Robust cell tracking in epithelial tissues through identification of maximum common subgraphs

Human Neuromuscular Junction on a Chip: Impact of Amniotic Fluid Stem Cell Extracellular Vesicles on Muscle Atrophy and NMJ Integrity

Neuromuscular junctions (NMJs) are specialized synapses, crucial for the communication between spinal motor neurons (MNs) and skeletal muscle. NMJs become vulnerable in degenerative diseases, such as muscle atrophy, where the crosstalk between the different cell populations fails, and the regenerative ability of the entire tissue is hampered. How skeletal muscle sends retrograde signals to MNs through NMJs represents an intriguing field of research, and the role of oxidative stress and its sources remain poorly understood. Recent works demonstrate the myofiber regeneration potential of stem cells, including amniotic fluid stem cells (AFSC), and secreted extracellular vesicles (EVs) as cell-free therapy. To study NMJ perturbations during muscle atrophy, we generated an MN/myotube co-culture system through Xona^TM microfluidic devices, and muscle atrophy was induced in vitro by Dexamethasone (Dexa). After atrophy induction, we treated muscle and MN compartments with AFSC-derived EVs (AFSC-EVs) to investigate their regenerative and anti-oxidative potential in counteracting NMJ alterations. We found that the presence of EVs reduced morphological and functional in vitro defects induced by Dexa. Interestingly, oxidative stress, occurring in atrophic myotubes and thus involving neurites as well, was prevented by EV treatment. Here, we provided and validated a fluidically isolated system represented by microfluidic devices for studying human MN and myotube interactions in healthy and Dexa-induced atrophic conditions-allowing the isolation of subcellular compartments for region-specific analyses-and demonstrated the efficacy of AFSC-EVs in counteracting NMJ perturbations.

The complex three-dimensional organization of epithelial tissues

Understanding the cellular organization of tissues is key to developmental biology. In order to deal with this complex problem, researchers have taken advantage of reductionist approaches to reveal fundamental morphogenetic mechanisms and quantitative laws. For epithelia, their two-dimensional representation as polygonal tessellations has proved successful for understanding tissue organization. Yet, epithelial tissues bend and fold to shape organs in three dimensions. In this context, epithelial cells are too often simplified as prismatic blocks with a limited plasticity. However, there is increasing evidence that a realistic approach, even from a reductionist perspective, must include apico-basal intercalations (i.e. scutoidal cell shapes) for explaining epithelial organization convincingly. Here, we present an historical perspective about the tissue organization problem. Specifically, we analyze past and recent breakthroughs, and discuss how and why simplified, but realistic, in silico models require scutoidal features to address key morphogenetic events.

Template-based mapping of dynamic motifs in tissue morphogenesis

Tissue morphogenesis relies on repeated use of dynamic behaviors at the levels of intracellular structures, individual cells, and cell groups. Rapidly accumulating live imaging datasets make it increasingly important to formalize and automate the task of mapping recurrent dynamic behaviors (motifs), as it is done in speech recognition and other data mining applications. Here, we present a "template-based search" approach for accurate mapping of sub- to multi-cellular morphogenetic motifs using a time series data mining framework. We formulated the task of motif mapping as a subsequence matching problem and solved it using dynamic time warping, while relying on high throughput graph-theoretic algorithms for efficient exploration of the search space. This formulation allows our algorithm to accurately identify the complete duration of each instance and automatically label different stages throughout its progress, such as cell cycle phases during cell division. To illustrate our approach, we mapped cell intercalations during germband extension in the early Drosophila embryo. Our framework enabled statistical analysis of intercalary cell behaviors in wild-type and mutant embryos, comparison of temporal dynamics in contracting and growing junctions in different genotypes, and the identification of a novel mode of iterative cell intercalation. Our formulation of tissue morphogenesis using time series opens new avenues for systematic decomposition of tissue morphogenesis.

Approximate Bayesian computation reveals the importance of repeated measurements for parameterising cell-based models of growing tissues

The growth and dynamics of epithelial tissues govern many morphogenetic processes in embryonic development. A recent quantitative transition in data acquisition, facilitated by advances in genetic and live-imaging techniques, is paving the way for new insights to these processes. Computational models can help us understand and interpret observations, and then make predictions for future experiments that can distinguish between hypothesised mechanisms. Increasingly, cell-based modelling approaches such as vertex models are being used to help understand the mechanics underlying epithelial morphogenesis. These models typically seek to reproduce qualitative phenomena, such as cell sorting or tissue buckling. However, it remains unclear to what extent quantitative data can be used to constrain these models so that they can then be used to make quantitative, experimentally testable predictions. To address this issue, we perform an in silico study to investigate whether vertex model parameters can be inferred from imaging data, and explore methods to quantify the uncertainty of such estimates. Our approach requires the use of summary statistics to estimate parameters. Here, we focus on summary statistics of cellular packing and of laser ablation experiments, as are commonly reported from imaging studies. We find that including data from repeated experiments is necessary to generate reliable parameter estimates that can facilitate quantitative model predictions.