Tissue Active Matter: Integrating Mechanics and Signaling into Dynamical Models.

The importance of physical forces in the morphogenesis, homeostatic function, and pathological dysfunction of multicellular tissues is being increasingly characterized, both theoretically and experimentally. Analogies between biological systems and inert materials such as foams, gels, and liquid crystals have provided striking insights into the core design principles underlying multicellular organization. However, these connections can seem surprising given that a key feature of multicellular systems is their ability to constantly consume energy, providing an active origin for the forces that they produce. Key emerging questions are, therefore, to understand whether and how this activity grants tissues novel properties that do not have counterparts in classical materials, as well as their consequences for biological function. Here, we review recent discoveries at the intersection of active matter and tissue biology, with an emphasis on how modeling and experiments can be combined to understand the dynamics of multicellular systems. These approaches suggest that a number of key biological tissue-scale phenomena, such as morphogenetic shape changes, collective migration, or fate decisions, share unifying design principles that can be described by physical models of tissue active matter.

Information content and optimization of self-organized developmental systems.

A key feature of many developmental systems is their ability to self-organize spatial patterns of functionally distinct cell fates. To ensure proper biological function, such patterns must be established reproducibly, by controlling and even harnessing intrinsic and extrinsic fluctuations. While the relevant molecular processes are increasingly well understood, we lack a principled framework to quantify the performance of such stochastic self-organizing systems. To that end, we introduce an information-theoretic measure for self-organized fate specification during embryonic development. We show that the proposed measure assesses the total information content of fate patterns and decomposes it into interpretable contributions corresponding to the positional and correlational information. By optimizing the proposed measure, our framework provides a normative theory for developmental circuits, which we demonstrate on lateral inhibition, cell type proportioning, and reaction-diffusion models of self-organization. This paves a way toward a classification of developmental systems based on a common information-theoretic language, thereby organizing the zoo of implicated chemical and mechanical signaling processes.

Learning dynamical models of single and collective cell migration: a review.

Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualising the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasise how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour. Together, these data-driven approaches are a promising avenue for building physical models of cell migration directly from experimental data, and for providing conceptual links between different length-scales of description.

Connecting theory and experiment in cell and tissue mechanics.

Understanding complex living systems, which are fundamentally constrained by physical phenomena, requires combining experimental data with theoretical physical and mathematical models. To develop such models, collaborations between experimental cell biologists and theoreticians are increasingly important but these two groups often face challenges achieving mutual understanding. To help navigate these challenges, this Perspective discusses different modelling approaches, including bottom-up hypothesis-driven and top-down data-driven models, and highlights their strengths and applications. Using cell mechanics as an example, we explore the integration of specific physical models with experimental data from the molecular, cellular and tissue level up to multiscale input. We also emphasize the importance of constraining model complexity and outline strategies for crosstalk between experimental design and model development. Furthermore, we highlight how physical models can provide conceptual insights and produce unifying and generalizable frameworks for biological phenomena. Overall, this Perspective aims to promote fruitful collaborations that advance our understanding of complex biological systems.

Stochastic motion and transcriptional dynamics of pairs of distal DNA loci on a compacted chromosome.

Chromosomes in the eukaryotic nucleus are highly compacted. However, for many functional processes, including transcription initiation, the pairwise motion of distal chromosomal elements such as enhancers and promoters is essential and necessitates dynamic fluidity. Here, we used a live-imaging assay to simultaneously measure the positions of pairs of enhancers and promoters and their transcriptional output while systematically varying the genomic separation between these two DNA loci. Our analysis reveals the coexistence of a compact globular organization and fast subdiffusive dynamics. These combined features cause an anomalous scaling of polymer relaxation times with genomic separation leading to long-ranged correlations. Thus, encounter times of DNA loci are much less dependent on genomic distance than predicted by existing polymer models, with potential consequences for eukaryotic gene expression.

Curvature induces active velocity waves in rotating spherical tissues.

The multicellular organization of diverse systems, including embryos, intestines, and tumors relies on coordinated cell migration in curved environments. In these settings, cells establish supracellular patterns of motion, including collective rotation and invasion. While such collective modes have been studied extensively in flat systems, the consequences of geometrical and topological constraints on collective migration in curved systems are largely unknown. Here, we discover a collective mode of cell migration in rotating spherical tissues manifesting as a propagating single-wavelength velocity wave. This wave is accompanied by an apparently incompressible supracellular flow pattern featuring topological defects as dictated by the spherical topology. Using a minimal active particle model, we reveal that this collective mode arises from the effect of curvature on the active flocking behavior of a cell layer confined to a spherical surface. Our results thus identify curvature-induced velocity waves as a mode of collective cell migration, impacting the dynamical organization of 3D curved tissues.

Stochastic motion and transcriptional dynamics of pairs of distal DNA loci on a compacted chromosome.

Chromosomes in the eukaryotic nucleus are highly compacted. However, for many functional processes, including transcription initiation, the 3D pair-wise motion of distal chromosomal elements, such as enhancers and promoters, is essential and necessitates dynamic fluidity. Therefore, the interplay of chromosome organization and dynamics is crucial for gene regulation. Here, we use a live imaging assay to simultaneously measure the positions of pairs of enhancers and promoters and their transcriptional output in the developing fly embryo while systematically varying the genomic separation between these two DNA loci. Our analysis reveals a combination of a compact globular organization and fast subdiffusive dynamics. These combined features cause an anomalous scaling of polymer relaxation times with genomic separation and lead to long-ranged correlations compared to existing polymer models. This scaling implies that encounter times of DNA loci are much less dependent on genomic separation than predicted by existing polymer models, with potentially significant consequences for eukaryotic gene expression.

3D printed protein-based robotic structures actuated by molecular motor assemblies.

Upscaling motor protein activity to perform work in man-made devices has long been an ambitious goal in bionanotechnology. The use of hierarchical motor assemblies, as realized in sarcomeres, has so far been complicated by the challenges of arranging sufficiently high numbers of motor proteins with nanoscopic precision. Here, we describe an alternative approach based on actomyosin cortex-like force production, allowing low complexity motor arrangements in a contractile meshwork that can be coated onto soft objects and locally activated by ATP. The design is reminiscent of a motorized exoskeleton actuating protein-based robotic structures from the outside. It readily supports the connection and assembly of micro-three-dimensional printed modules into larger structures, thereby scaling up mechanical work. We provide an analytical model of force production in these systems and demonstrate the design flexibility by three-dimensional printed units performing complex mechanical tasks, such as microhands and microarms that can grasp and wave following light activation.

Disentangling cadherin-mediated cell-cell interactions in collective cancer cell migration.

Cell dispersion from a confined area is fundamental in a number of biological processes, including cancer metastasis. To date, a quantitative understanding of the interplay of single-cell motility, cell proliferation, and intercellular contacts remains elusive. In particular, the role of E- and N-cadherin junctions, central components of intercellular contacts, is still controversial. Combining theoretical modeling with in vitro observations, we investigate the collective spreading behavior of colonies of human cancer cells (T24). The spreading of these colonies is driven by stochastic single-cell migration with frequent transient cell-cell contacts. We find that inhibition of E- and N-cadherin junctions decreases colony spreading and average spreading velocities, without affecting the strength of correlations in spreading velocities of neighboring cells. Based on a biophysical simulation model for cell migration, we show that the behavioral changes upon disruption of these junctions can be explained by reduced repulsive excluded volume interactions between cells. This suggests that in cancer cell migration, cadherin-based intercellular contacts sharpen cell boundaries leading to repulsive rather than cohesive interactions between cells, thereby promoting efficient cell spreading during collective migration.

Learning the dynamics of cell-cell interactions in confined cell migration.

The migratory dynamics of cells in physiological processes, ranging from wound healing to cancer metastasis, rely on contact-mediated cell-cell interactions. These interactions play a key role in shaping the stochastic trajectories of migrating cells. While data-driven physical formalisms for the stochastic migration dynamics of single cells have been developed, such a framework for the behavioral dynamics of interacting cells still remains elusive. Here, we monitor stochastic cell trajectories in a minimal experimental cell collider: a dumbbell-shaped micropattern on which pairs of cells perform repeated cellular collisions. We observe different characteristic behaviors, including cells reversing, following, and sliding past each other upon collision. Capitalizing on this large experimental dataset of coupled cell trajectories, we infer an interacting stochastic equation of motion that accurately predicts the observed interaction behaviors. Our approach reveals that interacting noncancerous MCF10A cells can be described by repulsion and friction interactions. In contrast, cancerous MDA-MB-231 cells exhibit attraction and antifriction interactions, promoting the predominant relative sliding behavior observed for these cells. Based on these experimentally inferred interactions, we show how this framework may generalize to provide a unifying theoretical description of the diverse cellular interaction behaviors of distinct cell types.

Inferring the Dynamics of Underdamped Stochastic Systems.

Many complex systems, ranging from migrating cells to animal groups, exhibit stochastic dynamics described by the underdamped Langevin equation. Inferring such an equation of motion from experimental data can provide profound insight into the physical laws governing the system. Here, we derive a principled framework to infer the dynamics of underdamped stochastic systems from realistic experimental trajectories, sampled at discrete times and subject to measurement errors. This framework yields an operational method, Underdamped Langevin Inference, which performs well on experimental trajectories of single migrating cells and in complex high-dimensional systems, including flocks with Viscek-like alignment interactions. Our method is robust to experimental measurement errors, and includes a self-consistent estimate of the inference error.

Area and Geometry Dependence of Cell Migration in Asymmetric Two-State Micropatterns.

Microstructured surfaces provide a unique framework to probe cell migration and cytoskeletal dynamics in a standardized manner. Here, we report on the steady-state occupancy probability of cells in asymmetric two-state microstructures that consist of two fibronectin-coated adhesion sites connected by a thin guidance cue. In these dumbbell-like structures, cells transition between the two sites in a repeated and stochastic manner, and average dwell times in the respective microenvironments are determined from the cell trajectories. We study the dynamics of human breast carcinoma cells (MDA-MB-231) in these microstructures as a function of area, shape, and orientation of the adhesion sites. On square adhesive sites with different areas, we find that the occupancy probability ratio is directly proportional to the ratio of corresponding adhesion site areas. These asymmetries are well captured by a simple model for the stochastic nonlinear dynamics of the cells, which reveals generic features of the motion. Sites of equal area but different shape lead to equal occupancy if shapes are isotropic (e.g., squared or circular). In contrast, an asymmetry in the occupancy is induced by anisotropic shapes like rhombi, triangles, or rectangles that enable motion in the direction perpendicular to the transition axis. Analysis of the two-dimensional motion of cells between two rectangles with orthogonal orientation suggests that cellular transition rates depend on the cell polarization induced by anisotropic micropatterns. Taken together, our results illustrate how two-state micropatterns provide a dynamic migration assay with distinct dwell times and relative cell occupancy as readouts, which may be useful to probe cell-microenvironment interactions.

Thermophoretic forces on a mesoscopic scale.

We use a mesoscale-molecular simulation technique known as Multi-Particle Collision Dynamics (MPC) to study the forces acting on a stationary colloid inside a temperature gradient. We compare our measurements of the thermophoretic force to a theoretical prediction based on Onsager's reciprocal relations, assuming that the temperature gradient is constant across the colloid. We find a good agreement between our measurements and theoretical predictions over a wide range of system parameters, even when the condition of uniform gradients is not strictly fulfilled. Our measurements further suggest that the magnitude of the thermophoretic force depends on the hydrodynamic boundary condition at the colloidal surface, thus highlighting the hydrodynamic character of colloidal thermophoresis. We also investigate how fluid advection disturbs the interfacial layer around the colloid and introduce a dimensionless number to quantify the validity of local thermodynamic equilibrium.

Guiding 3D cell migration in deformed synthetic hydrogel microstructures.

The ability of cells to navigate through the extracellular matrix, a network of biopolymers, is controlled by an interplay of cellular activity and mechanical network properties. Synthetic hydrogels with highly tuneable compositions and elastic properties are convenient model systems for the investigation of cell migration in 3D polymer networks. To study the impact of macroscopic deformations on single cell migration, we present a novel method to introduce uniaxial strain in matrices by microstructuring photo-polymerizable hydrogel strips with embedded cells in a channel slide. We find that such confined swelling results in a strained matrix in which cells exhibit an anisotropic migration response parallel to the strain direction. Surprisingly, however, the anisotropy of migration reaches a maximum at intermediate strain levels and decreases strongly at higher strains. We account for this non-monotonic response in the migration anisotropy with a computational model, in which we describe a cell performing durotactic and proteolytic migration in a deformable elastic meshwork. Our simulations reveal that the macroscopically applied strain induces a local geometric anisotropic stiffening of the matrix. This local anisotropic stiffening acts as a guidance cue for directed cell migration, resulting in a non-monotonic dependence on strain, as observed in our experiments. Our findings provide a mechanism for mechanical guidance that connects network properties on the cellular scale to cell migration behaviour.

Metabolic activity of mature biofilms of Mycobacterium tuberculosis and other non-tuberculous mycobacteria.

Mycobacteria are classified into two groups, fast- and slow-growing. Often, fast-growing mycobacteria are assumed to have a higher metabolic activity than their slower counterparts, but in mature biofilms this assumption might not be correct. Indeed, when measuring the metabolic activity of mycobacterial biofilms with two independent non-invasive techniques (isothermal microcalorimetry and tunable diode laser absorption spectrometry), mature biofilms of slow- and fast-growing species appeared more alike than expected. Metabolic heat production rate was 2298 ± 181 µW for M. smegmatis and 792 ± 81 µW for M. phlei, while M. tuberculosis and M. bovis metabolic heat production rates were between these values. These small differences were further confirmed by similar oxygen consumption rates (3.3 ± 0.2 nMole/s and 1.7 ± 0.3 nMole/s for M. smegmatis and M. tuberculosis, respectively). These data suggest that the metabolic potential of slow-growing mycobacterial biofilms has been underestimated, particularly for pathogenic species.

Acanthamoeba keratitis: the persistence of cases following a multistate outbreak.

PURPOSE: To describe the trend of Acanthamoeba keratitis case reports following an outbreak and the recall of a multipurpose contact lens disinfection solution. Acanthamoeba keratitis is a serious eye infection caused by the free-living amoeba Acanthamoeba that primarily affects contact lens users. METHODS: A convenience sample of 13 ophthalmology centers and laboratories in the USA, provided annual numbers of Acanthamoeba keratitis cases diagnosed between 1999-2009 and monthly numbers of cases diagnosed between 2007-2009. Data on ophthalmic preparations of anti-Acanthamoeba therapies were collected from a national compounding pharmacy. RESULTS: Data from sentinel site ophthalmology centers and laboratories revealed that the yearly number of cases gradually increased from 22 in 1999 to 43 in 2003, with a marked increase beginning in 2004 (93 cases) that continued through 2007 (170 cases; p < 0.0001). The outbreak identified from these sentinel sites resulted in the recall of a contact lens disinfecting solution. There was a statistically significant (p ≤ 0.0001) decrease in monthly cases reported from 28 cases in June 2007 (following the recall) to seven cases in June 2008, followed by an increase (p = 0.0004) in reported cases thereafter; cases have remained higher than pre-outbreak levels. A similar trend was seen in prescriptions for Acanthamoeba keratitis chemotherapy. Cases were significantly more likely to be reported during summer than during other seasons. CONCLUSION: The persistently elevated number of reported cases supports the need to understand the risk factors and environmental exposures associated with Acanthamoeba keratitis. Further prevention efforts are needed to reduce the number of cases occurring among contact lens wearers.

Identification of an atypical strain of toxoplasma gondii as the cause of a waterborne outbreak of toxoplasmosis in Santa Isabel do Ivai, Brazil.

Multilocus DNA sequencing has identified a nonarchetypal strain of Toxoplasma gondii as the causal agent of a waterborne outbreak in Brazil in 2001. The strain, isolated from a water supply epidemiologically linked to the outbreak, was virulent to mice, and it has previously been identified as BrI. Using a serologic assay that detects strain-specific antibodies, we found that 13 (65%) of 20 individuals who were immunoglobulin (Ig) M positive during the outbreak possessed the same serotype as mice infected with the purported epidemic strain. The remaining 7 individuals, plus additional IgM-negative, IgG-positive individuals, possessed 1 of 4 novel serotypes, the most common of which matched the serotype of mice infected with strains isolated from chickens foraging near the outbreak site. The latter strains likely reflect the genetic diversity of T. gondii circulating in highly endemic regions of Brazil. The serotyping assay proved a useful tool for identification of specific individuals infected with the outbreak agent.

Diagnosis of coccidioidomycosis with use of the Coccidioides antigen enzyme immunoassay.

BACKGROUND: We have previously shown antigenuria in patients with coccidioidomycosis through use of the Histoplasma antigen enzyme immunoassay (EIA), and now we have developed a specific Coccidioides antigen EIA. METHODS: The Coccidioides EIA uses antibodies to Coccidioides galactomannan. The sensitivity of the Coccidioides and Histoplasma EIAs was evaluated in patients with more-severe coccidioidomycosis, and the specificity of these EIAs was evaluated in patients with nonfungal infections, in patients with other endemic mycoses, and in healthy individuals. RESULTS: Among patients in the present study, antigenuria was detected in 70.8% of patients with coccidioidomycosis with use of the Coccidioides EIA and in 58.3% of patients with use of the Histoplasma EIA. Antigenuria was absent in 99.4% of healthy individuals, patients with nonfungal infections, and patients with noninfectious conditions. Cross-reactions with other endemic mycoses were observed in 10.7% of patients. CONCLUSIONS: The Coccidioides EIA has potential to be useful in the rapid diagnosis of more-severe forms of coccidioidomycosis.

Rapid antimicrobial susceptibility determination of uropathogens in clinical urine specimens by use of ATP bioluminescence.

We describe the first direct testing of the antimicrobial susceptibilities of bacterial pathogens in human clinical fluid samples by the use of ATP bioluminescence. We developed an ATP bioluminescence assay that eliminates somatic sources of ATP to selectively quantify the bacterial load in clinical urine specimens with a sensitivity of <1,000 CFU per milliliter. There was a log-log relationship between light emission and the numbers of CFU in clinical urine specimens. A clinical study was performed to evaluate the accuracy of the ATP bioluminescence assay for determination of the antimicrobial susceptibilities of uropathogens in clinical urine specimens tested in a blinded manner. ATP bioluminescent bacterial density quantitation was used to determine the inoculation volume in growth medium with and without antibiotics. After incubation at 37 degrees C for 120 min, the ATP bioluminescence assay was repeated to evaluate the uropathogen response to antibiotics. The ability of the ATP bioluminescence assay to discriminate between antimicrobial susceptibility and resistance was determined by comparison of the results obtained by the ATP bioluminescence assay with the results obtained by standard clinical microbiology methods. Receiver operator characteristic curves were used to determine the optimal threshold for discriminating between susceptibility and resistance. Susceptibility and resistance were correctly predicted in 87% and 95% of cases, respectively, for an overall unweighted accuracy of 91%, when the results were stratified by antibiotic. For samples in which the pathogen was susceptible, the accuracy improved to 95% when the results for samples with less than a 25-fold increase in the amount of bacterial ATP in the medium without antibiotics were excluded. These data indicate that a rapid bioluminescent antimicrobial susceptibility assay may be useful for the management of urinary tract infections.

Multicenter evaluation of the BD Phoenix Automated Microbiology System for antimicrobial susceptibility testing of Streptococcus species.

This multicenter study evaluated the BD Phoenix Automated Microbiology System STREP panel (BD Diagnostic Systems). Antimicrobial susceptibility testing (AST) with 13 agents was performed on 2,013 streptococci (938 Streptococcus pneumoniae isolates; 396 group B streptococci [GBS]; 369 viridans group streptococci [VGS]; 290 beta-hemolytic streptococcus groups A, C, and G; and 20 other streptococci) with the Phoenix system and a broth microdilution reference method. Clinical and challenge isolates were tested against cefepime, cefotaxime (CTX), ceftriaxone (CTR), clindamycin (CLI), erythromycin (ERY), gatifloxacin, levofloxacin, linezolid, meropenem, penicillin (PEN), tetracycline (TET), trimethoprim-sulfamethoxazole, and vancomycin. Clinical isolates with major errors or very major errors (VMEs) were retested in duplicate by both methods. The final results for clinical isolates showed the following trends. For all of the organism-antimicrobial agent combinations tested, categorical agreement (CA) was 92 to 100%, with one exception-VGS-PEN (87% CA; all errors were minor). For S. pneumoniae, there was one major error with CLI (0.1%) and one or two VMEs with CTX (4%), CTR (4.5%), ERY (0.9%), and TET (0.7%). For groups A, C, and G, the CA was 97 to 100% and the only VMEs were resolved by additional reference laboratory testing. For GBS, there was only one VME (TET, 0.3%) and D-zone testing of 23 isolates with CLI major errors (one isolate unavailable) revealed inducible CLI resistance. For VGS, the major error rates were 0 to 3% and VMEs occurred with seven agents (3.5 to 7.1%). The mean times required for organism groups to generate results ranged from 8.4 to 9.4 h. The Phoenix system provided reliable and rapid AST results for most of the organism-antimicrobial agent combinations tested.