From Molecules to Classrooms: A Comprehensive Guide to Single-Molecule Localization Microscopy.

Single-molecule localization microscopy (SMLM) has revolutionized our ability to visualize cellular structures, offering unprecedented detail. However, the intricate biophysical principles that underlie SMLM can be daunting for newcomers, particularly undergraduate and graduate students. To address this challenge, we introduce the fundamental concepts of SMLM, providing a solid theoretical foundation. In addition, we have developed an intuitive graphical interface APP that simplifies these core concepts, making them more accessible for students. This APP clarifies how super-resolved images are fitted and highlights the crucial factors determining image quality. Our approach deepens students' understanding of SMLM by combining theoretical instruction with practical learning. This development equips them with the skills to carry out single-molecule super-resolved experiments and explore the microscopic world beyond the diffraction limit.

Statistical and topological summaries aid disease detection for segmented retinal vascular images.

OBJECTIVE: Disease complications can alter vascular network morphology and disrupt tissue functioning. Microvascular diseases of the retina are assessed by visual inspection of retinal images, but this can be challenging when diseases exhibit silent symptoms or patients cannot attend in-person meetings. We examine the performance of machine learning algorithms in detecting microvascular disease when trained on statistical and topological summaries of segmented retinal vascular images. METHODS: We compute 13 separate descriptor vectors (5 statistical, 8 topological) to summarize the morphology of retinal vessel segmentation images and train support vector machines to predict each image's disease classification from the summary vectors. We assess the performance of each descriptor vector, using five-fold cross validation to estimate their accuracy. We apply these methods to four datasets that were assembled from four existing data repositories; three datasets contain segmented retinal vascular images from one of the repositories, whereas the fourth "All" dataset combines images from four repositories. RESULTS: Among the 13 total descriptor vectors considered, either a statistical Box-counting descriptor vector or a topological Flooding descriptor vector achieves the highest accuracy levels. On the combined "All" dataset, the Box-counting vector outperforms all other descriptors, including the topological Flooding vector which is sensitive to differences in the annotation styles between the different datasets. CONCLUSION: Our work represents a first step to establishing which computational methods are most suitable for identifying microvascular disease and assessing their current limitations. These methods could be incorporated into automated disease assessment tools.

Intra-nucleus mosaic pattern (InMop) and whole-cell Haralick combined-descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images.

Acute leukemia is usually diagnosed when a test of peripheral blood shows at least 20% of abnormal immature cells (blasts), a figure even lower in case of recurrent cytogenetic abnormalities. Blast identification is crucial for white blood cell (WBC) counting, which depends on both identifying the cell type and characterizing the cellular morphology, processes susceptible of inter- and intraobserver variability. The present work introduces an image combined-descriptor to detect blasts and determine their probable lineage. This strategy uses an intra-nucleus mosaic pattern (InMop) descriptor that captures subtle nuclei differences within WBCs, and Haralick's statistics which quantify the local structure of both nucleus and cytoplasm. The InMop captures WBC inner-nucleus structure by applying a multiscale Shearlet decomposition over a repetitive pattern (mosaic) of automatically-segmented nuclei. As a complement, Haralick's statistics characterize the local structure of the whole cell from an intensity co-occurrence matrix representation. Both InMoP and Haralick-based descriptors are calculated using the b-channel from Lab color-space. The combined-descriptor is assessed by differentiating blasts from nonleukemic cells with support vector machine (SVM) classifiers and different transformation kernels, in two public and independent databases. The first database-D1 (n = 260) is composed of healthy and acute lymphoid leukemia (ALL) single cell images, and second database-D2 contains acute myeloid leukemia (AML) blasts (n = 3294) and nonblast (n = 15,071) cell images. In a first experiment, blasts versus nonblast differentiation is performed by training with a subset of D2 (n = 6588) and testing in D1 (n = 260), obtaining a training AUC of 0.991 ± 0.002 and AUC = 0.782 for the independent validation. A second experiment automatically differentiates AML blasts (260 images from D2) from ALL blasts (260 images from D1), with an AUC of 0.93. In a third experiment, state-of-the-art strategies, VGG16 and RESNEXT convolutional neural networks (CNN), separate blast from nonblast cells in both databases. The VGG16 showed an AUC of 0.673 and the RESNEXT of 0.75. Reported metrics for all the experiments are area under the ROC curve (AUC), accuracy and F1-score.

Quantitative analysis of the roles of IRM cell adhesion molecules in column formation in the fly brain.

The Drosophila visual center shows columnar structures, basic structural and functional units of the brain, that are shared with the mammalian cerebral cortex. Visual information received in the ommatidia in the compound eye is transmitted to the columns in the brain. However, the developmental mechanisms of column formation are largely unknown. The Irre Cell Recognition Module (IRM) proteins are a family of immunoglobulin cell adhesion molecules. The four Drosophila IRM proteins are localized to the developing columns, the structure of which is affected in IRM mutants, suggesting that IRM proteins are essential for column formation. Since IRM proteins are cell adhesion molecules, they may regulate cell adhesion between columnar neurons. To test this possibility, we specifically knocked down IRM genes in columnar neurons and examined the defects in column formation. We developed a system that automatically extracts the individual column images and quantifies the column shape. Using this system, we demonstrated that IRM genes play critical roles in regulating column shape in a core columnar neuron, Mi1. We also show that their expression in the other columnar neurons, Mi4 and T4/5, is essential, suggesting that the interactions between IRM proteins and multiple neurons shape the columns in the fly brain.

Quantitative and dynamic cell polarity tracking in plant cells.

Quantitative information on the spatiotemporal distribution of polarised proteins is central for understanding cell-fate determination, yet collecting sufficient data for statistical analysis is difficult to accomplish with manual measurements. Here we present Polarity Measurement (Pome), a semi-automated pipeline for the quantification of cell polarity and demonstrate its application to a variety of developmental contexts. Pome analysis reveals that, during asymmetric cell divisions in the Arabidopsis thaliana stomatal lineage, polarity proteins BASL and BRXL2 are more asynchronous and less mutually dependent than previously thought. A similar analysis of the linearly arrayed stomatal lineage of Brachypodium distachyon revealed that the MAPKKK BdYDA1 is segregated and polarised following asymmetrical divisions. Our results demonstrate that Pome is a versatile tool, which by itself or combined with tissue-level studies and advanced microscopy techniques can help to uncover new mechanisms of cell polarity.

REAVER: A program for improved analysis of high-resolution vascular network images.

Alterations in vascular networks, including angiogenesis and capillary regression, play key roles in disease, wound healing, and development. The spatial structures of blood vessels can be captured through imaging, but effective characterization of network architecture requires both metrics for quantification and software to carry out the analysis in a high-throughput and unbiased fashion. We present Rapid Editable Analysis of Vessel Elements Routine (REAVER), an open-source tool that researchers can use to analyze high-resolution 2D fluorescent images of blood vessel networks, and assess its performance compared to alternative image analysis programs. Using a dataset of manually analyzed images from a variety of murine tissues as a ground-truth, REAVER exhibited high accuracy and precision for all vessel architecture metrics quantified, including vessel length density, vessel area fraction, mean vessel diameter, and branchpoint count, along with the highest pixel-by-pixel accuracy for the segmentation of the blood vessel network. In instances where REAVER's automated segmentation is inaccurate, we show that combining manual curation with automated analysis improves the accuracy of vessel architecture metrics. REAVER can be used to quantify differences in blood vessel architectures, making it useful in experiments designed to evaluate the effects of different external perturbations (eg, drugs or disease states).

SMoLR: visualization and analysis of single-molecule localization microscopy data in R.

BACKGROUND: Single-molecule localization microscopy is a super-resolution microscopy technique that allows for nanoscale determination of the localization and organization of proteins in biological samples. For biological interpretation of the data it is essential to extract quantitative information from the super-resolution data sets. Due to the complexity and size of these data sets flexible and user-friendly software is required. RESULTS: We developed SMoLR (Single Molecule Localization in R): a flexible framework that enables exploration and analysis of single-molecule localization data within the R programming environment. SMoLR is a package aimed at extracting, visualizing and analyzing quantitative information from localization data obtained by single-molecule microscopy. SMoLR is a platform not only to visualize nanoscale subcellular structures but additionally provides means to obtain statistical information about the distribution and localization of molecules within them. This can be done for individual images or SMoLR can be used to analyze a large set of super-resolution images at once. Additionally, we describe a method using SMoLR for image feature-based particle averaging, resulting in identification of common features among nanoscale structures. CONCLUSIONS: Embedded in the extensive R programming environment, SMoLR allows scientists to study the nanoscale organization of biomolecules in cells by extracting and visualizing quantitative information and hence provides insight in a wide-variety of different biological processes at the single-molecule level.

Lipid droplet quantification based on iterative image processing.

Lipid droplets (LDs) are ubiquitous and highly dynamic subcellular organelles required for the storage of neutral lipids. LD number and size distribution are key parameters affected not only by nutrient supply but also by lipotoxic stress and metabolic regulation. Current methods for LD quantification lack general applicability and are either based on time consuming manual evaluation or show limitations if LDs are high in numbers or closely clustered. Here, we present an ImageJ-based approach for the detection and quantification of LDs stained by neutral lipid dyes in images acquired by conventional wide-field fluorescence microscopy. The method features an adjustable preprocessing procedure that resolves LD clusters. LD identification is based on their circular edges and central fluorescence intensity maxima. Adaptation to different cell types is mediated by a set of interactive parameters. Validation was done for three different cell lines using manual evaluation of LD numbers and volume measurement by 3D rendering of confocal datasets. In an application example, we show that overexpression of the acyl-CoA synthetase, FATP4/ACSVL5, in oleate-treated COS7 cells increased the size of LDs but not their number.

Methods to label, image, and analyze the complex structural architectures of microvascular networks.

Microvascular networks play key roles in oxygen transport and nutrient delivery to meet the varied and dynamic metabolic needs of different tissues throughout the body, and their spatial architectures of interconnected blood vessel segments are highly complex. Moreover, functional adaptations of the microcirculation enabled by structural adaptations in microvascular network architecture are required for development, wound healing, and often invoked in disease conditions, including the top eight causes of death in the Unites States. Effective characterization of microvascular network architectures is not only limited by the available techniques to visualize microvessels but also reliant on the available quantitative metrics that accurately delineate between spatial patterns in altered networks. In this review, we survey models used for studying the microvasculature, methods to label and image microvessels, and the metrics and software packages used to quantify microvascular networks. These programs have provided researchers with invaluable tools, yet we estimate that they have collectively attained low adoption rates, possibly due to limitations with basic validation, segmentation performance, and nonstandard sets of quantification metrics. To address these existing constraints, we discuss opportunities to improve effectiveness, rigor, and reproducibility of microvascular network quantification to better serve the current and future needs of microvascular research.

Towards a Connectomic Description of the Osteocyte Lacunocanalicular Network in Bone.

PURPOSE OF REVIEW: Osteocytes are the most abundant bone cells. They are completely encased in mineralized tissue, sitting inside lacunae that are connected by a multitude of canaliculi. In recent years, the osteocyte network has been shown to fulfill endocrine functions and to communicate with a number of other organs. This review addresses emerging knowledge on the connectome of the lacunocanalicular network in different types of bone tissue. RECENT FINDINGS: Recent advances in three-dimensional imaging technology started to reveal parameters that are well known from general theory to characterize the function of networks, such as network density, degree of nodes, or shortest path length through the network. The connectome of the lacunocanalicular network differs in some aspects between lamellar and woven bone and seems to change with age. More research is needed to relate network structure to function, such as intercellular transport or communication and its role in mechanosensation, as well as to understand the effect of diseases.

Hessian-based quantitative image analysis of host-pathogen confrontation assays.

Host-fungus interactions have gained a lot of interest in the past few decades, mainly due to an increasing number of fungal infections that are often associated with a high mortality rate in the absence of effective therapies. These interactions can be studied at the genetic level or at the functional level via imaging. Here, we introduce a new image processing method that quantifies the interaction between host cells and fungal invaders, for example, alveolar macrophages and the conidia of Aspergillus fumigatus. The new technique relies on the information content of transmitted light bright field microscopy images, utilizing the Hessian matrix eigenvalues to distinguish between unstained macrophages and the background, as well as between macrophages and fungal conidia. The performance of the new algorithm was measured by comparing the results of our method with that of an alternative approach that was based on fluorescence images from the same dataset. The comparison shows that the new algorithm performs very similarly to the fluorescence-based version. Consequently, the new algorithm is able to segment and characterize unlabeled cells, thus reducing the time and expense that would be spent on the fluorescent labeling in preparation for phagocytosis assays. By extending the proposed method to the label-free segmentation of fungal conidia, we will be able to reduce the need for fluorescence-based imaging even further. Our approach should thus help to minimize the possible side effects of fluorescence labeling on biological functions. © 2017 International Society for Advancement of Cytometry.

Diagnostic performance of the quantification of myocardium at risk from MPI SPECT/CTA 2G fusion for detecting obstructive coronary disease: A multicenter trial.

BACKGROUND: The effective non-invasive identification of coronary artery disease (CAD) and its proper referral for invasive treatment are still unresolved issues. We evaluated our quantification of myocardium at risk (MAR) from our second generation 3D MPI/CTA fusion framework for the detection and localization of obstructive coronary disease. METHODS: Studies from 48 patients who had rest/stress MPI, CTA, and ICA were analyzed from 3 different institutions. From the CTA, a 3D biventricular surface of the myocardium with superimposed coronaries was extracted and fused to the perfusion distribution. Significant lesions were identified from CTA readings and positioned on the fused display. Three estimates of MAR were computed on the 3D LV surface on the basis of the MPI alone (MARp), the CTA alone (MARa), and the fused information (MARf). The extents of areas at risk were used to generate ROC curves using ICA anatomical findings as reference standard. RESULTS: Areas under the ROC curve (AUC) for CAD detection using MARf was 0.88 (CI = 0.75-0.95) and for MARp and MARa were, respectively 0.82 (CI = 0.69-0.92) and 0.75 (CI = 0.60-0.86) using the ≥70% stenosis criterion. AUCs for CAD localization (all vessels) using MARf showed significantly higher performance than either MARa or MARp or both. CONCLUSIONS: Using ICA as the reference standard, MAR as the quantitative parameter, and AUC to measure diagnostic performance, MPI-CTA fusion imaging provided incremental diagnostic information compared to MPI or CTA alone for the diagnosis and localization of CAD.

Clinical value of the VTIQ technology in the differential diagnosis of superficially enlarged lymph nodes.

Background Lymph node enlargement is a common clinical finding in clinical practice with different treatment strategies. Purpose To investigate the application of Virtual Touch Image Quantification (VTIQ) to diagnose benign and malignant superficial enlarged lymph nodes. Material and Methods Between December 2015 and August 2016, 116 superficial enlarged lymph nodes were examined by VTIQ. Maximum (Vmax), minimum (Vmin), and average (Vmean) shear wave velocities (SWV) were obtained from the lymph nodes and from normal muscular tissues (Vn) located at the same level and within 5 mm from the target lymph node. The pathological results were used as the gold standard to evaluate VTIQ. Results All 116 patients underwent fine-needle aspiration biopsy for pathological examination. Forty patients had malignant lymph nodes and 76 patients had benign lymph nodes. Lymph node characteristics on B-mode ultrasound showed no differences between malignant and benign lymph nodes, but there were differences in VTIQ parameters (all P < 0.001). Compared with pathological diagnosis as the gold standard, the area under the ROC curves of Vmax, Vmin, and Vmean were 0.815, 0.746, and 0.795. The Vmax cutoff value to diagnose benign from malignant lymph nodes was 3.045 m/s. The sensitivity, specificity, and positive and negative predictive values were 70%, 78.9%, 63.6%, and 83.3%. Conclusion VTIQ has a clinical application in the differential diagnosis of superficial enlarged lymph nodes.

Understanding the Influence of Flow Velocity, Wall Motion Filter, Pulse Repetition Frequency, and Aliasing on Power Doppler Image Quantification.

OBJECTIVES: Although power Doppler imaging has been used to quantify tissue and organ vascularity, many studies showed that limitations in defining adequate ultrasound machine settings and attenuation make such measurements complex to be achieved. However, most of these studies were conducted by using the output of proprietary software, such as Virtual Organ computer-aided analysis (GE Healthcare, Kretz, Zipf, Austria); therefore, many conclusions may not be generalizable because of unknown settings and parameters used by the software. To overcome this limitation, our goal was to evaluate the impact of the flow velocity, pulse repetition frequency (PRF), and wall motion filter (WMF) on power Doppler image quantification using beam-formed ultrasonic radiofrequency data. METHODS: The setup consisted of a blood-mimicking fluid flowing through a phantom. Radiofrequency signals were collected using PRFs ranging from 0.6 to 10 kHz for 6 different flow velocities (5-40 cm/s). Wall motion filter cutoff frequencies were varied between 50 and 250 Hz. RESULTS: The power Doppler magnitude was deeply influenced by the WMF cutoff frequency. The effect of using different WMF values varied with the PRF; therefore, the power Doppler signal intensity was dependent on the PRF. Finally, we verified that power Doppler quantification can be affected by the aliasing effect, especially when using a PRF lower than 1.3 kHz. CONCLUSIONS: The WMF and PRF greatly influenced power Doppler quantification, mainly when flow velocities lower than 20 cm/s were used. Although the experiments were conducted in a nonclinical environment, the evaluated parameters are equivalent to those used in clinical practice, which makes them valuable for aiding the interpretation of related data in future research.

Clinicopathological characteristics and genomic profile of primary sinonasal tract diffuse large B cell lymphoma (DLBCL) reveals gain at 1q31 and RGS1 encoding protein; high RGS1 immunohistochemical expression associates with poor overall survival in DLBCL not otherwise specified (NOS).

AIMS: We aimed to define the clinicopathological characteristics of 29 primary sinonasal diffuse large B cell lymphoma (DLBCLsn ) in a series of 240 cases of DLBCL not otherwise specified [DLBCLall (NOS) ], including DLBCLsn training set (n = 11) and validation set (n = 18), and DLBCLnon-sn (n = 211). METHODS AND RESULTS: In the training set, 82% had a non-germinal center B-cell-like (Hans' Classifier) (non-GCB) phenotype and 18% were Epstein-Barr virus-encoded small RNAs (EBER)+ . The genomic profile showed gains(+) of 1q21.3q31.2 (55%), 10q24.1 (46%), 11q14.1 (46%) and 18q12.1q23 (46%); losses(-) of 6q26q27 (55%) and 9p21.3 (64%); and copy number neutral loss of heterozygosity (LOH) (acquired uniparental disomy, UPD) at 6p25.3p21.31 (36%). This profile is comparable to DLBCLNOS (GSE11318, n = 203.) and closer to non-GCB/activated B-cell-like subtype (ABC). Nevertheless, +1q31, -9p21.3 and -10q11.1q26.2 were more characteristic of DLBCLsn (P < 0.001). Array results were verified successfully by fluorescence in situ hybridization (FISH) on +1q21.3 (CKS1B), -6q26 (PARK2), +8q24.21 (MYC), -9p21.3 (MTAP, CDKN2A/B), -17p13.1 (TP53) and +18q21.33 (BCL2) with 82-91% agreement. Minimal common regions included biologically relevant genes of MNDA (+1q23.1), RGS1 and RGS13 (+1q31.2), FOXP1 (+3p13), PRDM1 (BLIMP1) and PARK2 (-6q21q26), MYC (+8q24.21), CDKN2A (-9p21.3), PTEN (-10q23.31), MDM2 (+12q15), TP53 (-17p13.1) and BCL2 (+18q21.33). Correlation between DNA copy number and protein immunohistochemistry was confirmed for RGS1, RGS13, FOXP1, PARK2 and BCL2. The microenvironment had high infiltration of M2-like tumour associated macrophages (TAMs) and CD8+ T lymphocytes that associated with higher genomic instability. The DLBCLsn validation set confirmed the clinicopathological characteristics, all FISH loci and immunohistochemistry (IHC) for RGS1. RGS1, one of the most frequently altered genes, was analysed by IHC in DLBCLall and high RGS1 expression associated with non-GCB, EBER+ and unfavourable overall survival (hazard ratio = 1.794; P = 0.016). CONCLUSIONS: DLBCLsn has a characteristic genomic profile. High RGS1 IHC expression associates with poor overall survival in DLBCLall (NOS) .

Improved sampling and analysis of images in corneal confocal microscopy.

INTRODUCTION: Corneal confocal microscopy (CCM) is a noninvasive clinical method to analyse and quantify corneal nerve fibres in vivo. Although the CCM technique is in constant progress, there are methodological limitations in terms of sampling of images and objectivity of the nerve quantification. The aim of this study was to present a randomized sampling method of the CCM images and to develop an adjusted area-dependent image analysis. Furthermore, a manual nerve fibre analysis method was compared to a fully automated method. METHODS: 23 idiopathic small-fibre neuropathy patients were investigated using CCM. Corneal nerve fibre length density (CNFL) and corneal nerve fibre branch density (CNBD) were determined in both a manual and automatic manner. Differences in CNFL and CNBD between (1) the randomized and the most common sampling method, (2) the adjusted and the unadjusted area and (3) the manual and automated quantification method were investigated. RESULTS: The CNFL values were significantly lower when using the randomized sampling method compared to the most common method (p = 0.01). There was not a statistical significant difference in the CNBD values between the randomized and the most common sampling method (p = 0.85). CNFL and CNBD values were increased when using the adjusted area compared to the standard area. Additionally, the study found a significant increase in the CNFL and CNBD values when using the manual method compared to the automatic method (p ≤ 0.001). CONCLUSION: The study demonstrated a significant difference in the CNFL values between the randomized and common sampling method indicating the importance of clear guidelines for the image sampling. The increase in CNFL and CNBD values when using the adjusted cornea area is not surprising. The observed increases in both CNFL and CNBD values when using the manual method of nerve quantification compared to the automatic method are consistent with earlier findings. This study underlines the importance of improving the analysis of the CCM images in order to obtain more objective corneal nerve fibre measurements.

Comparison of three-dimensional analysis and stereological techniques for quantifying lithium-ion battery electrode microstructures.

Lithium-ion battery performance is intrinsically linked to electrode microstructure. Quantitative measurement of key structural parameters of lithium-ion battery electrode microstructures will enable optimization as well as motivate systematic numerical studies for the improvement of battery performance. With the rapid development of 3-D imaging techniques, quantitative assessment of 3-D microstructures from 2-D image sections by stereological methods appears outmoded; however, in spite of the proliferation of tomographic imaging techniques, it remains significantly easier to obtain two-dimensional (2-D) data sets. In this study, stereological prediction and three-dimensional (3-D) analysis techniques for quantitative assessment of key geometric parameters for characterizing battery electrode microstructures are examined and compared. Lithium-ion battery electrodes were imaged using synchrotron-based X-ray tomographic microscopy. For each electrode sample investigated, stereological analysis was performed on reconstructed 2-D image sections generated from tomographic imaging, whereas direct 3-D analysis was performed on reconstructed image volumes. The analysis showed that geometric parameter estimation using 2-D image sections is bound to be associated with ambiguity and that volume-based 3-D characterization of nonconvex, irregular and interconnected particles can be used to more accurately quantify spatially-dependent parameters, such as tortuosity and pore-phase connectivity.

Image-based quantification and mathematical modeling of spatial heterogeneity in ESC colonies.

Pluripotent embryonic stem cells (ESCs) have the potential to differentiate into cells of all three germ layers. This unique property has been extensively studied on the intracellular, transcriptional level. However, ESCs typically form clusters of cells with distinct size and shape, and establish spatial structures that are vital for the maintenance of pluripotency. Even though it is recognized that the cells' arrangement and local interactions play a role in fate decision processes, the relations between transcriptional and spatial patterns have not yet been studied. We present a systems biology approach which combines live-cell imaging, quantitative image analysis, and multiscale, mathematical modeling of ESC growth. In particular, we develop quantitative measures of the morphology and of the spatial clustering of ESCs with different expression levels and apply them to images of both in vitro and in silico cultures. Using the same measures, we are able to compare model scenarios with different assumptions on cell-cell adhesions and intercellular feedback mechanisms directly with experimental data. Applying our methodology to microscopy images of cultured ESCs, we demonstrate that the emerging colonies are highly variable regarding both morphological and spatial fluorescence patterns. Moreover, we can show that most ESC colonies contain only one cluster of cells with high self-renewing capacity. These cells are preferentially located in the interior of a colony structure. The integrated approach combining image analysis with mathematical modeling allows us to reveal potential transcription factor related cellular and intercellular mechanisms behind the emergence of observed patterns that cannot be derived from images directly.

Bile canalicular dynamics in hepatocyte sandwich cultures.

Many substances are hepatotoxic due to their ability to cause intrahepatic cholestasis. Therefore, there is a high demand for in vitro systems for the identification of cholestatic properties of new compounds. Primary hepatocytes cultivated in collagen sandwich cultures are known to establish bile canaliculi which enclose secreted biliary components. Cholestatic compounds are mainly known to inhibit bile excretion dynamics, but may also alter canalicular volume, or hepatocellular morphology. So far, techniques to assess time-resolved morphological changes of bile canaliculi in sandwich cultures are not available. In this study, we developed an automated system that quantifies dynamics of bile canaliculi recorded in conventional time-lapse image sequences. We validated the hepatocyte sandwich culture system as an appropriate model to study bile canaliculi in vitro by showing structural similarity measured as bile canaliculi length per hepatocyte to that observed in vivo. Moreover, bile canalicular excretion kinetics of CMFDA (5-chloromethylfluorescein diacetate) in sandwich cultures resembled closely the kinetics observed in vivo. The developed quantification technique enabled the quantification of dynamic changes in individual bile canaliculi. With this technique, we were able to clearly distinguish between sandwich cultures supplemented with dexamethasone and insulin from control cultures. In conclusion, the automated quantification system offers the possibility to systematically study the causal relationship between disturbed bile canalicular dynamics and cholestasis.

How predictive quantitative modelling of tissue organisation can inform liver disease pathogenesis.

From the more than 100 liver diseases described, many of those with high incidence rates manifest themselves by histopathological changes, such as hepatitis, alcoholic liver disease, fatty liver disease, fibrosis, and, in its later stages, cirrhosis, hepatocellular carcinoma, primary biliary cirrhosis and other disorders. Studies of disease pathogeneses are largely based on integrating -omics data pooled from cells at different locations with spatial information from stained liver structures in animal models. Even though this has led to significant insights, the complexity of interactions as well as the involvement of processes at many different time and length scales constrains the possibility to condense disease processes in illustrations, schemes and tables. The combination of modern imaging modalities with image processing and analysis, and mathematical models opens up a promising new approach towards a quantitative understanding of pathologies and of disease processes. This strategy is discussed for two examples, ammonia metabolism after drug-induced acute liver damage, and the recovery of liver mass as well as architecture during the subsequent regeneration process. This interdisciplinary approach permits integration of biological mechanisms and models of processes contributing to disease progression at various scales into mathematical models. These can be used to perform in silico simulations to promote unravelling the relation between architecture and function as below illustrated for liver regeneration, and bridging from the in vitro situation and animal models to humans. In the near future novel mechanisms will usually not be directly elucidated by modelling. However, models will falsify hypotheses and guide towards the most informative experimental design.