Peripheral microvascular dysfunction is also present in patients with ischemia and no obstructive coronary artery disease (INOCA)

BACKGROUND: In patients with ischemia and no obstructive coronary artery disease (INOCA), coronary microvascular dysfunction is associated with higher rate of major adverse cardiovascular events. OBJECTIVE: To demonstrate if microvascular dysfunction present in coronary microcirculation of patients with INOCA may be detected noninvasively in their peripheral circulation. METHODS: 25 patients with INOCA and 25 apparently healthy individuals (controls) were subjected to nailfold videocapillaroscopy (NVC) and venous occlusion plethysmography (VOP) to evaluate peripheral microvascular function and blood collection for biomarkers analysis, including soluble vascular cell adhesion molecule-1 (sVCAM-1), endothelin-1 (ET-1) and C-reactive protein (CRP). RESULTS: Red blood cell velocity (RBCV) before and after ischemia (RBCVmax) were significantly lower in patients with INOCA (p = 0.0001). Time to reach maximal red blood cell velocity (TRBCVmax) was significantly longer in INOCA group (p = 0.0004). Concerning VOP, maximal blood flow (p = 0.004) and its relative increment were significantly lower in patients with INOCA (p = 0.0004). RBCVmax showed significant correlations with sVCAM-1 (r = –0.38, p < 0.05), ET-1 (r = –0.73, p < 0.05) and CRP (r = –0.33, p < 0.05). Relative increment of maximal post-ischemic blood flow was significantly correlated with sVCAM-1 (r = –0.42, p < 0.05) and ET-1 (r = –0.48, p < 0.05). CONCLUSIONS: The impairment of microvascular function present in coronary microcirculation of patients with INOCA can be also detected in peripheral microcirculation.

Methodology for comprehensive cell-level analysis of wound healing experiments using deep learning in MATLAB

Background Endothelial healing after deployment of cardiovascular devices is particularly important in the context of clinical outcome. It is therefore of great interest to develop tools for a precise prediction of endothelial growth after injury in the process of implant deployment. For experimental investigation of re-endothelialization in vitro cell migration assays are routinely used. However, semi-automatic analyses of live cell images are often based on gray value distributions and are as such limited by image quality and user dependence. The rise of deep learning algorithms offers promising opportunities for application in medical image analysis. Here, we present an intelligent cell detection (iCD) approach for comprehensive assay analysis to obtain essential characteristics on cell and population scale. Results In an in vitro wound healing assay, we compared conventional analysis methods with our iCD approach. Therefore we determined cell density and cell velocity on cell scale and the movement of the cell layer as well as the gap closure between two cell monolayers on population scale. Our data demonstrate that cell density analysis based on deep learning algorithms is superior to an adaptive threshold method regarding robustness against image distortion. In addition, results on cell scale obtained with iCD are in agreement with manually velocity detection, while conventional methods, such as Cell Image Velocimetry (CIV), underestimate cell velocity by a factor of 0.5. Further, we found that iCD analysis of the monolayer movement gave results just as well as manual freehand detection, while conventional methods again shows more frayed leading edge detection compared to manual detection. Analysis of monolayer edge protrusion by ICD also produced results, which are close to manual estimation with an relative error of 11.7%. In comparison, the conventional Canny method gave a relative error of 76.4%. Conclusion The results of our experiments indicate that deep learning algorithms such as our iCD have the ability to outperform conventional methods in the field of wound healing analysis. The combined analysis on cell and population scale using iCD is very well suited for timesaving and high quality wound healing analysis enabling the research community to gain detailed understanding of endothelial movement.

Spontaneous polarization and cell guidance on asymmetric nanotopography

Asymmetric nanotopography with sub-cellular dimensions has recently been demonstrated to be able to provide a unidirectional bias in the migration of cells. The details of this guidance depend both on the type of cell studied and the design of the nanotopography. This behavior is not yet well understood, so there is a pressing need for a predictive description of cell migration on such nanotopography that captures both the initiation of migration and the manner in which cell migration evolves. Here, we employ a three-dimensional, physics-based model to study cell guidance on asymmetric nanosawteeth. In agreement with experimental data, our model predicts that asymmetric sawteeth lead both to spontaneous motion and changes in motion phenotypes. Our model demonstrates that asymmetric nanosawteeth induce a unidirectional bias in guidance direction that is dependent upon the actin polymerization rate and the sawtooth dimensions. Motivated by this model, an analysis of previously reported experimental data indicates that the degree of guidance by asymmetric nanosawteeth increases with the cell velocity.

Pressure drives rapid burst-like collective migration from 3D cancer aggregates

Collective migration of cells is a key behaviour observed during morphogenesis, wound healing and cancer cell invasion. Hence, understanding the different aspects of collective migration is at the core of further progress in describing and treating cancer and other pathological defects. The standard dogma in cell migration is that cells exert forces on the environment to move and cell-cell adhesion-based forces provide the coordination for collective migration. Here, we report a new collective migration mechanism that is independent of pulling forces on the extra-cellular matrix (ECM), as it is driven by the pressure difference generated inside model tumours. We observe a striking collective migration phenotype, where a rapid burst-like stream of HeLa cervical cancer cells emerges from the 3D aggregate embedded in matrices with low collagen concentration (0.5 mg ml−1). This invasion-like behaviour is recorded within 8 hours post embedding (hpe), and is characterised by high cell velocity and super-diffusive collective motion. We show that cellular swelling, triggered by the soft matrix, leads to a rise in intrinsic pressure, which eventually drives an invasion-like phenotype of HeLa cancer aggregates. These dynamic observations provide new evidence that pressure-driven effects need to be considered for a complete description of the mechanical forces involved in collective migration and invasion.

Symmetry and fluctuation of cell movements in neural crest-derived facial mesenchyme

In the face, symmetry is established when bilateral streams of neural crest cells leave the neural tube at the same time, follow identical migration routes and then give rise to the facial prominences. However developmental instability exists, particularly surrounding the steps of lip fusion. The causes of instability are unknown but inability to cope with developmental fluctuations are a likely cause of congenital malformations such as non-syndromic orofacial clefts. Here, we tracked cell movements over time in the frontonasal mass which forms the facial midline and participates in lip fusion using live-cell imaging. Our mathematical examination of cell velocity vectors uncovered temporal fluctuations in several parameters including order/disorder, symmetry/asymmetry and divergence/convergence. We found that treatment with a RhoGTPase inhibitor completely disrupted the temporal fluctuations in all measures and blocked morphogenesis. Thus we discovered that genetic control of symmetry extends to mesenchymal cell movements and that these movements are of the type that could be perturbed in in asymmetrical malformations such as non-syndromic cleft lip.

Single-cell motile behaviour of $$Trypanosoma brucei $$ in thin-layered fluid collectives

We describe a system for the analysis of an important unicellular eukaryotic flagellate in a confining and crowded environment. The parasite Trypanosoma brucei is arguably one of the most versatile microswimmers known. It has unique properties as a single microswimmer and shows remarkable adaptations (not only in motility, but prominently so), to its environment during a complex developmental cycle involving two different hosts. Specific life cycle stages show fascinating collective behaviour, as millions of cells can be forced to move together in extreme confinement. Our goal is to examine such motile behaviour directly in the context of the relevant environments. Therefore, for the first time, we analyse the motility behaviour of trypanosomes directly in a widely used assay, which aims to evaluate the parasites behaviour in collectives, in response to as yet unknown parameters. In a step towards understanding whether, or what type of, swarming behaviour of trypanosomes exists, we customised the assay for quantitative tracking analysis of motile behaviour on the single-cell level. We show that the migration speed of cell groups does not directly depend on single-cell velocity and that the system remains to be simplified further, before hypotheses about collective motility can be advanced. Graphic abstract

Tacc3 modulates microtubule network dynamicity and focal adhesion remodeling to affect cranial neural crest cell migration in Xenopus laevis

Coordinated cell migration is critical during embryogenesis, as cells must leave their point of origin, navigate a complex barrage of signals, and accurately position themselves to facilitate correct tissue and organ formation. The cell motility process relies on dynamic interactions of the F-actin and microtubule (MT) cytoskeletons. Our work focuses on how one MT plus-end regulator, Transforming Acidic Coiled-Coil 3 (Tacc3), can impact migration of cranial neural crest cells in Xenopus laevis. We previously demonstrated that tacc3 expression is expressed in cranial neural crest cells, and that Tacc3 can function as a MT plus-end tracking protein to regulate MT growth velocities. Here, we demonstrate that manipulation of Tacc3 protein levels is sufficient to alter cranial neural crest cell velocity in vitro. Tacc3 overexpression drives increased single-cell migration velocities, while Tacc3 KD results in reduced cell velocity and defective explant dispersion. We also show that Tacc3 can have spatially-enhanced effects on MT plus-end growth velocities as well as effects on focal adhesion remodeling. Together, we demonstrate that Tacc3 can facilitate neural crest cell motility through spatially-enhanced cytoskeletal remodeling, which may underlie the enhanced metastatic potential of Tacc3-overexpressing tumor cells.

Multi-Compartment Lymph-Node-on-a-Chip Enables Measurement of Immune Cell Motility in Response to Drugs

Organs On-a-Chip represent novel platforms for modelling human physiology and disease. The lymph node (LN) is a relevant immune organ in which B and T lymphocytes are spatially organized in a complex architecture, and it is the place where the immune response initiates. The present study addresses the utility of a recently designed LN-on-a-chip to dissect and understand the effect of drugs delivered to cells in a fluidic multicellular 3D setting that mimics the human LN. To do so, we analyzed the motility and viability of human B and T cells exposed to hydroxychloroquine (HCQ). We show that the innovative LN platform, which operates at a microscale level, allows real-time monitoring of co-cultured B and T cells by imaging, and supports cellular random movement. HCQ delivered to cells through a constant and continuous flow induces a reduction in T cell velocity while promotes persistent rotational motion. We also find that HCQ increases the production of reactive oxygen species in T cells. Taken together, these results highlight the potential of the LN-on-a-chip to be applied in drug screening and development, and in cellular dynamics studies.

Methodology for Comprehensive Cell-Level Analysis of Wound Healing Experiments Using Artificial Intelligence

BackgroundEndothelial healing after deployment of cardiovascular devices is particularly important in the context of clinical outcome. It is therefore of great interest to develop tools for a precise prediction of endothelial growth after injury in the process of implant deployment. For experimental investigation of re-endothelialization in vitro cell migration assays are routinely used. However, automatic analyses of live cell images are often based on gray value distributions and are as such limited by image quality and user dependence. The rise of deep learning algorithms offers promising opportunities for application in medical image analysis. Here, we present an intelligent cell detection (iCD) approach for comprehensive assay analysis to obtain essential characteristics on cell and population scale.ResultsIn an in vitro wound healing assay, we compared conventional analysis methods with our iCD approach. Therefore we determined cell density and cell velocity on cell scale and the movement of the cell layer as well as the gap closure between two cell monolayers on population scale. Our data demonstrate that cell density analysis based on deep learning algorithms is superior to an adaptive threshold method regarding robustness against image distortion. In addition, results on cell scale obtained with iCD are in agreement with manually velocity detection, while conventional methods, such as Cell Image Velocimetry (CIV), underestimate cell velocity by a factor of 0.5. Further, we found that iCD analysis of the monolayer movement gave results just as well as manual freehand detection, while conventional methods again shows more frayed leading edge detection compared to manual detection. Analysis of monolayer edge protrusion by ICD also produced results, which are close to manual estimation with an relative error of 13.2 %. In comparison, the conventional Canny method gave a relative error of 61 %. ConclusionThe results of our experiments indicate that deep learning algorithms such as our iCD have the ability to outperform conventional methods in the field of wound healing analysis. The combined analysis on cell and population scale using iCD is very well suited for timesaving and high quality wound healing analysis enabling the research community to gain detailed understanding of endothelial movement.