Motion sensing superpixels (MOSES) is a systematic computational framework to quantify and discover cellular motion phenotypes

RhoU forms homo-oligomers to regulate cellular responses

RhoU is an atypical member of the Rho family of small G-proteins, which has N- and C-terminal extensions compared to the classic Rho GTPases RhoA, Rac1 and Cdc42, and associates with membranes through C-terminal palmitoylation rather than prenylation. RhoU mRNA expression is upregulated in prostate cancer and is considered a marker for disease progression. Here, we show that RhoU overexpression in prostate cancer cells increases cell migration and invasion. To identify RhoU targets that contribute to its function, we found that RhoU homodimerizes in cells. We map the region involved in this interaction to the C-terminal extension and show that C-terminal palmitoylation is required for self-association. Expression of the isolated C-terminal extension reduces RhoU-induced activation of p21-activated kinases (PAKs), which are known downstream targets for RhoU, and induces cell morphological changes consistent with inhibiting RhoU function. Our results show for the first time that the activity of a Rho family member is stimulated by self-association, and this is important for its activity.

Unsupervised Contour Tracking of Live Cells by Mechanical and Cycle Consistency Losses

Analyzing the dynamic changes of cellular morphology is important for understanding the various functions and characteristics of live cells, including stem cells and metastatic cancer cells. To this end, we need to track all points on the highly deformable cellular contour in every frame of live cell video. Local shapes and textures on the contour are not evident, and their motions are complex, often with expansion and contraction of local contour features. The prior arts for optical flow or deep point set tracking are unsuited due to the fluidity of cells, and previous deep contour tracking does not consider point correspondence. We propose the first deep learning-based tracking of cellular (or more generally viscoelastic materials) contours with point correspondence by fusing dense representation between two contours with cross attention. Since it is impractical to manually label dense tracking points on the contour, unsupervised learning comprised of the mechanical and cyclical consistency losses is proposed to train our contour tracker. The mechanical loss forcing the points to move perpendicular to the contour effectively helps out. For quantitative evaluation, we labeled sparse tracking points along the contour of live cells from two live cell datasets taken with phase contrast and confocal fluorescence microscopes. Our contour tracker quantitatively outperforms compared methods and produces qualitatively more favorable results. Our code and data are publicly available at https://github.com/JunbongJang/contour-tracking/.

Surface-guided computing to analyze subcellular morphology and membrane-associated signals in 3D

Signal transduction and cell function are governed by the spatiotemporal organization of membrane-associated molecules. Despite significant advances in visualizing molecular distributions by 3D light microscopy, cell biologists still have limited quantitative understanding of the processes implicated in the regulation of molecular signals at the whole cell scale. In particular, complex and transient cell surface morphologies challenge the complete sampling of cell geometry, membrane-associated molecular concentration and activity and the computing of meaningful parameters such as the cofluctuation between morphology and signals. Here, we introduce u-Unwrap3D, a framework to remap arbitrarily complex 3D cell surfaces and membrane-associated signals into equivalent lower dimensional representations. The mappings are bidirectional, allowing the application of image processing operations in the data representation best suited for the task and to subsequently present the results in any of the other representations, including the original 3D cell surface. Leveraging this surface-guided computing paradigm, we track segmented surface motifs in 2D to quantify the recruitment of Septin polymers by blebbing events; we quantify actin enrichment in peripheral ruffles; and we measure the speed of ruffle movement along topographically complex cell surfaces. Thus, u-Unwrap3D provides access to spatiotemporal analyses of cell biological parameters on unconstrained 3D surface geometries and signals.

Surface-guided computing to analyze subcellular morphology and membrane-associated signals in 3D

Signal transduction and cell function are governed by the spatiotemporal organization of membrane-associated molecules. Despite significant advances in visualizing molecular distributions by 3D light microscopy, cell biologists still have limited quantitative understanding of the processes implicated in the regulation of molecular signals at the whole cell scale. In particular, complex and transient cell surface morphologies challenge the complete sampling of cell geometry, membrane-associated molecular concentration and activity and the computing of meaningful parameters such as the cofluctuation between morphology and signals. Here, we introduce u-Unwrap3D, a framework to remap arbitrarily complex 3D cell surfaces and membrane-associated signals into equivalent lower dimensional representations. The mappings are bidirectional, allowing the application of image processing operations in the data representation best suited for the task and to subsequently present the results in any of the other representations, including the original 3D cell surface. Leveraging this surface-guided computing paradigm, we track segmented surface motifs in 2D to quantify the recruitment of Septin polymers by blebbing events; we quantify actin enrichment in peripheral ruffles; and we measure the speed of ruffle movement along topographically complex cell surfaces. Thus, u-Unwrap3D provides access to spatiotemporal analyses of cell biological parameters on unconstrained 3D surface geometries and signals.

Moving beyond velocity: Opportunities and challenges to quantify immune cell behavior

The analysis of cellular behavior using intravital multi-photon microscopy has contributed substantially to our understanding of the priming and effector phases of immune responses. Yet, many questions remain unanswered and unexplored. Though advancements in intravital imaging techniques and animal models continue to drive new discoveries, continued improvements in analysis methods are needed to extract detailed information about cellular behavior. Focusing on dendritic cell (DC) and T cell interactions as an exemplar, here we discuss key limitations for intravital imaging studies and review and explore alternative approaches to quantify immune cell behavior. We touch upon current developments in deep learning models, as well as established methods from unrelated fields such as ecology to detect and track objects over time. As developments in open-source software make it possible to process and interactively view larger datasets, the challenge for the field will be to determine how best to combine intravital imaging with multi-parameter imaging of larger tissue regions to discover new facets of leukocyte dynamics and how these contribute to immune responses.

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.

Visualization of T Cell Migration in the Spleen Reveals a Network of Perivascular Pathways that Guide Entry into T Zones

Lymphocyte homeostasis and immune surveillance require that T and B cells continuously recirculate between secondary lymphoid organs. Here, we used intravital microscopy to define lymphocyte trafficking routes within the spleen, an environment of open blood circulation and shear forces unlike other lymphoid organs. Upon release from arterioles into the red pulp sinuses, T cells latched onto perivascular stromal cells in a manner that was independent of the chemokine receptor CCR7 but sensitive to Gi protein-coupled receptor inhibitors. This latching sheltered T cells from blood flow and enabled unidirectional migration to the bridging channels and then to T zones, entry into which required CCR7. Inflammatory responses modified the chemotactic cues along the perivascular homing paths, leading to rapid block of entry. Our findings reveal a role for vascular structures in lymphocyte recirculation through the spleen, indicating the existence of separate entry and exit routes and that of a checkpoint located at the gate to the T zone.

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Perivascular pathways support T cell entry into splenic T zones, but not egress from them

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Attachment to the homing paths requires activation of GPCRs other than CCR7

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CCR7 mediates one-directional migration and entry into T zones

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Inflammation leads to modification of the homing paths and to rapid block of entry

Characterization of the Dynamic Behavior of Neutrophils Following Influenza Vaccination

Neutrophils are amongst the first cells to respond to inflammation and infection. Although they play a key role in limiting the dissemination of pathogens, the study of their dynamic behavior in immune organs remains elusive. In this work, we characterized in vivo the dynamic behavior of neutrophils in the mouse popliteal lymph node (PLN) after influenza vaccination with UV-inactivated virus. To achieve this, we used an image-based systems biology approach to detect the motility patterns of neutrophils and to associate them to distinct actions. We described a prominent and rapid recruitment of neutrophils to the PLN following vaccination, which was dependent on the secretion of the chemokine CXCL1 and the alarmin molecule IL-1α. In addition, we observed that the initial recruitment occurred mainly via high endothelial venules located in the paracortical and interfollicular regions of the PLN. The analysis of the spatial-temporal patterns of neutrophil migration demonstrated that, in the initial stage, the majority of neutrophils displayed a patrolling behavior, followed by the formation of swarms in the subcapsular sinus of the PLN, which were associated with macrophages in this compartment. Finally, we observed using multiple imaging techniques, that neutrophils phagocytize and transport influenza virus particles. These processes might have important implications in the capacity of these cells to present viral antigens.

Characterization of Biological Motion Using Motion Sensing Superpixels

Precise spatiotemporal regulation is the foundation for the healthy development and maintenance of living organisms. All cells must correctly execute their function in the right place at the right time. Cellular motion is thus an important dynamic readout of signaling in key disease-relevant molecular pathways. However despite the rapid advancement of imaging technology, a comprehensive quantitative description of motion imaged under different imaging modalities at all spatiotemporal scales; molecular, cellular and tissue-level is still lacking. Generally, cells move either 'individually' or 'collectively' as a group with nearby cells. Current computational tools specifically focus on one or the other regime, limiting their general applicability. To address this, we recently developed and reported a new computational framework, Motion Sensing Superpixels (MOSES). Incorporating the individual advantages of single cell trackers for individual cell and particle image velocimetry (PIV) for collective cell motion analyses, MOSES enables 'mesoscale' analysis of both single-cell and collective motion over arbitrarily long times. At the same time, MOSES readily complements existing single-cell tracking workflows with additional characterization of global motion patterns and interaction analysis between cells and also operates directly on PIV extracted motion fields to yield rich motion trajectories analogous for single-cell tracks suitable for high-throughput motion phenotyping. This protocol provides a step-by-step practical guide for those interested in applying MOSES to their own datasets. The protocol highlights the salient features of a MOSES analysis and demonstrates the ease-of-use and wide applicability of MOSES to biological imaging through demo experimental analyses with ready-to-use code snippets of four datasets from different microscope modalities; phase-contrast, fluorescent, lightsheet and intra-vital microscopy. In addition we discuss critical points of consideration in the analysis.