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Abstract Multicellular spheroids have shown great promise in 3D biology. Many techniques exist to form spheroids, but how cells take mechanical advantage of native fibrous extracellular matrix (ECM) to form spheroids remains unknown. Here, we identify the role of fiber diameter, architecture, and cell contractility on spheroids’ spontaneous formation and growth in ECM-mimicking fiber networks. We show that matrix deformability revealed through force measurements on aligned fiber networks promotes spheroid formation independent of fiber diameter. At the same time, larger-diameter crosshatched networks of low deformability abrogate spheroid formation. Thus, designing fiber networks of varying diameters and architectures allows spatial patterning of spheroids and monolayers simultaneously. Forces quantified during spheroid formation revealed the contractile role of Rho-associated protein kinase in spheroid formation and maintenance. Interestingly, we observed spheroid–spheroid and multiple spheroid mergers initiated by cell exchanges to form cellular bridges connecting the two spheroids. Unexpectedly, we found large pericyte spheroids contract rhythmically. Transcriptomic analysis revealed striking changes in cell–cell, cell–matrix, and mechanosensing gene expression profiles concordant with spheroid assembly on fiber networks. Overall, we ascertained that contractility and network deformability work together to spontaneously form and pattern 3D spheroids, potentially connecting in vivo matrix biology with developmental, disease, and regenerative biology. 

Abstract Accurate positioning of the mitotic spindle within the rounded cell body is critical to physiological maintenance. Mitotic cells encounter confinement from neighboring cells or the extracellular matrix (ECM), which can cause rotation of mitotic spindles and tilting of the metaphase plate (MP). To understand the effect of confinement on mitosis by fibers (ECM confinement), we use flexible ECM-mimicking nanofibers that allow natural rounding of the cell body while confining it to differing levels. Rounded mitotic bodies are anchored in place by actin retraction fibers (RFs) originating from adhesions on fibers. We discover that the extent of confinement influences RF organization in 3D, forming triangular and band-like patterns on the cell cortex under low and high confinement, respectively. Our mechanistic analysis reveals that the patterning of RFs on the cell cortex is the primary driver of the MP rotation. A stochastic Monte Carlo simulation of the centrosome, chromosome, membrane interactions, and 3D arrangement of RFs recovers MP tilting trends observed experimentally. Under high ECM confinement, the fibers can mechanically pinch the cortex, causing the MP to have localized deformations at contact sites with fibers. Interestingly, high ECM confinement leads to low and high MP tilts, which we mechanistically show to depend upon the extent of cortical deformation, RF patterning, and MP position. We identify that cortical deformation and RFs work in tandem to limit MP tilt, while asymmetric positioning of MP leads to high tilts. Overall, we provide fundamental insights into how mitosis may proceed in ECM-confining microenvironments in vivo. 

Abstract Protrusions at the leading-edge of a cell play an important role in sensing the extracellular cues during cellular spreading and motility. Recent studies provided indications that these protrusions wrap (coil) around the extracellular fibers. However, the physics of this coiling process, and the mechanisms that drive it, are not well understood. We present a combined theoretical and experimental study of the coiling of cellular protrusions on fibers of different geometry. Our theoretical model describes membrane protrusions that are produced by curved membrane proteins that recruit the protrusive forces of actin polymerization, and identifies the role of bending and adhesion energies in orienting the leading-edges of the protrusions along the azimuthal (coiling) direction. Our model predicts that the cell’s leading-edge coils on fibers with circular cross-section (above some critical radius), but the coiling ceases for flattened fibers of highly elliptical cross-section. These predictions are verified by 3D visualization and quantitation of coiling on suspended fibers using Dual-View light-sheet microscopy (diSPIM). Overall, we provide a theoretical framework, supported by experiments, which explains the physical origin of the coiling phenomenon. 

Abstract Cytoskeleton‐mediated force transmission regulates nucleus morphology. How nuclei shaping occurs in fibrous in vivo environments remains poorly understood. Here suspended nanofiber networks of precisely tunable (nm–µm) diameters are used to quantify nucleus plasticity in fibrous environments mimicking the natural extracellular matrix. Contrary to the apical cap over the nucleus in cells on 2‐dimensional surfaces, the cytoskeleton of cells on fibers displays a uniform actin network caging the nucleus. The role of contractility‐driven caging in sculpting nuclear shapes is investigated as cells spread on aligned single fibers, doublets, and multiple fibers of varying diameters. Cell contractility increases with fiber diameter due to increased focal adhesion clustering and density of actin stress fibers, which correlates with increased mechanosensitive transcription factor Yes‐associated protein (YAP) translocation to the nucleus. Unexpectedly, large‐ and small‐diameter fiber combinations lead to teardrop‐shaped nuclei due to stress fiber anisotropy across the cell. As cells spread on fibers, diameter‐dependent nuclear envelope invaginations that run the nucleus's length are formed at fiber contact sites. The sharpest invaginations enriched with heterochromatin clustering and sites of DNA repair are insufficient to trigger nucleus rupture. Overall, the authors quantitate the previously unknown sculpting and adaptability of nuclei to fibrous environments with pathophysiological implications. 

Polarized fluorescence microscopy is a valuable tool for measuring molecular orientations in biological samples, but techniques for recovering three-dimensional orientations and positions of fluorescent ensembles are limited. We report a polarized dual-view light-sheet system for determining the diffraction-limited three-dimensional distribution of the orientations and positions of ensembles of fluorescent dipoles that label biological structures. We share a set of visualization, histogram, and profiling tools for interpreting these positions and orientations. We model the distributions based on the polarization-dependent efficiency of excitation and detection of emitted fluorescence, using coarse-grained representations we call orientation distribution functions (ODFs). We apply ODFs to create physics-informed models of image formation with spatio-angular point-spread and transfer functions. We use theory and experiment to conclude that light-sheet tilting is a necessary part of our design for recovering all three-dimensional orientations. We use our system to extend known two-dimensional results to three dimensions in FM1-43-labeled giant unilamellar vesicles, fast-scarlet-labeled cellulose in xylem cells, and phalloidin-labeled actin in U2OS cells. Additionally, we observe phalloidin-labeled actin in mouse fibroblasts grown on grids of labeled nanowires and identify correlations between local actin alignment and global cell-scale orientation, indicating cellular coordination across length scales. 

Tracking microrobots is challenging due to their minute size and high speed. In biomedical applications, this challenge is exacerbated by the dense surrounding environments with feature sizes and shapes comparable to microrobots. Herein, Motion Enhanced Multi‐level Tracker (MEMTrack) is introduced for detecting and tracking microrobots in dense and low‐contrast environments. Informed by the physics of microrobot motion, synthetic motion features for deep learning‐based object detection and a modified Simple Online and Real‐time Tracking (SORT)algorithm with interpolation are used for tracking. MEMTrack is trained and tested using bacterial micromotors in collagen (tissue phantom), achieving precision and recall of 76% and 51%, respectively. Compared to the state‐of‐the‐art baseline models, MEMTrack provides a minimum of 2.6‐fold higher precision with a reasonably high recall. MEMTrack's generalizability to unseen (aqueous) media and its versatility in tracking microrobots of different shapes, sizes, and motion characteristics are shown. Finally, it is shown that MEMTrack localizes objects with a root‐mean‐square error of less than 1.84 μm and quantifies the average speed of all tested systems with no statistically significant difference from the laboriously produced manual tracking data. MEMTrack significantly advances microrobot localization and tracking in dense and low‐contrast settings and can impact fundamental and translational microrobotic research. 

Tracking microrobots is challenging, considering their minute size and high speed. As the field progresses towards developing microrobots for biomedical applications and conducting mechanistic studies in physiologically relevant media (e.g., collagen), this challenge is exacerbated by the dense surrounding environments with feature size and shape comparable to microrobots. Herein, we report Motion Enhanced Multi-level Tracker (MEMTrack), a robust pipeline for detecting and tracking microrobots using synthetic motion features, deep learning-based object detection, and a modified Simple Online and Real-time Tracking (SORT) algorithm with interpolation for tracking. Our object detection approach combines different models based on the object's motion pattern. We trained and validated our model using bacterial micro-motors in collagen (tissue phantom) and tested it in collagen and aqueous media. We demonstrate that MEMTrack accurately tracks even the most challenging bacteria missed by skilled human annotators, achieving precision and recall of 77% and 48% in collagen and 94% and 35% in liquid media, respectively. Moreover, we show that MEMTrack can quantify average bacteria speed with no statistically significant difference from the laboriously-produced manual tracking data. MEMTrack represents a significant contribution to microrobot localization and tracking, and opens the potential for vision-based deep learning approaches to microrobot control in dense and low-contrast settings. All source code for training and testing MEMTrack and reproducing the results of the paper have been made publicly available this https URL. 

A high porosity (88%) and ultrathin (<3 μm) fibrous basement membrane mimic using (A) suspended nanofiber networks for a (B) brain endothelial–pericyte co-culture model. (C) Our approach achieved low cell membrane and nuclei separations.