A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study

BACKGROUND

Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to nonstandardized data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the health care system. Despite the existence of standardized data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remain limited.

OBJECTIVE

In this paper, we developed a data harmonization pipeline (DHP) for clinical data sets relying on the common FHIR data standard.

METHODS

We validated the performance and usability of our FHIR-DHP with data from the Medical Information Mart for Intensive Care IV database.

RESULTS

We present the FHIR-DHP workflow in respect of the transformation of "raw" hospital records into a harmonized, AI-friendly data representation. The pipeline consists of the following 5 key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonized data into the patient-model database, and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records.

CONCLUSIONS

Our approach enables the scalable and needs-driven data modeling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step toward increasing cooperation, interoperability, and quality of patient care in the clinical routine and for medical research.

Graph-Based Disease Prediction in Neuroimaging: Investigating the Impact of Feature Selection

In biomedical machine learning, data often appear in the form of graphs. Biological systems such as protein interactions and ecological or brain networks are instances of applications that benefit from graph representations. Geometric deep learning is an arising field of techniques that has extended deep neural networks to non-Euclidean domains such as graphs. In particular, graph convolutional neural networks have achieved advanced performance in semi-supervised learning in those domains. Over the last years, these methods have gained traction in neuroscience as they could be the key to a deeper understanding in clinical diagnosis at the systems or network level (for an individual brain but also for across a cohort of subjects). As a proof-of-principle, we study and validate a previous implementation of graph-based semi-supervised classification using a ridge classifier and graph convolutional neural networks. The models are trained on population graphs that integrate imaging and phenotypic information. Our analysis employs neuroimaging data of structural and functional connectivity for prediction of neurodevelopmental and neurodegenerative disorders. Here, we particularly study the effect of different strategies to reduce the dimensionality of the neuroimaging features on the graph nodes on the classification performance.

Automatic Lung Segmentation and Quantification of Aeration in Computed Tomography of the Chest Using 3D Transfer Learning

BACKGROUND

Identification of lung parenchyma on computer tomographic (CT) scans in the research setting is done semi-automatically and requires cumbersome manual correction. This is especially true in pathological conditions, hindering the clinical application of aeration compartment (AC) analysis. Deep learning based algorithms have lately been shown to be reliable and time-efficient in segmenting pathologic lungs. In this contribution, we thus propose a novel 3D transfer learning based approach to quantify lung volumes, aeration compartments and lung recruitability.

METHODS

Two convolutional neural networks developed for biomedical image segmentation (uNet), with different resolutions and fields of view, were implemented using Matlab. Training and evaluation was done on 180 scans of 18 pigs in experimental ARDS (u2Net Pig ) and on a clinical data set of 150 scans from 58 ICU patients with lung conditions varying from healthy, to COPD, to ARDS and COVID-19 (u2Net Human ). One manual segmentations (MS) was available for each scan, being a consensus by two experts. Transfer learning was then applied to train u2Net Pig on the clinical data set generating u2Net Transfer . General segmentation quality was quantified using the Jaccard index (JI) and the Boundary Function score (BF). The slope between JI or BF and relative volume of non-aerated compartment (S JI and S BF , respectively) was calculated over data sets to assess robustness toward non-aerated lung regions. Additionally, the relative volume of ACs and lung volumes (LV) were compared between automatic and MS.

RESULTS

On the experimental data set, u2Net Pig resulted in JI = 0.892 [0.88 : 091] (median [inter-quartile range]), BF = 0.995 [0.98 : 1.0] and slopes S JI = -0.2 {95% conf. int. -0.23 : -0.16} and S BF = -0.1 {-0.5 : -0.06}. u2Net Human showed similar performance compared to u2Net Pig in JI, BF but with reduced robustness S JI = -0.29 {-0.36 : -0.22} and S BF = -0.43 {-0.54 : -0.31}. Transfer learning improved overall JI = 0.92 [0.88 : 0.94], P < 0.001, but reduced robustness S JI = -0.46 {-0.52 : -0.40}, and affected neither BF = 0.96 [0.91 : 0.98] nor S BF = -0.48 {-0.59 : -0.36}. u2Net Transfer improved JI compared to u2Net Human in segmenting healthy (P = 0.008), ARDS (P < 0.001) and COPD (P = 0.004) patients but not in COVID-19 patients (P = 0.298). ACs and LV determined using u2Net Transfer segmentations exhibited < 5% volume difference compared to MS.

CONCLUSION

Compared to manual segmentations, automatic uNet based 3D lung segmentation provides acceptable quality for both clinical and scientific purposes in the quantification of lung volumes, aeration compartments, and recruitability.

How to predict relapse in leukemia using time series data: A comparative in silico study

Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improve the predictions for patient-specific treatment responses. We designed a synthetic experiment simulating response kinetics of 5,000 patients to compare different computational methods with respect to their ability to accurately predict relapse for chronic and acute myeloid leukemia treatment. Technically, we used clinical reference data to first fit a model and then generate de novo model simulations of individual patients' time courses for which we can systematically tune data quality (i.e. measurement error) and quantity (i.e. number of measurements). Based hereon, we compared the prediction accuracy of three different computational methods, namely mechanistic models, generalized linear models, and deep neural networks that have been fitted to the reference data. Reaching prediction accuracies between 60 and close to 100%, our results indicate that data quality has a higher impact on prediction accuracy than the specific choice of the particular method. We further show that adapted treatment and measurement schemes can considerably improve the prediction accuracy by 10 to 20%. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukemia patients.

Involvement of GPR17 in Neuronal Fibre Outgrowth

Characterization of new pharmacological targets is a promising approach in research of neurorepair mechanisms. The G protein-coupled receptor 17 (GPR17) has recently been proposed as an interesting pharmacological target, e.g., in neuroregenerative processes. Using the well-established ex vivo model of organotypic slice co-cultures of the mesocortical dopaminergic system (prefrontal cortex (PFC) and substantia nigra/ventral tegmental area (SN/VTA) complex), the influence of GPR17 ligands on neurite outgrowth from SN/VTA to the PFC was investigated. The growth-promoting effects of Montelukast (MTK; GPR17- and cysteinyl-leukotriene receptor antagonist), the glial cell line-derived neurotrophic factor (GDNF) and of two potent, selective GPR17 agonists (PSB-16484 and PSB-16282) were characterized. Treatment with MTK resulted in a significant increase in mean neurite density, comparable with the effects of GDNF. The combination of MTK and GPR17 agonist PSB-16484 significantly inhibited neuronal growth. qPCR studies revealed an MTK-induced elevated mRNA-expression of genes relevant for neuronal growth. Immunofluorescence labelling showed a marked expression of GPR17 on NG2-positive glia. Western blot and RT-qPCR analysis of untreated cultures suggest a time-dependent, injury-induced stimulation of GPR17. In conclusion, MTK was identified as a stimulator of neurite fibre outgrowth, mediating its effects through GPR17, highlighting GPR17 as an interesting therapeutic target in neuronal regeneration.

Simulating Local Deformations in the Human Cortex Due to Blood Flow-Induced Changes in Mechanical Tissue Properties: Impact on Functional Magnetic Resonance Imaging

Investigating human brain tissue is challenging due to the complexity and the manifold interactions between structures across different scales. Increasing evidence suggests that brain function and microstructural features including biomechanical features are related. More importantly, the relationship between tissue mechanics and its influence on brain imaging results remains poorly understood. As an important example, the study of the brain tissue response to blood flow could have important theoretical and experimental consequences for functional magnetic resonance imaging (fMRI) at high spatial resolutions. Computational simulations, using realistic mechanical models can predict and characterize the brain tissue behavior and give us insights into the consequent potential biases or limitations of in vivo, high-resolution fMRI. In this manuscript, we used a two dimensional biomechanical simulation of an exemplary human gyrus to investigate the relationship between mechanical tissue properties and the respective changes induced by focal blood flow changes. The model is based on the changes in the brain’s stiffness and volume due to the vasodilation evoked by neural activity. Modeling an exemplary gyrus from a brain atlas we assessed the influence of different potential mechanisms: (i) a local increase in tissue stiffness (at the level of a single anatomical layer), (ii) an increase in local volume, and (iii) a combination of both effects. Our simulation results showed considerable tissue displacement because of these temporary changes in mechanical properties. We found that the local volume increase causes more deformation and consequently higher displacement of the gyrus. These displacements introduced considerable artifacts in our simulated fMRI measurements. Our results underline the necessity to consider and characterize the tissue displacement which could be responsible for fMRI artifacts.

Domain-specific cues improve robustness of deep learning-based segmentation of CT volumes

Machine learning has considerably improved medical image analysis in the past years. Although data-driven approaches are intrinsically adaptive and thus, generic, they often do not perform the same way on data from different imaging modalities. In particular computed tomography (CT) data poses many challenges to medical image segmentation based on convolutional neural networks (CNNs), mostly due to the broad dynamic range of intensities and the varying number of recorded slices of CT volumes. In this paper, we address these issues with a framework that adds domain-specific data preprocessing and augmentation to state-of-the-art CNN architectures. Our major focus is to stabilise the prediction performance over samples as a mandatory requirement for use in automated and semi-automated workflows in the clinical environment. To validate the architecture-independent effects of our approach we compare a neural architecture based on dilated convolutions for parallel multi-scale processing (a modified Mixed-Scale Dense Network: MS-D Net) to traditional scaling operations (a modified U-Net). Finally, we show that an ensemble model combines the strengths across different individual methods. Our framework is simple to implement into existing deep learning pipelines for CT analysis. It performs well on a range of tasks such as liver and kidney segmentation, without significant differences in prediction performance on strongly differing volume sizes and varying slice thickness. Thus our framework is an essential step towards performing robust segmentation of unknown real-world samples.

Ontology patterns for the representation of quality changes of cells in time

Background

Cell tracking experiments, based on time-lapse microscopy, have become an important tool in biomedical research. The goal is the reconstruction of cell migration patterns, shape and state changes, and, comprehensive genealogical information from these data. This information can be used to develop process models of cellular dynamics. However, so far there has been no structured, standardized way of annotating and storing the tracking results, which is critical for comparative analysis and data integration. The key requirement to be satisfied by an ontology is the representation of a cell’s change over time. Unfortunately, popular ontology languages, such as Web Ontology Language (OWL), have limitations for the representation of temporal information. The current paper addresses the fundamental problem of modeling changes of qualities over time in biomedical ontologies specified in OWL.

Results

The presented analysis is a result of the lessons learned during the development of an ontology, intended for the annotation of cell tracking experiments. We present, discuss and evaluate various representation patterns for specifying cell changes in time. In particular, we discuss two patterns of temporally changing information: n-ary relation reification and 4d fluents. These representation schemes are formalized within the ontology language OWL and are aimed at the support for annotation of cell tracking experiments. We analyze the performance of each pattern with respect to standard criteria used in software engineering and data modeling, i.e. simplicity, scalability, extensibility and adequacy. We further discuss benefits, drawbacks, and the underlying design choices of each approach.

Conclusions

We demonstrate that patterns perform differently depending on the temporal distribution of modeled information. The optimal model can be constructed by combining two competitive approaches. Thus, we demonstrate that both reification and 4d fluents patterns can work hand in hand in a single ontology. Additionally, we have found that 4d fluents can be reconstructed by two patterns well known in the computer science community, i.e. state modeling and actor-role pattern.

Developing 3D microscopy with CLARITY on human brain tissue: Towards a tool for informing and validating MRI-based histology

Recent breakthroughs in magnetic resonance imaging (MRI) enabled quantitative relaxometry and diffusion-weighted imaging with sub-millimeter resolution. Combined with biophysical models of MR contrast the emerging methods promise in vivo mapping of cyto- and myelo-architectonics, i.e., in vivo histology using MRI (hMRI) in humans. The hMRI methods require histological reference data for model building and validation. This is currently provided by MRI on post mortem human brain tissue in combination with classical histology on sections. However, this well established approach is limited to qualitative 2D information, while a systematic validation of hMRI requires quantitative 3D information on macroscopic voxels. We present a promising histological method based on optical 3D imaging combined with a tissue clearing method, Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging compatible Tissue hYdrogel (CLARITY), adapted for hMRI validation. Adapting CLARITY to the needs of hMRI is challenging due to poor antibody penetration into large sample volumes and high opacity of aged post mortem human brain tissue. In a pilot experiment we achieved transparency of up to 8 mm-thick and immunohistochemical staining of up to 5 mm-thick post mortem brain tissue by a combination of active and passive clearing, prolonged clearing and staining times. We combined 3D optical imaging of the cleared samples with tailored image processing methods. We demonstrated the feasibility for quantification of neuron density, fiber orientation distribution and cell type classification within a volume with size similar to a typical MRI voxel. The presented combination of MRI, 3D optical microscopy and image processing is a promising tool for validation of MRI-based microstructure estimates.

Large Histological Serial Sections for Computational Tissue Volume Reconstruction

Objectives: A proof of principle study was conducted for microscopic tissue volume reconstructions using a new image processing chain operating on alternately stained large histological serial sections.

Methods: Digital histological images were obtained from conventional brightfield transmitted light microscopy. A powerful nonparametric nonlinear optical flow-based registration approach was used. In order to apply a simple but computationally feasible sum-of-squared-differences similarity measure even in case of differing histological stainings, a new consistent tissue segmentation procedure was placed upstream.

Results: Two reconstructions from uterine cervix carcinoma specimen were accomplished, one alternately stained with p16INK4a (surrogate tumor marker) and H&amp;E (routine reference), and another with three different alternate stainings, H&amp;E, p16INK4a, and CD3 (a T-lymphocyte marker). For both cases, due to our segmentation-based reference-free nonlinear registration procedure, resulting tissue reconstructions exhibit utmost smooth image-to-image transitions without impairing warpings.

Conclusions: Our combination of modern nonparametric nonlinear registration and consistent tissue segmentation has turned out to provide a superior tissue reconstruction quality.

Cell-accurate optical mapping across the entire developing heart

Organogenesis depends on orchestrated interactions between individual cells and morphogenetically relevant cues at the tissue level. This is true for the heart, whose function critically relies on well-ordered communication between neighboring cells, which is established and fine-tuned during embryonic development. For an integrated understanding of the development of structure and function, we need to move from isolated snap-shot observations of either microscopic or macroscopic parameters to simultaneous and, ideally continuous, cell-to-organ scale imaging. We introduce cell-accurate three-dimensional Ca²⁺-mapping of all cells in the entire electro-mechanically uncoupled heart during the looping stage of live embryonic zebrafish, using high-speed light sheet microscopy and tailored image processing and analysis. We show how myocardial region-specific heterogeneity in cell function emerges during early development and how structural patterning goes hand-in-hand with functional maturation of the entire heart. Our method opens the way to systematic, scale-bridging, in vivo studies of vertebrate organogenesis by cell-accurate structure-function mapping across entire organs.

The heart has a built-in pacemaker that sets the rhythm of the heartbeat. Pacemaker cells produce electrical signals that spread across the heart in a coordinated wave. As each cell receives its signal, ion channels open in its membrane. Calcium ions rush in from the spaces around the cells, triggering the release of more calcium ions from internal stores. The rise in calcium ion levels causes the heart muscle to contract.

Standard techniques for studying how the activation process spreads across the heart typically involve removing the organ from the animal. One reason for this is that no microscopy technique had been able to provide the detail needed to observe the activity of individual cells across the whole heart during its activation cycle.

Zebrafish embryos have a simple heart with two chambers that can be visually explored because the embryos are transparent. Their hearts are activated in a pattern that has been maintained throughout evolution with principal similarities in many different species. These properties make fish embryos well suited for the non-invasive examination of the heart.

Weber, Scherf et al. have studied genetically engineered zebrafish embryos whose heart muscle cells contained a calcium-sensitive fluorophore, using a technique called light sheet microscopy. This method illuminates the heart with a thin sheet of laser light, which causes the fluorescent dye to glow in a way that indicates changes in the concentration of calcium ions in the cells. A fast and sensitive camera detects these signals and stacks of movies are recorded and synchronized, allowing cardiac activation to be mapped in three dimensions as it spreads across the heart.

Applying this new technique revealed that different parts of the heart conduct activation signals at different speeds. These speeds finely match the anatomical features of the heart, yielding planar progression of the activation signal over the increasingly complex shape of the developing heart. Weber, Scherf et al. also showed that the heart only requires a handful of pacemaker cells to reliably set the heart’s rhythm.

Future modifications to the technique of Weber, Scherf et al. could help us investigate how the heart works in even finer detail. For example, it might reveal how electrical activity, calcium handling, and contraction influence one another, and how they individually and collectively respond to drug treatments. This will help us understand how the normal heart rhythm develops, how it can be modified, and how the heart adapts to changes in its environment, including damage during cardiac disease.

P2Y(1) receptor mediated neuronal fibre outgrowth in organotypic brain slice co-cultures

Extracellular purines have multiple functional roles in development, plastic remodelling, and regeneration of the CNS by stimulating certain P2X/Y receptor (R) subtypes. In the present study we elucidated the involvement of P2YRs in neuronal fibre outgrowth in the developing nervous system. We particularly focused on the P2Y1R subtype and the dopaminergic system, respectively. For this purpose, we used organotypic slice co-cultures consisting of the ventral tegmental area/substantia nigra (VTA/SN) and the prefrontal cortex (PFC). After detecting the presence of the P2Y1R in VTA/SN, PFC, and on outgrowing fibres in the border region (e.g. on glial processes) connecting both brain slices, we could show that pharmacological modulation of the receptor influenced neuronal fibre outgrowth. Biocytin-tracing and tyrosine hydroxylase-immunolabelling together with quantitative image analysis revealed a significant increase in fibre growth in the border region of the co-cultures after treatment with ADPβS (P2Y1,12,13R agonist). The observed stimulatory potential of ADPβS was inhibited by pre-treatment with the P2X/YR antagonist PPADS. In P2Y1R knockout (P2Y1R(-/-)) mice, the ADPβS-induced stimulatory effect was absent, while growth was significantly enhanced in the co-cultures of the respective wild-type. This observation was confirmed in entorhino-hippocampal co-cultures, an example of a different projection system, expressing the P2Y1R. Using wortmannin and PD98059 we further showed that PI3K/Akt and MAPK/ERK cascades are involved in the mechanism underlying ADPβS-induced fibre growth. In conclusion, the data of this study clearly indicate that activation of the P2Y1R stimulates fibre growth and thereby emphasises the general role of this particular receptor subtype during development and regeneration.

Mesenchymal stem cells support neuronal fiber growth in an organotypic brain slice co-culture model

Mesenchymal stem cells (MSCs) have been identified as promising candidates for neuroregenerative cell therapies. However, the impact of different isolation procedures on the functional and regenerative characteristics of MSC populations has not been studied thoroughly. To quantify these differences, we directly compared classically isolated bulk bone marrow-derived MSCs (bulk BM-MSCs) to the subpopulation Sca-1(+)Lin(-)CD45(-)-derived MSCs(-) (SL45-MSCs), isolated by fluorescence-activated cell sorting from bulk BM-cell suspensions. Both populations were analyzed with respect to functional readouts, that are, frequency of fibroblast colony forming units (CFU-f), general morphology, and expression of stem cell markers. The SL45-MSC population is characterized by greater morphological homogeneity, higher CFU-f frequency, and significantly increased nestin expression compared with bulk BM-MSCs. We further quantified the potential of both cell populations to enhance neuronal fiber growth, using an ex vivo model of organotypic brain slice co-cultures of the mesocortical dopaminergic projection system. The MSC populations were cultivated underneath the slice co-cultures without direct contact using a transwell system. After cultivation, the fiber density in the border region between the two brain slices was quantified. While both populations significantly enhanced fiber outgrowth as compared with controls, purified SL45-MSCs stimulated fiber growth to a larger degree. Subsequently, we analyzed the expression of different growth factors in both cell populations. The results show a significantly higher expression of brain-derived neurotrophic factor (BDNF) and basic fibroblast growth factor in the SL45-MSCs population. Altogether, we conclude that MSC preparations enriched for primary MSCs promote neuronal regeneration and axonal regrowth, more effectively than bulk BM-MSCs, an effect that may be mediated by a higher BDNF secretion.

Elucidating functional heterogeneity in hematopoietic progenitor cells: a combined experimental and modeling approach

A detailed understanding of the mechanisms maintaining the hierarchical balance of cell types in hematopoiesis will be important for the therapeutic manipulation of normal and leukemic cells. Mathematical modeling is expected to make an important contribution to this area, but the iterative development of increasingly accurate models will rely on repeated validation using experimental data of sufficient resolution to distinguish between alternative model scenarios. The multipotent hematopoietic progenitor FDCP-Mix cells maintain a hierarchy from self-renewal to post-mitotic differentiation in vitro and are accessible to detailed analysis. Here, we report the development of a combined mathematical modeling and experimental approach to study the principles underlying heterogeneity in FDCP-Mix cultures. We adapt a single-cell based model of hematopoiesis to the conditions of cell culture and describe an association between proliferative history and phenotype of FDCP-Mix cells. While data derived from population studies are incapable of distinguishing between three mechanistically different model scenarios, statistical analysis of single cell tracking data provides a resolution sufficient to select one of them. This scenario favors differences between granulocytic and monocytic lineage with respect to their proliferative behavior and death rates as a mechanistic explanation for the observed heterogeneity. Our results demonstrate the power of a combined experimental/modeling approach in which single cell fate analysis is the key to revealing regulatory principles at the cellular level.

High-speed panoramic light-sheet microscopy reveals global endodermal cell dynamics

The ever-increasing speed and resolution of modern microscopes make the storage and post-processing of images challenging and prevent thorough statistical analyses in developmental biology. Here, instead of deploying massive storage and computing power, we exploit the spherical geometry of zebrafish embryos by computing a radial maximum intensity projection in real time with a 240-fold reduction in data rate. In our four-lens selective plane illumination microscope (SPIM) setup the development of multiple embryos is recorded in parallel and a map of all labelled cells is obtained for each embryo in <10 s. In these panoramic projections, cell segmentation and flow analysis reveal characteristic migration patterns and global tissue remodelling in the early endoderm. Merging data from many samples uncover stereotypic patterns that are fundamental to endoderm development in every embryo. We demonstrate that processing and compressing raw image data in real time is not only efficient but indispensable for image-based systems biology.

Systematic large-scale analysis of embryonic development requires the processing of large amounts of microscopy data. Here Schmid et al. solve this problem by developing a high-speed imaging system that projects zebrafish embryos onto a ‘world map’ in real time, revealing characteristic migration patterns in the early endoderm.

Quantitative analysis of astrogliosis in drug-dependent humans

Drug addiction is a chronic, relapsing disease caused by neurochemical and molecular changes in the brain. In this human autopsy study qualitative and quantitative changes of glial fibrillary acidic protein (GFAP)-positive astrocytes in the hippocampus of 26 lethally intoxicated drug addicts and 35 matched controls are described. The morphological characterization of these cells reflected alterations representative for astrogliosis. But, neither quantification of GFAP-positive cells nor the Western blot analysis indicated statistical significant differences between drug fatalities versus controls. However, by semi-quantitative scoring a significant shift towards higher numbers of activated astrocytes in the drug group was detected. To assess morphological changes quantitatively, graph-based representations of astrocyte morphology were obtained from single cell images captured by confocal laser scanning microscopy. Their underlying structures were used to quantify changes in astroglial fibers in an automated fashion. This morphometric analysis yielded significant differences between the investigated groups for four different measures of fiber characteristics (Euclidean distance, graph distance, number of graph elements, fiber skeleton distance), indicating that, e.g., astrocytes in drug addicts on average exhibit significant elongation of fiber structures as well as two-fold increase in GFAP-positive fibers as compared with those in controls. In conclusion, the present data show characteristic differences in morphology of hippocampal astrocytes in drug addicts versus controls and further supports the involvement of astrocytes in human pathophysiology of drug addiction. The automated quantification of astrocyte morphologies provides a novel, testable way to assess the fiber structures in a quantitative manner as opposed to standard, qualitative descriptions.

Imaging, quantification and visualization of spatio-temporal patterning in mESC colonies under different culture conditions

MOTIVATION

Mouse embryonic stem cells (mESCs) have developed into a prime system to study the regulation of pluripotency in stable cell lines. It is well recognized that different, established protocols for the maintenance of mESC pluripotency support morphologically and functionally different cell cultures. However, it is unclear how characteristic properties of cell colonies develop over time and how they are re-established after cell passage depending on the culture conditions. Furthermore, it appears that cell colonies have an internal structure with respect to cell size, marker expression or biomechanical properties, which is not sufficiently understood. The analysis of these phenotypic properties is essential for a comprehensive understanding of mESC development and ultimately requires a bioinformatics approach to guarantee reproducibility and high-throughput data analysis.

RESULTS

We developed an automated image analysis and colony tracking framework to obtain an objective and reproducible quantification of structural properties of cell colonies as they evolve in space and time. In particular, we established a method that quantifies changes in colony shape and (internal) motion using fluid image registration and image segmentation. The methodology also allows to robustly track motion, splitting and merging of colonies over a sequence of images. Our results provide a first quantitative assessment of temporal mESC colony formation and estimates of structural differences between colony growth under different culture conditions. Furthermore, we provide a stream-based visualization of structural features of individual colonies over time for the whole experiment, facilitating visual comprehension of differences between experimental conditions. Thus, the presented method establishes the basis for the model-based analysis of mESC colony development. It can be easily extended to integrate further functional information using fluorescence signals and differentiation markers.

AVAILABILITY

The analysis tool is implemented C++ and Mathematica 8.0 (Wolfram Research Inc., Champaign, IL, USA). The tool is freely available from the authors. We will also provide the source code upon request.

CONTACT

nico.scherf@tu-dresden.de.

Phosphodiesterase 2 inhibitors promote axonal outgrowth in organotypic slice co-cultures

The development of appropriate models assessing the potential of substances for regeneration of neuronal circuits is of great importance. Here, we present procedures to analyze effects of substances on fiber outgrowth based on organotypic slice co-cultures of the nigrostriatal dopaminergic system in combination with biocytin tracing and tyrosine hydroxylase labeling and subsequent automated image quantification. Selected phosphodiesterase inhibitors (PDE-Is) were studied to identify their potential growth-promoting capacities. Immunohistochemical methods were used to visualize developing fibers in the border region between ventral tegmental area/substantia nigra co-cultivated with the striatum as well as the cellular expression of PDE2A and PDE10. The quantification shows a significant increase of fiber density in the border region induced by PDE2-Is (BAY60-7550; ND7001), comparable with the potential of the nerve growth factor and in contrast to PDE10-I (MP-10). Analysis of tyrosine hydroxylase-positive fibers indicated a significant increase after treatment with BAY60-7550 and nerve growth factor in relation to dimethyl sulfoxide. Additionally, a dose-dependent increase of intracellular cGMP levels in response to the applied PDE2-Is in PDE2-transfected HEK293 cells was found. In summary, our findings show that PDE2-Is are able to significantly promote axonal outgrowth in organotypic slice co-cultures, which are a suitable model to assess growth-related effects in neuro(re)generation.