Emergent Properties of Tumor Microenvironment in a Real-life Model of Multicell Tumor Spheroids

Multicellular tumor spheroids are an important {\it in vitro} model of the pre-vascular phase of solid tumors, for sizes well below the diagnostic limit: therefore a biophysical model of spheroids has the ability to shed light on the internal workings and organization of tumors at a critical phase of their development. To this end, we have developed a computer program that integrates the behavior of individual cells and their interactions with other cells and the surrounding environment. It is based on a quantitative description of metabolism, growth, proliferation and death of single tumor cells, and on equations that model biochemical and mechanical cell-cell and cell-environment interactions. The program reproduces existing experimental data on spheroids, and yields unique views of their microenvironment. Simulations show complex internal flows and motions of nutrients, metabolites and cells, that are otherwise unobservable with current experimental techniques, and give novel clues on tumor development and strong hints for future therapies.Comment: 20 pages, 10 figures. Accepted for publication in PLOS One. The published version contains links to a supplementary text and three video file

High-throughput spheroid screens using volume, resazurin reduction and acid phosphatase activity

Mainstream adoption of physiologically-relevant three-dimensional models has been slow in the last 50 years due to long, manual protocols with poor reproducibility, high price and closed commercial platforms. This chapter describes high-throughput, low-cost, open methods for spheroid viability assessment which use readily-available reagents and open-source software to analyse spheroid volume, metabolism and enzymatic activity. We provide two ImageJ macros for automated spheroid size determination - for both single images and for images in stacks. We also share an Excel template spreadsheet allowing users to rapidly process spheroid size data, analyse plate uniformity (such as edge effects and systematic seeding errors), detect outliers and calculate dose-response. The methods would be useful to researchers in preclinical and translational research planning to move away from simplistic monolayer studies and explore 3D spheroid screens for drug safety and efficacy without substantial investment in money or time

Spheroid arrays for high-throughput single-cell analysis of spatial patterns and biomarker expression in 3D

We describe and share a device, methodology and image analysis algorithms, which allow up to 66 spheroids to be arranged into a gel-based array directly from a culture plate for downstream processing and analysis. Compared to processing individual samples, the technique uses 11-fold less reagents, saves time and enables automated imaging. To illustrate the power of the technology, we showcase applications of the methodology for investigating 3D spheroid morphology and marker expression and for in vitro safety and efficacy screens. Firstly, spheroid arrays of 11 cell-lines were rapidly assessed for differences in spheroid morphology. Secondly, highly-positive (SOX-2), moderately-positive (Ki-67) and weakly-positive (βIII-tubulin) protein targets were detected and quantified. Third, the arrays enabled screening of ten media compositions for inducing differentiation in human neurospheres. Lastly, the application of spheroid microarrays for spheroid-based drug-screens was demonstrated by quantifying the dose-dependent drop in proliferation and increase in differentiation in etoposide-treated neurospheres

Classifying and scoring of molecules with the NGN: new datasets, significance tests, and generalization

<p>Abstract</p> <p/> <p>This paper demonstrates how a Neural Grammar Network learns to classify and score molecules for a variety of tasks in chemistry and toxicology. In addition to a more detailed analysis on datasets previously studied, we introduce three new datasets (BBB, FXa, and toxicology) to show the generality of the approach. A new experimental methodology is developed and applied to both the new datasets as well as previously studied datasets. This methodology is rigorous and statistically grounded, and ultimately culminates in a Wilcoxon significance test that proves the effectiveness of the system. We further include a complete generalization of the specific technique to arbitrary grammars and datasets using a mathematical abstraction that allows researchers in different domains to apply the method to their own work.</p> <p>Background</p> <p>Our work can be viewed as an alternative to existing methods to solve the quantitative structure-activity relationship (QSAR) problem. To this end, we review a number approaches both from a methodological and also a performance perspective. In addition to these approaches, we also examined a number of chemical properties that can be used by generic classifier systems, such as feed-forward artificial neural networks. In studying these approaches, we identified a set of interesting benchmark problem sets to which many of the above approaches had been applied. These included: ACE, AChE, AR, BBB, BZR, Cox2, DHFR, ER, FXa, GPB, Therm, and Thr. Finally, we developed our own benchmark set by collecting data on toxicology.</p> <p>Results</p> <p>Our results show that our system performs better than, or comparatively to, the existing methods over a broad range of problem types. Our method does not require the expert knowledge that is necessary to apply the other methods to novel problems.</p> <p>Conclusions</p> <p>We conclude that our success is due to the ability of our system to: 1) encode molecules losslessly before presentation to the learning system, and 2) leverage the design of molecular description languages to facilitate the identification of relevant structural attributes of the molecules over different problem domains.</p

Robustness Through Regime Flips in Collapsing Ecological Networks

© 2019, Crown. There has been considerable progress in our perception of organized complexity in recent years. Recurrent debates on the dynamics and stability of complex systems have provided several insights, but it is very difficult to find identifiable patterns in the relationship between complex network structure and dynamics. Traditionally an arena for theoreticians, much of this research has been invigorated by demonstration of alternate stable states in real world ecosystems such as lakes, coral reefs, forests and grasslands. In this work, we use topological connectivity attributes of eighty six ecological networks and link these with random and targeted perturbations, to obtain general patterns of behaviour of complex real world systems. We have analyzed the response of each ecological network to individual, grouped and cascading extinctions, and the results suggest that most networks are robust to loss of specialists until specific thresholds are reached in terms of network geodesics. If the extinctions persist beyond these thresholds, a state change or ‘flip’ occurs and the structural properties are altered drastically, although the network does not collapse. As opposed to simpler or smaller networks, we find larger networks to contain multiple states that may in turn, ensure long-term persistence, suggesting that complexity can endow resilience to ecosystems. The concept of critical transitions in ecological networks and the implications of these findings for complex systems characterized by networks are likely to be profound with immediate significance for ecosystem conservation, invasion biology and restoration ecology.Non

Environmental Factors in the Relapse and Recurrence of Inflammatory Bowel Disease:A Review of the Literature

The causes of relapse in patients with Crohn's disease (CD) and ulcerative colitis (UC) are largely unknown. This paper reviews the epidemiological and clinical data on how medications (non-steroidal anti-inflammatory drugs, estrogens and antibiotics), lifestyle factors (smoking, psychological stress, diet and air pollution) may precipitate clinical relapses and recurrence. Potential biological mechanisms include: increasing thrombotic tendency, imbalances in prostaglandin synthesis, alterations in the composition of gut microbiota, and mucosal damage causing increased permeability

DNA-Based Diet Analysis for Any Predator

Background: Prey DNA from diet samples can be used as a dietary marker; yet current methods for prey detection require a priori diet knowledge and/or are designed ad hoc, limiting their scope. I present a general approach to detect diverse prey in the feces or gut contents of predators. Methodology/Principal Findings: In the example outlined, I take advantage of the restriction site for the endonuclease Pac I which is present in 16S mtDNA of most Odontoceti mammals, but absent from most other relevant non-mammalian chordates and invertebrates. Thus in DNA extracted from feces of these mammalian predators Pac I will cleave and exclude predator DNA from a small region targeted by novel universal primers, while most prey DNA remain intact allowing prey selective PCR. The method was optimized using scat samples from captive bottlenose dolphins (Tursiops truncatus) fed a diet of 6–10 prey species from three phlya. Up to five prey from two phyla were detected in a single scat and all but one minor prey item (2% of the overall diet) were detected across all samples. The same method was applied to scat samples from free-ranging bottlenose dolphins; up to seven prey taxa were detected in a single scat and 13 prey taxa from eight teleost families were identified in total. Conclusions/Significance: Data and further examples are provided to facilitate rapid transfer of this approach to any predator. This methodology should prove useful to zoologists using DNA-based diet techniques in a wide variety of study systems

Early star-forming galaxies and the reionization of the Universe

Star forming galaxies represent a valuable tracer of cosmic history. Recent observational progress with Hubble Space Telescope has led to the discovery and study of the earliest-known galaxies corresponding to a period when the Universe was only ~800 million years old. Intense ultraviolet radiation from these early galaxies probably induced a major event in cosmic history: the reionization of intergalactic hydrogen. New techniques are being developed to understand the properties of these most distant galaxies and determine their influence on the evolution of the universe.Comment: Review article appearing in Nature. This posting reflects a submitted version of the review formatted by the authors, in accordance with Nature publication policies. For the official, published version of the review, please see http://www.nature.com/nature/archive/index.htm