Search

Non-small cell lung cancer is characterized by slow progression and high heterogeneity of tumors. Integrins play an important role in lung cancer development and metastasis and were suggested as a tumor marker; however their role in anticancer therapy remains controversial. In this work, we demonstrate the potential of integrin-targeted imaging to recognize early lesions in transgenic mouse model of lung cancer based on spontaneous introduction of mutated human gene bearing K-ras mutation. We conducted ex vivo and fluorescence molecular tomography-X-ray computed tomography (FMT-XCT) in vivo imaging and analysis for specific targeting of early lung lesions and tumors in rodent preclinical model for lung cancer. The lesions and tumors were characterized by histology, immunofluorescence and immunohistochemistry using a panel of cancer markers. Ex vivo, the integrin-targeted fluorescent signal significantly differed between wild type lung tissue and K-ras pulmonary lesions (PL) at all ages studied. The panel of immunofluorescence experiments demonstrated that PL, which only partially show cancer cell features were detected by αvβ3-integrin targeted imaging. Human patient material analysis confirmed the specificity of target localization in different lung cancer types. Most importantly, small tumors in the lungs of 4-week-old animals could be noninvasively detected in vivo on the fluorescence channel of FMT-XCT. Our findings demonstrated αvβ3-integrin targeted fluorescent imaging to specifically detect premalignant pleural lesions in K-ras mice. Integrin targeted imaging may find application areas in preclinical research and clinical practice, such as early lung cancer diagnostics, intraoperative assistance or therapy monitoring.

Tracking disease progression in vivo is essential for the development of treatments against bacterial infection. Optical imaging has become a central tool for in vivo tracking of bacterial population development and therapeutic response. For a precise understanding of in vivo imaging results in terms of disease mechanisms derived from detailed postmortem observations, however, a link between the two is needed. Here, we develop a model that provides that link for the investigation of Citrobacter rodentium infection, a mouse model for enteropathogenic Escherichia coli (EPEC). We connect in vivo disease progression of C57BL/6 mice infected with bioluminescent bacteria, imaged using optical tomography and X-ray computed tomography, to postmortem measurements of colonic immune cell infiltration. We use the model to explore changes to both the host immune response and the bacteria and to evaluate the response to antibiotic treatment. The developed model serves as a novel tool for the identification and development of new therapeutic interventions.

A hybrid imaging system for simultaneous fluorescence tomography and X-ray computed tomography (XCT) of small animals has been developed and presented. The system capitalizes on the imaging power of a 360 ( degrees )-projection free-space fluorescence tomography system, implemented within a microcomputed tomography scanner. Image acquisition is based on techniques that automatically adjust a series of imaging parameters to offer a high dynamic range dataset. Image segmentation further allows the incorporation of structural priors in the optical reconstruction problem to improve the imaging performance. The functional system characteristics are showcased, and images from a brain imaging study are shown, which are reconstructed using XCT-derived priors into the optical forward problem.

Motivation: Many biochemical systems require stochastic descriptions. Unfortunately these can only be solved for the simplest cases and their direct simulation can become prohibitively expensive, precluding thorough analysis. As an alternative, moment closure approximation methods generate equations for the time-evolution of the system’s moments and apply a closure ansatz to obtain a closed set of differential equations; that can become the basis for the deterministic analysis of the moments of the outputs of stochastic systems. Results: We present a free, user-friendly tool implementing an efficient moment expansion approximation with parametric closures that integrates well with the IPython interactive environment. Our package enables the analysis of complex stochastic systems without any constraints on the number of species and moments studied and the type of rate laws in the system. In addition to the approximation method our package provides numerous tools to help non-expert users in stochastic analysis. Availability and implementation: https://github.com/theosysbio/means Contacts: m.stumpf@imperial.ac.uk or e.lakatos13@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.

Two-component systems play a central part in bacterial signal transduction. Phosphorelay mechanisms have been linked to more robust and ultra-sensitive signalling dynamics. The molecular machinery that facilitates such a signalling is, however, only understood in outline. In particular the functional relevance of the dimerization of a non-orthodox or hybrid histidine kinase along which the phosphorelay takes place has been a subject of debate. We use a combination of molecular and genetic approaches, coupled to mathematical and statistical modelling, to demonstrate that the different possible intra- and inter-molecular mechanisms of phosphotransfer are formally non-identifiable in Escherichia coli expressing the ArcB non-orthodox histidine kinase used in anoxic redox control. In order to resolve this issue we further analyse the mathematical model in order to identify discriminatory experiments, which are then performed to address cis- and trans-phosphorelay mechanisms. The results suggest that exclusive cis- and trans-mechanisms will not be operating, instead the functional phosphorelay is likely to build around a sequence of allosteric interactions among the domain pairs in the histidine kinase. This is the first detailed mechanistic analysis of the molecular processes involved in non-orthodox two-component signalling and our results suggest strongly that dimerization facilitates more discriminatory proof-reading of external signals, via these allosteric reactions, prior to them being further processed.

The molecular reaction networks that coordinate the response of an organism to changing environmental conditions are central for survival and reproduction. Escherchia coli employs an accurate and flexible signalling system that is capable of processing ambient nitrogen availability rapidly and with high accuracy. Carefully orchestrated post-translational modifications of PII and the glutamine synthetase allow E. coli to trace nitrogen availability in a continuous, decidedly non-digital fashion. We measure the dynamic proteomic and metabolomic responses to trace the analog computations, and use an information theoretical framework to characterize the information capacity of E. coli's nitrogen sensing network: we find that this system can transmit up to 9bits of information about the nitrogen state. This allows cells to respond rapidly and accurately even to small differences in metabolite concentrations.

Purpose: A primary enabling feature of near-infrared fluorescent proteins (FPs) and fluorescent probes is the ability to visualize deeper in tissues than in the visible. The purpose of this work is to find which is the optimal visualization method that can exploit the advantages of this novel class of FPs in full-scale pre-clinical molecular imaging studies. Procedures: Nude mice were stereotactically implanted with near-infrared FP expressing glioma cells to from brain tumors. The feasibility and performance metrics of FPs were compared between planar epi-illumination and trans-illumination fluorescence imaging, as well as to hybrid Fluorescence Molecular Tomography (FMT) system combined with X-ray CT and Multispectral Optoacoustic (or Photoacoustic) Tomography (MSOT). Results: It is shown that deep-seated glioma brain tumors are possible to visualize both with fluorescence and optoacoustic imaging. Fluorescence imaging is straightforward and has good sensitivity; howe ver, it lacks resolution. FMT-XCT can provide an improved rough resolution of about 1 mm in deep tissue, while MSOT achieves 0.1 mm resolution in deep tissue and has comparable sensitivity. Conclusions: We show imaging capacity that can shift the visualization paradigm in biological discovery. The results are relevant not only to reporter gene imaging, but stand as cross-platform comparison for all methods imaging near infrared fluorescent contrast agents.

Moment approximation methods are gaining increasing attention for their use in the approximation of the stochastic kinetics of chemical reaction systems. In this paper we derive a general moment expansion method for any type of propensities and which allows expansion up to any number of moments. For some chemical reaction systems, more than two moments are necessary to describe the dynamic properties of the system, which the linear noise approximation (LNA) is unable to provide. Moreover, also for systems for which the mean does not have a strong dependence on higher order moments, moment approximation methods give information about higher order moments of the underlying probability distribution. We demonstrate the method using a dimerisation reaction, Michaelis-Menten kinetics and a model of an oscillating p53 system. We show that for the dimerisation reaction and Michaelis-Menten enzyme kinetics system higher order moments have limited influence on the estimation of the mean, while for the p53 system, the solution for the mean can require several moments to converge to the average obtained from many stochastic simulations. We also find that agreement between lower order moments does not guarantee that higher moments will agree. Compared to stochastic simulations our approach is numerically highly efficient at capturing the behaviour of stochastic systems in terms of the average and higher moments, and we provide expressions for the computational cost for different system sizes and orders of approximation. {We show how the moment expansion method can be employed to efficiently {quantify parameter sensitivity}.} Finally we investigate the effects of using too few moments on parameter estimation, and provide guidance on how to estimate if the distribution can be accurately approximated using only a few moments., 13 pages, 7 figures, 2 tables

Cardiomyocyte loss via apoptosis plays a crucial role in ventricular remodeling following myocardial infarction (MI). Cell-based therapy approaches using bone marrow derived c-kit(+) pluripotent cells may attenuate apoptosis following ischemic injury. We therefore thought to examine the early course of apoptosis following myocardial infarction - in-vivo - and non-invasively determine the effect of c-kit(+) bone marrow cells on post-MI remodeling. We studied apoptosis in wild-type Kit(+/+), c-kit mutant Kit(W)/Kit(W-v) and Kit(W)/Kit(W-v) mice after cell therapy with bone-marrow derived c-kit(+) cells after ischemia-reperfusion injury. Mice were followed by hybrid Fluorescence Molecular Tomography/X-ray Computed Tomography (FMT-XCT) at 6h, 24h and 7 days after ischemia-reperfusion injury using an Annexin V-based fluorescent nanosensor targeting phosphatidylserine. Kit(W)/Kit(W-v) mice showed increased and prolonged apoptosis compared to control Kit(+/+) mice while c-kit cell therapy was able to attenuate the altered apoptosis rates. Increased apoptosis was accompanied by severe decline in heart function, determined by cardiac Magnetic Resonance Imaging, and cell therapy was able to rescue the animals from deleterious heart failure. Post-mortem cryoslicing and immunohistochemistry localized the fluorescence signal of the Annexin V sensor within the infarcted myocardium. Flow cytometry of digested infarct specimens identified apoptotic cardiomyocytes as the major source for the in-vivo Annexin V signal. In-vivo molecular imaging using hybrid FMT-XCT reveals increased cardiomyocyte apoptosis in Kit(W)/Kit(W-v) mice and shows that c-kit(+) cardioprotective cells are able to attenuate post-MI apoptosis and rescue mice from progressive heart failure.

We examine the improvement in imaging performance, such as axial resolution and signal localization, when employing limited-projection-angle fluorescence molecular tomography (FMT) together with x-ray computed tomography (XCT) measurements versus stand-alone FMT. For this purpose, we employed living mice, bearing a spontaneous lung tumor model, and imaged them with FMT and XCT under identical geometrical conditions using fluorescent probes for cancer targeting. The XCT data was employed, herein, as structural prior information to guide the FMT reconstruction. Gold standard images were provided by fluorescence images of mouse cryoslices, providing the ground truth in fluorescence bio-distribution. Upon comparison of FMT images versus images reconstructed using hybrid FMT and XCT data, we demonstrate marked improvements in image accuracy. This work relates to currently disseminated FMT systems, using limited projection scans, and can be employed to enhance their performance.

The recent development of hybrid imaging scanners that integrate fluorescence molecular tomography (FMT) and x-ray computed tomography (XCT) allows the utilization of x-ray information as image priors for improving optical tomography reconstruction. To fully capitalize on this capacity, we consider a framework for the automatic and fast detection of different anatomic structures in murine XCT images. To accurately differentiate between different structures such as bone, lung, and heart, a combination of image processing steps including thresholding, seed growing, and signal detection are found to offer optimal segmentation performance. The algorithm and its utilization in an inverse FMT scheme that uses priors is demonstrated on mouse images.

The ability to visualize early stage lung cancer is important in the study of biomarkers and targeting agents that could lead to earlier diagnosis. The recent development of hybrid free-space 360-deg fluorescence molecular tomography (FMT) and x-ray computed tomography (XCT) imaging yields a superior optical imaging modality for three-dimensional small animal fluorescence imaging over stand-alone optical systems. Imaging accuracy was improved by using XCT information in the fluorescence reconstruction method. Despite this progress, the detection sensitivity of targeted fluorescence agents remains limited by nonspecific background accumulation of the fluorochrome employed, which complicates early detection of murine cancers. Therefore we examine whether x-ray CT information and bulk fluorescence detection can be combined to increase detection sensitivity. Correspondingly, we research the performance of a data-driven fluorescence background estimator employed for subtraction of background fluorescence from acquisition data. Using mice containing known fluorochromes ex vivo, we demonstrate the reduction of background signals from reconstructed images and sensitivity improvements. Finally, by applying the method to in vivo data from K-ras transgenic mice developing lung cancer, we find small tumors at an early stage compared with reconstructions performed using raw data. We conclude with the benefits of employing fluorescence subtraction in hybrid FMT-XCT for early detection studies.