An analysis toolbox to explore mesenchymal migration heterogeneity reveals adaptive switching between distinct modes

Interpretable Fine-Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning

Uncovering fine-grained phenotypes of live cell dynamics is pivotal for a comprehensive understanding of the heterogeneity in healthy and diseased biological processes. However, this endeavor poses significant technical challenges for unsupervised machine learning, requiring the extraction of features that not only faithfully preserve this heterogeneity but also effectively discriminate between established biological states, all while remaining interpretable. To tackle these challenges, a self-training deep learning framework designed for fine-grained and interpretable phenotyping is presented. This framework incorporates an unsupervised teacher model with interpretable features to facilitate feature learning in a student deep neural network (DNN). Significantly, an autoencoder-based regularizer is designed to encourage the student DNN to maximize the heterogeneity associated with molecular perturbations. This method enables the acquisition of features with enhanced discriminatory power, while simultaneously preserving the heterogeneity associated with molecular perturbations. This study successfully delineated fine-grained phenotypes within the heterogeneous protrusion dynamics of migrating epithelial cells, revealing specific responses to pharmacological perturbations. Remarkably, this framework adeptly captured a concise set of highly interpretable features uniquely linked to these fine-grained phenotypes, each corresponding to specific temporal intervals crucial for their manifestation. This unique capability establishes it as a valuable tool for investigating diverse cellular dynamics and their heterogeneity.

The F-actin bundler SWAP-70 promotes tumor metastasis

Dynamic rearrangements of the F-actin cytoskeleton are a hallmark of tumor metastasis. Thus, proteins that govern F-actin rearrangements are of major interest for understanding metastasis and potential therapies. We hypothesized that the unique F-actin binding and bundling protein SWAP-70 contributes importantly to metastasis. Orthotopic, ectopic, and short-term tail vein injection mouse breast and lung cancer models revealed a strong positive dependence of lung and bone metastasis on SWAP-70. Breast cancer cell growth, migration, adhesion, and invasion assays revealed SWAP-70's key role in these metastasis-related cell features and the requirement for SWAP-70 to bind F-actin. Biophysical experiments showed that tumor cell stiffness and deformability are negatively modulated by SWAP-70. Together, we present a hitherto undescribed, unique F-actin modulator as an important contributor to tumor metastasis.

Advances in Single-Cell Techniques for Linking Phenotypes to Genotypes

Single-cell analysis has become an essential tool in modern biological research, providing unprecedented insights into cellular behavior and heterogeneity. By examining individual cells, this approach surpasses conventional population-based methods, revealing critical variations in cellular states, responses to environmental cues, and molecular signatures. In the context of cancer, with its diverse cell populations, single-cell analysis is critical for investigating tumor evolution, metastasis, and therapy resistance. Understanding the phenotype-genotype relationship at the single-cell level is crucial for deciphering the molecular mechanisms driving tumor development and progression. This review highlights innovative strategies for selective cell isolation based on desired phenotypes, including robotic aspiration, laser detachment, microraft arrays, optical traps, and droplet-based microfluidic systems. These advanced tools facilitate high-throughput single-cell phenotypic analysis and sorting, enabling the identification and characterization of specific cell subsets, thereby advancing therapeutic innovations in cancer and other diseases.

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.

Microfluidic single-cell migration chip reveals insights into the impact of extracellular matrices on cell movement

Cell migration is a complex process that plays a crucial role in normal physiology and pathologies such as cancer, autoimmune diseases, and mental disorders. Conventional cell migration assays face limitations in tracking a large number of individual migrating cells. To address this challenge, we have developed a high-throughput microfluidic cell migration chip, which seamlessly integrates robotic liquid handling and computer vision to swiftly monitor the movement of 3200 individual cells, providing unparalleled single-cell resolution for discerning distinct behaviors of the fast-moving cell population. This study focuses on the ECM's role in regulating cellular migration, utilizing this cutting-edge microfluidic technology to investigate the impact of ten different ECMs on triple-negative breast cancer cell lines. We found that collagen IV, collagen III, and collagen I coatings were the top enhancers of cell movement. Combining these ECMs increased cell motility, but the effect was sub-additive. Furthermore, we examined 87 compounds and found that while some compounds inhibited migration on all substrates, significantly distinct effects on differently coated substrates were observed, underscoring the importance of considering ECM coating. We also utilized cells expressing a fluorescent actin reporter and observed distinct actin structures in ECM-interacting cells. ScRNA-Seq analysis revealed that ECM coatings induced EMT and enhanced cell migration. Finally, we identified genes that were particularly up-regulated by collagen IV and the selective inhibitors successfully blocked cell migration on collagen IV. Overall, the study provides insights into the impact of various ECMs on cell migration and dynamics of cell movement with implications for developing therapeutic strategies to combat diseases related to cell motility.

Actin polymerization and depolymerization in developing vertebrates

Development is a complex process that occurs throughout the life cycle. F-actin, a major component of the cytoskeleton, is essential for the morphogenesis of tissues and organs during development. F-actin is formed by the polymerization of G-actin, and the dynamic balance of polymerization and depolymerization ensures proper cellular function. Disruption of this balance results in various abnormalities and defects or even embryonic lethality. Here, we reviewed recent findings on the structure of G-actin and F-actin and the polymerization of G-actin to F-actin. We also focused on the functions of actin isoforms and the underlying mechanisms of actin polymerization/depolymerization in cellular and organic morphogenesis during development. This information will extend our understanding of the role of actin polymerization in the physiologic or pathologic processes during development and may open new avenues for developing therapeutics for embryonic developmental abnormalities or tissue regeneration.

An introduction to representation learning for single-cell data analysis

Single-cell-resolved systems biology methods, including omics- and imaging-based measurement modalities, generate a wealth of high-dimensional data characterizing the heterogeneity of cell populations. Representation learning methods are routinely used to analyze these complex, high-dimensional data by projecting them into lower-dimensional embeddings. This facilitates the interpretation and interrogation of the structures, dynamics, and regulation of cell heterogeneity. Reflecting their central role in analyzing diverse single-cell data types, a myriad of representation learning methods exist, with new approaches continually emerging. Here, we contrast general features of representation learning methods spanning statistical, manifold learning, and neural network approaches. We consider key steps involved in representation learning with single-cell data, including data pre-processing, hyperparameter optimization, downstream analysis, and biological validation. Interdependencies and contingencies linking these steps are also highlighted. This overview is intended to guide researchers in the selection, application, and optimization of representation learning strategies for current and future single-cell research applications.

Morphomigrational description as a new approach connecting cell's migration with its morphology

The examination of morphology and migration of cells plays substantial role in understanding the cellular behaviour, being described by plethora of quantitative parameters and models. These descriptions, however, treat cell migration and morphology as independent properties of temporal cell state, while not taking into account their strong interdependence in adherent cells. Here we present the new and simple mathematical parameter called signed morphomigrational angle (sMM angle) that links cell geometry with translocation of cell centroid, considering them as one morphomigrational behaviour. The sMM angle combined with pre-existing quantitative parameters enabled us to build a new tool called morphomigrational description, used to assign the numerical values to several cellular behaviours. Thus, the cellular activities that until now were characterized using verbal description or by complex mathematical models, are described here by a set of numbers. Our tool can be further used in automatic analysis of cell populations as well as in studies focused on cellular response to environmental directional signals.

Multisite assessment of reproducibility in high-content cell migration imaging data

High-content image-based cell phenotyping provides fundamental insights into a broad variety of life science disciplines. Striving for accurate conclusions and meaningful impact demands high reproducibility standards, with particular relevance for high-quality open-access data sharing and meta-analysis. However, the sources and degree of biological and technical variability, and thus the reproducibility and usefulness of meta-analysis of results from live-cell microscopy, have not been systematically investigated. Here, using high-content data describing features of cell migration and morphology, we determine the sources of variability across different scales, including between laboratories, persons, experiments, technical repeats, cells, and time points. Significant technical variability occurred between laboratories and, to lesser extent, between persons, providing low value to direct meta-analysis on the data from different laboratories. However, batch effect removal markedly improved the possibility to combine image-based datasets of perturbation experiments. Thus, reproducible quantitative high-content cell image analysis of perturbation effects and meta-analysis depend on standardized procedures combined with batch correction.

Extracellular alkaline pH enhances migratory behaviors of hepatocellular carcinoma cells as a caution against the indiscriminate application of alkalinizing drug therapy: In vitro microscopic studies

The migratory process is a highly organized, differentiated, and polarized stage by which many signaling pathways are regulated to control cell migration. Since the significant evidence of migrating cells is the reorganization of the cytoskeleton. In the recent study, the cell migration model was assessed on the fact that any disruption obtained in the cellular monolayer confluent, may cause stimulation for surrounding cells to migrate. We attempt to demonstrate the morphological alterations associated with these migrating cells. In this case, sterilized 1 N NaOH (1 µl) was used as alkaline burnt. It leads to scratching the monolayer of hepatocellular carcinoma (HLF cell line) allowing cells to lose their connection. Scanning electron microscopy (SEM), fluorescence microscopy, light inverted microscopy, and dark field were used for discovering the morphological alterations associated with migrating cancer cells. The findings show that cells exhibited distinctive alterations including a polarizing stage, accumulation of the actin nodules in front of the nucleus, and protrusions. Nuclei appeared as lobulated shapes during migration. Lamellipodia and uropod were extended as well. Additionally, TGFβ1 proved its expression in HLF and SNU449 after their stimulation. It is demonstrated that hepatocellular carcinoma cells can migrate after their stimulation and there is a caution against the indiscriminate application of alkalinizing drug therapy.

Data-driven microscopy allows for automated context-specific acquisition of high-fidelity image data

Light microscopy is a powerful single-cell technique that allows for quantitative spatial information at subcellular resolution. However, unlike flow cytometry and single-cell sequencing techniques, microscopy has issues achieving high-quality population-wide sample characterization while maintaining high resolution. Here, we present a general framework, data-driven microscopy (DDM) that uses real-time population-wide object characterization to enable data-driven high-fidelity imaging of relevant phenotypes based on the population context. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As a proof of concept, we develop and apply DDM with plugins for improved high-content screening and live adaptive microscopy for cell migration and infection studies that capture events of interest, rare or common, with high precision and resolution. We propose that DDM can reduce human bias, increase reproducibility, and place single-cell characteristics in the context of the sample population when interpreting microscopy data, leading to an increase in overall data fidelity.

Oscillatory ERK Signaling and Morphology Determine Heterogeneity of Breast Cancer Cell Chemotaxis via MEK-ERK and p38-MAPK Signaling Pathways

Chemotaxis, regulated by oscillatory signals, drives critical processes in cancer metastasis. Crucial chemoattractant molecules in breast cancer, CXCL12 and EGF, drive the activation of ERK and Akt. Regulated by feedback and crosstalk mechanisms, oscillatory signals in ERK and Akt control resultant changes in cell morphology and chemotaxis. While commonly studied at the population scale, metastasis arises from small numbers of cells that successfully disseminate, underscoring the need to analyze processes that cancer cells use to connect oscillatory signaling to chemotaxis at single-cell resolution. Furthermore, little is known about how to successfully target fast-migrating cells to block metastasis. We investigated to what extent oscillatory networks in single cells associate with heterogeneous chemotactic responses and how targeted inhibitors block signaling processes in chemotaxis. We integrated live, single-cell imaging with time-dependent data processing to discover oscillatory signal processes defining heterogeneous chemotactic responses. We identified that short ERK and Akt waves, regulated by MEK-ERK and p38-MAPK signaling pathways, determine the heterogeneous random migration of cancer cells. By comparison, long ERK waves and the morphological changes regulated by MEK-ERK signaling, determine heterogeneous directed motion. This study indicates that treatments against chemotaxis in consider must interrupt oscillatory signaling.

Anti-Human CD9 Fab Fragment Antibody Blocks the Extracellular Vesicle-Mediated Increase in Malignancy of Colon Cancer Cells

Intercellular communication between cancer cells themselves or with healthy cells in the tumor microenvironment and/or pre-metastatic sites plays an important role in cancer progression and metastasis. In addition to ligand-receptor signaling complexes, extracellular vesicles (EVs) are emerging as novel mediators of intercellular communication both in tissue homeostasis and in diseases such as cancer. EV-mediated transfer of molecular activities impacting morphological features and cell motility from highly metastatic SW620 cells to non-metastatic SW480 cells is a good in vitro example to illustrate the increased malignancy of colorectal cancer leading to its transformation and aggressive behavior. In an attempt to intercept the intercellular communication promoted by EVs, we recently developed a monovalent Fab fragment antibody directed against human CD9 tetraspanin and showed its effectiveness in blocking the internalization of melanoma cell-derived EVs and the nuclear transfer of their cargo proteins into recipient cells. Here, we employed the SW480/SW620 model to investigate the anti-cancer potential of the anti-CD9 Fab antibody. We first demonstrated that most EVs derived from SW620 cells contain CD9, making them potential targets. We then found that the anti-CD9 Fab antibody, but not the corresponding divalent antibody, prevented internalization of EVs from SW620 cells into SW480 cells, thereby inhibiting their phenotypic transformation, i.e., the change from a mesenchymal-like morphology to a rounded amoeboid-like shape with membrane blebbing, and thus preventing increased cell migration. Intercepting EV-mediated intercellular communication in the tumor niche with an anti-CD9 Fab antibody, combined with direct targeting of cancer cells, could lead to the development of new anti-cancer therapeutic strategies.

Local temporal Rac1-GTP nadirs and peaks restrict cell protrusions and retractions

Cells probe their microenvironment using membrane protrusion-retraction cycles. Spatiotemporal coordination of Rac1 and RhoA GTP-binding activities initiates and reinforces protrusions and retractions, but the control of their finite lifetime remains unclear. We examined the relations of Rac1 and RhoA GTP-binding levels to key protrusion and retraction events, as well as to cell-ECM traction forces at physiologically relevant ECM stiffness. High RhoA-GTP preceded retractions and Rac1-GTP elevation before protrusions. Notable temporal Rac1-GTP nadirs and peaks occurred at the maximal edge velocity of local membrane protrusions and retractions, respectively, followed by declined edge velocity. Moreover, altered local Rac1-GTP consistently preceded similarly altered traction force. Local optogenetic Rac1-GTP perturbations defined a function of Rac1 in restricting protrusions and retractions and in promoting local traction force. Together, we show that Rac1 plays a fundamental role in restricting the size and durability of protrusions and retractions, plausibly in part through controlling traction forces.

Decoding Single Cell Morphology in Osteotropic Breast Cancer Cells for Dissecting Their Migratory, Molecular and Biophysical Heterogeneity

Breast cancer is a heterogeneous disease and the mechanistic framework for differential osteotropism among intrinsic breast cancer subtypes is unknown. Hypothesizing that cell morphology could be an integrated readout for the functional state of a cancer cell, we established a catalogue of the migratory, molecular and biophysical traits of MDA-MB-231 breast cancer cells, compared it with two enhanced bone-seeking derivative cell lines and integrated these findings with single cell morphology profiles. Such knowledge could be essential for predicting metastatic capacities in breast cancer. High-resolution microscopy revealed a heterogeneous and specific spectrum of single cell morphologies in bone-seeking cells, which correlated with differential migration and stiffness. While parental MDA-MB-231 cells showed long and dynamic membrane protrusions and were enriched in motile cells with continuous and mesenchymal cell migration, bone-seeking cells appeared with discontinuous mesenchymal or amoeboid-like migration. Although non-responsive to CXCL12, bone-seeking cells responded to epidermal growth factor with a morphotype shift and differential expression of genes controlling cell shape and directional migration. Hence, single cell morphology encodes the molecular, migratory and biophysical architecture of breast cancer cells and is specifically altered among osteotropic phenotypes. Quantitative morpho-profiling could aid in dissecting breast cancer heterogeneity and in refining clinically relevant intrinsic breast cancer subtypes.

Enhanced substrate stress relaxation promotes filopodia-mediated cell migration

Cell migration on two-dimensional substrates is typically characterized by lamellipodia at the leading edge, mature focal adhesions and spread morphologies. These observations result from adherent cell migration studies on stiff, elastic substrates, because most cells do not migrate on soft, elastic substrates. However, many biological tissues are soft and viscoelastic, exhibiting stress relaxation over time in response to a deformation. Here, we have systematically investigated the impact of substrate stress relaxation on cell migration on soft substrates. We observed that cells migrate minimally on substrates with an elastic modulus of 2 kPa that are elastic or exhibit slow stress relaxation, but migrate robustly on 2-kPa substrates that exhibit fast stress relaxation. Strikingly, migrating cells were not spread out and did not extend lamellipodial protrusions, but were instead rounded, with filopodia protrusions extending at the leading edge, and exhibited small nascent adhesions. Computational models of cell migration based on a motor-clutch framework predict the observed impact of substrate stress relaxation on cell migration and filopodia dynamics. Our findings establish substrate stress relaxation as a key requirement for robust cell migration on soft substrates and uncover a mode of two-dimensional cell migration marked by round morphologies, filopodia protrusions and weak adhesions.

Emerging machine learning approaches to phenotyping cellular motility and morphodynamics

Cells respond heterogeneously to molecular and environmental perturbations. Phenotypic heterogeneity, wherein multiple phenotypes coexist in the same conditions, presents challenges when interpreting the observed heterogeneity. Advances in live cell microscopy allow researchers to acquire an unprecedented amount of live cell image data at high spatiotemporal resolutions. Phenotyping cellular dynamics, however, is a nontrivial task and requires machine learning (ML) approaches to discern phenotypic heterogeneity from live cell images. In recent years, ML has proven instrumental in biomedical research, allowing scientists to implement sophisticated computation in which computers learn and effectively perform specific analyses with minimal human instruction or intervention. In this review, we discuss how ML has been recently employed in the study of cell motility and morphodynamics to identify phenotypes from computer vision analysis. We focus on new approaches to extract and learn meaningful spatiotemporal features from complex live cell images for cellular and subcellular phenotyping.

ImageJ and CellProfiler: Complements in Open-Source Bioimage Analysis

ImageJ and CellProfiler have long been leading open-source platforms in the field of bioimage analysis. ImageJ's traditional strength is in single-image processing and investigation, while CellProfiler is designed for building large-scale, modular analysis pipelines. Although many image analysis problems can be well solved with one or the other, using these two platforms together in a single workflow can be powerful. Here, we share two pipelines demonstrating mechanisms for productively and conveniently integrating ImageJ and CellProfiler for (1) studying cell morphology and migration via tracking, and (2) advanced stitching techniques for handling large, tiled image sets to improve segmentation. No single platform can provide all the key and most efficient functionality needed for all studies. While both programs can be and are often used separately, these pipelines demonstrate the benefits of using them together for image analysis workflows. ImageJ and CellProfiler are both committed to interoperability between their platforms, with ongoing development to improve how both are leveraged from the other. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Studying cell morphology and cell migration in time-lapse datasets using TrackMate (Fiji) and CellProfiler Basic Protocol 2: Creating whole plate montages to easily assess adaptability of segmentation parameters.

Focal adhesion dynamics in cellular function and disease

Acting as a bridge between the cytoskeleton of the cell and the extra cellular matrix (ECM), the cell-ECM adhesions with integrins at their core, play a major role in cell signalling to direct mechanotransduction, cell migration, cell cycle progression, proliferation, differentiation, growth and repair. Biochemically, these adhesions are composed of diverse, yet an organised group of structural proteins, receptors, adaptors, various enzymes including protein kinases, phosphatases, GTPases, proteases, etc. as well as scaffolding molecules. The major integrin adhesion complexes (IACs) characterised are focal adhesions (FAs), invadosomes (podosomes and invadopodia), hemidesmosomes (HDs) and reticular adhesions (RAs). The varied composition and regulation of the IACs and their signalling, apart from being an integral part of normal cell survival, has been shown to be of paramount importance in various developmental and pathological processes. This review per-illustrates the recent advancements in the research of IACs, their crucial roles in normal as well as diseased states. We have also touched on few of the various methods that have been developed over the years to visualise IACs, measure the forces they exert and study their signalling and molecular composition. Having such pertinent roles in the context of various pathologies, these IACs need to be understood and studied to develop therapeutical targets. We have given an update to the studies done in recent years and described various techniques which have been applied to study these structures, thereby, providing context in furthering research with respect to IAC targeted therapeutics.

3D mesenchymal cell migration is driven by anterior cellular contraction that generates an extracellular matrix prestrain

We describe a cellular contractile mechanism employed by fibroblasts and mesenchymal cancer cells to migrate in 3D collagen gels. During 3D spreading, fibroblasts strongly deform the matrix. They protrude, polarize, and initiate migration in the direction of highest extracellular matrix (ECM) deformation (prestrain). This prestrain is maintained through anterior cellular contractions behind the leading edge prior to protrusion, coordinating a distinct 3D migration cycle that varies between cell types. Myosin IIA is required for strain polarization, generating anterior contractions, and maintaining prestrain for efficient directional cell migration. Local matrix severing disrupts the matrix prestrain, suppressing directional protrusion. We show that epithelial cancer and endothelial cells rarely demonstrate the sustained prestrain or anterior contractions. We propose that mesenchymal cells sense ECM stiffness in 3D and generate their own matrix prestrain. This requires myosin IIA to generate polarized periodic anterior contractions for maintaining a 3D migration cycle.

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.

Live-cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data

Recent advances in single-cell techniques catalyze an emerging field of studying how cells convert from one phenotype to another, in a step-by-step process. Two grand technical challenges, however, impede further development of the field. Fixed cell-based approaches can provide snapshots of high-dimensional expression profiles but have fundamental limits on revealing temporal information, and fluorescence-based live-cell imaging approaches provide temporal information but are technically challenging for multiplex long-term imaging. We first developed a live-cell imaging platform that tracks cellular status change through combining endogenous fluorescent labeling that minimizes perturbation to cell physiology and/or live-cell imaging of high-dimensional cell morphological and texture features. With our platform and an A549 VIM-RFP epithelial-to-mesenchymal transition (EMT) reporter cell line, live-cell trajectories reveal parallel paths of EMT missing from snapshot data due to cell-cell dynamic heterogeneity. Our results emphasize the necessity of extracting dynamical information of phenotypic transitions from multiplex live-cell imaging.

Cell matrix adhesion in cell migration

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance and wound healing. In order for cells to migrate, they must interact with their environment using adhesion receptors, such as integrins, and form specialized adhesion complexes that mediate responses to different extracellular cues. In this review, we discuss the role of integrin adhesion complexes (IACs) in cell migration, highlighting the layers of regulation that are involved, including intracellular signalling cascades, mechanosensing and reciprocal feedback to the extracellular environment. We also discuss the role of IACs in extracellular matrix remodeling and how they impact upon cell migration.

Community standards for open cell migration data

Cell migration research has become a high-content field. However, the quantitative information encapsulated in these complex and high-dimensional datasets is not fully exploited owing to the diversity of experimental protocols and non-standardized output formats. In addition, typically the datasets are not open for reuse. Making the data open and Findable, Accessible, Interoperable, and Reusable (FAIR) will enable meta-analysis, data integration, and data mining. Standardized data formats and controlled vocabularies are essential for building a suitable infrastructure for that purpose but are not available in the cell migration domain. We here present standardization efforts by the Cell Migration Standardisation Organisation (CMSO), an open community-driven organization to facilitate the development of standards for cell migration data. This work will foster the development of improved algorithms and tools and enable secondary analysis of public datasets, ultimately unlocking new knowledge of the complex biological process of cell migration.

Uncoupling Traditional Functionalities of Metastasis: The Parting of Ways with Real-Time Assays

The experimental evaluation of metastasis overly focuses on the gain of migratory and invasive properties, while disregarding the contributions of cellular plasticity, extra-cellular matrix heterogeneity, niche interactions, and tissue architecture. Traditional cell-based assays often restrict the inclusion of these processes and warrant the implementation of approaches that provide an enhanced spatiotemporal resolution of the metastatic cascade. Time lapse imaging represents such an underutilized approach in cancer biology, especially in the context of disease progression. The inclusion of time lapse microscopy and microfluidic devices in routine assays has recently discerned several nuances of the metastatic cascade. Our review emphasizes that a complete comprehension of metastasis in view of evolving ideologies necessitates (i) the use of appropriate, context-specific assays and understanding their inherent limitations; (ii) cautious derivation of inferences to avoid erroneous/overestimated clinical extrapolations; (iii) corroboration between multiple assay outputs to gauge metastatic potential; and (iv) the development of protocols with improved in situ implications. We further believe that the adoption of improved quantitative approaches in these assays can generate predictive algorithms that may expedite therapeutic strategies targeting metastasis via the development of disease relevant model systems. Such approaches could potentiate the restructuring of the cancer metastasis paradigm through an emphasis on the development of next-generation real-time assays.

Reticular adhesions are a distinct class of cell-matrix adhesions that mediate attachment during mitosis

Adhesion to the extracellular matrix persists during mitosis in most cell types. However, while classical adhesion complexes, such as focal adhesions, do and must disassemble to enable mitotic rounding, the mechanisms of residual mitotic cell-extracellular matrix adhesion remain undefined. Here, we identify 'reticular adhesions', a class of adhesion complex that is mediated by integrin αvβ5, formed during interphase, and preserved at cell-extracellular matrix attachment sites throughout cell division. Consistent with this role, integrin β5 depletion perturbs mitosis and disrupts spatial memory transmission between cell generations. Reticular adhesions are morphologically and dynamically distinct from classical focal adhesions. Mass spectrometry defines their unique composition, enriched in phosphatidylinositol-4,5-bisphosphate (PtdIns(4,5)P2)-binding proteins but lacking virtually all consensus adhesome components. Indeed, reticular adhesions are promoted by PtdIns(4,5)P2, and form independently of talin and F-actin. The distinct characteristics of reticular adhesions provide a solution to the problem of maintaining cell-extracellular matrix attachment during mitotic rounding and division.

Neuronal Growth Cone Size-Dependent and -Independent Parameters of Microtubule Polymerization

Migration and pathfinding of neuronal growth cones during neurite extension is critically dependent on dynamic microtubules. In this study we sought to determine, which aspects of microtubule polymerization relate to growth cone morphology and migratory characteristics. We conducted a multiscale quantitative microscopy analysis using automated tracking of microtubule plus ends in migrating growth cones of cultured murine dorsal root ganglion (DRG) neurons. Notably, this comprehensive analysis failed to identify any changes in microtubule polymerization parameters that were specifically associated with spontaneous extension vs. retraction of growth cones. This suggests that microtubule dynamicity is a basic mechanism that does not determine the polarity of growth cone response but can be exploited to accommodate diverse growth cone behaviors. At the same time, we found a correlation between growth cone size and basic parameters of microtubule polymerization including the density of growing microtubule plus ends and rate and duration of microtubule growth. A similar correlation was observed in growth cones of neurons lacking the microtubule-associated protein MAP1B. However, MAP1B-null growth cones, which are deficient in growth cone migration and steering, displayed an overall reduction in microtubule dynamicity. Our results highlight the importance of taking growth cone size into account when evaluating the influence on growth cone microtubule dynamics of different substrata, guidance factors or genetic manipulations which all can change growth cone morphology and size. The type of large scale multiparametric analysis performed here can help to separate direct effects that these perturbations might have on microtubule dynamics from indirect effects resulting from perturbation-induced changes in growth cone size.

Deconvolution of subcellular protrusion heterogeneity and the underlying actin regulator dynamics from live cell imaging

Cell protrusion is morphodynamically heterogeneous at the subcellular level. However, the mechanism of cell protrusion has been understood based on the ensemble average of actin regulator dynamics. Here, we establish a computational framework called HACKS (deconvolution of heterogeneous activity in coordination of cytoskeleton at the subcellular level) to deconvolve the subcellular heterogeneity of lamellipodial protrusion from live cell imaging. HACKS identifies distinct subcellular protrusion phenotypes based on machine-learning algorithms and reveals their underlying actin regulator dynamics at the leading edge. Using our method, we discover "accelerating protrusion", which is driven by the temporally ordered coordination of Arp2/3 and VASP activities. We validate our finding by pharmacological perturbations and further identify the fine regulation of Arp2/3 and VASP recruitment associated with accelerating protrusion. Our study suggests HACKS can identify specific subcellular protrusion phenotypes susceptible to pharmacological perturbation and reveal how actin regulator dynamics are changed by the perturbation.

Using Systems Microscopy to Understand the Emergence of Cell Migration from Cell Organization

Cell migration is a dynamic process that emerges from fine-tuned networks coordinated in three-dimensional space, spanning molecular, subcellular, and cellular scales, and over multiple temporal scales, from milliseconds to days. Understanding how cell migration arises from this complexity requires data collection and analyses that quantitatively integrate these spatial and temporal scales. To meet this need, we have combined quantitative live and fixed cell fluorescence microscopy, customized image analysis tools, multivariate statistical methods, and mathematical modeling. Collectively, this constitutes the systems microscopy strategy that we have applied to dissect how cells organize themselves to migrate. In this overview, we highlight key principles, concepts, and components of our systems microscopy methodology, and exemplify what we have learnt so far and where this approach may lead.

Method comparison for analyzing wound healing rates

Wound healing scratch assay is a frequently used method to characterize cell migration, which is an important biological process in the course of development, tissue repair, and immune response for example. The measurement of wound healing rate, however, varies among different studies. Here we summarized these measurements into three types: (I) direct rate average; (II) regression rate average; and (III) average distance regression rate. Using Chinese hamster ovary (CHO) cells as a model, we compared the three types of analyses on quantifying the wound closing rate, and discovered that type I & III measurements are more resistant to outliers, and type II analysis is more sensitive to outliers. We hope this study can help researchers to better use this simple yet effective assay.

Plasticity of Cell Migration In Vivo and In Silico

Cell migration results from stepwise mechanical and chemical interactions between cells and their extracellular environment. Mechanistic principles that determine single-cell and collective migration modes and their interconversions depend upon the polarization, adhesion, deformability, contractility, and proteolytic ability of cells. Cellular determinants of cell migration respond to extracellular cues, including tissue composition, topography, alignment, and tissue-associated growth factors and cytokines. Both cellular determinants and tissue determinants are interdependent; undergo reciprocal adjustment; and jointly impact cell decision making, navigation, and migration outcome in complex environments. We here review the variability, decision making, and adaptation of cell migration approached by live-cell, in vivo, and in silico strategies, with a focus on cell movements in morphogenesis, repair, immune surveillance, and cancer metastasis.

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.