Daniel J. Cohen

Representative raw images and control codes used for SCHEPHERD project. Manual, codes and design files will be continuously updated on GitHub (https://github.com/Cellherders-CohenLab/SCHEPHERD_Code/tree/main). SCHEPHERD_CODE-main contains the most recent github repository prior to publication.

Collective cell migration is critical in a range of biophysical processes spanning wound healing to tumor metastasis. It is therefore important to develop techniques for regulating cell motion, but while we have developed powerful migration tools from optogenetics and bioelectricity, the complex mechanics of collective systems make it difficult to determine where and when to apply these stimuli. For example, here we begin with a circular sheet of skin cells with a central hole, or “wound”, and show that globally stimulating all cells to migrate radially inwards to close the gap through electrotaxis causes catastrophic mechanical damage, emerging from strong cell-cell adhesion, which we explain with an active elasticity model. We propose a solution inspired by sheepherding based on using local stimulation to learn the collective perturbation-response of the group and then drive tissue motion. First, we induce local electrotaxis to characterize the impulse-response function of quasi-1D strips of skin tissue, discovering that hyper-local stimulation triggers long-range tissue response over a length scale set by cell-cell adhesion also predicted by our model. Based on this, we apply local, concentric ring fields to in vitro circular wounds. Continuously driving cells towards the wound core kinetically traps the tissue in a jammed state and freezes healing. Pulsing the stimuli allows the tissue to relax and fluidize again, accelerating migration. Finally, we integrate the key length and timescales of tissue mechanics into a biophysically-informed continuum control model. The model’s predictive framework helps determine where and when to apply stimulation for optimal tissue growth which, when tested, accelerates healing 5-fold.

Abstract Cell migration is a cornerstone of biological systems, enabling organisms to adapt to environmental stimuli and maintain homeostasis. Disruptions in this process can lead to functional impairment or system failure. In many cases, cells do not move randomly; instead, they migrate directionally in response to external cues, allowing them to perform essential biological functions. This directed movement is especially important in processes such as morphogenesis, cancer invasion, and wound healing. To unravel the complexities of directional cell migration, investigating natural guiding stimuli is crucial. Among these, electrical fields stand out as precise and physiologically relevant stimulus. Using a platform designed to apply programmable electric fields, the SCHEEPDOG device; we applied controlled electric field of varying intensities to keratocytes and quantitatively analyzed their migratory behavior. Our findings reveal that electric field stimulation not only induces robust directional migration but also enhances migration speed in an intensity-dependent manner. Additionally, cells initially moving in random directions gradually align with the field vector, with higher intensities accelerating the alignment. Intriguingly, while both speed and alignment time can be modulated through stimulation, the overall shape of migration trajectories remains unchanged. In other terms, for cells initially moving to the opposite direction of the field, the alignment is accompanied by making a turn and the size and shape of this turn is not affected by the magnitude of the electrical stimulation. Together, these results demonstrate that electrical stimulation can tune the speed and directional alignment of keratocyte migration without altering turning dynamics. These findings contribute to a deeper understanding of electrotaxis and offers new insights into how biophysical cues regulate cell migration in both physiological and pathological contexts.

Spanning frogs, fish, and humans, direct-current (DC) bioelectric cues play critical roles beyond neuro-muscular function, such as modulating morphogenesis, immune response, and healing through electrotaxis-electrically directed cell migration. Harnessing this potential requires new tools. However, standardized, accessible, and reproducible infrastructure capable of DC stimulation remains a challenge. We present SCHEPHERD: a universal, electrobioreactor integrating 8 stimulation channels and modular inserts to enable most electrotaxis assays in one device (cells, monolayers, and 3D spheroids), while enabling powerful, new capabilities. SCHEPHERD revealed through parameter sweeps that DC fields act like a 'steering wheel and gas pedal' for cell migration. We then used live confocal imaging to observe electrically reprogrammed F-actin dynamics. Finally, our multi-polar inserts generated complex spatial electrical patterns that reorganize engineered tissue dynamics. By significantly improving accessibility through modularity and an open-source, graphically programmed stimulator, we hope SCHEPHERD can help broaden the community studying these important DC bioelectric phenomena.

A cell can bind to itself and form a self-adhesion that can be engineered and harnessed as a new way to adhere cells to engineered materials-a key challenge for biomaterials are demonstrated. Here, a 3D structure smaller is developed than a single cell, that a Self-Adhesion-Tunnel (SAT) is called, that causes cells to wrap around it and bind to themselves. This process is driven through the cadherin proteins that regulate cell-cell adhesion, and it is shown that many of the key elements of a normal cell-cell adhesion are found in self-adhesions. Size and shape of the SAT determine the efficiency of self-adhesion formation, and >90% efficient formation of self-adhesions are observed in both kidney and skin cells per SAT. Self-adhesions can persist for at least 24 hrs and act to stabilize the cell-material interface and reduce migration. Overall, this ability to co-opt the native cell-cell adhesion machinery in cells and use it as an attachment strategy can provide new approaches for soft-tissue implant integration and tissue engineering scaffolds where stable tissue-material interfaces are critical.

Summary Receptor tyrosine kinases (RTKs) are thought to play key roles in coordinating cell movement at single-cell and tissue scales. The recent development of optogenetic tools for controlling RTKs and their downstream signaling pathways suggested these responses may be amenable to engineering-based control for sculpting tissue shape and function. Here, we report that a light-controlled EGF receptor (OptoEGFR) can be deployed in epithelial cell lines for precise, programmable control of long-range tissue movements. We show that in OptoEGFR-expressing tissues, light can drive millimeter-scale cell rearrangements to densify interior regions or produce rapid outgrowth at tissue edges. Light-controlled tissue movements are driven primarily by PI 3-kinase signaling, rather than diffusible ligands, tissue contractility, or ERK kinase signaling as seen in other RTK-driven migration contexts. Our study suggests that synthetic, light-controlled RTKs could serve as a powerful platform for controlling cell positions and densities for diverse applications including wound healing and tissue morphogenesis.

OBJECTIVE: To validate a universal neuropsychological model that suggests that disorders of the self are best conceptualized as disintegrated neuropsychological processes (ie, sensations, mental experiences) that lack a sense of relationship to the unified experience/sense of self. DESIGN: Cross-sectional observational study. SETTING: Rehabilitation hospital outpatient clinics. PARTICIPANTS: A total of 73 individuals including 33 with acquired brain injury and 40 with multiple sclerosis. INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: On the basis of the Cambridge Depersonalization Scale, a measure of general disintegration of sensations and mental experiences, a team of rehabilitation clinicians and researchers proposed 6 clinically derived indices of specific disintegrated neuropsychological inputs (ie, sensations), outputs (ie, mental experiences), and experiences of disintegration (ie, space, time, context). RESULTS: As hypothesized (1) a confirmatory factor analysis supported the proposed factors including disintegrated bodily sensations (root mean square error of approximation [RMSEA]=0.193, P=.009; comparative fit index [CFI]=0.909; Tucker-Lewis index [TLI]=0.819), disintegrated context (RMSEA=0.143, P=.129; CFI=0.970; TLI=0.911), disintegrated emotions (RMSEA=0.090, P=.266; CFI=0.967; TLI=0.902), disintegrated cognition (RMSEA=0.091, P=.210; CFI=0.963; TLI=0.939), disintegrated smell/taste, and disintegrated spatial perception (measures of model fit for these last 2 factors could not be determined given they included only 2 items); and (2) Pearson correlations indicated that all 7 Cambridge Depersonalization Scale indices were negatively correlated with a measure associated with right hemisphere functioning, with 5 achieving/approaching statistical significance. CONCLUSION: The results suggest that (1) neuropsychological abilities should be conceptualized in terms of relatively singular neuropsychological domains (ie, affect, behavior, cognition, sensation) and the experience of relationship that is created when they are integrated, and (2) disorders of the self are best conceptualized as disorders of disintegration that are associated with decreased relationship between specific neuropsychological processes and the unified experience/sense of self.

In search settings, calibrating the scores during the ranking process to quantities such as click-through rates or relevance levels enhances a system's usefulness and trustworthiness for downstream users.While previous research has improved this notion of calibration for low complexity learning-to-rank models, the larger data demands and parameter count specific to modern neural text rankers produce unique obstacles that hamper the efficacy of methods intended for the learning-to-rank setting.This paper proposes exploiting large language models (LLMs) to provide relevance and uncertainty signals for these neural text rankers to produce scale-calibrated scores through Monte Carlo sampling of natural language explanations (NLEs).Our approach transforms the neural ranking task from ranking textual querydocument pairs to ranking corresponding synthesized NLEs.Comprehensive experiments on two popular document ranking datasets show that the NLE-based calibration approach consistently outperforms past calibration methods and LLM-based methods for ranking, calibration, and query performance prediction tasks.

Despite the widespread popularity of the "scratch assay," where a pipette is dragged manually through cultured tissue to create a gap to study cell migration and healing, it carries significant drawbacks. Its heavy reliance on manual technique can complicate quantification, reduce throughput, and limit the versatility and reproducibility. We present an open-source, low-cost, accessible, robotic scratching platform that addresses all of the core issues. Compatible with nearly all standard cell culture dishes and usable directly in a sterile culture hood without specialized training, our robot makes highly reproducible scratches in a variety of complex cultured tissues with high throughput. Moreover, the robot demonstrates precise removal of tissues for sculpting arbitrary tissue and wound shapes, enabling complex co-culture experiments. This system significantly improves the usefulness of the conventional scratch assay and opens up new possibilities in complex tissue engineering for realistic wound healing and migration research.

The ability to precisely pattern cells and proteins is crucial in various scientific disciplines, including cell biology, bioengineering, and materials chemistry. Current techniques, such as microcontact stamping, 3D bioprinting, and direct photopatterning, have limitations in terms of cost, versatility, and throughput. In this Article, we present an accessible approach that combines the throughput of photomask systems with the versatility of programmable light patterning using a low-cost consumer LCD resin printer. The method involves utilizing a bioinert hydrogel, poly(ethylene glycol) diacrylate (PEGDA), and a 405 nm sensitive photoinitiator (LAP) that are selectively cross-linked to form a hydrogel upon light exposure, creating specific regions that are protein and cell-repellent. Our result highlights that a low-cost LCD resin printer can project virtual photomasks onto the hydrogel, allowing for reasonable resolution and large-area printing at a fraction of the cost of traditional systems. The study demonstrates the calibration of exposure times for optimal resolution and accuracy and shape corrections to overcome the inherent challenges of wide-field resin printing. The potential of this approach is validated through widely studied 2D and 3D stem cell applications, showcasing its biocompatibility and ability to replicate complex tissue engineering patterns. We also validate the method with a cell-adhesive polymer (gelatin methacrylate; GelMA). The combination of low cost, high throughput, and accessibility makes this method broadly applicable across fields for enabling rapid and precise fabrication of cells and tissues in standard laboratory culture vessels.

'scratch assay', where a pipette is dragged through cultured tissue to create an injury gap to study cell migration and healing, the manual nature of the assay carries significant drawbacks. So much of the process depends on individual manual technique, which can complicate quantification, reduce throughput, and limit the versatility and reproducibility of the approach. Here, we present a truly open-source, low-cost, accessible, and robotic scratching platform that addresses all of the core issues. Compatible with nearly all standard cell culture dishes and usable directly in a sterile culture hood, our robot makes highly reproducible scratches in a variety of complex cultured tissues with high throughput. Moreover, we demonstrate how scratching can be programmed to precisely remove areas of tissue to sculpt arbitrary tissue and wound shapes, as well as enable truly complex co-culture experiments. This system significantly improves the usefulness of the conventional scratch assay, and opens up new possibilities in complex tissue engineering and cell biological assays for realistic wound healing and migration research.

Receptor tyrosine kinases (RTKs) are thought to play key roles in coordinating cell movement at single-cell and tissue scales. The recent development of optogenetic tools for controlling RTKs and their downstream signaling pathways suggested these responses may be amenable to engineering-based control for sculpting tissue shape and function. Here, we report that a light-controlled EGF receptor (OptoEGFR) can be deployed in epithelial cell lines for precise, programmable control of long-range tissue movements. We show that in OptoEGFR-expressing tissues, light can drive millimeter-scale cell rearrangements to densify interior regions or produce rapid outgrowth at tissue edges. Light-controlled tissue movements are driven primarily by PI 3-kinase signaling, rather than diffusible signals, tissue contractility, or ERK kinase signaling as seen in other RTK-driven migration contexts. Our study suggests that synthetic, light-controlled RTKs could serve as a powerful platform for controlling cell positions and densities for diverse applications including wound healing and tissue morphogenesis.

Abstract Epithelial migration is implicit in processes ranging from gastrula development to the healing of skin, and involves the coordinated motion, force production, and resulting energy expenditure of thousands of constitutive cells. However, the spatiotemporal patterning and regulation of energy expenditure during epithelial migration remains poorly understood. Here, we propose a continuum mechan-ics framework and use it to explore how energy expenditure regulates epithelial migration. We use canonical mechanical metrics such as force, work and power to define what it means for a tissue to migrate ‘efficiently’ and show that freely expanding epithelia actively regulate themselves to operate within a maximally efficient regime. We then leverage electrotaxis (directed motion in response to an externally applied electric field) as a tool to study non-homeostatic migra-tion using this new framework. We show that regulation of migration is robust to external cues and acts to to attenuate a tissues response to stimuli.

The spatiotemporal coordination and regulation of cell proliferation is fundamental in many aspects of development and tissue maintenance. Cells have the ability to adapt their division rates in response to mechanical constraints, yet we do not fully understand how cell proliferation regulation impacts cell migration phenomena. Here, we present a minimal continuum model of cell migration with cell cycle dynamics, which includes density-dependent effects and hence can account for cell proliferation regulation. By combining minimal mathematical modeling, Bayesian inference, and recent experimental data, we quantify the impact of tissue crowding across different cell cycle stages in epithelial tissue expansion experiments. Our model suggests that cells sense local density and adapt cell cycle progression in response, during G1 and the combined S/G2/M phases, providing an explicit relationship between each cell-cycle-stage duration and local tissue density, which is consistent with several experimental observations. Finally, we compare our mathematical model's predictions to different experiments studying cell cycle regulation and present a quantitative analysis on the impact of density-dependent regulation on cell migration patterns. Our work presents a systematic approach for investigating and analyzing cell cycle data, providing mechanistic insights into how individual cells regulate proliferation, based on population-based experimental measurements.

Epithelial tissues sheath organs and electro-mechanically regulate ion and water transport to regulate development, homeostasis, and hydrostatic organ pressure. Here, we demonstrate how external electrical stimulation allows us to control these processes in living tissues. Specifically, we electrically stimulate hollow, 3D kidneyoids and gut organoids and find that physiological-strength electrical stimulation of ∼ 5 - 10 V/cm powerfully inflates hollow tissues; a process we call electro-inflation. Electro-inflation is mediated by increased ion flux through ion channels/transporters and triggers subsequent osmotic water flow into the lumen, generating hydrostatic pressure that competes against cytoskeletal tension. Our computational studies suggest that electro-inflation is strongly driven by field-induced ion crowding on the outer surface of the tissue. Electrically stimulated tissues also break symmetry in 3D resulting from electrotaxis and affecting tissue shape. The ability of electrical cues to regulate tissue size and shape emphasizes the role and importance of the electrical micro-environment for living tissues.

When employing mechanistic models to study biological phenomena, practical parameter identifiability is important for making accurate predictions across wide ranges of unseen scenarios, as well as for understanding the underlying mechanisms. In this work, we use a profile-likelihood approach to investigate parameter identifiability for four extensions of the Fisher-Kolmogorov-Petrovsky-Piskunov (Fisher-KPP) model, given experimental data from a cell invasion assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures, and that they require more data to be practically identifiable. As a result, we suggest that parameter identifiability should be considered alongside goodness-of-fit and model complexity as criteria for model selection.

Collective electrotaxis is a phenomenon that occurs when a cellular collective, for example an epithelial monolayer, is subjected to an electric field. Biologically, it is well known that the velocity of migration during the collective electrotaxis of large epithelia exhibits significant spatial heterogeneity. In this work, we demonstrate that the heterogeneity of velocities in the electrotaxing epithelium can be accounted for by a continuum model of cue competition in different tissue regions. Having established a working model of competing migratory cues in the migrating epithelium, we develop and validate a reaction-convection-diffusion model that describes the movement of an epithelial monolayer as it undergoes electrotaxis. We use the model to predict how tissue size and geometry affect the collective migration of MDCK monolayers, and to propose several ways in which electric fields can be designed such that they give rise to a desired spatial pattern of collective migration. We conclude with two examples that demonstrate practical applications of the method in designing bespoke stimulation protocols.

In search settings, calibrating the scores during the ranking process to quantities such as click-through rates or relevance levels enhances a system's usefulness and trustworthiness for downstream users. While previous research has improved this notion of calibration for low complexity learning-to-rank models, the larger data demands and parameter count specific to modern neural text rankers produce unique obstacles that hamper the efficacy of methods intended for the learning-to-rank setting. This paper proposes exploiting large language models (LLMs) to provide relevance and uncertainty signals for these neural text rankers to produce scale-calibrated scores through Monte Carlo sampling of natural language explanations (NLEs). Our approach transforms the neural ranking task from ranking textual query-document pairs to ranking corresponding synthesized NLEs. Comprehensive experiments on two popular document ranking datasets show that the NLE-based calibration approach consistently outperforms past calibration methods and LLM-based methods for ranking, calibration, and query performance prediction tasks.

Cells attach to the world through either cell-extracellular matrix adhesion or cell-cell adhesion, and traditional biomaterials imitate the matrix for integrin-based adhesion. However, materials incorporating cadherin proteins that mimic cell-cell adhesion offer an alternative to program cell behavior and integrate into living tissues. We investigated how cadherin substrates affect collective cell migration and cell cycling in epithelia. Our approach involved biomaterials with matrix proteins on one-half and E-cadherin proteins on the other, forming a "Janus" interface across which we grew a single sheet of cells. Tissue regions over the matrix side exhibited normal collective dynamics, but an abrupt behavior shift occurred across the Janus boundary onto the E-cadherin side, where cells attached to the substrate via E-cadherin adhesions, resulting in stalled migration and slowing of the cell cycle. E-cadherin surfaces disrupted long-range mechanical coordination and nearly doubled the length of the G0/G1 phase of the cell cycle, linked to the lack of integrin focal adhesions on the E-cadherin surface.

This is the dataset accompanying the paper "Parameter identifiability and model selection for partial differential equation models of cell invasion" (https://arxiv.org/abs/2309.01476). It consists of a series of images taken of a barrier assay experiment to study tissue expansion of MDCK cells, along with cell density data in MATLAB format. File structure: the contents of the four zip files should be combined (they were split into four files for practical reasons regarding file size). The data corresponds to eight experiments, four with circular initial conditions, and four with triangular initial conditions, the associated data are located in 04-05-22 exp1/Circle and 04-05-22 exp1/Triangle respectively, each labeled "xy<n>", where <n> from 1 to 8 is an identifier for the experiment. The images under the xy<n>_Phase folders are the raw images taken of the experiment, those under the xy<n>_mask folder are processed images indicating the extend of the spread of the cell population. The DensityCellcyleFraction folder contain data files in MATLAB format. The most relevant is the "density" variable, which is a rank-3 tensor of size 150x150x77 such that density(i,j,k) corresponds to the cell density at location (x_i,y_j) and time t_k. The process for calculating the cell density is described in the paper. Alternatively, the density data is also provided in csv format. In the csv_data folder, xy<n>/t<k>.csv encodes a matrix representing cell density for experiment <n> at time t_k. The code for processing and analysing these data are provided in the "code" folder. It is also available at https://github.com/liuyue002/woundhealing . Abstract of the paper: When employing a mechanistic model to study biological systems, practical parameter identifiability is important for making predictions in a wide range of scenarios, as well as for understanding the mechanisms driving the system behaviour. We argue that parameter identifiability should be considered alongside goodness-of-fit and model complexity as criteria for model selection. To demonstrate, we use a profile likelihood approach to investigate parameter identifiability for four extensions of the Fisher--KPP model, given experimental data from a cell invasion assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures, and require more data to be practically identifiable. The results from identifiability analysis can inform model selection, as well as data collection and experimental design.

This is the dataset accompanying the paper "Parameter identifiability and model selection for partial differential equation models of cell invasion" (https://arxiv.org/abs/2309.01476). It consists of a series of images taken of a barrier assay experiment to study tissue expansion of MDCK cells, along with cell density data in MATLAB format. File structure: the contents of the four zip files should be combined (they were split into four files for practical reasons regarding file size). The data corresponds to eight experiments, four with circular initial conditions, and four with triangular initial conditions, the associated data are located in 04-05-22 exp1/Circle and 04-05-22 exp1/Triangle respectively, each labeled "xy&lt;n&gt;", where &lt;n&gt; from 1 to 8 is an identifier for the experiment. The images under the xy&lt;n&gt;_Phase folders are the raw images taken of the experiment, those under the xy&lt;n&gt;_mask folder are processed images indicating the extend of the spread of the cell population. The DensityCellcyleFraction folder contain data files in MATLAB format. The most relevant is the "density" variable, which is a rank-3 tensor of size 150x150x77 such that density(i,j,k) corresponds to the cell density at location (x_i,y_j) and time t_k. The process for calculating the cell density is described in the paper. Alternatively, the density data is also provided in csv format. In the csv_data folder, xy&lt;n&gt;/t&lt;k&gt;.csv encodes a matrix representing cell density for experiment &lt;n&gt; at time t_k. The code for processing and analysing these data are provided at https://github.com/liuyue002/woundhealing . Abstract of the paper: When employing a mechanistic model to study biological systems, practical parameter identifiability is important for making predictions in a wide range of scenarios, as well as for understanding the mechanisms driving the system behaviour. We argue that parameter identifiability should be considered alongside goodness-of-fit and model complexity as criteria for model selection. To demonstrate, we use a profile likelihood approach to investigate parameter identifiability for four extensions of the Fisher--KPP model, given experimental data from a cell invasion assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures, and require more data to be practically identifiable. The results from identifiability analysis can inform model selection, as well as data collection and experimental design.

This is the dataset accompanying the paper "Parameter identifiability and model selection for partial differential equation models of cell invasion" (https://arxiv.org/abs/2309.01476). It consists of a series of images taken of a barrier assay experiment to study tissue expansion of MDCK cells, along with cell density data in MATLAB format. File structure: the contents of the four zip files should be combined (they were split into four files for practical reasons regarding file size). The data corresponds to eight experiments, four with circular initial conditions, and four with triangular initial conditions, the associated data are located in 04-05-22 exp1/Circle and 04-05-22 exp1/Triangle respectively, each labeled "xy&lt;n&gt;", where &lt;n&gt; from 1 to 8 is an identifier for the experiment. The images under the xy&lt;n&gt;_Phase folders are the raw images taken of the experiment, those under the xy&lt;n&gt;_mask folder are processed images indicating the extend of the spread of the cell population. The DensityCellcyleFraction folder contain data files in MATLAB format. The most relevant is the "density" variable, which is a rank-3 tensor of size 150x150x77 such that density(i,j,k) corresponds to the cell density at location (x_i,y_j) and time t_k. The process for calculating the cell density is described in the paper. Abstract of the paper: When employing a mechanistic model to study biological systems, practical parameter identifiability is important for making predictions in a wide range of scenarios, as well as for understanding the mechanisms driving the system behaviour. We argue that parameter identifiability should be considered alongside goodness-of-fit and model complexity as criteria for model selection. To demonstrate, we use a profile likelihood approach to investigate parameter identifiability for four extensions of the Fisher--KPP model, given experimental data from a cell invasion assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures, and require more data to be practically identifiable. The results from identifiability analysis can inform model selection, as well as data collection and experimental design.

Cells attach to the world around them in two ways-cell:extracellular-matrix adhesion and cell:cell adhesion-and conventional biomaterials are made to resemble the matrix to encourage integrin-based cell adhesion. However, interest is growing for cell-mimetic interfaces that mimic cell-cell interactions using cadherin proteins, as this offers a new way to program cell behavior and design synthetic implants and objects that can integrate directly into living tissues. Here, we explore how these cadherin-based materials affect collective cell behaviors, focusing specifically on collective migration and cell cycle regulation in cm-scale epithelia. We built culture substrates where half of the culture area was functionalized with matrix proteins and the contiguous half was functionalized with E-cadherin proteins, and we grew large epithelia across this 'Janus' interface. Parts of the tissues in contact with the matrix side of the Janus interface exhibited normal collective dynamics, but an abrupt shift in behaviors happened immediately across the Janus boundary onto the E-cadherin side, where cells formed hybrid E-cadherin junctions with the substrate, migration effectively froze in place, and cell-cycling significantly decreased. E-cadherin materials suppressed long-range mechanical correlations in the tissue and mechanical information reflected off the substrate interface. These effects could not be explained by conventional density, shape index, or contact inhibition explanations. E-cadherin surfaces nearly doubled the length of the G0/G1 phase of the cell cycle, which we ultimately connected to the exclusion of matrix focal adhesions induced by the E-cadherin culture surface.

Although tissues are usually studied in isolation, this situation rarely occurs in biology, as cells, tissues and organs coexist and interact across scales to determine both shape and function. Here, we take a quantitative approach combining data from recent experiments, mathematical modelling and Bayesian parameter inference, to describe the self-assembly of multiple epithelial sheets by growth and collision. We use two simple and well-studied continuum models, where cells move either randomly or following population pressure gradients. After suitable calibration, both models prove to be practically identifiable, and can reproduce the main features of single tissue expansions. However, our findings reveal that whenever tissue-tissue interactions become relevant, the random motion assumption can lead to unrealistic behaviour. Under this setting, a model accounting for population pressure from different cell populations is more appropriate and shows a better agreement with experimental measurements. Finally, we discuss how tissue shape and pressure affect multi-tissue collisions. Our work thus provides a systematic approach to quantify and predict complex tissue configurations with applications in the design of tissue composites and more generally in tissue engineering.

INTRODUCTION: Women frequently report pain associated with medical termination of pregnancy (MToP), and its management can differ largely between centres. This study aimed at evaluating in real-life settings pain related to MToP and its management in France. METHODS: This was a non-interventional prospective, longitudinal study run in 23 centres between 2015 and 2016 that included 893 pregnant women. Pain was reported by women prior any curative analgesic intake (CAI) through a five-level Likert scale (absence, mild, moderated, severe, extreme). Modalities of analgesic prophylaxis prescription (APP) and intake (API) and CAI were collected. Risk factors were investigated using ordinal logistic regression (for pain) or logistic regression (for CAI) with stepwise selection of variables. RESULTS: APP was prescribed to 657 (73.7%) women irrespective of the gestational age, among whom 386 (73.7%) took the treatment. Out of 740 women who documented their pain symptoms prior to any CAI, few declared no pain (n = 94, 12.7%) or intense pain (n = 88, 11.9%). The majority reported mild or moderate pain (n = 558, 75.4%). On multivariate analysis adjusted on gestational age, increasing initial [odds ratio (OR) 1.25, 95% confidence interval (CI) 1.06-1.47] or total dose (OR 1.15, 95% CI 1.05-1.26) of misoprostol taken were independent factors associated with risk of more pain. When adjusting for gestational age, initial dose of misoprostol (OR 1.69, 95% CI 1.45-2.66) and pain experienced (OR 3.58, 95% CI 2.82-4.55) were significantly associated with higher risk of CAI while API (OR 0.52, 95% CI 0.36; 0.75) was negatively associated. CONCLUSIONS: Most of the women received an APP, but not all used it. API and gestational age were not related to different risks of more pain following MToP, whereas history of at least one child showed a negative association. Higher doses of misoprostol were strongly associated with both pain and CAI. API was associated with a decreased risk of CAI.