A cell-based simulation software for multi-cellular systems

CellSys is a modular software tool for efficient off-lattice simulation of growth and organization processes in multi-cellular systems in 2D and 3D. It implements an agent-based model that approximates cells as isotropic, elastic and adhesive objects. Cell migration is modeled by an equation of motion for each cell. The software includes many modules specifically tailored to support the simulation and analysis of virtual tissues including real-time 3D visualization and VRML 2.0 support. All cell and environment parameters can be independently varied which facilitates species specific simulations and allows for detailed analyses of growth dynamics and links between cellular and multi-cellular phenotypes

Modeling the impact of granular embedding media, and pulling versus pushing cells on growing cell clones

International audienceIn this paper, we explore how potential biomechanical influences on cell cycle entrance and cell migration affect the growth dynamics of cell populations. We consider cell populations growing in free, granular and tissuelike environments using a mathematical single-cell-based model. In a free environment we study the effect of pushing movements triggered by proliferation versus active pulling movements of cells stretching cell-cell contacts on the multi-cellular kinetics and the cell population morphotype. By growing cell clones embedded in agarose gel or cells of another type, one can mimic aspects of embedding tissues. We perform simulation studies of cell clones expanding in an environment of granular objects and of chemically inert cells. In certain parameter ranges, we find the formation of invasive fingers reminiscent of viscous fingering. Since the simulation studies are highly computation-time consuming, we mainly study one-cell-thick monolayers and show that for selected parameter settings the results also hold for multi-cellular spheroids. Finally, we compare our model to the experimentally observed growth dynamics of multi-cellular spheroids in agarose gel

Guided interactive image segmentation using machine learning and color based data set clustering

We present a novel approach that combines machine learning based interactive image segmentation using supervoxels with a clustering method for the automated identification of similarly colored images in large data sets which enables a guided reuse of classifiers. Our approach solves the problem of significant color variability prevalent and often unavoidable in biological and medical images which typically leads to deteriorated segmentation and quantification accuracy thereby greatly reducing the necessary training effort. This increase in efficiency facilitates the quantification of much larger numbers of images thereby enabling interactive image analysis for recent new technological advances in high-throughput imaging. The presented methods are applicable for almost any image type and represent a useful tool for image analysis tasks in general

Guided interactive image segmentation using machine learning and color-based image set clustering

International audienceOver the last decades, image processing and analysis has become one of the key technologies in systems biology and medicine. The quantification of anatomical structures and dynamic processes in living systems is essential for understanding the complex underlying mechanisms and allows, i.a., the construction of spatio-temporal models that illuminate the interplay between architecture and function. Recently, deep learning significantly improved the performance of traditional image analysis in cases where imaging techniques provide large amounts of data. However, if only few images are available or qualified annotations are expensive to produce, the applicability of deep learning is still limited.We present a novel approach that combines machine learning based interactive image segmentation using supervoxels with a clustering method for the automated identification of similarly colored images in large image sets which enables a guided reuse of interactively trained classifiers. Our approach solves the problem of deteriorated segmentation and quantification accuracy when reusing trained classifiers which is due to significant color variability prevalent and often unavoidable in biological and medical images. This increase in efficiency improves the suitability of interactive segmentation for larger image sets, enabling efficient quantification or the rapid generation of training data for deep learning with minimal effort. The presented methods are applicable for almost any image type and represent a useful tool for image analysis tasks in general.The provided free software TiQuant makes the presented methods easily and readily usable and can be downloaded at tiquant.hoehme.com

Macrophage transactivation for chemokine production identified as a negative regulator of granulomatous inflammation using agent-based modeling

Cellular activation in trans by interferons, cytokines and chemokines is a commonly recognized mechanism to amplify immune effector function and limit pathogen spread. However, an optimal host response also requires that collateral damage associated with inflammation is limited. This may be particularly so in the case of granulomatous inflammation, where an excessive number and / or excessively florid granulomas can have significant pathological consequences. Here, we have combined transcriptomics, agent-based modeling and in vivo experimental approaches to study constraints on hepatic granuloma formation in a murine model of experimental leishmaniasis. We demonstrate that chemokine production by non-infected Kupffer cells in the Leishmania donovani-infected liver promotes competition with infected KCs for available iNKT cells, ultimately inhibiting the extent of granulomatous inflammation. We propose trans-activation for chemokine production as a novel broadly applicable mechanism that may operate early in infection to limit excessive focal inflammation

Model prediction and validation of an order mechanism controlling the spatio-temporal phenotype of early hepatocellular carcinoma

The aggressiveness of a tumor may be reflected by its micro-architecture. To gain a deeper understanding of the mechanisms controlling spatial organization of tumors at early stages after tumor initiation, we used an agent-based spatio-temporal model previously established to simulate features of liver regeneration. Here, this model was further developed to simulate scenarios in early tumor development, when individual initiated hepatocytes gain increased proliferation capacity. The model simulations were performed in realistic liver microarchitectures obtained from 3D reconstruction of confocal laser scanning micrographs. Interestingly, the here established model predicted that initially initiated hepatocytes arrange in elongated patterns. Only when the tumor progresses to cell numbers of approximately 4,000, it adopts spherical structures. This model prediction was validated by the analysis of initiated cells in a rat liver tumor initiation study using single doses of 250 mg/kg of the genotoxic carcinogen Nnitrosomorpholine (NNM). Indeed, small clusters of GST-P positive cells induced by NNM were elongated, almost columnar, while larger GDT-P positive foci of approximately the size of liver lobuli, adopted spherical shapes. Simulation of numerous possible mechanisms demonstrated that only hepatocyte-sinusoidal-alignment (HSA), a previously discovered order mechanism involved in coordination of liver tissue architecture, could explain the experimentally observed initial deviation from sphericalshape. The present study demonstrates that the architecture of small hepatocellular tumor cell clusters early after initiation is still controlled by physiological control mechanisms. However, this coordinating influence is lost when the tumor grows to approximately 4,000 cells, leading to further growth in spherical shape. Our findings stress the potential importance of organ micro-architecture in understanding tumor phenotypes

MultiCellDS: a standard and a community for sharing multicellular data

Cell biology is increasingly focused on cellular heterogeneity and multicellular systems. To make the fullest use of experimental, clinical, and computational efforts, we need standardized data formats, community-curated "public data libraries", and tools to combine and analyze shared data. To address these needs, our multidisciplinary community created MultiCellDS (MultiCellular Data Standard): an extensible standard, a library of digital cell lines and tissue snapshots, and support software. With the help of experimentalists, clinicians, modelers, and data and library scientists, we can grow this seed into a community-owned ecosystem of shared data and tools, to the benefit of basic science, engineering, and human health

MultiCellDS: a community-developed standard for curating microenvironment-dependent multicellular data

Exchanging and understanding scientific data and their context represents a significant barrier to advancing research, especially with respect to information siloing. Maintaining information provenance and providing data curation and quality control help overcome common concerns and barriers to the effective sharing of scientific data. To address these problems in and the unique challenges of multicellular systems, we assembled a panel composed of investigators from several disciplines to create the MultiCellular Data Standard (MultiCellDS) with a use-case driven development process. The standard includes (1) digital cell lines, which are analogous to traditional biological cell lines, to record metadata, cellular microenvironment, and cellular phenotype variables of a biological cell line, (2) digital snapshots to consistently record simulation, experimental, and clinical data for multicellular systems, and (3) collections that can logically group digital cell lines and snapshots. We have created a MultiCellular DataBase (MultiCellDB) to store digital snapshots and the 200+ digital cell lines we have generated. MultiCellDS, by having a fixed standard, enables discoverability, extensibility, maintainability, searchability, and sustainability of data, creating biological applicability and clinical utility that permits us to identify upcoming challenges to uplift biology and strategies and therapies for improving human health

Interruption of bile acid uptake by hepatocytes after acetaminophen overdose ameliorates hepatotoxicity.

Background & aimsAcetaminophen (APAP) overdose remains a frequent cause of acute liver failure, which is generally accompanied by increased levels of serum bile acids (BAs). However, the pathophysiological role of BAs remains elusive. Herein, we investigated the role of BAs in APAP-induced hepatotoxicity.MethodsWe performed intravital imaging to investigate BA transport in mice, quantified endogenous BA concentrations in the serum of mice and patients with APAP overdose, analyzed liver tissue and bile by mass spectrometry and MALDI-mass spectrometry imaging, assessed the integrity of the blood-bile barrier and the role of oxidative stress by immunostaining of tight junction proteins and intravital imaging of fluorescent markers, identified the intracellular cytotoxic concentrations of BAs, and performed interventions to block BA uptake from blood into hepatocytes.ResultsPrior to the onset of cell death, APAP overdose causes massive oxidative stress in the pericentral lobular zone, which coincided with a breach of the blood-bile barrier. Consequently, BAs leak from the bile canaliculi into the sinusoidal blood, which is then followed by their uptake into hepatocytes via the basolateral membrane, their secretion into canaliculi and repeated cycling. This, what we termed 'futile cycling' of BAs, led to increased intracellular BA concentrations that were high enough to cause hepatocyte death. Importantly, however, the interruption of BA re-uptake by pharmacological NTCP blockage using Myrcludex B and Oatp knockout strongly reduced APAP-induced hepatotoxicity.ConclusionsAPAP overdose induces a breach of the blood-bile barrier which leads to futile BA cycling that causes hepatocyte death. Prevention of BA cycling may represent a therapeutic option after APAP intoxication.Lay summaryOnly one drug, N-acetylcysteine, is approved for the treatment of acetaminophen overdose and it is only effective when given within ∼8 hours after ingestion. We identified a mechanism by which acetaminophen overdose causes an increase in bile acid concentrations (to above toxic thresholds) in hepatocytes. Blocking this mechanism prevented acetaminophen-induced hepatotoxicity in mice and evidence from patients suggests that this therapy may be effective for longer periods after ingestion compared to N-acetylcysteine