Segmenting and Tracking Multiple Dividing Targets Using ilastik

Genetic control over biogenic crystal morphogenesis in zebrafish

Organisms evolve mechanisms that regulate the properties of biogenic crystals to support a wide range of functions, from vision and camouflage to communication and thermal regulation. Yet, the mechanism underlying the formation of diverse intracellular crystals remains enigmatic. Here we unravel the biochemical control over crystal morphogenesis in zebrafish iridophores. We show that the chemical composition of the crystals determines their shape, particularly through the ratio between the nucleobases guanine and hypoxanthine. We reveal that these variations in composition are genetically controlled through tissue-specific expression of specialized paralogs, which exhibit remarkable substrate selectivity. This orchestrated combination grants the organism with the capacity to generate a broad spectrum of crystal morphologies. Overall, our findings suggest a mechanism for the morphological and functional diversity of biogenic crystals and may, thus, inspire the development of genetically designed biomaterials and medical therapeutics.

Morphological heterogeneity of neurons in the human central amygdaloid nucleus

The central amygdaloid nucleus (CeA) has an ancient phylogenetic development and functions relevant for animal survival. Local cells receive intrinsic amygdaloidal information that codes emotional stimuli of fear, integrate them, and send cortical and subcortical output projections that prompt rapid visceral and social behavior responses. We aimed to describe the morphology of the neurons that compose the human CeA (N = 8 adult men). Cells within CeA coronal borders were identified using the thionine staining and were further analyzed using the "single-section" Golgi method followed by open-source software procedures for two-dimensional and three-dimensional image reconstructions. Our results evidenced varied neuronal cell body features, number and thickness of primary shafts, dendritic branching patterns, and density and shape of dendritic spines. Based on these criteria, we propose the existence of 12 morphologically different spiny neurons in the human CeA and discuss the variability in the dendritic architecture within cellular types, including likely interneurons. Some dendritic shafts were long and straight, displayed few collaterals, and had planar radiation within the coronal neuropil volume. Most of the sampled neurons showed a few to moderate density of small stubby/wide spines. Long spines (thin and mushroom) were observed occasionally. These novel data address the synaptic processing and plasticity in the human CeA. Our morphological description can be combined with further transcriptomic, immunohistochemical, and electrophysiological/connectional approaches. It serves also to investigate how neurons are altered in neurological and psychiatric disorders with hindered emotional perception, in anxiety, following atrophy in schizophrenia, and along different stages of Alzheimer's disease.

Single-Cell Radiation Response Scoring with the Deep Learning Algorithm CeCILE 2.0

External stressors, such as ionizing radiation, have massive effects on life, survival, and the ability of mammalian cells to divide. Different types of radiation have different effects. In order to understand these in detail and the underlying mechanisms, it is essential to study the radiation response of each cell. This allows abnormalities to be characterized and laws to be derived. Tracking individual cells over several generations of division generates large amounts of data that can no longer be meaningfully analyzed by hand. In this study, we present a deep-learning-based algorithm, CeCILE (Cell classification and in vitro lifecycle evaluation) 2.0, that can localize, classify, and track cells in live cell phase-contrast videos. This allows conclusions to be drawn about the viability of the cells, the cell cycle, cell survival, and the influence of X-ray radiation on these. Furthermore, radiation-specific abnormalities during division could be characterized. In summary, CeCILE 2.0 is a powerful tool to characterize and quantify the cellular response to external stressors such as radiation and to put individual responses into a larger context. To the authors knowledge, this is the first algorithm with a fully integrated workflow that is able to do comprehensive single-cell and cell composite analysis, allowing them to draw conclusions on cellular radiation response.

Advanced Computational Methods to Evaluate Vascular Heterogeneity in Tumor Tissue Based on Single Plane Illumination Microscopy

During tumor growth, the complex composition of vasculature is prone to dynamic changes due to mechanic and biochemical challenges. Perivascular invasion of tumor cells to co-opt existing vasculature, but also formation of de-novo vasculature and other effects on the vascular network, may lead to altered geometric vessel properties as well as changes in vascular network topology, which is defined by vascular multifurcations and connections between vessel segments. The intricate organization and heterogeneity of the vascular network can be analyzed with advanced computational methods to uncover vascular network signatures that may allow differentiating between pathological and physiological vessel regions. Herein, we present a protocol to evaluate vascular heterogeneity in whole vascular networks, using morphological and topological measures. The protocol was developed for single plane illumination microscopy images of mice brain vasculature but can be applied to any vascular network.

Analysing Key Steps of the Photogrammetric Pipeline for Museum Artefacts 3D Digitisation

In recent years, massive digitisation of cultural heritage (CH) assets has become a focus of European programmes and initiatives. Among CH settings, attention is reserved to the immense and precious museum collections, whose digital 3D reproduction can support broader non-invasive analyses and stimulate the realisation of more attractive and interactive exhibitions. The reconstruction pipeline typically includes numerous processing steps when passive techniques are selected to deal with object digitisation. This article presents some insights on critical operations, which, based on our experience, can rule the quality of the final models and the reconstruction times for delivering 3D heritage results, while boosting the sustainability of digital cultural contents. The depth of field (DoF) problem is explored in the acquisition phase when surveying medium and small-sized objects. Techniques for deblurring images and masking object backgrounds are examined relative to the pre-processing stage. Some point cloud denoising and mesh simplification procedures are analysed in data post-processing. Hints on physically-based rendering (PBR) materials are also presented as closing operations of the reconstruction pipeline. This paper explores these processes mainly through experiments, providing a practical guide, tricks, and suggestions when tackling museum digitisation projects.

Profiling Immune Cells in the Kidney Using Tissue Cytometry and Machine Learning

The immune system governs key functions that maintain renal homeostasis through various effector cells that reside in or infiltrate the kidney. These immune cells play an important role in shaping adaptive or maladaptive responses to local or systemic stress and injury. We increasingly recognize that microenvironments within the kidney are characterized by a unique distribution of immune cells, the function of which depends on this unique spatial localization. Therefore, quantitative profiling of immune cells in intact kidney tissue becomes essential, particularly at a scale and resolution that allow the detection of differences between the various "nephro-ecosystems" in health and disease. In this review, we discuss advancements in tissue cytometry of the kidney, performed through multiplexed confocal imaging and analysis using the Volumetric Tissue Exploration and Analysis (VTEA) software. We highlight how this tool has improved our understanding of the role of the immune system in the kidney and its relevance in the pathobiology of renal disease. We also discuss how the field is increasingly incorporating machine learning to enhance the analytic potential of imaging data and provide unbiased methods to explore and visualize multidimensional data. Such novel analytic methods could be particularly relevant when applied to profiling immune cells. Furthermore, machine-learning approaches applied to cytometry could present venues for nonexhaustive exploration and classification of cells from existing data and improving tissue economy. Therefore, tissue cytometry is transforming what used to be a qualitative assessment of the kidney into a highly quantitative, imaging-based "omics" assessment that complements other advanced molecular interrogation technologies.

Furry is required for cell movements during gastrulation and functionally interacts with NDR1

Gastrulation is a key event in animal embryogenesis during which germ layer precursors are rearranged and the embryonic axes are established. Cell polarization is essential during gastrulation, driving asymmetric cell division, cell movements, and cell shape changes. The furry (fry) gene encodes an evolutionarily conserved protein with a wide variety of cellular functions, including cell polarization and morphogenesis in invertebrates. However, little is known about its function in vertebrate development. Here, we show that in Xenopus, Fry plays a role in morphogenetic processes during gastrulation, in addition to its previously described function in the regulation of dorsal mesoderm gene expression. Using morpholino knock-down, we demonstrate a distinct role for Fry in blastopore closure and dorsal axis elongation. Loss of Fry function drastically affects the movement and morphological polarization of cells during gastrulation and disrupts dorsal mesoderm convergent extension, responsible for head-to-tail elongation. Finally, we evaluate a functional interaction between Fry and NDR1 kinase, providing evidence of an evolutionarily conserved complex required for morphogenesis.

A framework for automated time-resolved analysis of cell colony growth after irradiation

Understanding dose-dependent survival of irradiated cells is a pivotal goal in radiotherapy and radiobiology. To this end, the clonogenic assay is the standard in vitro method, classifying colonies into either clonogenic or non-clonogenic based on a size threshold at a fixed time. Here we developed a methodological framework for the automated analysis of time course live-cell image data to examine in detail the growth dynamics of large numbers of colonies that occur during such an experiment. We developed a segmentation procedure that exploits the characteristic composition of phase-contrast images to identify individual colonies. Colony tracking allowed us to characterize colony growth dynamics as a function of dose by extracting essential information: (a) colony size distributions across time; (b) fractions of differential growth behavior; and (c) distributions of colony growth rates across all tested doses. We analyzed three data sets from two cell lines (H3122 and RENCA) and made consistent observations in line with already published results: (i) colony growth rates are normally distributed with a large variance; (ii) with increasing dose, the fraction of exponentially growing colonies decreases, whereas the fraction of delayed abortive colonies increases; as a novel finding, we observed that (iii) mean exponential growth rates decrease linearly with increasing dose across the tested range (0-10 Gy). The presented method is a powerful tool to examine live colony growth on a large scale and will help to deepen our understanding of the dynamic, stochastic processes underlying the radiation response in vitro.

OpSeF: Open Source Python Framework for Collaborative Instance Segmentation of Bioimages

Various pre-trained deep learning models for the segmentation of bioimages have been made available as developer-to-end-user solutions. They are optimized for ease of use and usually require neither knowledge of machine learning nor coding skills. However, individually testing these tools is tedious and success is uncertain. Here, we present the Open Segmentation Framework (OpSeF), a Python framework for deep learning-based instance segmentation. OpSeF aims at facilitating the collaboration of biomedical users with experienced image analysts. It builds on the analysts' knowledge in Python, machine learning, and workflow design to solve complex analysis tasks at any scale in a reproducible, well-documented way. OpSeF defines standard inputs and outputs, thereby facilitating modular workflow design and interoperability with other software. Users play an important role in problem definition, quality control, and manual refinement of results. OpSeF semi-automates preprocessing, convolutional neural network (CNN)-based segmentation in 2D or 3D, and postprocessing. It facilitates benchmarking of multiple models in parallel. OpSeF streamlines the optimization of parameters for pre- and postprocessing such, that an available model may frequently be used without retraining. Even if sufficiently good results are not achievable with this approach, intermediate results can inform the analysts in the selection of the most promising CNN-architecture in which the biomedical user might invest the effort of manually labeling training data. We provide Jupyter notebooks that document sample workflows based on various image collections. Analysts may find these notebooks useful to illustrate common segmentation challenges, as they prepare the advanced user for gradually taking over some of their tasks and completing their projects independently. The notebooks may also be used to explore the analysis options available within OpSeF in an interactive way and to document and share final workflows. Currently, three mechanistically distinct CNN-based segmentation methods, the U-Net implementation used in Cellprofiler 3.0, StarDist, and Cellpose have been integrated within OpSeF. The addition of new networks requires little; the addition of new models requires no coding skills. Thus, OpSeF might soon become both an interactive model repository, in which pre-trained models might be shared, evaluated, and reused with ease.

Saving time maintaining reliability: a new method for quantification of Tetranychus urticae damage in Arabidopsis whole rosettes

BACKGROUND

The model species Tetranychus urticae produces important plant injury and economic losses in the field. The current accepted method for the quantification of the spider mite damage in Arabidopsis whole rosettes is time consuming and entails a bottleneck for large-scale studies such as mutant screening or quantitative genetic analyses. Here, we describe an improved version of the existing method by designing an automatic protocol. The accuracy, precision, reproducibility and concordance of the new enhanced approach are validated in two Arabidopsis accessions with opposite damage phenotypes. Results are compared to the currently available manual method.

RESULTS

Image acquisition experiments revealed that the automatic settings plus 10 values of brightness and the black background are the optimal conditions for a specific recognition of spider mite damage by software programs. Among the different tested methods, the Ilastik-Fiji tandem based on machine learning was the best procedure able to quantify the damage maintaining the differential range of damage between accessions. In addition, the Ilastik-Fiji tandem method showed the lowest variability within a set of conditions and the highest stability under different lighting or background surroundings. Bland-Altman concordance results pointed out a negative value for Ilastik-Fiji, which implies a minor estimation of the damage when compared to the manual standard method.

CONCLUSIONS

The novel approach using Ilastik and Fiji programs entails a great improvement for the quantification of the specific spider mite damage in Arabidopsis whole rosettes. The automation of the proposed method based on interactive machine learning eliminates the subjectivity and inter-rater-variability of the previous manual protocol. Besides, this method offers a robust tool for time saving and to avoid the damage overestimation observed with other methods.

BMP Signaling Gradient Scaling in the Zebrafish Pectoral Fin

Secreted growth factors can act as morphogens that form spatial concentration gradients in developing organs, thereby controlling growth and patterning. For some morphogens, adaptation of the gradients to tissue size allows morphological patterns to remain proportioned as the organs grow. In the zebrafish pectoral fin, we found that BMP signaling forms a two-dimensional gradient. The length of the gradient scales with tissue length and its amplitude increases with fin size according to a power-law. Gradient scaling and amplitude power-laws are signatures of growth control by time derivatives of morphogenetic signaling: cell division correlates with the fold change over time of the cellular signaling levels. We show that Smoc1 regulates BMP gradient scaling and growth in the fin. Smoc1 scales the gradient by means of a feedback loop: Smoc1 is a BMP agonist and BMP signaling represses Smoc1 expression. Our work uncovers a layer of morphogen regulation during vertebrate appendage development.

Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer

Genomic alterations shape cell phenotypes and the structure of tumor ecosystems in poorly defined ways. To investigate these relationships, we used imaging mass cytometry to quantify the expression of 37 proteins with subcellular spatial resolution in 483 tumors from the METABRIC cohort. Single-cell analysis revealed cell phenotypes spanning epithelial, stromal and immune types. Distinct combinations of cell phenotypes and cell-cell interactions were associated with genomic subtypes of breast cancer. Epithelial luminal cell phenotypes separated into those predominantly impacted by mutations and those affected by copy number aberrations. Several features of tumor ecosystems, including cellular neighborhoods, were linked to prognosis, illustrating their clinical relevance. In summary, systematic analysis of single-cell phenotypic and spatial correlates of genomic alterations in cancer revealed how genomes shape both the composition and architecture of breast tumor ecosystems and will enable greater understanding of the phenotypic impact of genomic alterations.

Primary cilia deficiency in neural crest cells models anterior segment dysgenesis in mouse

Defects affecting tissues of the anterior segment (AS) of the eye lead to a group of highly debilitating disorders called Anterior Segment Dysgenesis (ASD). Despite the identification of some causative genes, the pathogenesis of ASD remains unclear. Interestingly, several ciliopathies display conditions of the AS. Using conditional targeting of Ift88 with Wnt1-Cre, we show that primary cilia of neural crest cells (NCC), precursors of most AS structures, are indispensable for normal AS development and their ablation leads to ASD conditions including abnormal corneal dimensions, defective iridocorneal angle, reduced anterior chamber volume and corneal neovascularization. Mechanistically, NCC cilia ablation abolishes hedgehog (Hh) signaling in the periocular mesenchyme (POM) canonically activated by choroid-secreted Indian Hh, reduces proliferation of POM cells surrounding the retinal pigment epithelium and decreases the expression of Foxc1 and Pitx2, two transcription factors identified as major ASD causative genes. Thus, we uncovered a signaling axis linking cilia and ASD.

2D + Time Object Tracking Using Fiji and ilastik

Tracking cells is one of the main challenges in biology, as it often requires time-consuming annotations and the images can have a low signal-to-noise ratio while containing a large number of cells. Here we present two methods for detecting and tracking cells using the open-source Fiji and ilastik frameworks. A straightforward approach is described using Fiji, consisting of a pre-processing and segmentation phase followed by a tracking phase, based on the overlapping of objects along the image sequence. Using ilastik, a classifier is trained through manual annotations to both detect cells over the background and be able to recognize false detections and merging cells. We describe these two methods in a step-by-step fashion, using as example a time-lapse microscopy movie of HeLa cells.

The tumor cell-secreted matricellular protein WISP1 drives pro-metastatic collagen linearization

Collagen linearization is a hallmark of aggressive tumors and a key pathogenic event that promotes cancer cell invasion and metastasis. Cell-generated mechanical tension has been proposed to contribute to collagen linearization in tumors, but it is unknown whether other mechanisms play prominent roles in this process. Here, we show that the secretome of cancer cells is by itself able to induce collagen linearization independently of cell-generated mechanical forces. Among the tumor cell-secreted factors, we find a key role in this process for the matricellular protein WISP1 (CCN4). Specifically, WISP1 directly binds to type I collagen to promote its linearization in vitro (in the absence of cells) and in vivo in tumors. Consequently, WISP1-induced type I collagen linearization facilitates tumor cell invasion and promotes spontaneous breast cancer metastasis, without significantly affecting gene expression. Furthermore, higher WISP1 expression in tumors from cancer patients correlates with faster progression to metastatic disease and poor prognosis. Altogether, these findings reveal a conceptually novel mechanism whereby pro-metastatic collagen linearization critically depends on a cancer cell-secreted factor.

Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures

Pathogens face varying microenvironments in vivo, but suitable experimental systems and analysis tools to dissect how three-dimensional (3D) tissue environments impact pathogen spread are lacking. Here we develop an Integrative method to Study Pathogen spread by Experiment and Computation within Tissue-like 3D cultures (INSPECT-3D), combining quantification of pathogen replication with imaging to study single-cell and cell population dynamics. We apply INSPECT-3D to analyze HIV-1 spread between primary human CD4 T-lymphocytes using collagen as tissue-like 3D-scaffold. Measurements of virus replication, infectivity, diffusion, cellular motility and interactions are combined by mathematical analyses into an integrated spatial infection model to estimate parameters governing HIV-1 spread. This reveals that environmental restrictions limit infection by cell-free virions but promote cell-associated HIV-1 transmission. Experimental validation identifies cell motility and density as essential determinants of efficacy and mode of HIV-1 spread in 3D. INSPECT-3D represents an adaptable method for quantitative time-resolved analyses of 3D pathogen spread.

Here, using an integrative experimental and computational approach, Imle et al. show how cell motility and density affect HIV cell-associated transmission in a three-dimensional tissue-like culture system of CD4⁺ T cells and collagen, and how different collagen matrices restrict infection by cell-free virions.

Imaging Peripheral Nerve Regeneration: A New Technique for 3D Visualization of Axonal Behavior

BACKGROUND

Peripheral nerve assessment has traditionally been studied through histological and immunological staining techniques in a limited cross-sectional modality, making detailed analysis difficult. A new application of serial section electron microscopy is presented to overcome these limitations.

METHODS

Direct nerve repairs were performed on the posterior auricular nerve of transgenic YFP-H mice. Six weeks postoperatively the nerves were imaged using confocal fluorescent microscopy then excised and embedded in resin. Resin blocks were sequentially sectioned at 100 nm, and sections were serially imaged with an electron microscope. Images were aligned and autosegmented to allow for 3D reconstruction.

RESULTS

Basic morphometry and axonal counts were fully automated. Using full 3D reconstructions, the relationships between the axons, the Nodes of Ranvier, and Schwann cells could be fully appreciated. Interactions of individual axons with their surrounding environment could be visualized and explored in a virtual three-dimensional space.

CONCLUSIONS

Serial section electron microscopy allows the detailed pathway of the regenerating axon to be visualized in a 3D virtual space in comparison to isolated individual traditional histological techniques. Fully automated histo-morphometry can now give accurate axonal counts, provide information regarding the quality of nerve regeneration, and reveal the cell-to-cell interaction at a super-resolution scale. It is possible to fully visualize and "fly-through" the nerve to help understand the behavior of a regenerating axon within its environment. This technique provides future opportunities to evaluate the effect different treatment modalities have on the neuroregenerative potential and help us understand the impact different surgical techniques have when treating nerve injuries.

Correlated MRI and Ultramicroscopy (MR-UM) of Brain Tumors Reveals Vast Heterogeneity of Tumor Infiltration and Neoangiogenesis in Preclinical Models and Human Disease

Diffuse tumor infiltration into the adjacent parenchyma is an effective dissemination mechanism of brain tumors. We have previously developed correlated high field magnetic resonance imaging and ultramicroscopy (MR-UM) to study neonangiogenesis in a glioma model. In the present study we used MR-UM to investigate tumor infiltration and neoangiogenesis in a translational approach. We compare infiltration and neoangiogenesis patterns in four brain tumor models and the human disease: whereas the U87MG glioma model resembles brain metastases with an encapsulated growth and extensive neoangiogenesis, S24 experimental gliomas mimic IDH1 wildtype glioblastomas, exhibiting infiltration into the adjacent parenchyma and along white matter tracts to the contralateral hemisphere. MR-UM resolves tumor infiltration and neoangiogenesis longitudinally based on the expression of fluorescent proteins, intravital dyes or endogenous contrasts. Our study demonstrates the huge morphological diversity of brain tumor models regarding their infiltrative and neoangiogenic capacities and further establishes MR-UM as a platform for translational neuroimaging.

Machine Learning: Advanced Image Segmentation Using ilastik

Segmentation is one of the most ubiquitous problems in biological image analysis. Here we present a machine learning-based solution to it as implemented in the open source ilastik toolkit. We give a broad description of the underlying theory and demonstrate two workflows: Pixel Classification and Autocontext. We illustrate their use on a challenging problem in electron microscopy image segmentation. After following this walk-through, we expect the readers to be able to apply the necessary steps to their own data and segment their images by either workflow.

Cryo-soft X-ray tomography: using soft X-rays to explore the ultrastructure of whole cells

Cryo-soft X-ray tomography is an imaging technique that addresses the need for mesoscale imaging of cellular ultrastructure of relatively thick samples without the need for staining or chemical modification. It allows the imaging of cellular ultrastructure to a resolution of 25–40 nm and can be used in correlation with other imaging modalities, such as electron tomography and fluorescence microscopy, to further enhance the information content derived from biological samples. An overview of the technique, discussion of sample suitability and information about sample preparation, data collection and data analysis is presented here. Recent developments and future outlook are also discussed.

Data integration through brain atlasing: Human Brain Project tools and strategies

The Human Brain Project (HBP), an EU Flagship Initiative, is currently building an infrastructure that will allow integration of large amounts of heterogeneous neuroscience data. The ultimate goal of the project is to develop a unified multi-level understanding of the brain and its diseases, and beyond this to emulate the computational capabilities of the brain. Reference atlases of the brain are one of the key components in this infrastructure. Based on a new generation of three-dimensional (3D) reference atlases, new solutions for analyzing and integrating brain data are being developed. HBP will build services for spatial query and analysis of brain data comparable to current online services for geospatial data. The services will provide interactive access to a wide range of data types that have information about anatomical location tied to them. The 3D volumetric nature of the brain, however, introduces a new level of complexity that requires a range of tools for making use of and interacting with the atlases. With such new tools, neuroscience research groups will be able to connect their data to atlas space, share their data through online data systems, and search and find other relevant data through the same systems. This new approach partly replaces earlier attempts to organize research data based only on a set of semantic terminologies describing the brain and its subdivisions.

Large-scale 3-dimensional quantitative imaging of tissues: state-of-the-art and translational implications

Recent developments in automated optical sectioning microscope systems have enabled researchers to conduct high resolution, three-dimensional (3D) microscopy at the scale of millimeters in various types of tissues. This powerful technology allows the exploration of tissues at an unprecedented level of detail, while preserving the spatial context. By doing so, such technology will also enable researchers to explore cellular and molecular signatures within tissue and correlate with disease course. This will allow an improved understanding of pathophysiology and facilitate a precision medicine approach to assess the response to treatment. The ability to perform large-scale imaging in 3D cannot be realized without the widespread availability of accessible quantitative analysis. In this review, we will outline recent advances in large-scale 3D imaging and discuss the available methodologies to perform meaningful analysis and potential applications in translational research.

Automatic Endosomal Structure Detection And Localization in Fluorescence Microscopic Images

This paper proposes a modified spatially-constrained similarity measure (mSCSM) method for endosomal structure detection and localization under the bag-of-words (BoW) framework. To our best knowledge, the proposed mSCSM is the first method for fully automatic detection and localization of complex subcellular compartments like endosomes. Essentially, a new similarity score and a novel two-stage output control scheme are proposed for localization by extracting discriminative information within a group of query images. Compared with the original SCSM which is formulated for instance localization, the proposed mSCSM can address category based localization problems. The preliminary experimental results show the proposed mSCSM can correctly detect and localize 79.17% of the existing endosomal structures in the microscopic images of human myeloid endothelial cells.