Formation of recurring transient Ca2+-based intercellular communities during Drosophila hematopoiesis.

Tissue development occurs through a complex interplay between many individual cells. Yet, the fundamental question of how collective tissue behavior emerges from heterogeneous and noisy information processing and transfer at the single-cell level remains unknown. Here, we reveal that tissue scale signaling regulation can arise from local gap-junction mediated cell-cell signaling through the spatiotemporal establishment of an intermediate-scale of transient multicellular communication communities over the course of tissue development. We demonstrated this intermediate scale of emergent signaling using Ca2+ signaling in the intact, ex vivo cultured, live developing Drosophila hematopoietic organ, the lymph gland. Recurrent activation of these transient signaling communities defined self-organized signaling "hotspots" that gradually formed over the course of larva development. These hotspots receive and transmit information to facilitate repetitive interactions with nonhotspot neighbors. Overall, this work bridges the scales between single-cell and emergent group behavior providing key mechanistic insight into how cells establish tissue-scale communication networks.

Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion.

Cells modify their internal organization during continuous state transitions, supporting functions from cell division to differentiation. However, tools to measure dynamic physiological states of individual transitioning cells are lacking. We combined live-cell imaging and machine learning to monitor ERK1/2-inhibited primary murine skeletal muscle precursor cells, that transition rapidly and robustly from proliferating myoblasts to post-mitotic myocytes and then fuse, forming multinucleated myotubes. Our models, trained using motility or actin intensity features from single-cell tracking data, effectively tracked real-time continuous differentiation, revealing that differentiation occurs 7.5-14.5 h post induction, followed by fusion ~3 h later. Co-inhibition of ERK1/2 and p38 led to differentiation without fusion. Our model inferred co-inhibition leads to terminal differentiation, indicating that p38 is specifically required for transitioning from terminal differentiation to fusion. Our model also predicted that co-inhibition leads to changes in actin dynamics. Mass spectrometry supported these in silico predictions and suggested novel fusion and maturation regulators downstream of differentiation. Collectively, this approach can be adapted to various biological processes to uncover novel links between dynamic single-cell states and their functional outcomes.

Cytokinetic abscission is part of the midblastula transition in early zebrafish embryogenesis.

Animal cytokinesis ends with the formation of a thin intercellular membrane bridge that connects the two newly formed sibling cells, which is ultimately resolved by abscission. While mitosis is completed within 15 min, the intercellular bridge can persist for hours, maintaining a physical connection between sibling cells and allowing exchange of cytosolic components. Although cell-cell communication is fundamental for development, the role of intercellular bridges during embryogenesis has not been fully elucidated. In this work, we characterized the spatiotemporal characteristics of the intercellular bridge during early zebrafish development. We found that abscission is delayed during the rapid division cycles that occur in the early embryo, giving rise to the formation of interconnected cell clusters. Abscission was accelerated when the embryo entered the midblastula transition (MBT) phase. Components of the ESCRT machinery, which drives abscission, were enriched at intercellular bridges post-MBT and, interfering with ESCRT function, extended abscission beyond MBT. Hallmark features of MBT, including transcription onset and cell shape modulations, were more similar in interconnected sibling cells compared to other neighboring cells. Collectively, our findings suggest that delayed abscission in the early embryo allows clusters of cells to coordinate their behavior during embryonic development.

Data science in cell imaging.

Cell imaging has entered the 'Big Data' era. New technologies in light microscopy and molecular biology have led to an explosion in high-content, dynamic and multidimensional imaging data. Similar to the 'omics' fields two decades ago, our current ability to process, visualize, integrate and mine this new generation of cell imaging data is becoming a critical bottleneck in advancing cell biology. Computation, traditionally used to quantitatively test specific hypotheses, must now also enable iterative hypothesis generation and testing by deciphering hidden biologically meaningful patterns in complex, dynamic or high-dimensional cell image data. Data science is uniquely positioned to aid in this process. In this Perspective, we survey the rapidly expanding new field of data science in cell imaging. Specifically, we highlight how data science tools are used within current image analysis pipelines, propose a computation-first approach to derive new hypotheses from cell image data, identify challenges and describe the next frontiers where we believe data science will make an impact. We also outline steps to ensure broad access to these powerful tools - democratizing infrastructure availability, developing sensitive, robust and usable tools, and promoting interdisciplinary training to both familiarize biologists with data science and expose data scientists to cell imaging.

Coalescing replication compartments provide the opportunity for recombination between coinfecting herpesviruses.

Homologous recombination (HR) is considered a major driving force of evolution because it generates and expands genetic diversity. Evidence of HR between coinfecting herpesvirus DNA genomes can be found frequently both in vitro and in clinical isolates. Each herpes simplex virus type 1 (HSV-1) replication compartment (RC) derives from a single incoming genome and maintains a specific territory within the nucleus. This raises intriguing questions about where and when coinfecting viral genomes interact. To study the spatiotemporal requirements for intergenomic recombination, we developed an assay with dual-color FISH that enables detection of HR between different pairs of coinfecting HSV-1 genomes. Our results revealed that HR increases intermingling of RCs derived from different genomes. Furthermore, inhibition of RC movement reduces the rate of HR events among coinfecting viruses. Finally, we observed correlation between nuclear size and the number of RCs per nucleus. Our findings suggest that both viral replication and recombination are subject to nuclear spatial constraints. Other DNA viruses and cellular DNA are likely to encounter similar restrictions.-Tomer, E., Cohen, E. M., Drayman, N., Afriat, A., Weitzman, M. D., Zaritsky, A., Kobiler, O. Coalescing replication compartments provide the opportunity for recombination between coinfecting herpesviruses.

Quantitative Live Imaging of Human Embryonic Stem Cell Derived Neural Rosettes Reveals Structure-Function Dynamics Coupled to Cortical Development.

Neural stem cells (NSCs) are progenitor cells for brain development, where cellular spatial composition (cytoarchitecture) and dynamics are hypothesized to be linked to critical NSC capabilities. However, understanding cytoarchitectural dynamics of this process has been limited by the difficulty to quantitatively image brain development in vivo. Here, we study NSC dynamics within Neural Rosettes--highly organized multicellular structures derived from human pluripotent stem cells. Neural rosettes contain NSCs with strong epithelial polarity and are expected to perform apical-basal interkinetic nuclear migration (INM)--a hallmark of cortical radial glial cell development. We developed a quantitative live imaging framework to characterize INM dynamics within rosettes. We first show that the tendency of cells to follow the INM orientation--a phenomenon we referred to as radial organization, is associated with rosette size, presumably via mechanical constraints of the confining structure. Second, early forming rosettes, which are abundant with founder NSCs and correspond to the early proliferative developing cortex, show fast motions and enhanced radial organization. In contrast, later derived rosettes, which are characterized by reduced NSC capacity and elevated numbers of differentiated neurons, and thus correspond to neurogenesis mode in the developing cortex, exhibit slower motions and decreased radial organization. Third, later derived rosettes are characterized by temporal instability in INM measures, in agreement with progressive loss in rosette integrity at later developmental stages. Finally, molecular perturbations of INM by inhibition of actin or non-muscle myosin-II (NMII) reduced INM measures. Our framework enables quantification of cytoarchitecture NSC dynamics and may have implications in functional molecular studies, drug screening, and iPS cell-based platforms for disease modeling.

Seeds of Locally Aligned Motion and Stress Coordinate a Collective Cell Migration.

We find how collective migration emerges from mechanical information transfer between cells. Local alignment of cell velocity and mechanical stress orientation-a phenomenon dubbed "plithotaxis"-plays a crucial role in inducing coordinated migration. Leader cells at the monolayer edge better align velocity and stress to migrate faster toward the open space. Local seeds of enhanced motion then generate stress on neighboring cells to guide their migration. Stress-induced motion propagates into the monolayer as well as along the monolayer boundary to generate increasingly larger clusters of coordinately migrating cells that move faster with enhanced alignment of velocity and stress. Together, our analysis provides a model of long-range mechanical communication between cells, in which plithotaxis translates local mechanical fluctuations into globally collective migration of entire tissues.

The metastatic microenvironment: Claudin-1 suppresses the malignant phenotype of melanoma brain metastasis.

Brain metastases occur frequently in melanoma patients with advanced disease whereby the prognosis is dismal. The underlying mechanisms of melanoma brain metastasis development are not well understood. Identification of molecular determinants regulating melanoma brain metastasis would advance the development of prevention and therapy strategies for this disease. Gene expression profiles of cutaneous and brain-metastasizing melanoma variants from three xenograft tumor models established in our laboratory revealed that expression of tight junction component CLDN1 was lower in the brain-metastasizing variants than in cutaneous variants from the same melanoma. The objective of our study was to determine the significance of CLDN1 downregulation/loss in metastatic melanoma and its role in melanoma brain metastasis. An immunohistochemical analysis of human cells of the melanocyte lineage indicated a significant CLDN1 downregulation in metastatic melanomas. Transduction of melanoma brain metastatic cells expressing low levels of CLDN1 with a CLDN1 retrovirus suppressed their metastatic phenotype. CLDN1-overexpressing melanoma cells expressed a lower ability to migrate and adhere to extracellular matrix, reduced tumor aggressiveness in nude mice and, most importantly, eliminated the formation of micrometastases in the brain. In sharp contrast, the ability of the CLDN1-overexpressing cells to form lung micrometastases was not impaired. CLDN1-mediated interactions between these cells and brain endothelial cells constitute the mechanism underlying these results. Taken together, we demonstrated that downregulation or loss of CLDN1 supports the formation of melanoma brain metastasis, and that CLDN1 expression could be a useful prognostic predictor for melanoma patients with a high risk of brain metastasis.

Propagating waves of directionality and coordination orchestrate collective cell migration.

The ability of cells to coordinately migrate in groups is crucial to enable them to travel long distances during embryonic development, wound healing and tumorigenesis, but the fundamental mechanisms underlying intercellular coordination during collective cell migration remain elusive despite considerable research efforts. A novel analytical framework is introduced here to explicitly detect and quantify cell clusters that move coordinately in a monolayer. The analysis combines and associates vast amount of spatiotemporal data across multiple experiments into transparent quantitative measures to report the emergence of new modes of organized behavior during collective migration of tumor and epithelial cells in wound healing assays. First, we discovered the emergence of a wave of coordinated migration propagating backward from the wound front, which reflects formation of clusters of coordinately migrating cells that are generated further away from the wound edge and disintegrate close to the advancing front. This wave emerges in both normal and tumor cells, and is amplified by Met activation with hepatocyte growth factor/scatter factor. Second, Met activation was found to induce coinciding waves of cellular acceleration and stretching, which in turn trigger the emergence of a backward propagating wave of directional migration with about an hour phase lag. Assessments of the relations between the waves revealed that amplified coordinated migration is associated with the emergence of directional migration. Taken together, our data and simplified modeling-based assessments suggest that increased velocity leads to enhanced coordination: higher motility arises due to acceleration and stretching that seems to increase directionality by temporarily diminishing the velocity components orthogonal to the direction defined by the monolayer geometry. Spatial and temporal accumulation of directionality thus defines coordination. The findings offer new insight and suggest a basic cellular mechanism for long-term cell guidance and intercellular communication during collective cell migration.

In silico labeling in cell biology: Potential and limitations.

In silico labeling is the computational cross-modality image translation where the output modality is a subcellular marker that is not specifically encoded in the input image, for example, in silico localization of organelles from transmitted light images. In principle, in silico labeling has the potential to facilitate rapid live imaging of multiple organelles with reduced photobleaching and phototoxicity, a technology enabling a major leap toward understanding the cell as an integrated complex system. However, five years have passed since feasibility was attained, without any demonstration of using in silico labeling to uncover new biological insight. In here, we discuss the current state of in silico labeling, the limitations preventing it from becoming a practical tool, and how we can overcome these limitations to reach its full potential.

Anomaly detection for high-content image-based phenotypic cell profiling.

High-content image-based phenotypic profiling combines automated microscopy and analysis to identify phenotypic alterations in cell morphology and provide insight into the cell's physiological state. Classical representations of the phenotypic profile can not capture the full underlying complexity in cell organization, while recent weakly machine-learning based representation-learning methods are hard to biologically interpret. We used the abundance of control wells to learn the in-distribution of control experiments and use it to formulate a self-supervised reconstruction anomaly-based representation that encodes the intricate morphological inter-feature dependencies while preserving the representation interpretability. The performance of our anomaly-based representations was evaluated for downstream tasks with respect to two classical representations across four public Cell Painting datasets. Anomaly-based representations improved reproducibility, Mechanism of Action classification, and complemented classical representations. Unsupervised explainability of autoencoder-based anomalies identified specific inter-feature dependencies causing anomalies. The general concept of anomaly-based representations can be adapted to other applications in cell biology.

Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization.

The success of deep learning in identifying complex patterns exceeding human intuition comes at the cost of interpretability. Non-linear entanglement of image features makes deep learning a "black box" lacking human meaningful explanations for the models' decision. We present DISCOVER, a generative model designed to discover the underlying visual properties driving image-based classification models. DISCOVER learns disentangled latent representations, where each latent feature encodes a unique classification-driving visual property. This design enables "human-in-the-loop" interpretation by generating disentangled exaggerated counterfactual explanations. We apply DISCOVER to interpret classification of in vitro fertilization embryo morphology quality. We quantitatively and systematically confirm the interpretation of known embryo properties, discover properties without previous explicit measurements, and quantitatively determine and empirically verify the classification decision of specific embryo instances. We show that DISCOVER provides human-interpretable understanding of "black box" classification models, proposes hypotheses to decipher underlying biomedical mechanisms, and provides transparency for the classification of individual predictions.

Benchmark for multi-cellular segmentation of bright field microscopy images.

BACKGROUND: Multi-cellular segmentation of bright field microscopy images is an essential computational step when quantifying collective migration of cells in vitro. Despite the availability of various tools and algorithms, no publicly available benchmark has been proposed for evaluation and comparison between the different alternatives. DESCRIPTION: A uniform framework is presented to benchmark algorithms for multi-cellular segmentation in bright field microscopy images. A freely available set of 171 manually segmented images from diverse origins was partitioned into 8 datasets and evaluated on three leading designated tools. CONCLUSIONS: The presented benchmark resource for evaluating segmentation algorithms of bright field images is the first public annotated dataset for this purpose. This annotated dataset of diverse examples allows fair evaluations and comparisons of future segmentation methods. Scientists are encouraged to assess new algorithms on this benchmark, and to contribute additional annotated datasets.

Inference of long-range cell-cell force transmission from ECM remodeling fluctuations.

Cells sense, manipulate and respond to their mechanical microenvironment in a plethora of physiological processes, yet the understanding of how cells transmit, receive and interpret environmental cues to communicate with distant cells is severely limited due to lack of tools to quantitatively infer the complex tangle of dynamic cell-cell interactions in complicated environments. We present a computational method to systematically infer and quantify long-range cell-cell force transmission through the extracellular matrix (cell-ECM-cell communication) by correlating ECM remodeling fluctuations in between communicating cells and demonstrating that these fluctuations contain sufficient information to define unique signatures that robustly distinguish between different pairs of communicating cells. We demonstrate our method with finite element simulations and live 3D imaging of fibroblasts and cancer cells embedded in fibrin gels. While previous studies relied on the formation of a visible fibrous 'band' extending between cells to inform on mechanical communication, our method detected mechanical propagation even in cases where visible bands never formed. We revealed that while contractility is required, band formation is not necessary, for cell-ECM-cell communication, and that mechanical signals propagate from one cell to another even upon massive reduction in their contractility. Our method sets the stage to measure the fundamental aspects of intercellular long-range mechanical communication in physiological contexts and may provide a new functional readout for high content 3D image-based screening. The ability to infer cell-ECM-cell communication using standard confocal microscopy holds the promise for wide use and democratizing the method.

Using Unlabeled Information of Embryo Siblings from the Same Cohort Cycle to Enhance In Vitro Fertilization Implantation Prediction.

High-content time-lapse embryo imaging assessed by machine learning is revolutionizing the field of in vitro fertilization (IVF). However, the vast majority of IVF embryos are not transferred to the uterus, and these masses of embryos with unknown implantation outcomes are ignored in current efforts that aim to predict implantation. Here, whether, and to what extent the information encoded within "sibling" embryos from the same IVF cohort contributes to the performance of machine learning-based implantation prediction is explored. First, it is shown that the implantation outcome is correlated with attributes derived from the cohort siblings. Second, it is demonstrated that this unlabeled data boosts implantation prediction performance. Third, the cohort properties driving embryo prediction, especially those that rescued erroneous predictions, are characterized. The results suggest that predictive models for embryo implantation can benefit from the overlooked, widely available unlabeled data of sibling embryos by reducing the inherent noise of the individual transferred embryo.

Emergence of synchronized multicellular mechanosensing from spatiotemporal integration of heterogeneous single-cell information transfer.

Multicellular synchronization is a ubiquitous phenomenon in living systems. However, how noisy and heterogeneous behaviors of individual cells are integrated across a population toward multicellular synchronization is unclear. Here, we study the process of multicellular calcium synchronization of the endothelial cell monolayer in response to mechanical stimuli. We applied information theory to quantify the asymmetric information transfer between pairs of cells and defined quantitative measures to how single cells receive or transmit information within a multicellular network. Our analysis revealed that multicellular synchronization was established by gradual enhancement of information spread from the single cell to the multicellular scale. Synchronization was associated with heterogeneity in the cells' communication properties, reinforcement of the cells' state, and information flow. Altogether, we suggest a phenomenological model where cells gradually learn their local environment, adjust, and reinforce their internal state to stabilize the multicellular network architecture to support information flow from local to global scales toward multicellular synchronization.

Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma.

Deep learning has emerged as the technique of choice for identifying hidden patterns in cell imaging data but is often criticized as "black box." Here, we employ a generative neural network in combination with supervised machine learning to classify patient-derived melanoma xenografts as "efficient" or "inefficient" metastatic, validate predictions regarding melanoma cell lines with unknown metastatic efficiency in mouse xenografts, and use the network to generate in silico cell images that amplify the critical predictive cell properties. These exaggerated images unveiled pseudopodial extensions and increased light scattering as hallmark properties of metastatic cells. We validated this interpretation using live cells spontaneously transitioning between states indicative of low and high metastatic efficiency. This study illustrates how the application of artificial intelligence can support the identification of cellular properties that are predictive of complex phenotypes and integrated cell functions but are too subtle to be identified in the raw imagery by a human expert. A record of this paper's transparent peer review process is included in the supplemental information. VIDEO ABSTRACT.

ERK1/2 inhibition promotes robust myotube growth via CaMKII activation resulting in myoblast-to-myotube fusion.

Myoblast fusion is essential for muscle development and regeneration. Yet, it remains poorly understood how mononucleated myoblasts fuse with preexisting fibers. We demonstrate that ERK1/2 inhibition (ERKi) induces robust differentiation and fusion of primary mouse myoblasts through a linear pathway involving RXR, ryanodine receptors, and calcium-dependent activation of CaMKII in nascent myotubes. CaMKII activation results in myotube growth via fusion with mononucleated myoblasts at a fusogenic synapse. Mechanistically, CaMKII interacts with and regulates MYMK and Rac1, and CaMKIIδ/γ knockout mice exhibit smaller regenerated myofibers following injury. In addition, the expression of a dominant negative CaMKII inhibits the formation of large multinucleated myotubes. Finally, we demonstrate the evolutionary conservation of the pathway in chicken myoblasts. We conclude that ERK1/2 represses a signaling cascade leading to CaMKII-mediated fusion of myoblasts to myotubes, providing an attractive target for the cultivated meat industry and regenerative medicine.