Biology subjects

Sedzinski, J.

Publications and source records attributed to Sedzinski, J..

8 recordsLinked to original sources

bioRxiv2026

StressNET: an adaptable deep-learning model for mechanical stress inference in tissues

Mechanical interactions between cells are fundamental to tissue morphogenesis during development and regeneration. Computational methods that infer intercellular stresses from microscopy images of cell shapes offer a non-invasive alternative to experimental perturbation techniques, yet all existing approaches rely on explicit physical models. Here we present StressNET, a Graph Neural Network (GNN) that infers intercellular mechanical stresses directly from tissue geometry, without assuming any underlying physical model. We generated synthetic datasets to train and benchmark StressNET, and demonstrate that its predictions achieve state-of-the-art correlation with experimental stress proxies in zebrafish neuromasts and Xenopus embryos. Analysis of the network's latent space reveals that StressNET learns global organizational principles of mechanical stress distribution, beyond local cell-cell interactions. StressNET is open-source and provides pre-trained models that can be fine-tuned on in vivo data, making it a broadly adaptable tool for studying tissue mechanics across biological systems.

bioinformatics↗

bioRxiv2026

Actin cross-linking organizes basal body patterning through anomalous diffusion transitions

Subcellular protein complexes and organelles exhibit diverse dynamic behaviors that reflect the mechanical constraints and organization of the intracellular environment. Although some structures follow classical Brownian motion, many display anomalous dynamics, including subdiffusion and superdiffusion, driven by viscoelasticity, molecular crowding, and cytoskeletal interactions. Transitions between these regimes are increasingly recognized as critical for subcellular organization, yet how they are regulated and influence pattern formation remains unclear. Here, we investigate the spatial arrangement of cilia on the apical surface of multiciliated cells (MCCs) in developing Xenopus laevis embryos, where coordinated ciliary beating depends on the precise organization of hundreds of centriole-derived basal bodies (BBs). Using quantitative confocal, high-resolution and high-speed TIRF imaging together with theoretical modeling, we show that BB trajectories undergo time-resolved transitions between diffusive and anomalous motion, with distinct regimes that correlate with apical surface expansion. During the early stages, actin remodeling facilitates the dispersal of BBs by providing a permissive, low-confinement environment. As development progresses, the actin network becomes increasingly cross-linked, forming a dense meshwork that constrains BB movement and promotes uniform spacing across the apical domain. Disruption of -actinin-1, a major actin cross-linking protein, impairs the integrity of the apical actin meshwork, weakens BB confinement, and disrupts regular spatial patterning, ultimately compromising the spatial arrangement of BBs required for proper cilia alignment. Together, we show that progressive apical actin cross-linking coordinates BB positioning and regulates their dynamic state, guiding the shift from diffusive to confined motion. This transition in dynamics enables the emergence of a uniform BB pattern, which in turn ensures the aligned deployment of motile cilia necessary for effective directional fluid flow.

biophysics↗

bioRxiv2025

Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation.

Cell state transitions underlie the emergence of diverse cell types and are traditionally defined by changes in gene expression. Yet these transitions also involve coordinated shifts in cell morphology and behaviour, which remain poorly characterized in densely packed epithelia. We developed a quantitative live-imaging and computational framework to track thousands of individual cells over time in the rapidly differentiating Xenopus mucociliary epithelium (MCE). From segmentations and trajectories, we extracted dynamic features-cell and nuclear shape, movement, position-to create a time-resolved morphodynamic dataset spanning the full course of differentiation. While single features showed high noise and low separability of ground-truth cell types, supervised machine learning revealed that integrating time-resolved features robustly predicts final cell fate. Gradient-boosted trees and multinomial logistic regression achieved moderate but consistent accuracy, especially for abundant epithelial lineages. Key discriminants included normalized Z-position, membrane-nucleus offset, and absolute experimental time, whereas movement contributed minimally to the results. Our results show that morphodynamic signatures encode predictive information about cell identity and provide a framework linking physical dynamics with molecular state.

developmental biology↗

bioRxiv2024

ForSys: non-invasive stress inference from time-lapse microscopy

During tissue development and regeneration, cells interpret and exert mechanical forces that are challenging to measure in vivo. Therefore, stress inference algorithms have emerged as powerful tools to estimate tissue stresses. However, how to incorporate tissue dynamics effectively into the inference remains elusive. Here, we present ForSys, a Python-based software that estimates intercellular stresses and intracellular pressures using time-lapse microscopy. We validated ForSys in silico and in vivo using the well-characterized mucociliary epithelium of the Xenopus embryo. We applied ForSys to study the migrating zebrafish lateral line primordium. We found that stress increases during cell rounding just before cell division and predicted the onset of epithelial rosettogenesis with high accuracy. Finally, we analyzed the development of the zebrafish neuromast and inferred mechanical asymmetries in a cell type-specific adhesion pattern. The versatility and simplicity of ForSys enhance the toolkit for studying spatiotemporal patterns of mechanical forces during tissue morphogenesis in vivo.

systems biology↗

bioRxiv2023

nf-core/marsseq: systematic pre-processing pipeline for MARS-seq experiments

MotivationAs a result of advancing single sequencing technology (scRNA-seq), it has become possible to study gene regulatory mechanism(s) and their influence on evolving cell states in time at the level of individual cells. Since 2009, numerous scRNA-seq protocols have been developed, each with its own advantages, disadvantages and library preparation complexities (Ziegenhain et al. 2017). However, the interpretation of data arising from these techniques often shares similar limitations, such as the lack of a standardized pre-processing workflow and consistent data reproducibility. Here we focus on the standardization of the plate based Massively Parallel RNA Single cell Sequencing (MARS-seq, Jaitin et al. 2014) pre-processing pipeline as described in MARS-seq2.0 (Keren-Shaul et al. 2019), which was developed at the Weizmann Institute of Science. ResultsTo overcome the limitations mentioned above, we have taken the original MARS-seq2.0 pipeline and revised it to enable implementation using the nf-core framework (Ewels et al. 2020). By doing so, we have simplified pipeline execution enabling streamlined application, with increased transparency and scalability. Additionally, we have further improved the pipeline by implementing a custom workflow for RNA velocity estimation. Availability and implementationThe pipeline is part of the nf-core bioinformatics community and is freely available at https://github.com/nf-core/marsseq with data analysis at https://github.com/brickmanlab/proks-et-al-2023.

bioinformatics↗

bioRxiv2023

A single-cell, time-resolved profiling of Xenopus mucociliary epithelium reveals non-hierarchical model of development

The specialized cell-types of the mucociliary epithelium (MCE) lining the respiratory tract enable continuous airway clearing, with its defects leading to chronic respiratory diseases. The molecular mechanisms driving cell-fate acquisition and temporal specialization during mucociliary epithelial development remain largely unknown. Here, we profile the developing Xenopus MCE from pluripotent to mature stages by single-cell transcriptomics, identifying novel, multipotent early epithelial progenitors that execute multi-lineage cues before specialising into late-stage ionocytes, goblet and basal cells. Combining in silico lineage inference, in situ hybridization and single-cell multiplexed RNA imaging, we capture the initial bifurcation into early epithelial and multiciliated progenitors, chart cell- type emergence and fate progression into specialized cell-types. Comparative analysis of nine airway atlases reveals an evolutionary conserved transcriptional module in ciliated cells, whereas secretory and basal types execute distinct function-specific programmes across vertebrates. We uncover a continuous non-hierarchical model of MCE development alongside a significant data resource for understanding respiratory biology.

developmental biology↗

bioRxiv2021

Mechanics of cell integration in vivo

During embryonic development, regeneration and homeostasis, cells have to physically integrate into their target tissues, where they ultimately execute their function. Despite a significant body of research on how mechanical forces instruct cellular behaviors within the plane of an epithelium, very little is known about the mechanical interplay at the interface between migrating cells and their surrounding tissue, which has its own dynamics, architecture and identity. Here, using quantitative in vivo imaging and molecular perturbations, together with a theoretical model, we reveal that multiciliated cell (MCC) precursors in the Xenopus embryo form dynamic filopodia that pull at the vertices of the overlying epithelial sheet to probe their stiffness and identify the preferred positions for their integration into the tissue. Moreover, we report a novel function for a structural component of vertices, the lipolysis-stimulated lipoprotein receptor (LSR), in filopodia dynamics and show its critical role in cell intercalation. Remarkably, we find that pulling forces equip the MCCs to remodel the epithelial junctions of the neighboring tissue, enabling them to generate a permissive environment for their integration. Our findings reveal the intricate physical crosstalk at the cell-tissue interface and uncover previously unknown functions for mechanical forces in orchestrating cell integration.

biophysics↗

bioRxiv2017

High-content protein localization screening in vivo reveals novel regulators of multiciliated cell development and function

Multiciliated cells (MCCs) drive fluid flow in diverse tubular organs and are essential for development and homeostasis of the vertebrate central nervous system, airway, and reproductive tracts. These cells are characterized by dozens or hundreds of long, motile cilia that beat in a coordinated and polarized manner (Brooks and Wallingford, 2014). In recent years, genomic studies have not only elucidated the transcriptional hierarchy for MCC specification, but also identified myriad new proteins that govern MCC ciliogenesis, cilia beating, or cilia polarization (e.g. (Choksi et al., 2014b; Chung et al., 2014; Hoh et al., 2012; Ma et al., 2014; Treutlein et al., 2014)). Interestingly, this burst of genomic data has also highlighted the obvious importance of the \"ignorome,\" that large fraction of vertebrate genes that remain only poorly characterized (Pandey et al., 2014). Understanding the function of novel proteins with little prior history of study presents a special challenge, especially when faced with large numbers of such proteins. Here, we explored the MCC ignorome by defining the subcellular localization of 260 poorly defined proteins in vertebrate MCCs in vivo. Moreover, functional analyses that arose from results of the screen provide novel insights into the mechanisms by which the actin cytoskeleton simultaneously influences diverse aspects of MCC biology, including basal body docking, and ciliogenesis.

cell biology↗