Biology subjects

Sbalzarini, I. F.

Publications and source records attributed to Sbalzarini, I. F..

12 recordsLinked to original sources

bioRxiv2026

Self-centering steady-state flows emerge in confined actomyosin networks

The actin cytoskeleton drives shape changes and transport in cells and supports mechanical signal transmission. However, how cells control and use active cytoskeletal flows at the mesoscale is not well understood. We reconstituted an active cytoskeleton in water-in-oil emulsion droplets of Xenopus laevis egg extract and observed, above a critical droplet size, the emergence of a 3D radially convergent steady-state flow of polymeric actin, maintained by continuous actin turnover. The flow condensed lipid-rich cellular debris into a centered inclusion. Steady-state F-actin density and flow velocity profiles roughly collapse onto scale-invariant master curves. This behavior can be explained by a physical model representing the network as an isotropic active viscous fluid with a percolation threshold. The contracting network behaves as an active swimmer with complex internal dynamics that centers itself and the central inclusion inside the droplets without physical boundary attachment. Active contraction, crosslinking and polymerization dynamics in an actin network can thus generate cell-scale flow patterns that sense the confining geometry and external signals and exert forces that are likely sufficient to move and localize organelles in cells.

biophysics↗

bioRxiv2024

Mechanics of knee meniscus results from precise balance between material microstructure and synovial fluid viscosity

The meniscus plays a crucial role in the biomechanics of the knee, serving as load transmitter, and reducing friction between joints. Understanding the biome-chanics of the meniscus is essential to effective treatments of knee injuries and degenerative conditions. In this study, we used two central meniscus samples extracted from a human knee and acquired high-resolution {micro}-CT images. Using an implicit immersed boundary technique, we reconstructed two 3D computational models of the menisci. By eroding the channels of the original meniscus geometry, we created new microstructures with varying porosities (0.53 to 0.8) whilst preserving the connectivity of the porous structure. We investigate the fluid dynamics of the meniscus using a mesh-free numerical method, considering various inlet pressure conditions and analysing the fluid flow within the microstructures. The results of the original microstructure associated with a physiological dynamic viscosity of synovial fluid are in accordance with biophysical experiments on menisci. Furthermore, the eroded microstructure with a 33% increase in porosity exhibited a remarkable 120% increase in flow velocity. This emphasises the sensitivity of meniscus physiology to porous microstructure properties, showing that detailed computational models can explore physiological and pathological conditions, advancing further knee biomechanics research.

bioengineering↗

bioRxiv2024

Morphogen gradients are regulated by porous media characteristics of the developing tissue

Long-range morphogen gradients have been proposed to form by morphogen diffusion from a localized source to distributed sinks in the target tissue. The role of the complex tissue geometry in this process is, however, less well understood and has not been explicitly resolved in existing models. Here, we numerically reconstruct pore-scale 3D geometries of zebrafish epiboly from light-sheet microscopy images. In these high-resolution 3D geometries, we simulate Fgf8a gradient formation in the tortuous extracellular space. Our simulations show that when realistic embryo geometries are considered, a source-diffusion-degradation mechanism with additional binding to extracellular matrix polymers is sufficient to explain self-organized emergence and robust maintenance of Fgf8a gradients. The predicted normalized gradient is robust against changes in source and sink rates but sensitive to changes in the pore connectivity of the extracellular space, with lower connectivity leading to steeper and shorter gradients. This demonstrates the importance of considering realistic geometries when studying morphogen gradients. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/588250v2_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@66c705org.highwire.dtl.DTLVardef@1943009org.highwire.dtl.DTLVardef@1dda6dcorg.highwire.dtl.DTLVardef@f5db66_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2023

Efficient image analysis for large-scale next generation histopathology using pAPRica

The large size of imaging datasets generated by next-generation histology methods limits the adoption of those approaches in research and the clinic. We propose pAPRica (pipelines for Adaptive Particle Representation image compositing and analysis), a framework based on the Adaptive Particle Representation (APR) to enable efficient analysis of large microscopy datasets, scalable up to petascale on a regular workstation. pAPRica includes stitching, merging, segmentation, registration, and mapping to an atlas as well as visualization of the large 3D image data, achieving 100+ fold speedup in computation and commensurate data-size reduction.

bioinformatics↗

bioRxiv2022

Variational inference accelerates accurate DNA mixture deconvolution

We investigate a class of DNA mixture deconvolution algorithms based on variational inference, and we show that this can significantly reduce computational runtimes with little or no effect on the accuracy and precision of the result. In particular, we consider Stein Variational Gradient Descent (SVGD) and Variational Inference (VI) with an evidence lower-bound objective. Both provide alternatives to the commonly used Markov-Chain Monte-Carlo methods for estimating the model posterior in Bayesian probabilistic genotyping. We demonstrate that both SVGD and VI significantly reduce computational costs over the current state of the art. Importantly, VI does so without sacrificing precision or accuracy, presenting an overall improvement over previously published methods.

bioinformatics↗

bioRxiv2022

Analysis of the Hamiltonian Monte Carlo genotyping algorithm on PROVEDIt mixtures including a novel precision benchmark

We provide an internal validation study of a recently published precise DNA mixture algorithm based on Hamiltonian Monte Carlo sampling [1]. We provide results for all 428 mixtures analysed by Riman et al. [2] and compare the results with two state-of-the-art software products: STRmix v2.6 and Euroformix v3.4.0. The comparison shows that the Hamiltonian Monte Carlo method provides reliable values of likelihood ratios (LRs) close to the other methods. We further propose a novel large-scale precision benchmark and quantify the precision of the Hamiltonian Monte Carlo method, indicating its improvements over existing solutions. Finally, we analyse the influence of the factors discussed by Buckleton et al. [3].

bioinformatics↗

bioRxiv2022

Hamiltonian Monte Carlo with strict convergence criteria reduces run-to-run variability in forensic DNA mixture deconvolution

MotivationAnalysing mixed DNA profiles is a common task in forensic genetics. Due to the complexity of the data, such analysis is often performed using Markov Chain Monte Carlo (MCMC)-based genotyping algorithms. These trade off precision against execution time. When the default settings are used, as large as a 10-fold changes in inferred likelihood ratios (LR) are observed when the software is run twice on the same case. So far, this uncertainty has been attributed to the stochasticity of MCMC algorithms. Since LRs translate directly to strength of the evidence in a criminal trial, forensic laboratories desire LR with small run-to-run variability. ResultsWe present a Hamiltonian Monte Carlo (HMC) algorithm that reduces run-to-run variability in forensic DNA mixture deconvolution by around an order of magnitude without increased runtime. We achieve this by enforcing strict convergence criteria. We show that the choice of convergence metric strongly influences precision. We validate our method by reproducing previously published results for benchmark DNA mixtures (MIX05, MIX13, and ProvedIt). We also present a complete software implementation of our algorithm that is able to leverage GPU acceleration, accelerating the inference process. In the benchmark mixtures, on consumer-grade hardware, the runtime is less than 7 minutes for 3 contributors, less than 35 minutes for 4 contributors, and less than an hour for 5 contributors with one known contributor.

genetics↗

bioRxiv2022

Matrix topology guides collective cell migration in vivo

Diverse modes of cell migration shape organisms in health and disease and much research has focused on the role of intracellular and extracellular components in different cell migration phenomena. What is less explored, however, is how the arrangement of the underlying extracellular matrix that many cells move upon in vivo influences migration. Combining novel transgenic lines and image analysis pipelines, reveals that during zebrafish optic cup formation cells use cryptopodia-like protrusions to migrate collectively and actively over a topologically changing matrix. These changing topologies correspond to different cell-matrix interactions. Interference with matrix topology results in loss of cryptopodia and inefficient migration. Thus, matrix topology influences the efficiency of directed collective cell migration during eye morphogenesis, a concept likely conserved in other developmental and disease contexts. One-Sentence SummaryDynamic cell-matrix interactions, crucial for successful collective rim cell migration, rely on extracellular matrix topologies during optic cup development in vivo.

developmental biology↗

bioRxiv2019

A non-linear system patterns Rab5 GTPase on the membrane

Proteins can self-organize into spatial patterns via non-linear dynamic interactions on cellular membranes. Modelling and simulations have shown that small GTPases can generate patterns by coupling guanine nucleotide exchange factors (GEF) to effector binding, generating a positive feedback of GTPase activation and membrane recruitment. Here, we reconstituted the patterning of the small GTPase Rab5 and its GEF/effector complex Rabex5/Rabaptin5 on supported lipid bilayers as a model system for membrane patterning. We show that there is a "handover" of Rab5 from Rabex5 to Rabaptin5 upon nucleotide exchange. A minimal system consisting of Rab5, RabGDI and a complex of full length Rabex5/Rabaptin5 was necessary to pattern Rab5 into membrane domains. Surprisingly, a lipid membrane composition mimicking that of the early endosome was required for Rab5 patterning. The prevalence of GEF/effector coupling in nature suggests a possible universal system for small GTPase patterning involving both protein and lipid interactions.

biochemistry↗

bioRxiv2018

An algorithm-centric Monte Carlo method to empiricallyquantify motion type estimation uncertainty in single-particle tracking

Quantitative analysis of microscopy images is ideally suited for understanding the functional biological correlates of individual molecular species identified by one of the several available \"omics\" techniques. Due to advances in fluorescent labeling, microscopy engineering and image processing, it is now possible to routinely observe and quantitatively analyze at high temporal and spatial resolution the real-time behavior of thousands of individual cellular structures as they perform their functional task inside living systems. Despite the central role of microscopic imaging in modern biology, unbiased inference, valid interpretation, scientific reproducibility and results dissemination are hampered by the still prevalent need for subjective interpretation of image data and by the limited attention given to the quantitative assessment and reporting of the error associated with each measurement or calculation, and on its effect on downstream analysis steps (i.e., error propagation). One of the mainstays of bioimage analysis is represented by single-particle tracking (SPT)1-5, which coupled with the mathematical analysis of trajectories and with the interpretative modelling of motion modalities, is of key importance for the quantitative understanding of the heterogeneous intracellular dynamic behavior of fluorescently-labeled individual cellular structures, vesicles, virions and single-molecules. Despite substantial advances, the evaluation of analytical error propagation through SPT and motion analysis pipelines is absent from most available tools 6. This severely hinders the critical evaluation, comparison, reproducibility and integration of results emerging from different laboratories, at different times, under different experimental conditions and using different model systems. Here we describe a novel, algorithmic-centric, Monte Carlo method to assess the effect of experimental parameters such as signal to noise ratio (SNR), particle detection error, trajectory length, and the diffusivity characteristics of the moving particle on the uncertainty associated with motion type classification The method is easily extensible to a wide variety of SPT algorithms, is made widely available via its implementation in our Open Microscopy Environment inteGrated Analysis (OMEGA) software tool for the management and analysis of tracking data 7, and forms an integral part of our Minimum Information About Particle Tracking Experiments (MIAPTE) data model 8.

bioinformatics↗

bioRxiv2018

OMEGA: a software tool for the management, analysis, and dissemination of intracellular trafficking data that incorporates motion type classification and quality control

MOTIVATIONParticle tracking coupled with time-lapse microscopy is critical for understanding the dynamics of intracellular processes of clinical importance. Spurred on by advances in the spatiotemporal resolution of microscopy and automated computational methods, this field is increasingly amenable to multi-dimensional high-throughput data collection schemes (Snijder et al., 2012). Typically, complex particle tracking datasets generated by individual laboratories are produced with incompatible methodologies that preclude comparison to each other. There is therefore an unmet need for data management systems that facilitate data standardization, meta-analysis, and structured data dissemination. The integration of analysis, visualization, and quality control capabilities into such systems would eliminate the need for manual transfer of data to diverse downstream analysis tools. At the same time, it would lay the foundation for shared trajectory data, particle tracking, and motion analysis standards. RESULTSHere, we present Open Microscopy Environment inteGrated Analysis (OMEGA), a cross-platform data management, analysis, and visualization system, for particle tracking data, with particular emphasis on results from viral and vesicular trafficking experiments. OMEGA provides intuitive graphical interfaces to implement integrated particle tracking and motion analysis workflows while providing easy to use facilities to automatically keep track of error propagation, harvest data provenance and ensure the persistence of analysis results and metadata. Specifically, OMEGA: 1) imports image data and metadata from data management tools such as the Open Microscopy Environment Remote Objects (OMERO; Allan et al., 2012); 2) tracks intracellular particles movement; 3) facilitates parameter optimization and trajectory results inspection and validation; 4) performs downstream trajectory analysis and motion type classification; 5) estimates the uncertainty propagating through the motion analysis pipeline; and, 6) facilitates storage and dissemination of analysis results, and analysis definition metadata, on the basis of our newly proposed FAIRsharing.org complainant Minimum Information About Particle Tracking Experiments (MIAPTE; Rigano and Strambio-De-Castillia, 2016; 2017) guidelines in combination with the OME-XML data model (Goldberg et al., 2005). In so doing, OMEGA maintains a persistent link between raw image data, intermediate analysis steps, the overall analysis output, and all necessary metadata to repeat the analysis process and reproduce its results. Availability and implementationOMEGA is a cross-platform, open-source software developed in Java. Source code and cross-platform binaries are freely available on GitHub at https://github.com/OmegaProject/Omega (doi: 10.5281/zenodo.2535523), under the GNU General Public License v.3. Contactcaterina.strambio@umassmed.edu and alex.rigano@umassmed.edu Supplementary informationSupplementary Material is available at BioRxiv.org

bioinformatics↗

bioRxiv2018

Forget Pixels: Adaptive Particle Representation of Fluorescence Microscopy Images

Modern microscopy modalities create a data deluge with gigabytes of data generated each second, and terabytes per day. Storing and processing these data is a severe bottleneck, not fully alleviated by data compression. We argue that this is because images are processed as regular grids of pixels. To address the root of the problem, we here propose a content-adaptive representation of fluorescence microscopy images called the Adaptive Particle Representation (APR). The APR replaces the regular grid of pixels with particles positioned according to image content. This overcomes storage bottlenecks, as data compression does, but additionally overcomes memory and processing bottlenecks, since the APR can directly be used in processing without going back to pixels. We present the ideas, concepts, and algorithms of the APR and validate them using noisy 3D image data. We show that the APR represents the content of an image while maintaining image quality. We then show that the adaptivity of the APR provides orders of magnitude benefits across a range of image processing tasks. Therefore, the APR provides a simple, extendable, and efficient content-aware representation of images that relaxes current data and processing bottlenecks.

bioinformatics↗