Quantifying Modes of 3D Cell Migration

Learning dynamical models of single and collective cell migration: a review

Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualising the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasise how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour. Together, these data-driven approaches are a promising avenue for building physical models of cell migration directly from experimental data, and for providing conceptual links between different length-scales of description.

u-track3D: Measuring, navigating, and validating dense particle trajectories in three dimensions

We describe u-track3D, a software package that extends the versatile u-track framework established in 2D to address the specific challenges of 3D particle tracking. First, we present the performance of the new package in quantifying a variety of intracellular dynamics imaged by multiple 3D microcopy platforms and on the standard 3D test dataset of the particle tracking challenge. These analyses indicate that u-track3D presents a tracking solution that is competitive to both conventional and deep-learning-based approaches. We then present the concept of dynamic region of interest (dynROI), which allows an experimenter to interact with dynamic 3D processes in 2D views amenable to visual inspection. Third, we present an estimator of trackability that automatically defines a score for every trajectory, thereby overcoming the challenges of trajectory validation by visual inspection. With these combined strategies, u-track3D provides a complete framework for unbiased studies of molecular processes in complex volumetric sequences.

Cellular harmonics for the morphology-invariant analysis of molecular organization at the cell surface

The spatiotemporal organization of membrane-associated molecules is central to the regulation of cellular signals. Powerful new microscopy techniques enable the three-dimensional visualization of localization and activation of these molecules; however, the quantitative interpretation and comparison of molecular organization on the three-dimensional cell surface remains challenging because cells themselves vary greatly in morphology. Here we introduce u-signal3D, a framework to assess the spatial scales of molecular organization at the cell surface in a cell-morphology-invariant manner. We validated the framework by analyzing synthetic signaling patterns painted onto observed cell morphologies, as well as measured distributions of cytoskeletal and signaling molecules. To demonstrate the framework's versatility, we further compared the spatial organization of cell surface signals both within, and between, cell populations, and powered an upstream machine-learning-based analysis of signaling motifs.

Adhesion tunes speed and persistence by coordinating protrusions and extracellular matrix remodeling

Cell migration through 3D environments is essential to development, disease, and regeneration processes. Conceptual models of migration have been developed primarily on the basis of 2D cell behaviors, but a general understanding of 3D cell migration is still lacking due to the added complexity of the extracellular matrix. Here, using a multiplexed biophysical imaging approach for single-cell analysis of human cell lines, we show how the subprocesses of adhesion, contractility, actin cytoskeletal dynamics, and matrix remodeling integrate to produce heterogeneous migration behaviors. This single-cell analysis identifies three modes of cell speed and persistence coupling, driven by distinct modes of coordination between matrix remodeling and protrusive activity. The framework that emerges establishes a predictive model linking cell trajectories to distinct subprocess coordination states.

Deep learning techniques and mathematical modeling allow 3D analysis of mitotic spindle dynamics

Time-lapse microscopy movies have transformed the study of subcellular dynamics. However, manual analysis of movies can introduce bias and variability, obscuring important insights. While automation can overcome such limitations, spatial and temporal discontinuities in time-lapse movies render methods such as 3D object segmentation and tracking difficult. Here, we present SpinX, a framework for reconstructing gaps between successive image frames by combining deep learning and mathematical object modeling. By incorporating expert feedback through selective annotations, SpinX identifies subcellular structures, despite confounding neighbor-cell information, non-uniform illumination, and variable fluorophore marker intensities. The automation and continuity introduced here allows the precise 3D tracking and analysis of spindle movements with respect to the cell cortex for the first time. We demonstrate the utility of SpinX using distinct spindle markers, cell lines, microscopes, and drug treatments. In summary, SpinX provides an exciting opportunity to study spindle dynamics in a sophisticated way, creating a framework for step changes in studies using time-lapse microscopy.

A machine learning based approach for quantitative evaluation of cell migration in Transwell assays based on deformation characteristics

Many pathological and physiological processes, including embryonic development, immune response and cancer metastasis, involve studies on cell migration, and especially detection methods, for which it is difficult to satisfy the requirements for rapid and quantitative evaluation and analysis. In view of the shortcomings in simultaneously quantifying the number of migrated cells and non-migrated cells using Transwell assays, we propose a novelty approach for the evaluation of cell migration by distinguishing whether the cells have migrated based on the regularity of the cell morphology changes. Traditionally, the status of living cells and dead cells are detected and analyzed by machine learning using some common morphological characteristics, e.g., area and perimeter of the cells. However, the accuracy of detecting whether cells have migrated or not using these common characteristics is not high, and the characteristics are not appropriate for our studies. Therefore, from the point of view of mechanism analysis for the migration behavior, we examined the regularity of different morphology changes of migrated cells and non-migrated cells, and thus discovered the distinguishable morphological characteristics. Then, two deformation characteristics, deformation index and taper index are proposed. Then, a machine learning based algorithm that can identify migrated cells according to the proposed deformation characteristics was devised. In addition, images of migrated cells and non-migrated cells were obtained from the Transwell assays. This algorithm was trained, and was able to successfully identify migrated cells with an accuracy of 84% using the proposed morphological characteristics. This method greatly improves the identification accuracy when compared with the identification of traditional characteristics of which the accuracy was about 54.7%. This machine learning based method might be employed as a potential tool for cell counting and evaluation of cell migration with the aim of reducing time and improving automation compared with the traditional method. This method is effective, rapid, and incorporate advances in artificial intelligence which could be used for adapting the current evaluation methods.

Fine-grained, nonlinear registration of live cell movies reveals spatiotemporal organization of diffuse molecular processes

We present an application of nonlinear image registration to align in microscopy time lapse sequences for every frame the cell outline and interior with the outline and interior of the same cell in a reference frame. The registration relies on a subcellular fiducial marker, a cell motion mask, and a topological regularization that enforces diffeomorphism on the registration without significant loss of granularity. This allows spatiotemporal analysis of extremely noisy and diffuse molecular processes across the entire cell. We validate the registration method for different fiducial markers by measuring the intensity differences between predicted and original time lapse sequences of Actin cytoskeleton images and by uncovering zones of spatially organized GEF- and GTPase signaling dynamics visualized by FRET-based activity biosensors in MDA-MB-231 cells. We then demonstrate applications of the registration method in conjunction with stochastic time-series analysis. We describe distinct zones of locally coherent dynamics of the cytoplasmic protein Profilin in U2OS cells. Further analysis of the Profilin dynamics revealed strong relationships with Actin cytoskeleton reorganization during cell symmetry-breaking and polarization. This study thus provides a framework for extracting information to explore functional interactions between cell morphodynamics, protein distributions, and signaling in cells undergoing continuous shape changes. Matlab code implementing the proposed registration method is available at https://github.com/DanuserLab/Mask-Regularized-Diffeomorphic-Cell-Registration.

T cell morphodynamics reveal periodic shape oscillations in three-dimensional migration

T cells use sophisticated shape dynamics (morphodynamics) to migrate towards and neutralize infected and cancerous cells. However, there is limited quantitative understanding of the migration process in three-dimensional extracellular matrices (ECMs) and across timescales. Here, we leveraged recent advances in lattice light-sheet microscopy to quantitatively explore the three-dimensional morphodynamics of migrating T cells at high spatio-temporal resolution. We first developed a new shape descriptor based on spherical harmonics, incorporating key polarization information of the uropod. We found that the shape space of T cells is low-dimensional. At the behavioural level, run-and-stop migration modes emerge at approximately 150 s, and we mapped the morphodynamic composition of each mode using multiscale wavelet analysis, finding 'stereotyped' motifs. Focusing on the run mode, we found morphodynamics oscillating periodically (every approx. 100 s) that can be broken down into a biphasic process: front-widening with retraction of the uropod, followed by a rearward surface motion and forward extension, where intercalation with the ECM in both of these steps likely facilitates forward motion. Further application of these methods may enable the comparison of T cell migration across different conditions (e.g. differentiation, activation, tissues and drug treatments) and improve the precision of immunotherapeutic development.

Coupling traction force patterns and actomyosin wave dynamics reveals mechanics of cell motion

Motile cells can use and switch between different modes of migration. Here, we use traction force microscopy and fluorescent labeling of actin and myosin to quantify and correlate traction force patterns and cytoskeletal distributions in Dictyostelium discoideum cells that move and switch between keratocyte-like fan-shaped, oscillatory, and amoeboid modes. We find that the wave dynamics of the cytoskeletal components critically determine the traction force pattern, cell morphology, and migration mode. Furthermore, we find that fan-shaped cells can exhibit two different propulsion mechanisms, each with a distinct traction force pattern. Finally, the traction force patterns can be recapitulated using a computational model, which uses the experimentally determined spatiotemporal distributions of actin and myosin forces and a viscous cytoskeletal network. Our results suggest that cell motion can be generated by friction between the flow of this network and the substrate.

The matrix-dependent 3D spheroid model of the migration of non-small cell lung cancer: a step towards a rapid automated screening

In vitro 3D cell culture systems utilizing multicellular tumor spheroids (MCTS) are widely used in translational oncology, including for studying cell migration and in personalized therapy. However, early stages of cellular migration from MCTS and cross-talk between spheroids are overlooked, which was addressed in the current study. Here, we investigated cell migration from MCTS derived from human non-small cell lung cancer (NSCLC) cell line A549 cultured on different substrates, collagen gel or plastic, at different time points. We found that migration starts at 4–16 h time points after the seeding and its speed is substrate-dependent. We also demonstrated that co-culture of two NSCLC-derived MCTS on collagen gel, but not on plastic, facilitates cell migration compared with single MTCS. This finding should be considered when designing MCTS-based functional assays for personalized therapeutic approach and drug screenings. Overall, our work characterizes the in vitro 3D cell culture model resembling NSCLC cell migration from the clusters of CTCs into surgical wound, and describes microscopy-based tools and approaches for image data analysis with a potential for further automation. These tools and approaches also might be used to predict patterns of CTCs migration based on ex vivo analysis of patient biopsy in a 3D culture system.

A mathematical framework for modelling 3D cell motility: applications to glioblastoma cell migration

The collection of 3D cell tracking data from live images of micro-tissues is a recent innovation made possible due to advances in imaging techniques. As such there is increased interest in studying cell motility in 3D in vitro model systems but a lack of rigorous methodology for analysing the resulting data sets. One such instance of the use of these in vitro models is in the study of cancerous tumours. Growing multicellular tumour spheroids in vitro allows for modelling of the tumour microenvironment and the study of tumour cell behaviours, such as migration, which improves understanding of these cells and in turn could potentially improve cancer treatments. In this paper, we present a workflow for the rigorous analysis of 3D cell tracking data, based on the persistent random walk model, but adaptable to other biologically informed mathematical models. We use statistical measures to assess the fit of the model to the motility data and to estimate model parameters and provide confidence intervals for those parameters, to allow for parametrization of the model taking correlation in the data into account. We use in silico simulations to validate the workflow in 3D before testing our method on cell tracking data taken from in vitro experiments on glioblastoma tumour cells, a brain cancer with a very poor prognosis. The presented approach is intended to be accessible to both modellers and experimentalists alike in that it provides tools for uncovering features of the data set that may suggest amendments to future experiments or modelling attempts.

Imaging mitotic processes in three dimensions with lattice light-sheet microscopy

There are few technologies that can capture mitotic processes occurring in three-dimensional space with the desired spatiotemporal resolution. Due to such technical limitations, our understanding of mitosis, which has been studied since the early 1880s, is still incomplete with regard to mitotic processes and their regulatory mechanisms at a molecular level. A recently developed high-resolution type of light-sheet microscopy, lattice light-sheet microscopy (LLSM), has achieved unprecedented spatiotemporal resolution scans of intracellular spaces at the whole-cell level. This technology enables experiments that were not possible before (e.g., tracking of growth of every spindle microtubule end and discrimination of individual chromosomes in living cells), thus providing a new avenue for the analysis of mitotic processes. Herein, principles of LLSM technology are introduced, as well as experimental techniques that became possible with LLSM. In addition, issues remaining to be solved for use of this technology in mitosis research, big image data problems, are presented to help guide mitosis research into a new era.

Bioimage Analysis and Cell Motility

Bioimage analysis (BIA) has historically helped study how and why cells move; biological experiments evolved in intimate feedback with the most classical image processing techniques because they contribute objectivity and reproducibility to an eminently qualitative science. Cell segmentation, tracking, and morphology descriptors are all discussed here. Using ameboid motility as a case study, these methods help us illustrate how proper quantification can augment biological data, for example, by choosing mathematical representations that amplify initially subtle differences, by statistically uncovering general laws or by integrating physical insight. More recently, the non-invasive nature of quantitative imaging is fertilizing two blooming fields: mechanobiology, where many biophysical measurements remain inaccessible, and microenvironments, where the quest for physiological relevance has exploded data size. From relief to remedy, this trend indicates that BIA is to become a main vector of biological discovery as human visual analysis struggles against ever more complex data.

Critical comparison of image analysis workflows for quantitative cell morphological evaluation in assessing cell response to biomaterials

Quantitative image analysis is an important tool in understanding cell fate processes through the study of cell morphological changes in terms of size, shape, number, and orientation. In this context, this work explores systematically the main challenges involved in the quantitative analysis of fluorescence microscopy images and also proposes a new protocol while comparing its outcome with the widely used Image J analysis. It is important to mention that fluorescence microscopy is by far most widely used in biocompatibility analysis (observing cell fate changes) of implantable biomaterials. In this study, we employed two different image analyses toolsets: (i) the conventionally employed ImageJ software, and (ii) a recently developed automated digital image analyses framework, called ImageMKS. While ImageJ offers a powerful toolset for image analyses, it requires sophisticated user expertise to design and iteratively refine the analyses workflow. This workflow primarily comprises a sequence of image transformations that typically involve de-noising and labelling of features. On the other hand, ImageMKS automates the image analyses protocol to a large extent, and thereby mitigates the influence of the user bias on the final results. This aspect is addressed using a case study of C2C12 mouse myoblast cells grown on Poly(vinyldiene difluoride) based polymeric substrates in the presence of an external electric field. In particular, we used a number of fluorescence microscopy images of murine myoblasts (muscle precursor cells) grown on Poly (vinylidene difluoride), PVDF based nanobiocomposites under the influence of electric field. It was observed that when compared with the findings obtained from ImageJ, ImageMKS workflows consistently produced more reliable results that correlated better with the prior studies. Furthermore, the MKS workflows required much less user time, because of their automation.

Cell Tracking Profiler - a user-driven analysis framework for evaluating 4D live-cell imaging data

Accurate measurements of cell morphology and behaviour are fundamentally important for understanding how disease, molecules and drugs affect cell function in vivo Here, by using muscle stem cell (muSC) responses to injury in zebrafish as our biological paradigm, we established a 'ground truth' for muSC behaviour. This revealed that segmentation and tracking algorithms from commonly used programs are error-prone, leading us to develop a fast semi-automated image analysis pipeline that allows user-defined parameters for segmentation and correction of cell tracking. Cell Tracking Profiler (CTP) is a package that runs two existing programs, HK Means and Phagosight within the Icy image analysis suite, to enable user-managed cell tracking from 3D time-lapse datasets to provide measures of cell shape and movement. We demonstrate how CTP can be used to reveal changes to cell behaviour of muSCs in response to manipulation of the cell cytoskeleton by small-molecule inhibitors. CTP and the associated tools we have developed for analysis of outputs thus provide a powerful framework for analysing complex cell behaviour in vivo from 4D datasets that are not amenable to straightforward analysis.

Of Cell Shapes and Motion: The Physical Basis of Animal Cell Migration

Motile cells have developed a variety of migration modes relying on diverse traction-force-generation mechanisms. Before the behavior of intracellular components could be easily imaged, cell movements were mostly classified by different types of cellular shape dynamics. Indeed, even though some types of cells move without any significant change in shape, most cell propulsion mechanisms rely on global or local deformations of the cell surface. In this review, focusing mostly on metazoan cells, we discuss how different types of local and global shape changes underlie distinct migration modes. We then discuss mechanical differences between force-generation mechanisms and finish by speculating on how they may have evolved.

Dynamic spherical harmonics approach for shape classification of migrating cells

Cell migration involves dynamic changes in cell shape. Intricate patterns of cell shape can be analyzed and classified using advanced shape descriptors, including spherical harmonics (SPHARM). Though SPHARM have been used to analyze and classify migrating cells, such classification did not exploit SPHARM spectra in their dynamics. Here, we examine whether additional information from dynamic SPHARM improves classification of cell migration patterns. We combine the static and dynamic SPHARM approach with a support-vector-machine classifier and compare their classification accuracies. We demonstrate that the dynamic SPHARM analysis classifies cell migration patterns more accurately than the static one for both synthetic and experimental data. Furthermore, by comparing the computed accuracies with that of a naive classifier, we can identify the experimental conditions and model parameters that significantly affect cell shape. This capability should – in the future – help to pinpoint factors that play an essential role in cell migration.

Mechanophenotyping of 3D multicellular clusters using displacement arrays of rendered tractions

Epithelial tissues mechanically deform the surrounding extracellular matrix during embryonic development, wound repair, and tumor invasion. Ex vivo measurements of such multicellular tractions within three-dimensional (3D) biomaterials could elucidate collective dissemination during disease progression and enable preclinical testing of targeted antimigration therapies. However, past 3D traction measurements have been low throughput due to the challenges of imaging and analyzing information-rich 3D material deformations. Here, we demonstrate a method to profile multicellular clusters in a 96-well-plate format based on spatially heterogeneous contractile, protrusive, and circumferential tractions. As a case study, we profile multicellular clusters across varying states of the epithelial-mesenchymal transition, revealing a successive loss of protrusive and circumferential tractions, as well as the formation of localized contractile tractions with elongated cluster morphologies. These cluster phenotypes were biochemically perturbed by using drugs, biasing toward traction signatures of different epithelial or mesenchymal states. This higher-throughput analysis is promising to systematically interrogate and perturb aberrant mechanobiology, which could be utilized with human-patient samples to guide personalized therapies.

A minimal computational model for three-dimensional cell migration

During migration, eukaryotic cells can continuously change their three-dimensional morphology, resulting in a highly dynamic and complex process. Further complicating this process is the observation that the same cell type can rapidly switch between different modes of migration. Modelling this complexity necessitates models that are able to track deforming membranes and that can capture the intracellular dynamics responsible for changes in migration modes. Here we develop an efficient three-dimensional computational model for cell migration, which couples cell mechanics to a simple intracellular activator-inhibitor signalling system. We compare the computational results to quantitative experiments using the social amoeba Dictyostelium discoideum. The model can reproduce the observed migration modes generated by varying either mechanical or biochemical model parameters and suggests a coupling between the substrate and the biomechanics of the cell.

LFA-1 Controls Th1 and Th17 Motility Behavior in the Inflamed Central Nervous System

Leukocyte trafficking is a key event during autoimmune and inflammatory responses. The subarachnoid space (SAS) and cerebrospinal fluid are major routes for the migration of encephalitogenic T cells into the central nervous system (CNS) during experimental autoimmune encephalomyelitis (EAE), the animal model of multiple sclerosis, and are sites of T cell activation before the invasion of CNS parenchyma. In particular, autoreactive Th1 and Th17 cell trafficking and reactivation in the CNS are required for the pathogenesis of EAE. However, the molecular mechanisms controlling T cell dynamics during EAE are unclear. We used two-photon laser microscopy to show that autoreactive Th1 and Th17 cells display distinct motility behavior within the SAS in the spinal cords of mice immunized with the myelin oligodendrocyte glycoprotein peptide MOG₃₅₋₅₅. Th1 cells showed a strong directional bias at the disease peak, moving in a straight line and covering long distances, whereas Th17 cells exhibited more constrained motility. The dynamics of both Th1 and Th17 cells were strongly affected by blocking the integrin LFA-1, which interfered with the deformability and biomechanics of Th1 but not Th17 cells. The intrathecal injection of a blocking anti-LFA-1 antibody at the onset of disease significantly inhibited EAE progression and also strongly reduced neuro-inflammation in the immunized mice. Our results show that LFA-1 plays a pivotal role in T cell motility during EAE and suggest that interfering with the molecular mechanisms controlling T cell motility can help to reduce the pathogenic potential of autoreactive lymphocytes.

Robust and automated detection of subcellular morphological motifs in 3D microscopy images

Rapid developments in live-cell 3D microscopy enable imaging of cell morphology and signaling with unprecedented detail. However, tools to systematically measure and visualize the intricate relationships between intracellular signaling, cytoskeletal organization, and downstream cell morphological outputs do not exist. Here we introduce u-shape3D, a computer graphics and machine-learning pipeline to probe molecular mechanisms underlying 3D cell morphogenesis and to test the intriguing possibility that morphogenesis itself affects intracellular signaling. We demonstrate a generic morphological motif detector that automatically finds lamellipodia, filopodia, blebs, and other motifs. Combining motif detection with molecular localization, we measure the differential association of PIP₂ and Kras^V12 with blebs. Both signals associate with bleb edges, as expected for membrane-localized proteins, but only PIP₂ is enhanced on blebs. This indicates that sub-cellular signaling processes are differentially modulated by local morphological motifs. Overall, our computational workflow enables the objective, 3D analysis of the coupling of cell shape and signaling.

3-D Quantification of Filopodia in Motile Cancer Cells

We present a 3D bioimage analysis workflow to quantitatively analyze single, actin-stained cells with filopodial protrusions of diverse structural and temporal attributes, such as number, length, thickness, level of branching, and lifetime, in time-lapse confocal microscopy image data. Our workflow makes use of convolutional neural networks trained using real as well as synthetic image data, to segment the cell volumes with highly heterogeneous fluorescence intensity levels and to detect individual filopodial protrusions, followed by a constrained nearest-neighbor tracking algorithm to obtain valuable information about the spatio-temporal evolution of individual filopodia. We validated the workflow using real and synthetic 3-D time-lapse sequences of lung adenocarcinoma cells of three morphologically distinct filopodial phenotypes and show that it achieves reliable segmentation and tracking performance, providing a robust, reproducible and less time-consuming alternative to manual analysis of the 3D+t image data.

FiloGen: A Model-Based Generator of Synthetic 3-D Time-Lapse Sequences of Single Motile Cells With Growing and Branching Filopodia

The existence of diverse image datasets accompanied by reference annotations is a crucial prerequisite for an objective benchmarking of bioimage analysis methods. Nevertheless, such a prerequisite is hard to satisfy for time lapse, multidimensional fluorescence microscopy image data, manual annotations of which are laborious and often impracticable. In this paper, we present a simulation system capable of generating 3-D time-lapse sequences of single motile cells with filopodial protrusions of user-controlled structural and temporal attributes, such as the number, thickness, length, level of branching, and lifetime of filopodia, accompanied by inherently generated reference annotations. The proposed simulation system involves three globally synchronized modules, each being responsible for a separate task: the evolution of filopodia on a molecular level, linear elastic deformation of the entire cell with filopodia, and the synthesis of realistic, time-coherent cell texture. Its flexibility is demonstrated by generating multiple synthetic 3-D time-lapse sequences of single lung cancer cells of two different phenotypes, qualitatively and quantitatively resembling their real counterparts acquired using a confocal fluorescence microscope.

Effect of Cytoskeleton Elasticity on Amoeboid Swimming

Recently, it has been reported that the cells of the immune system, as well as Dictyostelium amoebae, can swim in a bulk fluid by changing their shape repeatedly. We refer to this motion as amoeboid swimming. Here, we explore how the propulsion and the deformation of the cell emerge as an interplay between the active forces that the cell employs to activate the shape changes and the passive, viscoelastic response of the cell membrane, the cytoskeleton, and the surrounding environment. We introduce a model in which the cell is represented by an elastic capsule enclosing a viscous liquid. The motion of the cell is activated by time-dependent forces distributed along its surface. The model is solved numerically using the boundary integral formulation. The cell can swim in a fluid medium using cyclic deformations or strokes. We measure the swimming velocity of the cell as a function of the force amplitude, the stroke frequency, and the viscoelastic properties of the cell and the medium. We show that an increase in the shear modulus leads both to a regular slowdown of the swimming, which is more pronounced for more deflated swimmers, and to a tendency toward cell buckling. For a given stroke frequency, the swimming velocity shows a quadratic dependence on force amplitude for small forces, as expected, but saturates for large forces. We propose a scaling relationship for the dependence of swimming velocity on the relevant parameters that qualitatively reproduces the numerical results and allows us to define regimes in which the cell motility is dominated by elastic response or by the effective cortex viscosity. This leads to an estimate of the effective cortex viscosity of 103 Pa ⋅ s for which the two effects are comparable, which is close to that provided by several experiments.

Quantitative phase imaging unravels new insight into dynamics of mesenchymal and amoeboid cancer cell invasion

Observation and analysis of cancer cell behaviour in 3D environment is essential for full understanding of the mechanisms of cancer cell invasion. However, label-free imaging of live cells in 3D conditions is optically more challenging than in 2D. Quantitative phase imaging provided by coherence controlled holographic microscopy produces images with enhanced information compared to ordinary light microscopy and, due to inherent coherence gate effect, enables observation of live cancer cells' activity even in scattering milieu such as the 3D collagen matrix. Exploiting the dynamic phase differences method, we for the first time describe dynamics of differences in cell mass distribution in 3D migrating mesenchymal and amoeboid cancer cells, and also demonstrate that certain features are shared by both invasion modes. We found that amoeboid fibrosarcoma cells' membrane blebbing is enhanced upon constriction and is also occasionally present in mesenchymally invading cells around constricted nuclei. Further, we demonstrate that both leading protrusions and leading pseudopods of invading fibrosarcoma cells are defined by higher cell mass density. In addition, we directly document bundling of collagen fibres by protrusions of mesenchymal fibrosarcoma cells. Thus, such a non-invasive microscopy offers a novel insight into cellular events during 3D invasion.

TASI: A software tool for spatial-temporal quantification of tumor spheroid dynamics

Spheroid cultures derived from explanted cancer specimens are an increasingly utilized resource for studying complex biological processes like tumor cell invasion and metastasis, representing an important bridge between the simplicity and practicality of 2-dimensional monolayer cultures and the complexity and realism of in vivo animal models. Temporal imaging of spheroids can capture the dynamics of cell behaviors and microenvironments, and when combined with quantitative image analysis methods, enables deep interrogation of biological mechanisms. This paper presents a comprehensive open-source software framework for Temporal Analysis of Spheroid Imaging (TASI) that allows investigators to objectively characterize spheroid growth and invasion dynamics. TASI performs spatiotemporal segmentation of spheroid cultures, extraction of features describing spheroid morpho-phenotypes, mathematical modeling of spheroid dynamics, and statistical comparisons of experimental conditions. We demonstrate the utility of this tool in an analysis of non-small cell lung cancer spheroids that exhibit variability in metastatic and proliferative behaviors.

Revealing chiral cell motility by 3D Riesz transform-differential interference contrast microscopy and computational kinematic analysis

Left–right asymmetry is a fundamental feature of body plans, but its formation mechanisms and roles in functional lateralization remain unclear. Accumulating evidence suggests that left–right asymmetry originates in the cellular chirality. However, cell chirality has not yet been quantitatively investigated, mainly due to the absence of appropriate methods. Here we combine 3D Riesz transform-differential interference contrast (RT-DIC) microscopy and computational kinematic analysis to characterize chiral cellular morphology and motility. We reveal that filopodia of neuronal growth cones exhibit 3D left-helical motion with retraction and right-screw rotation. We next apply the methods to amoeba Dictyostelium discoideum and discover right-handed clockwise cell migration on a 2D substrate and right-screw rotation of subcellular protrusions along the radial axis in a 3D substrate. Thus, RT-DIC microscopy and the computational kinematic analysis are useful and versatile tools to reveal the mechanisms of left–right asymmetry formation and the emergence of lateralized functions.

The lack of an appropriate method has hampered quantitative measurements of cell chirality. Here, the authors combine Riesz transform-differential interference contrast microscopy and computational kinematic analysis to reveal chiral cell motility of neuronal growth cone filopodia and cellular slime mold.

Bleb Expansion in Migrating Cells Depends on Supply of Membrane from Cell Surface Invaginations

Cell migration is essential for morphogenesis, organ formation, and homeostasis, with relevance for clinical conditions. The migration of primordial germ cells (PGCs) is a useful model for studying this process in the context of the developing embryo. Zebrafish PGC migration depends on the formation of cellular protrusions in form of blebs, a type of protrusion found in various cell types. Here we report on the mechanisms allowing the inflation of the membrane during bleb formation. We show that the rapid expansion of the protrusion depends on membrane invaginations that are localized preferentially at the cell front. The formation of these invaginations requires the function of Cdc42, and their unfolding allows bleb inflation and dynamic cell-shape changes performed by migrating cells. Inhibiting the formation and release of the invaginations strongly interfered with bleb formation, cell motility, and the ability of the cells to reach their target.

Liquid Marble as Bioreactor for Engineering Three-Dimensional Toroid Tissues

Liquid marble is a liquid droplet coated with hydrophobic powder that can be used as a bioreactor. This paper reports the three-dimensional self-assembly and culture of a cell toroid in a slow-releasing, non-adhesive and evaporation-reducing bioreactor platform based on a liquid marble. The bioreactor is constructed by embedding a hydrogel sphere containing growth factor into a liquid marble filled with a suspension of dissociated cells. The hydrogel maintains the water content and concurrently acts as a slow-release carrier. The concentration gradient of growth factor induces cell migration and assembly into toroidal aggregates. An optimum cell concentration resulted in the toroidal (doughnut-like) tissue after 12 hours. The harvested cell toroids showed rapid closure of the inner opening when treated with the growth factor. We also present a geometric growth model to describe the shape of the toroidal tissue over time. In analogy to the classical two-dimensional scratch assay, we propose that the cell toroids reported here open up new possibilities to screen drugs affecting cell migration in three dimensions.

Multicellular tumor invasion and plasticity in biomimetic materials

Cancer cell invasion through the extracellular matrix is associated with metastatic spread and therapeutic resistance. In carcinomas, the detachment and dissemination of individual cells has been associated with an epithelial-mesenchymal transition, but tumors can also invade using collective, multicellular phenotypes. This malignant tumor progression is also associated with alignment and stiffening of the surrounding extracellular matrix. Historically, tumor invasion has been investigated using 2D monolayer culture, small animal models or patient histology. These assays have been complemented by the use of natural biomaterials such as reconstituted basement membrane and collagen I. More recently, engineered materials with well-defined physical, chemical and biomolecular properties have enabled more controlled microenvironments. In this review, we highlight recent developments in multicellular tumor invasion based on microfabricated structures or hydrogels. We emphasize the role of interfacial geometries, biomaterial stiffness, matrix remodeling, and co-culture models. Finally, we discuss future directions for the field, particularly integration with precision measurements of biomaterial properties and single cell heterogeneity, standardization and scale-up of these platforms, as well as integration with patient-derived samples.

Independent modes of ganglion cell translocation ensure correct lamination of the zebrafish retina

Icha et al. show that retinal ganglion cells (RGCs) can move by two different modes across the embryonic zebrafish retina and that correct RGC translocation is crucial for neuronal lamination and retinal development.

The arrangement of neurons into distinct layers is critical for neuronal connectivity and function. During development, most neurons move from their birthplace to the appropriate layer, where they polarize. However, kinetics and modes of many neuronal translocation events still await exploration. In this study, we investigate retinal ganglion cell (RGC) translocation across the embryonic zebrafish retina. After completing their translocation, RGCs establish the most basal retinal layer where they form the optic nerve. Using in toto light sheet microscopy, we show that somal translocation of RGCs is a fast and directed event. It depends on basal process attachment and stabilized microtubules. Interestingly, interference with somal translocation induces a switch to multipolar migration. This multipolar mode is less efficient but still leads to successful RGC layer formation. When both modes are inhibited though, RGCs fail to translocate and induce lamination defects. This indicates that correct RGC translocation is crucial for subsequent retinal lamination.

Quantitative Multiscale Cell Imaging in Controlled 3D Microenvironments

The microenvironment determines cell behavior, but the underlying molecular mechanisms are poorly understood because quantitative studies of cell signaling and behavior have been challenging due to insufficient spatial and/or temporal resolution and limitations on microenvironmental control. Here we introduce microenvironmental selective plane illumination microscopy (meSPIM) for imaging and quantification of intracellular signaling and submicrometer cellular structures as well as large-scale cell morphological and environmental features. We demonstrate the utility of this approach by showing that the mechanical properties of the microenvironment regulate the transition of melanoma cells from actin-driven protrusion to blebbing, and we present tools to quantify how cells manipulate individual collagen fibers. We leverage the nearly isotropic resolution of meSPIM to quantify the local concentration of actin and phosphatidylinositol 3-kinase signaling on the surfaces of cells deep within 3D collagen matrices and track the many small membrane protrusions that appear in these more physiologically relevant environments.

Multiple mechanisms of 3D migration: the origins of plasticity

Cells migrate through 3D environments using a surprisingly wide variety of molecular mechanisms. These distinct modes of migration often rely on the same intracellular components, which are used in different ways to achieve cell motility. Recent work reveals that how a cell moves can be dictated by the relative amounts of cell-matrix adhesion and actomyosin contractility. A current concept is that the level of difficulty in squeezing the nucleus through a confining 3D environment determines the amounts of adhesion and contractility required for cell motility. Ultimately, determining how the nucleus controls the mode of cell migration will be essential for understanding both physiological and pathological processes dependent on cell migration in the body.