and the long tail phospholipids organize asymmetrically across the membrane's leaflets naturally. In these extruded unilamellar vesicles, most cholesterol as well as the saturated lipid - dipalmitoylphosphatidylcholine (DPPC) or sphingomyelin (SM) - segregated to the inner leaflet while POPC preferentially localized in the outer leaflet. This asymmetric arrangement generated a slight phospholipid number imbalance favoring the outer leaflet and thus opposite to where cholesterol and the saturated lipids preferentially partitioned. These results were obtained using Magic Angle Spinning (MAS) NMR in combination with Small Angle Neutron Scattering (SANS) using isotope labeling to differentiate lipid species. We suggest that sidedness in membranes can be driven by thermodynamic processes. In addition, our MAS NMR results show that the lower bound for cholesterol's flip-flop half-time at 45°C is 10ms, which is at least two orders of magnitude slower than current MD simulations predict. This result stands in stark contrast to previous work that suggested that cholesterol's flip-flop half-time at 37°C has an upper bound of 10ms.

Multiple particle tracking (MPT) is a microscopy technique capable of simultaneously tracking hundreds to thousands of nanoparticles in a biological sample and has been used extensively to characterize biological microenvironments, including the brain extracellular space (ECS). Machine learning techniques have been applied to MPT datasets to predict the diffusion mode of nanoparticle trajectories as well as more complex biological variables, such age biological age. In this study, we develop a machine learning pipeline to predict and investigate changes to the brain ECS due to injury using supervised classification and feature importance calculations. We first validate the pipeline on three related but distinct MPT datasets from the living brain ECS - age differences, region differences, and enzymatic degradation of ECS structure. We predict three ages with 86% accuracy, three regions with 90% accuracy, and healthy versus enzyme-treated tissue with 69% accuracy. Since injury across groups is normally compared with traditional statistical approaches, we first used linear mixed effects models to compare features between healthy control conditions and injury induced by two different oxygen glucose deprivation [1] exposure times. We then used machine learning to predict injury state using MPT features. We show that the pipeline predicts between the healthy control, 0.5-hour OGD treatment, and 1.5-hour OGD treatment with 59% accuracy in the cortex and 66% in the striatum and identifies nonlinear relationships between trajectory features that were not evident from traditional linear models. Our work demonstrates that machine learning applied to MPT data is effective across multiple experimental conditions and can find unique biologically relevant features of nanoparticle diffusion.

Lipid membranes play a crucial role in regulating the body's water balance by adjusting their properties in response to hydration. The intercellular lipid matrix of the stratum corneum (SC), the outermost skin layer, serves as the body's primary defense against environmental factors. Osmolytes, including urocanic acid (UCA) and glycerol, are key components of the natural moisturizing factor that help the SC resist osmotic stress from dry environments. This study examines the effects of UCA and glycerol (each at 5 mol%) on isolated human SC lipids. For this, different techniques were employed, offering complementary information of the system's multiscale characteristics, including humidity-scanning quartz crystal microbalance with dissipation monitoring, infrared spectroscopy, X-ray diffraction, electrical impedance spectroscopy, and studies of water loss and permeability. Our results show that UCA increases water sorption and makes lipid films more liquid-like at high relative humidity, without significantly altering the lipid lamellar structure, chain order, or orthorhombic chain packing. Lipid films containing UCA exhibited higher water loss, significantly higher model drug permeability, and kinetically faster changes in electrical properties upon contact with aqueous solution compared to control lipids. These observations suggest that UCA reduces lipid cohesion in regions other than the acyl chain-rich leaflets, which may impact SC desquamation. In contrast, glycerol did not influence the hydration or permeability of the SC lipid matrix. However, it increased the proportion of orthorhombic domains at high humidities and slowed the kinetics of the hydration process, as evidenced by slower changes in the dielectric properties of the lipid film. These findings suggest that glycerol enhances lipid cohesion rather than increasing water uptake, which is typically the expected function of humectants. Consequently, UCA and glycerol appear to have distinct roles in maintaining epidermal homeostasis.

Gastrulation is a critical process during embryonic development that transforms a single-layered blastula into a multi-layered embryo with distinct germ layers, which eventually give rise to all the tissues and organs of the organism. Studies across species have uncovered the mechanisms underlying the building blocks of gastrulation movements, such as localized in-plane and out-of-plane epithelial deformations. The next challenge is to understand dynamics on the scale of the embryo: this requires quantifying strain tensors, which rigorously describe the differences between the deformed configurations taken on by local clusters of cells at time instants of observation and their reference configuration at an initial time. We present a systematic strategy for computing such tensors from the local dynamics of cell clusters, which are chosen across the embryo from several regions whose morphogenetic fate is central to viable gastrulation. As an application of our approach, we demonstrate a strategy of identifying distinct Drosophila morphological domains using strain tensors.

DEAD-box helicases use ATP to unwind short double-stranded RNA (dsRNA). The helicase core consists of two discrete domains, termed RecA_N and RecA_C. The nucleotide binding site is harbored in RecA_N, while both RecA_N and RecA_C are involved in RNA recognition and ATP hydrolysis. In the absence of nucleotides or RNA, RecA_N and RecA_C do not interact ("open" form of the enzyme). In the presence of both RNA and ATP the two domains come together ("closed" form), building the composite RNA binding site and stimulating ATP hydrolysis. Because of the different roles and thermodynamic properties of the ADP-bound and ATP-bound states in the catalytic cycle, the conformations of DEAD-box helicases in complex with ATP and ADP are assumed to be different. However, the available crystal structures do not recapitulate these supposed differences and show identical conformations of DEAD-box helicases independent of the identity of the bound nucleotide. Here, we use NMR to demonstrate that the conformations of the ATP- and ADP-bound forms of the DEAD-box helicase Vasa are indeed different, contrary to the results from x-ray crystallography. These differences do not relate to the populations of the open and closed forms, but are intrinsic to the RecA_N domain. NMR chemical shift analysis reveals the regions of RecA_N where the average conformations of Vasa-ADP and Vasa-ATP are most different and indicates that these differences may contribute to modulating the affinity of the two nucleotide-bound complexes for RNA substrates.

We present the first-ever, fully-discrete, stochastic model of triggered cardiac calcium dynamics. Using anatomically accurate subcellular cardiac myocyte geometries, we simulate the molecular players involved in calcium handling using high-resolution stochastic and explicit-particle methods at the level of an individual cardiac dyadic junction. Integrating data from multiple experimental sources, the model not only replicates the findings of traditional in silico studies and complements in vitro experimental data, but also reveals new insights into the molecular mechanisms driving cardiac dysfunction under stress and disease conditions. We improve upon older, non-discrete models using the same realistic geometry by incorporating molecular mechanisms for spontaneous, as well as triggered Calcium-Induced Calcium Release (CICR). Action potentials are used to activate L-type calcium channels (LTCCs), triggering CICR through Ryanodine receptors (RyR) on the surface of the sarcoplasmic reticulum. These improvements allow for the specific focus on the couplon: the structure-function relationship between LTCC and RyR. We investigate the electrophysical effects of normal and diseased action potentials on CICR and interrogate the effects of dyadic junction deformation through detubulation and orphaning of RyR. Our work demonstrates the importance of the electrophysical integrity of the CRU on CICR fidelity, giving insights into the molecular basis of heart disease. Finally, we provide a unique, detailed, molecular view of the CICR process using advanced rendering techniques. This easy-to-use model comes complete with tutorials and all necessary software for use and analysis so as to maximize usability and reproducibility. Our work focuses on quantifying, qualifying, and visualizing the behavior of the molecular species that underlie the function and dysfunction of subcellular cardiomyocyte systems.