Fibrin-fiber architecture influences cell spreading and differentiation

ABSTRACT

The mechanical and structural properties of the extracellular matrix (ECM) play an important role in regulating cell fate. The natural ECM has a complex fibrillar structure and shows nonlinear mechanical properties, which are both difficult to mimic synthetically. Therefore, systematically testing the influence of ECM properties on cellular behavior is very challenging. In this work we show two different approaches to tune the fibrillar structure and mechanical properties of fibrin hydrogels. Addition of extra thrombin before gelation increases the protein density within the fibrin fibers without significantly altering the mechanical properties of the resulting hydrogel. On the other hand, by forming a composite hydrogel with a synthetic biomimetic polyisocyanide network the protein density within the fibrin fibers decreases, and the mechanics of the composite material can be tuned by the PIC/fibrin mass ratio. The effect of the changes in gel structure and mechanics on cellular behavior are investigated, by studying human mesenchymal stem cell (hMSC) spreading and differentiation on these gels. We find that the trends observed in cell spreading and differentiation cannot be explained by the bulk mechanics of the gels, but correlate to the density of the fibrin fibers the gels are composed of. These findings strongly suggest that the microscopic properties of individual fibers in fibrous networks play an essential role in determining cell behavior.     

An improved neutral landscape model for recreating real landscapes and generating landscape series for spatial ecological simulations

Many studies have assessed the effect of landscape patterns on spatial ecological processes by simulating these processes in computer‐generated landscapes with varying composition and configuration. To generate such landscapes, various neutral landscape models have been developed. However, the limited set of landscape‐level pattern variables included in these models is often inadequate to generate landscapes that reflect real landscapes. In order to achieve more flexibility and variability in the generated landscapes patterns, a more complete set of class‐ and patch‐level pattern variables should be implemented in these models. These enhancements have been implemented in Landscape Generator (LG), which is a software that uses optimization algorithms to generate landscapes that match user‐defined target values. Developed for participatory spatial planning at small scale, we enhanced the usability of LG and demonstrated how it can be used for larger scale ecological studies. First, we used LG to recreate landscape patterns from a real landscape (i.e., a mountainous region in Switzerland). Second, we generated landscape series with incrementally changing pattern variables, which could be used in ecological simulation studies. We found that LG was able to recreate landscape patterns that approximate those of real landscapes. Furthermore, we successfully generated landscape series that would not have been possible with traditional neutral landscape models. LG is a promising novel approach for generating neutral landscapes and enables testing of new hypotheses regarding the influence of landscape patterns on ecological processes. LG is freely available online.     

Accounting for the nested nature of genetic variation across levels of organization improves our understanding of biodiversity and community ecology

Recent work has demonstrated that the presence or abundance of specific genotypes, populations, species and phylogenetic clades may influence community and ecosystem properties such as resilience or productivity. Many ecological studies, however, use simple linear models to test for such relationships, including species identity as the predictor variable and some measured trait or function as the response variable without accounting for the nestedness of genetic variation across levels of organization. This omission may lead to incorrect inference about which source of variation influences community and ecosystem properties. Here, we explicitly compare this common approach to alternative ways of modeling variation in trait data, using simulated trait data and empirical results of common‐garden trials using multiple levels of genetic variation within Eucalyptus, Populus and Picea. We show that: 1) when nested variation is ignored, an incorrect conclusion of species effect is drawn in up to 20% of cases; 2) overestimation of the species effect increases – up to 60% in some scenarios – as the nested term explains more of the variation; and 3) the sample sizes needed to overcome these potential problems associated with aggregating nested hierarchical variation may be impractically large. In common‐garden trials, incorporating nested models increased explanatory power twofold for mammal browsing rate in Eucalyptus, threefold for leaf area in Populus, and tenfold for branch number in Picea. Thoroughly measuring intraspecific variation and characterizing hierarchical genetic variation beyond the species level has implications for developing more robust theory in community ecology, managing invaded natural systems, and improving inference in biodiversity–ecosystem functioning research. Synthesis Until recently, ecologists acknowledged the ubiquity of within‐species trait variation, but paid scant attention to how much it affects communities and ecosystems. Here, the authors used simulated trait data and common‐garden studies to demonstrate that we ignore intraspecific trait variation at our peril. In both simulated and experimental systems, in many cases ignoring intraspecific variation led to incorrect statistical inferences and inflated the effect size of species identity. This study shows that ecologists must characterize hierarchically nested genetic and phenotypic variation to fully understand the links between individual traits, community structure and ecosystem functioning.     

When and where plant‐soil feedback may promote plant coexistence: a meta‐analysis

Plant‐soil feedback (PSF) theory provides a powerful framework for understanding plant dynamics by integrating growth assays into predictions of whether soil communities stabilise plant–plant interactions. However, we lack a comprehensive view of the likelihood of feedback‐driven coexistence, partly because of a failure to analyse pairwise PSF, the metric directly linked to plant species coexistence. Here, we determine the relative importance of plant evolutionary history, traits, and environmental factors for coexistence through PSF using a meta‐analysis of 1038 pairwise PSF measures. Consistent with eco‐evolutionary predictions, feedback is more likely to mediate coexistence for pairs of plant species (1) associating with similar guilds of mycorrhizal fungi, (2) of increasing phylogenetic distance, and (3) interacting with native microbes. We also found evidence for a primary role of pathogens in feedback‐mediated coexistence. By combining results over several independent studies, our results confirm that PSF may play a key role in plant species coexistence, species invasion, and the phylogenetic diversification of plant communities.     

Signs of stabilisation and stable coexistence

Many empirical studies motivated by an interest in stable coexistence have quantified negative density dependence, negative frequency dependence, or negative plant–soil feedback, but the links between these empirical results and ecological theory are not straightforward. Here, we relate these analyses to theoretical conditions for stabilisation and stable coexistence in classical competition models. By stabilisation, we mean an excess of intraspecific competition relative to interspecific competition that inherently slows or even prevents competitive exclusion. We show that most, though not all, tests demonstrating negative density dependence, negative frequency dependence, and negative plant–soil feedback constitute sufficient conditions for stabilisation of two‐species interactions if applied to data for per capita population growth rates of pairs of species, but none are necessary or sufficient conditions for stable coexistence of two species. Potential inferences are even more limited when communities involve more than two species, and when performance is measured at a single life stage or vital rate. We then discuss two approaches that enable stronger tests for stable coexistence‐invasibility experiments and model parameterisation. The model parameterisation approach can be applied to typical density‐dependence, frequency‐dependence, and plant–soil feedback data sets, and generally enables better links with mechanisms and greater insights, as demonstrated by recent studies.     

A simple, robust and semi-automated parasite egg isolation protocol

Large-scale parasite quantification is required for improving our understanding of the epidemiology and genetics of host-parasite interactions. We describe a protocol that uses a low-density salt solution for flotation and centrifugation of nematode eggs. Subsequently, sucrose flotation and precipitation are used to obtain clear egg preparations. Most traditional quantification protocols such as the McMaster technique are unsuited for the standardized processing of large numbers of samples and the analysis of large amounts of feces per sample. Consequently, they are suited only for small-scale surveys. Our protocol, which can be used to analyze up to 6 g of feces, results in clear egg preparations that are concentrated in wells of a microtiter plate and that are suited for digital recording and automated counting. Starting from a fecal suspension in the first flotation solution to a digital recording requires approximately 40 min per 24 samples.     

Imaging mass spectrometry at cellular length scales

Imaging mass spectrometry (IMS) allows the direct investigation of both the identity and the spatial distribution of the molecular content directly in tissue sections, single cells and many other biological surfaces. In this protocol, we present the steps required to retrieve the molecular information from tissue sections using matrix-enhanced (ME) and metal-assisted (MetA) secondary ion mass spectrometry (SIMS) as well as matrix-assisted laser desorption/ionization (MALDI) IMS. These techniques require specific sample preparation steps directed at optimal signal intensity with minimal redistribution or modification of the sample analytes. After careful sample preparation, different IMS methods offer a unique discovery tool in, for example, the investigation of (i) drug transport and uptake, (ii) biological processing steps and (iii) biomarker distributions. To extract the relevant information from the huge datasets produced by IMS, new bioinformatics approaches have been developed. The duration of the protocol is highly dependent on sample size and technique used, but on average takes approximately 5 h.