scholarly journals Mechanistic description of spatial processes using integrative modelling of noise-corrupted imaging data

2018 ◽  
Author(s):  
Sabrina Hross ◽  
Fabian J. Theis ◽  
Michael Sixt ◽  
Jan Hasenauer
Keyword(s):  
Experimental Data ◽  
Imaging Techniques ◽  
Quantitative Information ◽  
Integrated Modelling ◽  
Parameter Estimates ◽  
Published Data ◽  
Imaging Data ◽  
Microscopy Imaging ◽  
Modelling Approach

AbstractSpatial patterns are ubiquitous on the subcellular, cellular and tissue level, and can be studied using imaging techniques such as light and fluorescence microscopy. Imaging data provide quantitative information about biological systems, however, mechanisms causing spatial patterning often remain illusive. In recent years, spatio-temporal mathematical modelling helped to overcome this problem. Yet, outliers and structured noise limit modelling of whole imaging data, and models often consider spatial summary statistics. Here, we introduce an integrated data-driven modelling approach that can cope with measurement artefacts and whole imaging data. Our approach combines mechanistic models of the biological processes with robust statistical models of the measurement process. The parameters of the integrated model are calibrated using a maximum likelihood approach. We used this integrated modelling approach to study in vivo gradients of the chemokine (C-C motif) ligand 21 (CCL21). CCL21 gradients guide dendritic cells and are important in the adaptive immune response. Using artificial data, we verified that the integrated modelling approach provides reliable parameter estimates in the presence of measurement noise and that bias and variance of these estimates are reduced compared to conventional approaches. The application to experimental data allowed the parameterisation and subsequent refinement of the model using additional mechanisms. Among others, model-based hypothesis testing predicted lymphatic vessel dependent concentration of heparan sulfate, the binding partner of CCL21. The selected model provided an accurate description of the experimental data and was partially validated using published data. Our findings demonstrate that integrated statistical modelling of whole imaging data is computationally feasible and can provide novel biological insights.

scholarly journals Mechanistic description of spatial processes using integrative modelling of noise-corrupted imaging data

2018 ◽  
Vol 15 (149) ◽  
pp. 20180600 ◽  
Author(s):  
Sabrina Hross ◽  
Fabian J. Theis ◽  
Michael Sixt ◽  
Jan Hasenauer
Keyword(s):  
Experimental Data ◽  
Imaging Techniques ◽  
Quantitative Information ◽  
Integrated Modelling ◽  
Parameter Estimates ◽  
Published Data ◽  
Imaging Data ◽  
Microscopy Imaging ◽  
Modelling Approach

Spatial patterns are ubiquitous on the subcellular, cellular and tissue level, and can be studied using imaging techniques such as light and fluorescence microscopy. Imaging data provide quantitative information about biological systems; however, mechanisms causing spatial patterning often remain elusive. In recent years, spatio-temporal mathematical modelling has helped to overcome this problem. Yet, outliers and structured noise limit modelling of whole imaging data, and models often consider spatial summary statistics. Here, we introduce an integrated data-driven modelling approach that can cope with measurement artefacts and whole imaging data. Our approach combines mechanistic models of the biological processes with robust statistical models of the measurement process. The parameters of the integrated model are calibrated using a maximum-likelihood approach. We used this integrated modelling approach to studyin vivogradients of the chemokine (C-C motif) ligand 21 (CCL21). CCL21 gradients guide dendritic cells and are important in the adaptive immune response. Using artificial data, we verified that the integrated modelling approach provides reliable parameter estimates in the presence of measurement noise and that bias and variance of these estimates are reduced compared to conventional approaches. The application to experimental data allowed the parametrization and subsequent refinement of the model using additional mechanisms. Among other results, model-based hypothesis testing predicted lymphatic vessel-dependent concentration of heparan sulfate, the binding partner of CCL21. The selected model provided an accurate description of the experimental data and was partially validated using published data. Our findings demonstrate that integrated statistical modelling of whole imaging data is computationally feasible and can provide novel biological insights.

Imaging Microphysiological Systems: A Review

2020 ◽  
Author(s):  
Samantha Peel ◽  
Mark Jackman
Keyword(s):  
Drug Discovery ◽  
Animal Studies ◽  
Imaging Techniques ◽  
Quantitative Information ◽  
In Vitro Models ◽  
Microscopy Imaging ◽  
In Vivo Models ◽  
Microphysiological Systems

Microphysiological Systems (MPS), often referred to as 'organ-on-chips' are microfluidic-based in vitro models that aim to recapitulate the dynamic chemical and mechanical microenvironment of living organs. MPS promise to bridge the gap between in vitro and in vivo models, and ultimately improve the translation from pre-clinical animal studies to clinical trials. However, despite the explosion of interest in recent years, and the obvious rewards for such models which could improve R&D efficiency and reduce drug attrition in the clinic, the pharmaceutical industry has been slow to fully adopt this technology. The ability to extract robust, quantitative information from MPS at scale is a key requirement if these models are to impact drug discovery and the subsequent drug development process. Microscopy imaging remains a core technology that enables the capture of information at the single cell level and with subcellular resolution. Furthermore, such imaging techniques can be automated, increasing throughput, enabling compound screening. In this review we discuss a range of imaging techniques that have been applied to MPS of varying focus, such as organoids and organ-chip-type models. We outline the opportunities these technologies can bring in terms of understanding mechanistic biology, but also how they could be used in higher-throughput screens, widening the scope of their impact in drug discovery. We discuss the associated challenges of imaging these complex models and the steps required to enable full exploitation. Finally, we discuss the requirements for MPS, if they are to be applied at a scale necessary to support drug discovery projects.

scholarly journals Recovery of photoacoustic images based on accurate ultrasound positioning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yinhao Pan ◽  
Ningbo Chen ◽  
Liangjian Liu ◽  
Chengbo Liu ◽  
Zhiqiang Xu ◽  
...  
Keyword(s):  
Biological Tissue ◽  
Large Field ◽  
Photoacoustic Microscopy ◽  
Label Free ◽  
Imaging Data ◽  
Motor Speed ◽  
Image Correction ◽  
Microscopy Imaging ◽  
Scanning Method

AbstractPhotoacoustic microscopy is an in vivo imaging technology based on the photoacoustic effect. It is widely used in various biomedical studies because it can provide high-resolution images while being label-free, safe, and harmless to biological tissue. Polygon-scanning is an effective scanning method in photoacoustic microscopy that can realize fast imaging of biological tissue with a large field of view. However, in polygon-scanning, fluctuations of the rotating motor speed and the geometric error of the rotating mirror cause image distortions, which seriously affect the photoacoustic-microscopy imaging quality. To improve the image quality of photoacoustic microscopy using polygon-scanning, an image correction method is proposed based on accurate ultrasound positioning. In this method, the photoacoustic and ultrasound imaging data of the sample are simultaneously obtained, and the angle information of each mirror used in the polygon-scanning is extracted from the ultrasonic data to correct the photoacoustic images. Experimental results show that the proposed method can significantly reduce image distortions in photoacoustic microscopy, with the image dislocation offset decreasing from 24.774 to 10.365 μm.

On the interpretation of age–prevalence curves for trypanosome infections of tsetse flies

Parasitology ◽  
1998 ◽  
Vol 116 (2) ◽  
pp. 149-156 ◽  
Author(s):  
M. E. J. WOOLHOUSE ◽  
J. W. HARGROVE
Keyword(s):  
Experimental Data ◽  
Glossina Pallidipes ◽  
Tsetse Fly ◽  
Tsetse Flies ◽  
Parameter Estimates ◽  
Published Data ◽  
Data Sets ◽  
Trypanosoma Vivax ◽  
Epidemiological Models ◽  
Maturation Period

Epidemiological models are used to analyse 8 published data sets reporting age–prevalence curves for trypanosome infections of the tsetse fly Glossina pallidipes. A model assuming a fixed maturation period and a rate of infection which is independent of fly age is adequate for Trypanosoma vivax-type infections, explaining 98% of observed variance in prevalence by site and age, allowing that the rate of infection may be site dependent. This model is not adequate for T. congolense-type infections and the fit can be improved by allowing (i) the rates of infection to decline with age (although non-teneral flies remain susceptible), (ii) a fraction of resistant flies, which may vary between sites, (iii) increased mortality of infected flies and (iv) variation in the maturation period. Models with these features can explain up to 97% of observed variance. Parameter estimates from published experimental data suggest that all may contribute in practice but that (i) and/or (ii) are likely to be the most important.

scholarly journals Modeling Amantadine Treatment of Inuenza A Virus In Vitro

2021 ◽  
Author(s):  
Catherine A. A. Beauchemin ◽  
James J. McSharry ◽  
George L. Drusano ◽  
Jack T. Nguyen ◽  
Gregory T. Went ◽  
...  
Keyword(s):  
Experimental Data ◽  
Viral Infection ◽  
Mathematical Models ◽  
Influenza A ◽  
Mdck Cells ◽  
Rapid Development ◽  
Human Model ◽  
Parameter Estimates

We analyzed the dynamics of an influenza A/Albany/1/98 (H3N2) viral infection, using a set of mathematical models highlighting the differences between in vivo and in vitro infection. For example, we found that including virion loss due to cell entry was critical for the in vitro model but not for the in vivo model. Experiments were performed on influenza virus-infected MDCK cells in vitro inside a hollow-fiber (HF) system, which was used to continuously deliver the drug amantadine. The HF system captures the dynamics of an influenza infection, and is a controlled environment for producing experimental data which lend themselves well to mathematical modeling. The parameter estimates obtained from fitting our mathematical models to the HF experimental data are consistent with those obtained earlier for a primary infection in a human model. We found that influenza A/Albany/1/98 (H3N2) virions under normal experimental conditions at 37°C rapidly lose infectivity with a half-life of ~ 6.6 ± 0.2 h, and that the lifespan of productively infected MDCK cells is ~ 13 h. Finally, using our models we estimated that the maximum efficacy of amantadine in blocking viral infection is ~ 74%, and showed that this low maximum efficacy is likely due to the rapid development of drug resistance.

scholarly journals In Vivo and Ex Vivo Microscopy: Moving Toward the Integration of Optical Imaging Technologies Into Pathology Practice

2018 ◽  
Vol 143 (3) ◽  
pp. 288-298 ◽  
Author(s):  
Wendy A. Wells ◽  
Michael Thrall ◽  
Anastasia Sorokina ◽  
Jeffrey Fine ◽  
Savitri Krishnamurthy ◽  
...  
Keyword(s):  
Optical Imaging ◽  
Ex Vivo ◽  
Imaging Techniques ◽  
Clinical Care ◽  
Imaging Data ◽  
Imaging Technologies ◽  
Optical Images ◽  
Tissue Removal ◽  
Excised Tissue

The traditional surgical pathology assessment requires tissue to be removed from the patient, then processed, sectioned, stained, and interpreted by a pathologist using a light microscope. Today, an array of alternate optical imaging technologies allow tissue to be viewed at high resolution, in real time, without the need for processing, fixation, freezing, or staining. Optical imaging can be done in living patients without tissue removal, termed in vivo microscopy, or also in freshly excised tissue, termed ex vivo microscopy. Both in vivo and ex vivo microscopy have tremendous potential for clinical impact in a wide variety of applications. However, in order for these technologies to enter mainstream clinical care, an expert will be required to assess and interpret the imaging data. The optical images generated from these imaging techniques are often similar to the light microscopic images that pathologists already have expertise in interpreting. Other clinical specialists do not have this same expertise in microscopy, therefore, pathologists are a logical choice to step into the developing role of microscopic imaging expert. Here, we review the emerging technologies of in vivo and ex vivo microscopy in terms of the technical aspects and potential clinical applications. We also discuss why pathologists are essential to the successful clinical adoption of such technologies and the educational resources available to help them step into this emerging role.

scholarly journals Morphometric analysis of aerobic Eimeria bovis sporogony using live cell 3D holotomographic microscopy imaging

2021 ◽  
Author(s):  
Sara Lopez-Osorio ◽  
Zahady D. Velasquez ◽  
Iván Conejeros ◽  
Anja Taubert ◽  
Carlos Hermosilla
Keyword(s):  
Fluorescent Dyes ◽  
Nuclear Division ◽  
Imaging Techniques ◽  
Dry Mass ◽  
Live Cell ◽  
Efficient Tool ◽  
Microscopy Imaging ◽  
Fast Evaluation

AbstractM onoxenous Eimeria species are widespread enteropathogenic apicomplexan protozoa with a high economic impact on livestock. In cattle, tenacious oocysts shed by E. bovis-infected animals are ubiquitously found and making infection of calves almost inevitable. To become infectious oocysts, exogenous oxygen-dependent E. bovis sporogony must occur leading to the formation of sporulated oocysts containing four sporocysts each harboring two sporozoites. Investigations on sporogony by live cell imaging techniques of ruminant Eimeria species are still absent in literature as commonly used fluorescent dyes do not penetrate resistant oocyst bi-layered wall. Sporogonial oocysts were daily analyzed by a 3D Cell Explorer Nanolive microscope to explore ongoing aerobic-dependent sporogony as close as possible to an in vivo situation. Subsequently, 3D holotomographic images of sporulating E. bovis oocysts were digitally stained based on refractive indices (RI) of oocyst bi-layered wall and sub-compartments of circumplasm using STEVE software (Nanolive), and the cellular morphometric parameters were obtained. Overall, three different E. bovis sporogony phases, each of them divided into two sub-phases, were documented: (i) sporoblast/sporont transformation into sporogonial stages, (ii) cytokinesis followed by nuclear division, and finally (iii) formation of four sporocysts with two fully developed sporozoites. Approximately 60% of sporulating E. bovis oocysts accomplished aerobic sporogony in a synchronized manner. E. bovis sporogony was delayed (i.e., 6 days) when compared to an in vivo situation where 2–3 days are required but under optimal environmental conditions. Live cell 3D holotomography analysis might facilitate the evaluation of either novel disinfectants- or anti-coccidial drug-derived effects on ruminant/avian Eimeria sporogony in vitro as discrimination of sporogony degrees based on compactness, and dry mass was here successfully achieved. Main changes were observed in the oocyst area, perimeter, compactness, extent, and granularity suggesting those parameters as an efficient tool for a fast evaluation of the sporulation degree.

scholarly journals Approximate Bayesian computation reveals the importance of repeated measurements for parameterising cell-based models of growing tissues

2017 ◽  
Author(s):  
Jochen Kursawe ◽  
Ruth E. Baker ◽  
Alexander G. Fletcher
Keyword(s):  
Computational Models ◽  
Imaging Techniques ◽  
Quantitative Model ◽  
Repeated Measurements ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Summary Statistics ◽  
Imaging Data ◽  
Reliable Parameter ◽  
Approximate Bayesian

AbstractThe growth and dynamics of epithelial tissues govern many morphogenetic processes in embryonic development. A recent quantitative transition in data acquisition, facilitated by advances in genetic and live-imaging techniques, is paving the way for new insights to these processes. Computational models can help us understand and interpret observations, and then make predictions for future experiments that can distinguish between hypothesised mechanisms. Increasingly, cell-based modelling approaches such as vertex models are being used to help understand the mechanics underlying epithelial morphogenesis. These models typically seek to reproduce qualitative phenomena, such as cell sorting or tissue buckling. However, it remains unclear to what extent quantitative data can be used to constrain these models so that they can then be used to make quantitative, experimentally testable predictions. To address this issue, we perform an in silico study to investigate whether vertex model parameters can be inferred from imaging data, and explore methods to quantify the uncertainty of such estimates. Our approach requires the use of summary statistics to estimate parameters. Here, we focus on summary statistics of cellular packing and of laser ablation experiments, as are commonly reported from imaging studies. We find that including data from repeated experiments is necessary to generate reliable parameter estimates that can facilitate quantitative model predictions.

scholarly journals Antibiotic Drug screening and Image Characterization Toolbox (A.D.I.C.T.): a robust imaging workflow to monitor antibiotic stress response in bacterial cells in vivo

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 277
Author(s):  
Benjamin Mayer ◽  
Meike Schwan ◽  
Kai M. Thormann ◽  
Peter L. Graumann
Keyword(s):  
Stress Response ◽  
Data Processing ◽  
Time Course ◽  
Imaging Techniques ◽  
Cellular Stress Response ◽  
Imaging Data ◽  
Binary Images ◽  
Antibiotic Drug ◽  
Reproducible Manner

The search for novel drugs that efficiently eliminate prokaryotic pathogens is one of the most urgent health topics of our time. Robust evaluation methods for monitoring the antibiotic stress response in prokaryotes are therefore necessary for developing respective screening strategies. Besides advantages of common in vitro techniques, there is a growing demand for in vivo information based on imaging techniques that allow to screen antibiotic candidates in a dynamic manner. Gathering information from imaging data in a reproducible manner, robust data processing and analysis workflows demand advanced (semi-)automation and data management to increase reproducibility. Here we demonstrate a versatile and robust semi-automated image acquisition, processing and analysis workflow to investigate bacterial cell morphology in a quantitative manner. The presented workflow, A.D.I.C.T, covers aspects of experimental setup deployment, data acquisition and handling, image processing (e.g. ROI management, data transformation into binary images, background subtraction, filtering, projections) as well as statistical evaluation of the cellular stress response (e.g. shape measurement distributions, cell shape modeling, probability density evaluation of fluorescence imaging micrographs) towards antibiotic-induced stress, obtained from time-course experiments. The imaging workflow is based on regular brightfield images combined with live-cell imaging data gathered from bacteria, in our case from recombinant Shewanella cells, which are processed as binary images. The model organism expresses target proteins relevant for membrane-biogenesis that are functionally fused to respective fluorescent proteins. Data processing and analysis are based on customized scripts using ImageJ2/FIJI, Celltool and R packages that can be easily reproduced and adapted by users. Summing up, our approach aims at supporting life-scientists to establish their own imaging-pipeline in order to exploit their data as versatile as possible and in a reproducible manner.

scholarly journals Modeling Amantadine Treatment of Inuenza A Virus In Vitro

2021 ◽  
Author(s):  
Catherine A. A. Beauchemin ◽  
James J. McSharry ◽  
George L. Drusano ◽  
Jack T. Nguyen ◽  
Gregory T. Went ◽  
...  
Keyword(s):  
Experimental Data ◽  
Viral Infection ◽  
Mathematical Models ◽  
Influenza A ◽  
Mdck Cells ◽  
Rapid Development ◽  
Human Model ◽  
Parameter Estimates

We analyzed the dynamics of an influenza A/Albany/1/98 (H3N2) viral infection, using a set of mathematical models highlighting the differences between in vivo and in vitro infection. For example, we found that including virion loss due to cell entry was critical for the in vitro model but not for the in vivo model. Experiments were performed on influenza virus-infected MDCK cells in vitro inside a hollow-fiber (HF) system, which was used to continuously deliver the drug amantadine. The HF system captures the dynamics of an influenza infection, and is a controlled environment for producing experimental data which lend themselves well to mathematical modeling. The parameter estimates obtained from fitting our mathematical models to the HF experimental data are consistent with those obtained earlier for a primary infection in a human model. We found that influenza A/Albany/1/98 (H3N2) virions under normal experimental conditions at 37°C rapidly lose infectivity with a half-life of ~ 6.6 ± 0.2 h, and that the lifespan of productively infected MDCK cells is ~ 13 h. Finally, using our models we estimated that the maximum efficacy of amantadine in blocking viral infection is ~ 74%, and showed that this low maximum efficacy is likely due to the rapid development of drug resistance.

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