Visualization and correction of automated segmentation, tracking and lineaging from 5-D stem cell image sequences

Optical microscopic imaging, manipulation, and analysis methods for morphogenesis research

Morphogenesis is a developmental process of organisms being shaped through complex and cooperative cellular movements. To understand the interplay between genetic programs and the resulting multicellular morphogenesis, it is essential to characterize the morphologies and dynamics at the single-cell level and to understand how physical forces serve as both signaling components and driving forces of tissue deformations. In recent years, advances in microscopy techniques have led to improvements in imaging speed, resolution and depth. Concurrently, the development of various software packages has supported large-scale, analyses of challenging images at the single-cell resolution. While these tools have enhanced our ability to examine dynamics of cells and mechanical processes during morphogenesis, their effective integration requires specialized expertise. With this background, this review provides a practical overview of those techniques. First, we introduce microscopic techniques for multicellular imaging and image analysis software tools with a focus on cell segmentation and tracking. Second, we provide an overview of cutting-edge techniques for mechanical manipulation of cells and tissues. Finally, we introduce recent findings on morphogenetic mechanisms and mechanosensations that have been achieved by effectively combining microscopy, image analysis tools and mechanical manipulation techniques.

Spatiotemporal control of ERK pulse frequency coordinates fate decisions during mammary acinar morphogenesis

The signaling events controlling proliferation, survival, and apoptosis during mammary epithelial acinar morphogenesis remain poorly characterized. By imaging single-cell ERK activity dynamics in MCF10A acini, we find that these fates depend on the average frequency of non-periodic ERK pulses. High pulse frequency is observed during initial acinus growth, correlating with rapid cell motility and proliferation. Subsequent decrease in motility correlates with lower ERK pulse frequency and quiescence. Later, during lumen formation, coordinated multicellular ERK waves emerge, correlating with high and low ERK pulse frequencies in outer surviving and inner dying cells, respectively. Optogenetic entrainment of ERK pulses causally connects high ERK pulse frequency with inner cell survival. Acini harboring the PIK3CA H1047R mutation display increased ERK pulse frequency and inner cell survival. Thus, fate decisions during acinar morphogenesis are coordinated by different spatiotemporal modalities of ERK pulse frequency.

LEVERSC: Cross-Platform Scriptable Multichannel 3-D Visualization for Fluorescence Microscopy Images

We describe a new open-source program called LEVERSC to address the challenges of visualizing the multi-channel 3-D images prevalent in biological microscopy. LEVERSC uses a custom WebGL hardware-accelerated raycasting engine unique in its combination of rendering quality and performance, particularly for multi-channel data. Key features include platform independence, quantitative visualization through interactive voxel localization, and reproducible dynamic visualization via the scripting interface. LEVERSC is fully scriptable and interactive, and works with MATLAB, Python and Java/ImageJ.

CellTrackVis: analyzing the performance of cell tracking algorithms

Live-cell imaging is a common data acquisition technique used by biologists to analyze cell behavior. Since manually tracking cells in a video sequence is extremely time-consuming, many automatic algorithms have been developed in the last twenty years to accomplish the task. However, none of these algorithms can yet claim robust tracking performance at the varying of acquisition conditions (e.g., cell type, acquisition device, cell treatments). While many visualization tools exist to help with cell behavior analysis, there are no tools to help with the algorithm's validation. This paper proposes CellTrackVis, a new visualization tool for evaluating cell tracking algorithms. CellTrackVis allows comparing automatically generated cell tracks with ground truth data to help biologists select the best-suited algorithm for their experimented pipeline. Moreover, CellTackVis can be used as a debugging tool while developing a new cell tracking algorithm to investigate where, when, and why each tracking error occurred.

2D-GolgiTrack-a semi-automated tracking system to quantify morphological changes and dynamics of the Golgi apparatus and Golgi-derived membrane tubules

The Golgi apparatus and membrane tubules derived from this organelle play essential roles in membrane trafficking in eukaryotic cells. High-resolution live cell imaging is one highly suitable method for studying the molecular mechanisms of dynamics of organelles during membrane trafficking events. Due to the complex morphological changes and dynamic movements of the Golgi apparatus and associated membrane tubules during membrane trafficking, it is challenging to accurately quantify them. In this study, a semi-automated 2D tracking system, 2D-GolgiTrack, has been established for quantifying morphological changes and movements of Golgi elements, specifically encompassing the Golgi apparatus and its associated tubules, the fission and fusion of Golgi tubules, and the kinetics of formation of Golgi tubules and redistribution of the Golgi-associated protein Rab6A to the endoplasmic reticulum. The Golgi apparatus and associated tubules are segmented by a combination of Otsu's method and adaptive local normalization thresholding. Curvilinear skeletons and tips of skeletons of segmented tubules are used for calculating tubule length by the Geodesic method. The k-nearest neighbor is applied to search the possible candidate objects in the next frame and link the correct objects of adjacent frames by a tracking algorithm to calculate changes in morphological features of each Golgi object or tubule, e.g., number, length, shape, branch point and position, and fission or fusion events. Tracked objects are classified into morphological subtypes, and the Track-Map function of morphological evolution visualizes events of fission and fusion. Our 2D-GolgiTrack not only provides tracking results with 95% accuracy, but also maps morphological evolution for fast visual interpretation of the fission and fusion events. Our tracking system is able to characterize key morphological and dynamic features of the Golgi apparatus and associated tubules, enabling biologists to gain a greater understanding of the molecular mechanisms of membrane traffic involving this essential organelle. Graphical Abstract Overview of the semi-automated 2D tracking system. There are two main parts to the system, namely detection and tracking. The workflow process requires a raw sequence of images (a), which is filtered by the Gaussian filter method (c), and threshold intensity (b) to segment elements of Golgi cisternae (d) and tubules (e). Post-processing outputs are binary images of the cisternae area and tubule skeletons. The tubules are classified into three lengths, namely short, medium, and long tubules (f). Outputs of segmentation are calculated as morphological features (g). The tracking processing starts by loading the segmented outputs (h) and key-inputs of direction reference (i; (DR)) and interval setting of the start ((S)) and end ((E)) frame numbers (j). A tubule of interest is selected by the user (k; (GT_{interest, S}) as the tubule input ((GT_IN)) at the current frame ((i = S)). The tracking algorithm tracks and links the correct tubules at each subsequent frame ((i = i + 1)). The locations of tubule tips are determined for detecting tubule branches using the (DR) to identify the direction of tubule growth (l: (1); (GT_{tipBr, i}); Golgi cisternae: white area; Golgi tubule: white skeleton; tubule tips: green dots; branched tubules: two branches due to the (DR) of growth of the simulated tubule moving from left-to-right away from the Golgi cisternae location). According to the position of the (GT_IN), five candidates ((GT_{candidates, i})) are searched using the k-nearest neighbor method (l: (2)). Matching of tubules between the (GT_IN) and those (GT_{candidates, i}) uses the bounding box technique to check the amount of tubule-overlap based on the tracking conditions (l: (3)). If there is tubule-overlap, the system collects that tubule as the final output ((GT_{OUT, i})). By contrast, shape (see the Extent feature in Table reftab:1) and distance features are used to generate the tracked output, which has a priority of a minimum of both of these features ((Min_{DIST, EXTENT})); otherwise, it is from the minimum of the distance ((Min_DIST)). Once a loop of the interval track to the last frame is finished ((i = E + 1)), a Track-Map is generated allowing visualization of the morphological pattern of tubule formation and movement, including identification of fission and fusion events (m). Dynamic features are calculated (n). Related outputs are saved, and all features obtained from the detection and tracking processing are exported as MS Excel files (o).

Hydra image processor: 5-D GPU image analysis library with MATLAB and python wrappers

SUMMARY

Light microscopes can now capture data in five dimensions at very high frame rates producing terabytes of data per experiment. Five-dimensional data has three spatial dimensions (x, y, z), multiple channels (λ) and time (t). Current tools are prohibitively time consuming and do not efficiently utilize available hardware. The hydra image processor (HIP) is a new library providing hardware-accelerated image processing accessible from interpreted languages including MATLAB and Python. HIP automatically distributes data/computation across system and video RAM allowing hardware-accelerated processing of arbitrarily large images. HIP also partitions compute tasks optimally across multiple GPUs. HIP includes a new kernel renormalization reducing boundary effects associated with widely used padding approaches.

AVAILABILITY AND IMPLEMENTATION

HIP is free and open source software released under the BSD 3-Clause License. Source code and compiled binary files will be maintained on http://www.hydraimageprocessor.com. A comprehensive description of all MATLAB and Python interfaces and user documents are provided. HIP includes GPU-accelerated support for most common image processing operations in 2-D and 3-D and is easily extensible. HIP uses the NVIDIA CUDA interface to access the GPU. CUDA is well supported on Windows and Linux with macOS support in the future.

Macrophages of diverse phenotypes drive vascularization of engineered tissues

Macrophages are key contributors to vascularization, but the mechanisms behind their actions are not understood. Here, we show that diverse macrophage phenotypes have distinct effects on endothelial cell behavior, with resulting effects on vascularization of engineered tissues. In Transwell coculture, proinflammatory M1 macrophages caused endothelial cells to up-regulate genes associated with sprouting angiogenesis, whereas prohealing (M2a), proremodeling (M2c), and anti-inflammatory (M2f) macrophages promoted up-regulation of genes associated with pericyte cell differentiation. In 3D tissue-engineered human blood vessel networks in vitro, short-term exposure (1 day) to M1 macrophages increased vessel formation, while long-term exposure (3 days) caused regression. When human tissue-engineered blood vessel networks were implanted into athymic mice, macrophages expressing markers of both M1 and M2 phenotypes wrapped around and bridged adjacent vessels and formed vessel-like structures themselves. Last, depletion of host macrophages inhibited remodeling of engineered vessels, infiltration of host vessels, and anastomosis with host vessels.

Frontiers in Intravital Multiphoton Microscopy of Cancer

Background

Cancer is a highly complex disease which involves the co-operation of tumor cells with multiple types of host cells and the extracellular matrix. Cancer studies which rely solely on static measurements of individual cell types are insufficient to dissect this complexity. In the last two decades, intravital microscopy has established itself as a powerful technique that can significantly improve our understanding of cancer by revealing the dynamic interactions governing cancer initiation, progression and treatment effects, in living animals. This review focuses on intravital multiphoton microscopy (IV-MPM) applications in mouse models of cancer.

Recent Findings

IV-MPM studies have already enabled a deeper understanding of the complex events occurring in cancer, at the molecular, cellular and tissue levels. Multiple cells types, present in different tissues, influence cancer cell behavior via activation of distinct signaling pathways. As a result, the boundaries in the field of IV-MPM are continuously being pushed to provide an integrated comprehension of cancer. We propose that optics, informatics and cancer (cell) biology are co-evolving as a new field. We have identified four emerging themes in this new field. First, new microscopy systems and image processing algorithms are enabling the simultaneous identification of multiple interactions between the tumor cells and the components of the tumor microenvironment. Second, techniques from molecular biology are being exploited to visualize subcellular structures and protein activities within individual cells of interest, and relate those to phenotypic decisions, opening the door for "in vivo cell biology". Third, combining IV-MPM with additional imaging modalities, or omics studies, holds promise for linking the cell phenotype to its genotype, metabolic state or tissue location. Finally, the clinical use of IV-MPM for analyzing efficacy of anti-cancer treatments is steadily growing, suggesting a future role of IV-MPM for personalized medicine.

Conclusion

IV-MPM has revolutionized visualization of tumor-microenvironment interactions in real time. Moving forward, incorporation of novel optics, automated image processing, and omics technologies, in the study of cancer biology, will not only advance our understanding of the underlying complexities but will also leverage the unique aspects of IV-MPM for clinical use.

Separating Touching Cells Using Pixel Replicated Elliptical Shape Models

One of the most important and error-prone tasks in biological image analysis is the segmentation of touching or overlapping cells. Particularly for optical microscopy, including transmitted light and confocal fluorescence microscopy, there is often no consistent discriminative information to separate cells that touch or overlap. It is desired to partition touching foreground pixels into cells using the binary threshold image information only, and optionally incorporating gradient information. The most common approaches for segmenting touching and overlapping cells in these scenarios are based on the watershed transform. We describe a new approach called pixel replication for the task of segmenting elliptical objects that touch or overlap. Pixel replication uses the image Euclidean distance transform in combination with Gaussian mixture models to better exploit practically effective optimization for delineating objects with elliptical decision boundaries. Pixel replication improves significantly on commonly used methods based on watershed transforms, or based on fitting Gaussian mixtures directly to the thresholded image data. Pixel replication works equivalently on both 2-D and 3-D image data, and naturally combines information from multi-channel images. The accuracy of the proposed technique is measured using both the segmentation accuracy on simulated ellipse data and the tracking accuracy on validated stem cell tracking results extracted from hundreds of live-cell microscopy image sequences. Pixel replication is shown to be significantly more accurate compared with other approaches. Variance relationships are derived, allowing a more practically effective Gaussian mixture model to extract cell boundaries for data generated from the threshold image using the uniform elliptical distribution and from the distance transform image using the triangular elliptical distribution.

SPF-CellTracker: Tracking Multiple Cells with Strongly-Correlated Moves Using a Spatial Particle Filter

Tracking many cells in time-lapse 3D image sequences is an important challenging task of bioimage informatics. Motivated by a study of brain-wide 4D imaging of neural activity in C. elegans, we present a new method of multi-cell tracking. Data types to which the method is applicable are characterized as follows: (i) cells are imaged as globular-like objects, (ii) it is difficult to distinguish cells on the basis of shape and size only, (iii) the number of imaged cells in the several-hundred range, (iv) movements of nearly-located cells are strongly correlated, and (v) cells do not divide. We developed a tracking software suite that we call SPF-CellTracker. Incorporating dependency on the cells' movements into the prediction model is the key for reducing the tracking errors: the cell switching and the coalescence of the tracked positions. We model the target cells' correlated movements as a Markov random field and we also derive a fast computation algorithm, which we call spatial particle filter. With the live-imaging data of the nuclei of C. elegans neurons in which approximately 120 nuclei of neurons were imaged, the proposed method demonstrated improved accuracy compared to the standard particle filter and the method developed by Tokunaga et al. (2014).

Syntaphilin Ubiquitination Regulates Mitochondrial Dynamics and Tumor Cell Movements

Syntaphilin (SNPH) inhibits the movement of mitochondria in tumor cells, preventing their accumulation at the cortical cytoskeleton and limiting the bioenergetics of cell motility and invasion. Although this may suppress metastasis, the regulation of the SNPH pathway is not well understood. Using a global proteomics screen, we show that SNPH associates with multiple regulators of ubiquitin-dependent responses and is ubiquitinated by the E3 ligase CHIP (or STUB1) on Lys111 and Lys153 in the microtubule-binding domain. SNPH ubiquitination did not result in protein degradation, but instead anchored SNPH on tubulin to inhibit mitochondrial motility and cycles of organelle fusion and fission, that is dynamics. Expression of ubiquitination-defective SNPH mutant Lys111→Arg or Lys153→Arg increased the speed and distance traveled by mitochondria, repositioned mitochondria to the cortical cytoskeleton, and supported heightened tumor chemotaxis, invasion, and metastasis in vivo Interference with SNPH ubiquitination activated mitochondrial dynamics, resulting in increased recruitment of the fission regulator dynamin-related protein-1 (Drp1) to mitochondria and Drp1-dependent tumor cell motility. These data uncover nondegradative ubiquitination of SNPH as a key regulator of mitochondrial trafficking and tumor cell motility and invasion. In this way, SNPH may function as a unique, ubiquitination-regulated suppressor of metastasis.Significance: These findings reveal a new mechanism of metastasis suppression by establishing the role of SNPH ubiquitination in inhibiting mitochondrial dynamics, chemotaxis, and metastasis. Cancer Res; 78(15); 4215-28. ©2018 AACR.

Contact guidance is cell cycle-dependent

Cancer cell migration is essential for metastasis, during which cancer cells move through the tumor and reach the blood vessels. In vivo, cancer cells are exposed to contact guidance and chemotactic cues. Depending on the strength of such cues, cells will migrate in a random or directed manner. While similar cues may also stimulate cell proliferation, it is not clear whether cell cycle progression affects migration of cancer cells and whether this effect is different in random versus directed migration. In this study, we tested the effect of cell cycle progression on contact guided migration in 2D and 3D environments, in the breast carcinoma cell line, FUCCI-MDA-MB-231. The results were quantified from live cell microscopy images using the open source lineage editing and validation image analysis tools (LEVER). In 2D, cells were placed inside 10 μm-wide microchannels to stimulate contact guidance, with or without an additional chemotactic gradient of the soluble epidermal growth factor. In 3D, contact guidance was modeled by aligned collagen fibers. In both 2D and 3D, contact guidance was cell cycle-dependent, while the addition of the chemo-attractant gradient in 2D increased cell velocity and persistence in directionally migrating cells, regardless of their cell cycle phases. In both 2D and 3D contact guidance, cells in the G1 phase of the cell cycle outperformed cells in the S/G2 phase in terms of migration persistence and instantaneous velocity. These data suggest that in the presence of contact guidance cues in vivo, breast carcinoma cells in the G1 phase of the cell cycle may be more efficient in reaching the neighboring vasculature.

Dynamical localization of a thylakoid membrane binding protein is required for acquisition of photosynthetic competency

Vipp1 is highly conserved and essential for photosynthesis, but its function is unclear as it does not participate directly in light-dependent reactions. We analyzed Vipp1 localization in live cyanobacterial cells and show that Vipp1 is highly dynamic, continuously exchanging between a diffuse fraction that is uniformly distributed throughout the cell and a punctate fraction that is concentrated at high curvature regions of the thylakoid located at the cell periphery. Experimentally perturbing the spatial distribution of Vipp1 by relocalizing it to the nucleoid causes a severe growth defect during the transition from non-photosynthetic (dark) to photosynthetic (light) growth. However, the same perturbation of Vipp1 in dark alone or light alone growth conditions causes no growth or thylakoid morphology defects. We propose that the punctuated dynamics of Vipp1 at the cell periphery in regions of high thylakoid curvature enable acquisition of photosynthetic competency, perhaps by facilitating biogenesis of photosynthetic complexes involved in light-dependent reactions of photosynthesis.

Cell and small animal models for phenotypic drug discovery

The phenotype-based drug discovery (PDD) approach is re-emerging as an alternative platform for drug discovery. This review provides an overview of the various model systems and technical advances in imaging and image analyses that strengthen the PDD platform. In PDD screens, compounds of therapeutic value are identified based on the phenotypic perturbations produced irrespective of target(s) or mechanism of action. In this article, examples of phenotypic changes that can be detected and quantified with relative ease in a cell-based setup are discussed. In addition, a higher order of PDD screening setup using small animal models is also explored. As PDD screens integrate physiology and multiple signaling mechanisms during the screening process, the identified hits have higher biomedical applicability. Taken together, this review highlights the advantages gained by adopting a PDD approach in drug discovery. Such a PDD platform can complement target-based systems that are currently in practice to accelerate drug discovery.

Applying systems-level spectral imaging and analysis to reveal the organelle interactome

The organization of the eukaryotic cell into discrete membrane-bound organelles allows for the separation of incompatible biochemical processes, but the activities of these organelles must be coordinated. For example, lipid metabolism is distributed between the endoplasmic reticulum for lipid synthesis, lipid droplets for storage and transport, mitochondria and peroxisomes for β-oxidation, and lysosomes for lipid hydrolysis and recycling. It is increasingly recognized that organelle contacts have a vital role in diverse cellular functions. However, the spatial and temporal organization of organelles within the cell remains poorly characterized, as fluorescence imaging approaches are limited in the number of different labels that can be distinguished in a single image. Here we present a systems-level analysis of the organelle interactome using a multispectral image acquisition method that overcomes the challenge of spectral overlap in the fluorescent protein palette. We used confocal and lattice light sheet instrumentation and an imaging informatics pipeline of five steps to achieve mapping of organelle numbers, volumes, speeds, positions and dynamic inter-organelle contacts in live cells from a monkey fibroblast cell line. We describe the frequency and locality of two-, three-, four- and five-way interactions among six different membrane-bound organelles (endoplasmic reticulum, Golgi, lysosome, peroxisome, mitochondria and lipid droplet) and show how these relationships change over time. We demonstrate that each organelle has a characteristic distribution and dispersion pattern in three-dimensional space and that there is a reproducible pattern of contacts among the six organelles, that is affected by microtubule and cell nutrient status. These live-cell confocal and lattice light sheet spectral imaging approaches are applicable to any cell system expressing multiple fluorescent probes, whether in normal conditions or when cells are exposed to disturbances such as drugs, pathogens or stress. This methodology thus offers a powerful descriptive tool and can be used to develop hypotheses about cellular organization and dynamics.

Generalizing cell segmentation and quantification

Background

In recent years, the microscopy technology for imaging cells has developed greatly and rapidly. The accompanying requirements for automatic segmentation and quantification of the imaged cells are becoming more and more. After studied widely in both scientific research and industrial applications for many decades, cell segmentation has achieved great progress, especially in segmenting some specific types of cells, e.g. muscle cells. However, it lacks a framework to address the cell segmentation problems generally. On the contrary, different segmentation methods were proposed to address the different types of cells, which makes the research work divergent. In addition, most of the popular segmentation and quantification tools usually require a great part of manual work.

Results

To make the cell segmentation work more convergent, we propose a framework that is able to segment different kinds of cells automatically and robustly in this paper. This framework evolves the previously proposed method in segmenting the muscle cells and generalizes it to be suitable for segmenting and quantifying a variety of cell images by adding more union cases. Compared to the previous methods, the segmentation and quantification accuracy of the proposed framework is also improved by three novel procedures: (1) a simplified calibration method is proposed and added for the threshold selection process; (2) a noise blob filter is proposed to get rid of the noise blobs. (3) a boundary smoothing filter is proposed to reduce the false seeds produced by the iterative erosion. As it turned out, the quantification accuracy of the proposed framework increases from 93.4 to 96.8% compared to the previous method. In addition, the accuracy of the proposed framework is also better in quantifying the muscle cells than two available state-of-the-art methods.

Conclusions

The proposed framework is able to automatically segment and quantify more types of cells than state-of-the-art methods.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1604-1) contains supplementary material, which is available to authorized users.

A neuronal network of mitochondrial dynamics regulates metastasis

The role of mitochondria in cancer is controversial. Using a genome-wide shRNA screen, we now show that tumours reprogram a network of mitochondrial dynamics operative in neurons, including syntaphilin (SNPH), kinesin KIF5B and GTPase Miro1/2 to localize mitochondria to the cortical cytoskeleton and power the membrane machinery of cell movements. When expressed in tumours, SNPH inhibits the speed and distance travelled by individual mitochondria, suppresses organelle dynamics, and blocks chemotaxis and metastasis, in vivo. Tumour progression in humans is associated with downregulation or loss of SNPH, which correlates with shortened patient survival, increased mitochondrial trafficking to the cortical cytoskeleton, greater membrane dynamics and heightened cell invasion. Therefore, a SNPH network regulates metastatic competence and may provide a therapeutic target in cancer.

A Critical and Comparative Review of Fluorescent Tools for Live-Cell Imaging

Fluorescent tools have revolutionized our ability to probe biological dynamics, particularly at the cellular level. Fluorescent sensors have been developed on several platforms, utilizing either small-molecule dyes or fluorescent proteins, to monitor proteins, RNA, DNA, small molecules, and even cellular properties, such as pH and membrane potential. We briefly summarize the impressive history of tool development for these various applications and then discuss the most recent noteworthy developments in more detail. Particular emphasis is placed on tools suitable for single-cell analysis and especially live-cell imaging applications. Finally, we discuss prominent areas of need in future fluorescent tool development-specifically, advancing our capability to analyze and integrate the plethora of high-content data generated by fluorescence imaging.

Robust cell tracking in epithelial tissues through identification of maximum common subgraphs

Tracking of cells in live-imaging microscopy videos of epithelial sheets is a powerful tool for investigating fundamental processes in embryonic development. Characterizing cell growth, proliferation, intercalation and apoptosis in epithelia helps us to understand how morphogenetic processes such as tissue invagination and extension are locally regulated and controlled. Accurate cell tracking requires correctly resolving cells entering or leaving the field of view between frames, cell neighbour exchanges, cell removals and cell divisions. However, current tracking methods for epithelial sheets are not robust to large morphogenetic deformations and require significant manual interventions. Here, we present a novel algorithm for epithelial cell tracking, exploiting the graph-theoretic concept of a 'maximum common subgraph' to track cells between frames of a video. Our algorithm does not require the adjustment of tissue-specific parameters, and scales in sub-quadratic time with tissue size. It does not rely on precise positional information, permitting large cell movements between frames and enabling tracking in datasets acquired at low temporal resolution due to experimental constraints such as phototoxicity. To demonstrate the method, we perform tracking on the Drosophila embryonic epidermis and compare cell-cell rearrangements to previous studies in other tissues. Our implementation is open source and generally applicable to epithelial tissues.

Measuring Process Dynamics and Nuclear Migration for Clones of Neural Progenitor Cells

Neural stem and progenitor cells (NPCs) generate processes that extend from the cell body in a dynamic manner. The NPC nucleus migrates along these processes with patterns believed to be tightly coupled to mechanisms of cell cycle regulation and cell fate determination. Here, we describe a new segmentation and tracking approach that allows NPC processes and nuclei to be reliably tracked across multiple rounds of cell division in phase-contrast microscopy images. Results are presented for mouse adult and embryonic NPCs from hundreds of clones, or lineage trees, containing tens of thousands of cells and millions of segmentations. New visualization approaches allow the NPC nuclear and process features to be effectively visualized for an entire clone. Significant differences in process and nuclear dynamics were found among type A and type C adult NPCs, and also between embryonic NPCs cultured from the anterior and posterior cerebral cortex.

LEVER: software tools for segmentation, tracking and lineaging of proliferating cells

The analysis of time-lapse images showing cells dividing to produce clones of related cells is an important application in biological microscopy. Imaging at the temporal resolution required to establish accurate tracking for vertebrate stem or cancer cells often requires the use of transmitted light or phase-contrast microscopy. Processing these images requires automated segmentation, tracking and lineaging algorithms. There is also a need for any errors in the automated processing to be easily identified and quickly corrected. We have developed LEVER, an open source software tool that combines the automated image analysis for phase-contrast microscopy movies with an easy-to-use interface for validating the results and correcting any errors.

AVAILABILITY AND IMPLEMENTATION

LEVER is available free and open source, licensed under the GNU GPLv3. Details on obtaining and using LEVER are available at http://n2t.net/ark:/87918/d9rp4t CONTACT: acohen@coe.drexel.edu.

Automated Measurement of Cobblestone Morphology for Characterizing Stem Cell Derived Retinal Pigment Epithelial Cell Cultures

PURPOSE

Assessing the morphologic properties of cells in microscopy images is an important task to evaluate cell health, identity, and purity. Typically, subjective visual assessments are accomplished by an experienced researcher. This subjective human step makes transfer of the evaluation process from the laboratory to the cell manufacturing facility difficult and time consuming.

METHODS

Automated image analysis can provide rapid, objective measurements of cultured cells, greatly aiding manufacturing, regulatory, and research goals. Automated algorithms for classifying images based on appearance characteristics typically either extract features from the image and use those features for classification or use the images directly as input to the classification algorithm. In this study we have developed both feature and nonfeature extraction methods for automatically measuring "cobblestone" structure in human retinal pigment epithelial (RPE) cell cultures.

RESULTS

A new approach using image compression combined with a Kolmogorov complexity-based distance metric enables robust classification of microscopy images of RPE cell cultures. The automated measurements corroborate determinations made by experienced cell biologists. We have also developed an approach for using steerable wavelet filters for extracting features to characterize the individual cellular junctions.

CONCLUSIONS

Two image analysis techniques enable robust and accurate characterization of the cobblestone morphology that is indicative of viable RPE cultures for therapeutic applications.

A Genome-wide RNAi Screen for Microtubule Bundle Formation and Lysosome Motility Regulation in Drosophila S2 Cells

Long-distance intracellular transport of organelles, mRNA, and proteins ("cargo") occurs along the microtubule cytoskeleton by the action of kinesin and dynein motor proteins, but the vast network of factors involved in regulating intracellular cargo transport are still unknown. We capitalize on the Drosophila melanogaster S2 model cell system to monitor lysosome transport along microtubule bundles, which require enzymatically active kinesin-1 motor protein for their formation. We use an automated tracking program and a naive Bayesian classifier for the multivariate motility data to analyze 15,683 gene phenotypes and find 98 proteins involved in regulating lysosome motility along microtubules and 48 involved in the formation of microtubule filled processes in S2 cells. We identify innate immunity genes, ion channels, and signaling proteins having a role in lysosome motility regulation and find an unexpected relationship between the dynein motor, Rab7a, and lysosome motility regulation.

CellProfiler Tracer: exploring and validating high-throughput, time-lapse microscopy image data

Background

Time-lapse analysis of cellular images is an important and growing need in biology. Algorithms for cell tracking are widely available; what researchers have been missing is a single open-source software package to visualize standard tracking output (from software like CellProfiler) in a way that allows convenient assessment of track quality, especially for researchers tuning tracking parameters for high-content time-lapse experiments. This makes quality assessment and algorithm adjustment a substantial challenge, particularly when dealing with hundreds of time-lapse movies collected in a high-throughput manner.

Results

We present CellProfiler Tracer, a free and open-source tool that complements the object tracking functionality of the CellProfiler biological image analysis package. Tracer allows multi-parametric morphological data to be visualized on object tracks, providing visualizations that have already been validated within the scientific community for time-lapse experiments, and combining them with simple graph-based measures for highlighting possible tracking artifacts.

Conclusions

CellProfiler Tracer is a useful, free tool for inspection and quality control of object tracking data, available from http://www.cellprofiler.org/tracer/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0759-x) contains supplementary material, which is available to authorized users.

Shaped 3D singular spectrum analysis for quantifying gene expression, with application to the early zebrafish embryo

Recent progress in microscopy technologies, biological markers, and automated processing methods is making possible the development of gene expression atlases at cellular-level resolution over whole embryos. Raw data on gene expression is usually very noisy. This noise comes from both experimental (technical/methodological) and true biological sources (from stochastic biochemical processes). In addition, the cells or nuclei being imaged are irregularly arranged in 3D space. This makes the processing, extraction, and study of expression signals and intrinsic biological noise a serious challenge for 3D data, requiring new computational approaches. Here, we present a new approach for studying gene expression in nuclei located in a thick layer around a spherical surface. The method includes depth equalization on the sphere, flattening, interpolation to a regular grid, pattern extraction by Shaped 3D singular spectrum analysis (SSA), and interpolation back to original nuclear positions. The approach is demonstrated on several examples of gene expression in the zebrafish egg (a model system in vertebrate development). The method is tested on several different data geometries (e.g., nuclear positions) and different forms of gene expression patterns. Fully 3D datasets for developmental gene expression are becoming increasingly available; we discuss the prospects of applying 3D-SSA to data processing and analysis in this growing field.

Survey statistics of automated segmentations applied to optical imaging of mammalian cells

BACKGROUND

The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements.

METHODS

We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories.

RESULTS

The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue.

CONCLUSIONS

The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html.

Effects of nitric oxide on stem cell therapy

The use of stem cells as a research tool and a therapeutic vehicle has demonstrated their great potential in the treatment of various diseases. With unveiling of nitric oxide synthase (NOS) universally present at various levels in nearly all types of body tissues, the potential therapeutic implication of nitric oxide (NO) has been magnified, and thus scientists have explored new treatment strategies involved with stem cells and NO against various diseases. As the functionality of NO encompasses cardiovascular, neuronal and immune systems, NO is involved in stem cell differentiation, epigenetic regulation and immune suppression. Stem cells trigger cellular responses to external signals on the basis of both NO specific pathways and concerted action with endogenous compounds including stem cell regulators. As potency and interaction of NO with stem cells generally depend on the concentrations of NO and the presence of the cofactors at the active site, the suitable carriers for NO delivery is integral for exerting maximal efficacy of stem cells. The innovative utilization of NO functionality and involved mechanisms would invariably alter the paradigm of therapeutic application of stem cells. Future prospects in NO-involved stem cell research which promises to enhance drug discovery efforts by opening new era to improve drug efficacy, reduce drug toxicity and understand disease mechanisms and pathways, were also addressed.

Computational Image Analysis Reveals Intrinsic Multigenerational Differences between Anterior and Posterior Cerebral Cortex Neural Progenitor Cells

Time-lapse microscopy can capture patterns of development through multiple divisions for an entire clone of proliferating cells. Images are taken every few minutes over many days, generating data too vast to process completely by hand. Computational analysis of this data can benefit from occasional human guidance. Here we combine improved automated algorithms with minimized human validation to produce fully corrected segmentation, tracking, and lineaging results with dramatic reduction in effort. A web-based viewer provides access to data and results. The improved approach allows efficient analysis of large numbers of clones. Using this method, we studied populations of progenitor cells derived from the anterior and posterior embryonic mouse cerebral cortex, each growing in a standardized culture environment. Progenitors from the anterior cortex were smaller, less motile, and produced smaller clones compared to those from the posterior cortex, demonstrating cell-intrinsic differences that may contribute to the areal organization of the cerebral cortex.

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Open-source automated software designed to track stem/progenitor clones in time-lapse movies

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Software tools for easy data validation and visualization greatly improve efficiency

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Lineage tree reconstruction from hundreds of embryonic mouse forebrain clones

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Intrinsic differences in progenitor behavior from anterior/posterior cerebral cortex

Extracting meaning from biological imaging data

Biological imaging continues to improve, capturing continually longer-term, richer, and more complex data, penetrating deeper into live tissue. How do we gain insight into the dynamic processes of disease and development from terabytes of multidimensional image data? Here I describe a collaborative approach to extracting meaning from biological imaging data. The collaboration consists of teams of biologists and engineers working together. Custom computational tools are built to best exploit application-specific knowledge in order to visualize and analyze large and complex data sets. The image data are summarized, extracting and modeling the features that capture the objects and relationships in the data. The summarization is validated, the results visualized, and errors corrected as needed. Finally, the customized analysis and visualization tools together with the image data and the summarization results are shared. This Perspective provides a brief guide to the mathematical ideas that rigorously quantify the notion of extracting meaning from biological image, and to the practical approaches that have been used to apply these ideas to a wide range of applications in cell and tissue optical imaging.

Multisensory interface for 5D stem cell image volumes

Biological imaging of live cell and tissue using 3D microscopy is able to capture time-lapse image sequences showing multiple molecular markers labeling different biological structures simultaneously. In order to analyze this complex multi-dimensional image sequence data, there is a need for automated quantitative algorithms, and for methods to visualize and interact with both the data and the analytical results. Traditional computational human input devices such as the keyboard and mouse are no longer adequate for complex tasks such as manipulating and navigating 3+ dimensional volumes. In this paper, we have developed a new interaction system for interfacing with big data sets using the human visual system together with touch, force and audio feedback. This system includes real-time dynamic 3D visualization, haptic interaction via exoskeletal glove, and tonal auditory components that seamlessly create an immersive environment for efficient qualitative analysis.