Spatiotemporal analysis of F-actin polymerization with micropillar arrays reveals synchronization between adhesion sites.

We repurposed micropillar arrays to quantify spatiotemporal inter-adhesion communication. Following the observation that integrin adhesions formed around pillar tops we relied on the precise repetitive spatial control of the pillars to reliably monitor F-actin dynamics in mouse embryonic fibroblasts as a model for spatiotemporal adhesion-related intracellular signaling. Using correlation-based analyses, we revealed localized information flows propagating between adjacent pillars that were integrated over space and time to synchronize the adhesion dynamics within the entire cell.

Making the most of bioimaging data through interdisciplinary interactions.

The increasing technical complexity of all aspects involving bioimages, ranging from their acquisition to their analysis, has led to a diversification in the expertise of scientists engaged at the different stages of the discovery process. Although this diversity of profiles comes with the major challenge of establishing fruitful interdisciplinary collaboration, such collaboration also offers a superb opportunity for scientific discovery. In this Perspective, we review the different actors within the bioimaging research universe and identify the primary obstacles that hinder their interactions.

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.

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.

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.

Formation of recurring transient Ca-based intercellular communities during 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.

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.

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.

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.

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.

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.

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.

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.

Ferroptosis occurs through an osmotic mechanism and propagates independently of cell rupture.

Ferroptosis is a regulated form of necrotic cell death that is caused by the accumulation of oxidized phospholipids, leading to membrane damage and cell lysis. Although other types of necrotic death such as pyroptosis and necroptosis are mediated by active mechanisms of execution, ferroptosis is thought to result from the accumulation of unrepaired cell damage. Previous studies have suggested that ferroptosis has the ability to spread through cell populations in a wave-like manner, resulting in a distinct spatiotemporal pattern of cell death.

Differential dynamics of early stages of platelet adhesion and spreading on collagen IV- and fibrinogen-coated surfaces.

Upon wound formation, platelets adhere to the neighboring extracellular matrix and spread on it, a process which is critical for physiological wound healing. Multiple external factors, such as the molecular composition of the environment and its mechanical properties, play a key role in this process and direct its speed and outcome. We combined live cell imaging, quantitative interference reflection microscopy and cryo-electron tomography to characterize, at a single platelet level, the differential spatiotemporal dynamics of the adhesion process to fibrinogen- and collagen IV-functionalized surfaces.

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 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.

Sharing and reusing cell image data.

The rapid growth in content and complexity of cell image data creates an opportunity for synergy between experimental and computational scientists. Sharing microscopy data enables computational scientists to develop algorithms and tools for data analysis, integration, and mining. These tools can be applied by experimentalists to promote hypothesis-generation and discovery.

Diverse roles of guanine nucleotide exchange factors in regulating collective cell migration.

Efficient collective migration depends on a balance between contractility and cytoskeletal rearrangements, adhesion, and mechanical cell-cell communication, all controlled by GTPases of the RHO family. By comprehensive screening of guanine nucleotide exchange factors (GEFs) in human bronchial epithelial cell monolayers, we identified GEFs that are required for collective migration at large, such as SOS1 and β-PIX, and RHOA GEFs that are implicated in intercellular communication. Down-regulation of the latter GEFs differentially enhanced front-to-back propagation of guidance cues through the monolayer and was mirrored by down-regulation of RHOA expression and myosin II activity.

Decoupling global biases and local interactions between cell biological variables.

Analysis of coupled variables is a core concept of cell biological inference, with co-localization of two molecules as a proxy for protein interaction being a ubiquitous example. However, external effectors may influence the observed co-localization independently from the local interaction of two proteins. Such global bias, although biologically meaningful, is often neglected when interpreting co-localization.