Semi-automated Image Analysis Pipeline Research Articles

A low-cost open-source imaging platform reveals spatiotemporal insight into leaf elongation and movement.

Plant organs move throughout the diurnal cycle, changing leaf and petiole positions to balance light capture, leaf temperature, and water loss under dynamic environmental conditions. Upward movement of the petiole, called hyponasty, is one of several traits of the shade avoidance syndrome (SAS). SAS traits are elicited upon perception of vegetation shade signals such as far-red light (FR) and improve light capture in dense vegetation. Monitoring plant movement at a high temporal resolution allows studying functionality and molecular regulation of hyponasty. However, high temporal resolution imaging solutions are often very expensive, making this unavailable to many researchers. Here, we present a modular and low-cost imaging set-up, based on small Raspberry Pi computers, that can track leaf movements and elongation growth with high temporal resolution. We also developed an open-source, semi-automated image analysis pipeline. Using this setup we followed responses to FR enrichment, light intensity, and their interactions. Tracking both elongation and the angle of the petiole, lamina, and entire leaf in Arabidopsis (Arabidopsis thaliana) revealed insight into R:FR sensitivities of leaf growth and movement dynamics and the interactions of R:FR with background light intensity. The detailed imaging options of this system allowed us to identify spatially separate bending points for petiole and lamina positioning of the leaf.

Quantifying microbial robustness in dynamic environments using microfluidic single-cell cultivation

BackgroundMicroorganisms must respond to changes in their environment. Analysing the robustness of functions (i.e. performance stability) to such dynamic perturbations is of great interest in both laboratory and industrial settings. Recently, a quantification method capable of assessing the robustness of various functions, such as specific growth rate or product yield, across different conditions, time frames, and populations has been developed for microorganisms grown in a 96-well plate. In micro-titer-plates, environmental change is slow and undefined. Dynamic microfluidic single-cell cultivation (dMSCC) enables the precise maintenance and manipulation of microenvironments, while tracking single cells over time using live-cell imaging. Here, we combined dMSCC and a robustness quantification method to a pipeline for assessing performance stability to changes occurring within seconds or minutes.ResultsSaccharomyces cerevisiae CEN.PK113-7D, harbouring a biosensor for intracellular ATP levels, was exposed to glucose feast-starvation cycles, with each condition lasting from 1.5 to 48 min over a 20 h period. A semi-automated image and data analysis pipeline was developed and applied to assess the performance and robustness of various functions at population, subpopulation, and single-cell resolution. We observed a decrease in specific growth rate but an increase in intracellular ATP levels with longer oscillation intervals. Cells subjected to 48 min oscillations exhibited the highest average ATP content, but the lowest stability over time and the highest heterogeneity within the population.ConclusionThe proposed pipeline enabled the investigation of function stability in dynamic environments, both over time and within populations. The strategy allows for parallelisation and automation, and is easily adaptable to new organisms, biosensors, cultivation conditions, and oscillation frequencies. Insights on the microbial response to changing environments will guide strain development and bioprocess optimisation.Graphical

The modified Polsby-Popper score, a novel quantitative histomorphological biomarker and its potential to predict lymph node positivity and cancer-specific survival in oral tongue squamous cell carcinoma.

The significance of different histological spreading patterns of tumor tissue in oral tongue squamous cell carcinoma (TSCC) is well known. Our aim was to construct a numeric parameter on a continuous scale, that is, the modified Polsby-Popper (MPP) score, to describe the aggressiveness of tumor growth and infiltration, with the potential to analyze hematoxylin and eosin-stained whole slide images (WSIs) in an automated manner. We investigated the application of the MPP score in predicting survival and cervical lymph node metastases as well as in determining patients at risk in the context of different surgical margin scenarios. We developed a semiautomated image analysis pipeline to detect areas belonging to the tumor tissue compartment. Perimeter and area measurements of all detected tissue regions were derived, and a specific mathematical formula was applied to reflect the perimeter/area ratio in a comparable, observer-independent manner across digitized WSIs. We demonstrated the plausibility of the MPP score by correlating it with well-established clinicopathologic parameters. We then performed survival analysis to assess the relevance of the MPP score, with an emphasis on different surgical margin scenarios. Machine learning models were developed to assess the relevance of the MPP score in predicting survival and occult cervical nodal metastases. The MPP score was associated with unfavorable tumor growth and infiltration patterns, the presence of lymph node metastases, the extracapsular spread of tumor cells, and higher tumor thickness. Higher MPP scores were associated with worse overall survival (OS) and tongue carcinoma-specific survival (TCSS), both when assessing all pT-categories and pT1-pT2 categories only; moreover, higher MPP scores were associated with a significantly worse TCSS in cases where a cancer-free surgical margin of <5 mm could be achieved on the main surgical specimen. This discriminatory capacity remained constant when examining pT1-pT2 categories only. Importantly, the MPP score could successfully define cases at risk in terms of metastatic disease in pT1-pT2 cancer where tumor thickness failed to exhibit a significant predictive value. Machine learning (ML) models incorporating the MPP score could predict the 5-year TCSS efficiently. Furthermore, we demonstrated that machine learning models that predict occult cervical lymph node involvement can benefit from including the MPP score. We introduced an objective, quantifiable, and observer-independent parameter, the MPP score, representing the aggressiveness of tumor growth and infiltration in TSCC. We showed its prognostic relevance especially in pT1-pT2 category TSCC, and its possible use in ML models predicting TCSS and occult lymph node metastases.

A new in vitro monitoring system reveals a specific influence of Arabidopsis nitrogen nutrition on its susceptibility to Alternaria brassicicola at the seedling stage.

Seedling growth is an early phase of plant development highly susceptible to environmental factors such as soil nitrogen (N) availability or presence of seed-borne pathogens. Whereas N plays a central role in plant-pathogen interactions, its role has never been studied during this early phase for the interaction between Arabidopsis thaliana and Alternaria brassicicola, a seed-transmitted necrotrophic fungus. The aim of the present work was to develop an in vitro monitoring system allowing to study the impact of the fungus on A. thaliana seedling growth, while modulating N nutrition. The developed system consists of square plates placed vertically and filled with nutrient agar medium allowing modulation of N conditions. Seeds are inoculated after sowing by depositing a droplet of conidial suspension. A specific semi-automated image analysis pipeline based on the Ilastik software was developed to quantify the impact of the fungus on seedling aerial development, calculating an index accounting for every aspect of fungal impact, namely seedling death, necrosis and developmental delay. The system also permits to monitor root elongation. The interest of the system was then confirmed by characterising how N media composition [0.1 and 5mM of nitrate (NO3-), 5mM of ammonium (NH4+)] affects the impact of the fungus on three A. thaliana ecotypes. Seedling development was strongly and negatively affected by the fungus. However, seedlings grown with 5mM NO3- were less susceptible than those grown with NH4+ or 0.1mM NO3-, which differed from what was observed with adult plants (rosette stage). The developed monitoring system allows accurate determination of seedling growth characteristics (both on aerial and root parts) and symptoms. Altogether, this system could be used to study the impact of plant nutrition on susceptibility of various genotypes to fungi at the seedling stage.

Image-based axon model highlights heterogeneity in initiation of damage

Head injury simulations predict the occurrence of traumatic brain injury by placing a threshold on the calculated strains for axon tracts within the brain. However, a current roadblock to accurate injury prediction is the selection of an appropriate axon damage threshold. While several computational studies have used models of the axon cytoskeleton to investigate damage initiation, these models all employ an idealized, homogeneous axonal geometry. This homogeneous geometry with regularly spaced microtubules, evenly distributed throughout the model, overestimates axon strength because, in reality, the axon cytoskeleton is heterogeneous. In the heterogeneous cytoskeleton, the weakest cross section determines the initiation of failure, but these weak spots are not present in a homogeneous model. Addressing one source of heterogeneity in the axon cytoskeleton, we present a new semiautomated image analysis pipeline for using serial-section transmission electron micrographs to reconstruct the microtubule geometry of an axon. The image analysis procedure locates microtubules within the images, traces them throughout the image stack, and reconstructs the microtubule structure as a finite element mesh. We demonstrate the image analysis approach using a C. elegans touch receptor neuron due to the availability of high-quality serial-section transmission electron micrograph data sets. The results of the analysis highlight the heterogeneity of the microtubule structure in the spatial variation of both microtubule number and length. Simulations comparing this image-based geometry with homogeneous geometries show that structural heterogeneity in the image-based model creates significant spatial variation in deformation. The homogeneous geometries, on the other hand, deform more uniformly. Since no single homogeneous model can replicate the mechanical behavior of the image-based model, our results argue that heterogeneity in axon microtubule geometry should be considered in determining accurate axon failure thresholds.

Machine learning-based classification of mitochondrial morphology in primary neurons and brain

The mitochondrial network continually undergoes events of fission and fusion. Under physiologic conditions, the network is in equilibrium and is characterized by the presence of both elongated and punctate mitochondria. However, this balanced, homeostatic mitochondrial profile can change morphologic distribution in response to various stressors. Therefore, it is imperative to develop a method that robustly measures mitochondrial morphology with high accuracy. Here, we developed a semi-automated image analysis pipeline for the quantitation of mitochondrial morphology for both in vitro and in vivo applications. The image analysis pipeline was generated and validated utilizing images of primary cortical neurons from transgenic mice, allowing genetic ablation of key components of mitochondrial dynamics. This analysis pipeline was further extended to evaluate mitochondrial morphology in vivo through immunolabeling of brain sections as well as serial block-face scanning electron microscopy. These data demonstrate a highly specific and sensitive method that accurately classifies distinct physiological and pathological mitochondrial morphologies. Furthermore, this workflow employs the use of readily available, free open-source software designed for high throughput image processing, segmentation, and analysis that is customizable to various biological models.

Cell Tracking Profiler - a user-driven analysis framework for evaluating 4D live-cell imaging data.

Accurate measurements of cell morphology and behaviour are fundamentally important for understanding how disease, molecules and drugs affect cell function in vivo Here, by using muscle stem cell (muSC) responses to injury in zebrafish as our biological paradigm, we established a 'ground truth' for muSC behaviour. This revealed that segmentation and tracking algorithms from commonly used programs are error-prone, leading us to develop a fast semi-automated image analysis pipeline that allows user-defined parameters for segmentation and correction of cell tracking. Cell Tracking Profiler (CTP) is a package that runs two existing programs, HK Means and Phagosight within the Icy image analysis suite, to enable user-managed cell tracking from 3D time-lapse datasets to provide measures of cell shape and movement. We demonstrate how CTP can be used to reveal changes to cell behaviour of muSCs in response to manipulation of the cell cytoskeleton by small-molecule inhibitors. CTP and the associated tools we have developed for analysis of outputs thus provide a powerful framework for analysing complex cell behaviour in vivo from 4D datasets that are not amenable to straightforward analysis.

VAPYRIN Marks an Endosomal Trafficking Compartment Involved in Arbuscular Mycorrhizal Symbiosis.

Arbuscular mycorrhiza (AM) is a symbiosis between plants and AM fungi that requires the intracellular accommodation of the fungal partner in the host. For reciprocal nutrient exchange, AM fungi form intracellular arbuscules that are surrounded by the peri-arbuscular membrane. This membrane, together with the fungal plasma membrane, and the space in between, constitute the symbiotic interface, over which nutrients are exchanged. Intracellular establishment of AM fungi requires the VAPYRIN protein which is induced in colonized cells, and which localizes to numerous small mobile structures of unknown identity (Vapyrin-bodies). In order to characterize the identity and function of the Vapyrin-bodies we pursued a dual strategy. First, we co-expressed fluorescently tagged VAPYRIN with a range of subcellular marker proteins, and secondly, we employed biochemical tools to identify interacting partner proteins of VAPYRIN. As an important tool for the quantitative analysis of confocal microscopic data sets from co-expression of fluorescent proteins, we developed a semi-automated image analysis pipeline that allows for precise spatio-temporal quantification of protein co-localization and of the dynamics of organelle association from movies. Taken together, these experiments revealed that Vapyrin-bodies have an endosomal identity with trans-Golgi features, and that VAPYRIN interacts with a symbiotic R-SNARE of the VAMP721 family, that localizes to the same compartment.

High-throughput microfluidic micropipette aspiration device to probe time-scale dependent nuclear mechanics in intact cells.

The mechanical properties of the cell nucleus are increasingly recognized as critical in many biological processes. The deformability of the nucleus determines the ability of immune and cancer cells to migrate through tissues and across endothelial cell layers, and changes to the mechanical properties of the nucleus can serve as novel biomarkers in processes such as cancer progression and stem cell differentiation. However, current techniques to measure the viscoelastic nuclear mechanical properties are often time consuming, limited to probing one cell at a time, or require expensive, highly specialized equipment. Furthermore, many current assays do not measure time-dependent properties, which are characteristic of viscoelastic materials. Here, we present an easy-to-use microfluidic device that applies the well-established approach of micropipette aspiration, adapted to measure many cells in parallel. The device design allows rapid loading and purging of cells for measurements, and minimizes clogging by large particles or clusters of cells. Combined with a semi-automated image analysis pipeline, the microfluidic device approach enables significantly increased experimental throughput. We validated the experimental platform by comparing computational models of the fluid mechanics in the device with experimental measurements of fluid flow. In addition, we conducted experiments on cells lacking the nuclear envelope protein lamin A/C and wild-type controls, which have well-characterized nuclear mechanical properties. Fitting time-dependent nuclear deformation data to power law and different viscoelastic models revealed that loss of lamin A/C significantly altered the elastic and viscous properties of the nucleus, resulting in substantially increased nuclear deformability. Lastly, to demonstrate the versatility of the devices, we characterized the viscoelastic nuclear mechanical properties in a variety of cell lines and experimental model systems, including human skin fibroblasts from an individual with a mutation in the lamin gene associated with dilated cardiomyopathy, healthy control fibroblasts, induced pluripotent stem cells (iPSCs), and human tumor cells. Taken together, these experiments demonstrate the ability of the microfluidic device and automated image analysis platform to provide robust, high throughput measurements of nuclear mechanical properties, including time-dependent elastic and viscous behavior, in a broad range of applications.

SiSSR: Simultaneous subdivision surface registration for the quantification of cardiac function from computed tomography in canines.

Recent improvements in cardiac computed tomography (CCT) allow for whole-heart functional studies to be acquired at low radiation dose (<2mSv) and high-temporal resolution (<100ms) in a single heart beat. Although the extraction of regional functional information from these images is of great clinical interest, there is a paucity of research into the quantification of regional function from CCT, contrasting with the large body of work in echocardiography and cardiac MR. Here we present the Simultaneous Subdivision Surface Registration (SiSSR) method: a fast, semi-automated image analysis pipeline for quantifying regional function from contrast-enhanced CCT. For each of thirteen adult male canines, we construct an anatomical reference mesh representing the left ventricular (LV) endocardium, obviating the need for a template mesh to be manually sculpted and initialized. We treat this generated mesh as a Loop subdivision surface, and adapt a technique previously described in the context of 3-D echocardiography to register these surfaces to the endocardium efficiently across all cardiac frames simultaneously. Although previous work performs the registration at a single resolution, we observe that subdivision surfaces naturally suggest a multiresolution approach, leading to faster convergence and avoiding local minima. We additionally make two notable changes to the cost function of the optimization, explicitly encouraging plausible biological motion and high mesh quality. Finally, we calculate an accepted functional metric for CCT from the registered surfaces, and compare our results to an alternate state-of-the-art CCT method.

Detection of Mutations Affecting Heterogeneously Expressed Phenotypes by Colony Immunoblot and Dedicated Semi-Automated Image Analysis Pipeline.

To understand how bacteria evolve and adapt to their environment, it can be relevant to monitor phenotypic changes that occur in a population. Single cell level analyses and sorting of mutant cells according to a particular phenotypic readout can constitute efficient strategies. However, when the phenotype of interest is expressed heterogeneously in ancestral isogenic populations of cells, single cell level sorting approaches are not optimal. Phenotypic heterogeneity can for instance make no-expression mutant cells indistinguishable from a subpopulation of wild-type cells transiently not expressing the phenotype. The analysis of clonal populations (e.g., isolated colonies), in which the average phenotype is measured, can circumvent this issue. Indeed, no-expression mutants form negative populations while wild-type clones form populations in which average expression of the phenotype yields a positive signal. We present here an optimized colony immunoblot protocol and a semi-automated image analysis pipeline (ImageJ macro) allowing for rapid detection of clones harboring mutations that affect the heterogeneous (i.e., bimodal) expression of the Type Three Secretion System-1 (TTSS-1) in Salmonella enterica serovar Typhimurium. We show that this protocol can efficiently differentiate clones expressing TTSS-1 at various levels in mixed populations. We were able to detect the emergence of hilC mutants in which the proportion of cells expressing TTSS-1 was reduced compared to the ancestor. We could also follow changes in the frequency of different mutants during long-term infections. This demonstrates that our protocol constitutes a tractable approach to assess semi-quantitatively the evolutionary dynamics of heterogeneous phenotypes, such as the expression of virulence genes, in bacterial populations.

Single-cell-level spatial gene expression in the embryonic neural differentiation niche.

With the rapidly increasing availability of high-throughput in situ hybridization images, how to effectively analyze these images at high resolution for global patterns and testable hypotheses has become an urgent challenge. Here we developed a semi-automated image analysis pipeline to analyze in situ hybridization images of E14.5 mouse embryos at single-cell resolution for more than 1600 telencephalon-expressed genes from the Eurexpress database. Using this pipeline, we derived the spatial gene expression profiles at single-cell resolution across the cortical layers to gain insight into the key processes occurring during cerebral cortex development. These profiles displayed high spatial modularity in gene expression, precisely recapitulated known differentiation zones, and uncovered additional unknown transition zones or cellular states. In particular, they revealed a distinctive spatial transition phase dedicated to chromatin remodeling events during neural differentiation, which can be validated by genomic clustering patterns, epigenetic modifications switches, and network modules. Our analysis further revealed a role of mitotic checkpoints during spatial gene expression state transition. As a novel approach to analyzing at the single-cell level the spatial modularity, dynamic trajectory, and transient states of gene expression during embryonic neural differentiation and to inferring regulatory events, our approach will be useful and applicable in many different systems for understanding the dynamic differentiation processes in vivo and at high resolution.

Studies in spina bifida cystica. III. Seasonal variation in the frequency of spina bifida births.

<h3>Abstract</h3> Accurate measurements of cell morphology and behaviour are fundamentally important for understanding how disease, molecules and drugs affect cell function in vivo. Using muscle stem cell (muSC) responses to injury in zebrafish as our biological paradigm we have established a ground truth for muSC cell behaviour. This revealed that variability in segmentation and tracking algorithms from commonly used programs are error-prone, leading us to develop a fast semi-automated image analysis pipeline that allows user defined parameters for segmentation and correction of cell tracking. Cell Tracking Profiler (CTP) operates through the freely available Icy platform, and allows user-managed cell tracking from 3D time-lapsed datasets to provide measures of cell shape and movement. Using dimensionality reduction methods, multiple correlation and regression analyses we identify myosin II-dependent parameters of muSC behaviour during regeneration. CTP and the associated statistical tools we have developed thus provide a powerful framework for analysing complex cell behaviour <i>in vivo</i> from 4D datasets. <h3>Summary</h3> Analysis of cell shape and movement from 3D time-lapsed datasets is currently very challenging. We therefore designed Cell Tracking Profiler for analysing cell behaviour from complex datasets and demonstrate its effectiveness by analysing stem cell behaviour during muscle regeneration in zebrafish.