EmbryoMiner: A new framework for interactive knowledge discovery in large-scale cell tracking data of developing embryos.

State-of-the-art light-sheet and confocal microscopes allow recording of entire embryos in 3D and over time (3D+t) for many hours. Fluorescently labeled structures can be segmented and tracked automatically in these terabyte-scale 3D+t images, resulting in thousands of cell migration trajectories that provide detailed insights to large-scale tissue reorganization at the cellular level. Here we present EmbryoMiner, a new interactive open-source framework suitable for in-depth analyses and comparisons of entire embryos, including an extensive set of trajectory features. Starting at the whole-embryo level, the framework can be used to iteratively focus on a region of interest within the embryo, to investigate and test specific trajectory-based hypotheses and to extract quantitative features from the isolated trajectories. Thus, the new framework provides a valuable new way to quantitatively compare corresponding anatomical regions in different embryos that were manually selected based on biological prior knowledge. As a proof of concept, we analyzed 3D+t light-sheet microscopy images of zebrafish embryos, showcasing potential user applications that can be performed using the new framework.

On the statistical analysis of single cell lineage trees.

Stem cells play a central role in the regeneration and repair of multicellular organisms. However, it remains far from trivial to reliably identify them. Despite decades of work, current techniques to isolate hematopoietic stem cells (HSCs) based on cell-surface markers only result in 50% purity, i.e. half of the sorted cells are not stem cells when functionally tested. Modern microscopy techniques allow us to follow single cells and their progeny for up to weeks in vitro, while recording the cell fates and lifetime of each individual cell. This cell tracking generates so-called lineage trees. Here, we propose statistical techniques to determine if the initial cell in a lineage tree was a HSC. We apply these techniques to murine hematopoietic lineage trees, revealing that 18% of the trees in our HSC dataset display a unique signature, and this signature is compatible with these trees having started from a true stem cell. Assuming 50% purity of HSC empirical datasets, this corresponds to a 0.35 power of the test, and the type-1-error is estimated to be 0.047. In summary, this study shows that statistical analysis of lineage trees could improve the classification of cells, which is currently done based on bio-markers only. Our statistical techniques are not limited to mammalian stem cell biology. Any type of single cell lineage trees, be it from bacteria, single cell eukaryotes, or single cells in a multicellular organism can be investigated. We expect this to contribute to a better understanding of the molecules influencing cellular dynamics at the single cell level.

Mathematical imaging methods for mitosis analysis in live-cell phase contrast microscopy.

In this paper we propose a workflow to detect and track mitotic cells in time-lapse microscopy image sequences. In order to avoid the requirement for cell lines expressing fluorescent markers and the associated phototoxicity, phase contrast microscopy is often preferred over fluorescence microscopy in live-cell imaging. However, common specific image characteristics complicate image processing and impede use of standard methods. Nevertheless, automated analysis is desirable due to manual analysis being subjective, biased and extremely time-consuming for large data sets. Here, we present the following workflow based on mathematical imaging methods. In the first step, mitosis detection is performed by means of the circular Hough transform. The obtained circular contour subsequently serves as an initialisation for the tracking algorithm based on variational methods. It is sub-divided into two parts: in order to determine the beginning of the whole mitosis cycle, a backwards tracking procedure is performed. After that, the cell is tracked forwards in time until the end of mitosis. As a result, the average of mitosis duration and ratios of different cell fates (cell death, no division, division into two or more daughter cells) can be measured and statistics on cell morphologies can be obtained. All of the tools are featured in the user-friendly MATLAB®Graphical User Interface MitosisAnalyser.

TrackMate: An open and extensible platform for single-particle tracking.

We present TrackMate, an open source Fiji plugin for the automated, semi-automated, and manual tracking of single-particles. It offers a versatile and modular solution that works out of the box for end users, through a simple and intuitive user interface. It is also easily scriptable and adaptable, operating equally well on 1D over time, 2D over time, 3D over time, or other single and multi-channel image variants. TrackMate provides several visualization and analysis tools that aid in assessing the relevance of results. The utility of TrackMate is further enhanced through its ability to be readily customized to meet specific tracking problems. TrackMate is an extensible platform where developers can easily write their own detection, particle linking, visualization or analysis algorithms within the TrackMate environment. This evolving framework provides researchers with the opportunity to quickly develop and optimize new algorithms based on existing TrackMate modules without the need of having to write de novo user interfaces, including visualization, analysis and exporting tools. The current capabilities of TrackMate are presented in the context of three different biological problems. First, we perform Caenorhabditis-elegans lineage analysis to assess how light-induced damage during imaging impairs its early development. Our TrackMate-based lineage analysis indicates the lack of a cell-specific light-sensitive mechanism. Second, we investigate the recruitment of NEMO (NF-κB essential modulator) clusters in fibroblasts after stimulation by the cytokine IL-1 and show that photodamage can generate artifacts in the shape of TrackMate characterized movements that confuse motility analysis. Finally, we validate the use of TrackMate for quantitative lifetime analysis of clathrin-mediated endocytosis in plant cells.

Quantifying intrinsic and extrinsic control of single-cell fates in cancer and stem/progenitor cell pedigrees with competing risks analysis.

The molecular control of cell fate and behaviour is a central theme in biology. Inherent heterogeneity within cell populations requires that control of cell fate is studied at the single-cell level. Time-lapse imaging and single-cell tracking are powerful technologies for acquiring cell lifetime data, allowing quantification of how cell-intrinsic and extrinsic factors control single-cell fates over time. However, cell lifetime data contain complex features. Competing cell fates, censoring, and the possible inter-dependence of competing fates, currently present challenges to modelling cell lifetime data. Thus far such features are largely ignored, resulting in loss of data and introducing a source of bias. Here we show that competing risks and concordance statistics, previously applied to clinical data and the study of genetic influences on life events in twins, respectively, can be used to quantify intrinsic and extrinsic control of single-cell fates. Using these statistics we demonstrate that 1) breast cancer cell fate after chemotherapy is dependent on p53 genotype; 2) granulocyte macrophage progenitors and their differentiated progeny have concordant fates; and 3) cytokines promote self-renewal of cardiac mesenchymal stem cells by symmetric divisions. Therefore, competing risks and concordance statistics provide a robust and unbiased approach for evaluating hypotheses at the single-cell level.

Segmenting and Tracking Multiple Dividing Targets Using ilastik.

Tracking crowded cells or other targets in biology is often a challenging task due to poor signal-to-noise ratio, mutual occlusion, large displacements, little discernibility, and the ability of cells to divide. We here present an open source implementation of conservation tracking (Schiegg et al., IEEE international conference on computer vision (ICCV). IEEE, New York, pp 2928-2935, 2013) in the ilastik software framework. This robust tracking-by-assignment algorithm explicitly makes allowance for false positive detections, undersegmentation, and cell division. We give an overview over the underlying algorithm and parameters, and explain the use for a light sheet microscopy sequence of a Drosophila embryo. Equipped with this knowledge, users will be able to track targets of interest in their own data.

A Novel Multiobject Tracking Approach in the Presence of Collision and Division.

This paper aims to develop a general framework for accurately tracking and quantitatively characterizing multiple cells (objects) when collision and division between cells arise. Through introducing three types of interaction events among cells, namely, independence, collision, and division, the corresponding dynamic models are defined and an augmented interacting multiple model particle filter tracking algorithm is first proposed for spatially adjacent cells with varying size. In addition, to reduce the ambiguity of correspondence between frames, both the estimated cell dynamic parameters and cell size are further utilized to identify cells of interest. The experiments have been conducted on two real cell image sequences characterized with cells collision, division, or number variation, and the resulting dynamic parameters such as instant velocity, turn rate were obtained and analyzed.

Segmentation and Tracking of Lymphocytes Based on Modified Active Contour Models in Phase Contrast Microscopy Images.

The paper proposes an improved active contour model for segmenting and tracking accurate boundaries of the single lymphocyte in phase-contrast microscopic images. Active contour models have been widely used in object segmentation and tracking. However, current external-force-inspired methods are weak at handling low-contrast edges and suffer from initialization sensitivity. In order to segment low-contrast boundaries, we combine the region information of the object, extracted by morphology gray-scale reconstruction, and the edge information, extracted by the Laplacian of Gaussian filter, to obtain an improved feature map to compute the external force field for the evolution of active contours. To alleviate initial location sensitivity, we set the initial contour close to the real boundaries by performing morphological image processing. The proposed method was tested on live lymphocyte images acquired through the phase-contrast microscope from the blood samples of mice, and comparative experimental results showed the advantages of the proposed method in terms of the accuracy and the speed. Tracking experiments showed that the proposed method can accurately segment and track lymphocyte boundaries in microscopic images over time even in the presence of low-contrast edges, which will provide a good prerequisite for the quantitative analysis of lymphocyte morphology and motility.

Automated characterization and parameter-free classification of cell tracks based on local migration behavior.

Cell migration is the driving force behind the dynamics of many diverse biological processes. Even though microscopy experiments are routinely performed today by which populations of cells are visualized in space and time, valuable information contained in image data is often disregarded because statistical analyses are performed at the level of cell populations rather than at the single-cell level. Image-based systems biology is a modern approach that aims at quantitatively analyzing and modeling biological processes by developing novel strategies and tools for the interpretation of image data. In this study, we take first steps towards a fully automated characterization and parameter-free classification of cell track data that can be generally applied to tracked objects as obtained from image data. The requirements to achieve this aim include: (i) combination of different measures for single cell tracks, such as the confinement ratio and the asphericity of the track volume, and (ii) computation of these measures in a staggered fashion to retrieve local information from all possible combinations of track segments. We demonstrate for a population of synthetic cell tracks as well as for in vitro neutrophil tracks obtained from microscopy experiment that the information contained in the track data is fully exploited in this way and does not require any prior knowledge, which keeps the analysis unbiased and general. The identification of cells that show the same type of migration behavior within the population of all cells is achieved via agglomerative hierarchical clustering of cell tracks in the parameter space of the staggered measures. The recognition of characteristic patterns is highly desired to advance our knowledge about the dynamics of biological processes.

A strategy for identifying fluorescence intensity profiles of single rod-shaped cells.

The extraction of fluorescence intensity profiles of single cells from image data is a common challenge in cell biology. The manual segmentation of cells, the extraction of cell orientation and finally the extraction of intensity profiles are time-consuming tasks. This article proposes a routine for the segmentation of single rod-shaped cells (i.e. without neighboring cells in a distance of the cell length) from image data combined with an extraction of intensity distributions along the longitudinal cell axis under the aggravated conditions of (i) a low spatial resolution and (ii) lacking information on the imaging system i.e. the point spread function and signal-to-noise ratio. The algorithm named cipsa transfers a new approach from particle streak velocimetry to cell classification interpreting the rod-shaped as streak-like structures. An automatic reduction of systematic errors such as photobleaching and defocusing is included to guarantee robustness of the proposed approach under the described conditions and to the convenience of end-users unfamiliar with image processing. Performance of the algorithm has been tested on image sequences with high noise level produced by an overlay of different error sources. The developed algorithm provides a user-friendly, stand-alone procedure.

[Tracking of neural stem cells in high density image sequence based on Topological constraint combined with Hungarian algorithm].

Analysis of neural stem cells' movements is one of the important parts in the fields of cellular and biological research. The main difficulty existing in cells' movement study is whether the cells tracking system can simultaneously track and analyze thousands of neural stem cells (NSCs) automatically. We present a novel cells' tracking algorithm which is based on segmentation and data association in this paper, aiming to improve the tracking accuracy further in high density NSCs' image. Firstly, we adopted different methods of segmentation base on the characteristics of the two cell image sequences in our experiment. Then we formed a data association and constituted a coefficient matrix by all cells between two adjacent frames according to topological constraints. Finally we applied The Hungarian algorithm to implement inter-cells matching optimally. Cells' tracking can be achieved according to this model from the second frame to the last one in a sequence. Experimental results showed that this approaching method has higher accuracy compared with that using the topological constraints tracking alone. The final tracking accuracies of average of sequence I and sequence II have been improved 10.17% and 4%, respectively.

Effect of intrastriatal mesenchymal stromal cell injection on progression of a murine model of Krabbe disease.

One of a family of devastating lysosomal storage disorders, Krabbe disease is characterized by demyelination, psychosine accumulation, and inflammation. Affected infants rarely survive longer than 2 years. Using the twitcher mouse model of the disease, this study evaluated the potential of intrastriatal injection of adipose or bone marrow-derived mesenchymal stromal cells (MSCs) as a treatment option. Neonatal pups were injected with MSCs at 3-4 days of age and subjected to a battery of behavioral tests beginning at 15 days. While MSC injection failed to increase lifespan of twitchers, improvements in rotarod performance and twitching severity were observed at 27-38 days of age using MSCs derived from bone marrow. This study tested several different tasks developed in adult mice for evaluation of disease progression in immature twitchers. Rotarod was both reliable and extremely sensitive. Automated gait analysis using the Treadscan program was also useful for early evaluation of differences prior to overt gait dysfunction. Finally, this study represents the first use of the Stone T-maze in immature mice. Validation of rotarod and automated gait analysis for detection of subtle differences in disease progression is important for early stage efforts to develop treatments for juvenile disorders.

Topological constraint in high-density cells' tracking of image sequences.

The multi-target tracking in cell image sequences is the main difficulty in cells' locomotion study. Aim to study cells' complexity movement in high-density cells' image, this chapter has proposed a system of segmentation and tracking. The proposed tracking algorithm has combined overlapping and topological constraints with track inactive and active cells, respectively. In order to improve performance of algorithm, size factor has been introduced as a new restriction to quantification criterion of similarity based on Zhang's method. And the distance threshold for transforming segmented image into graph is adjusted on considering the local distribution of cells' district in one image. The improved algorithm has been tested in two different image sequences, which have high or low contrast ration separately. Experimental results show that our approach has improved tracking accuracy from 3% to 9% compared with Zhang's algorithm, especially when cells are in high density and cells' splitting occurred frequently. And the final tracking accuracy can reach 90.24% and 77.08%.