Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences

Results: We present an application that enables the quantitative analysis of multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks. Conclusions: By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. There is a pressing need for visualization and analysis tools for 5-D live cell image data. We combine accurate unsupervised processes with an intuitive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.Comment: BioVis 2014 conferenc

Spatiotemporal Identification of Cell Divisions Using Symmetry Properties in Time-Lapse Phase Contrast Microscopy

A variety of biological and pharmaceutical studies, such as for anti-cancer drugs, require the quantification of cell responses over long periods of time. This is performed with time-lapse video microscopy that gives a long sequence of frames. For this purpose, phase contrast imaging is commonly used since it is minimally invasive. The cell responses of interest in this study are the mitotic cell divisions. Their manual measurements are tedious, subjective, and restrictive. This study introduces an automated method for these measurements. The method starts with preprocessing for restoration and reconstruction of the phase contrast time-lapse sequences. The data are first restored from intensity non-uniformities. Subsequently, the circular symmetry of the contour of the mitotic cells in phase contrast images is used by applying a Circle Hough Transform (CHT) to reconstruct the entire cells. The CHT is also enhanced with the ability to “vote” exclusively towards the center of curvature. The CHT image sequence is then registered for misplacements between successive frames. The sequence is subsequently processed to detect cell centroids in individual frames and use them as starting points to form spatiotemporal trajectories of cells along the positive as well as along the negative time directions, that is, anti-causally. The connectivities of different trajectories enhanced by the symmetry of the trajectories of the daughter cells provide as topological by-products the events of cell divisions together with the corresponding entries into mitoses as well as exits from cytokineses. The experiments use several experimental video sequences from three different cell lines with many cells undergoing mitoses and divisions. The quantitative validations of the results of the processing demonstrate the high performance and efficiency of the method

Time-Lapse Microscopy

Time-lapse microscopy is a powerful, versatile and constantly developing tool for real-time imaging of living cells. This review outlines the advances of time-lapse microscopy and refers to the most interesting reports, thus pointing at the fact that the modern biology and medicine are entering the thrilling and promising age of molecular cinematography

Astroglial-axonal interactions during early stages of myelination in mixed cultures using in vitro and ex vivo imaging techniques

<b>Background</b><p></p> Myelination is a very complex process that requires the cross talk between various neural cell types. Previously, using cytosolic or membrane associated GFP tagged neurospheres, we followed the interaction of oligodendrocytes with axons using time-lapse imaging in vitro and ex vivo and demonstrated dynamic changes in cell morphology. In this study we focus on GFP tagged astrocytes differentiated from neurospheres and their interactions with axons.<p></p> <b>Results</b><p></p> We show the close interaction of astrocyte processes with axons and with oligodendrocytes in mixed mouse spinal cord cultures with formation of membrane blebs as previously seen for oligodendrocytes in the same cultures. When GFP-tagged neurospheres were transplanted into the spinal cord of the dysmyelinated shiverer mouse, confirmation of dynamic changes in cell morphology was provided and a prevalence for astrocyte differentiation compared with oligodendroglial differentiation around the injection site. Furthermore, we were able to image GFP tagged neural cells in vivo after transplantation and the cells exhibited similar membrane changes as cells visualised in vitro and ex vivo.<p></p> <b>Conclusion</b><p></p> These data show that astrocytes exhibit dynamic cell process movement and changes in their membrane topography as they interact with axons and oligodendrocytes during the process of myelination, with the first demonstration of bleb formation in astrocytes

Live Imaging at the Onset of Cortical Neurogenesis Reveals Differential Appearance of the Neuronal Phenotype in Apical versus Basal Progenitor Progeny

The neurons of the mammalian brain are generated by progenitors dividing either at the apical surface of the ventricular zone (neuroepithelial and radial glial cells, collectively referred to as apical progenitors) or at its basal side (basal progenitors, also called intermediate progenitors). For apical progenitors, the orientation of the cleavage plane relative to their apical-basal axis is thought to be of critical importance for the fate of the daughter cells. For basal progenitors, the relationship between cell polarity, cleavage plane orientation and the fate of daughter cells is unknown. Here, we have investigated these issues at the very onset of cortical neurogenesis. To directly observe the generation of neurons from apical and basal progenitors, we established a novel transgenic mouse line in which membrane GFP is expressed from the beta-III-tubulin promoter, an early pan-neuronal marker, and crossed this line with a previously described knock-in line in which nuclear GFP is expressed from the Tis21 promoter, a pan-neurogenic progenitor marker. Mitotic Tis21-positive basal progenitors nearly always divided symmetrically, generating two neurons, but, in contrast to symmetrically dividing apical progenitors, lacked apical-basal polarity and showed a nearly randomized cleavage plane orientation. Moreover, the appearance of beta-III-tubulin–driven GFP fluorescence in basal progenitor-derived neurons, in contrast to that in apical progenitor-derived neurons, was so rapid that it suggested the initiation of the neuronal phenotype already in the progenitor. Our observations imply that (i) the loss of apical-basal polarity restricts neuronal progenitors to the symmetric mode of cell division, and that (ii) basal progenitors initiate the expression of neuronal phenotype already before mitosis, in contrast to apical progenitors

Pipeline for Tracking Neural Progenitor Cells

Automated methods for neural stem cell lineage construction become increasingly important due to the large amount of data produced from time lapse imagery of in vitro cell growth experiments. Segmentation algorithms with the ability to adapt to the problem at hand and robust tracking methods play a key role in constructing these lineages. We present here a tracking pipeline based on learning a dictionary of discriminative image patches for segmentation and a graph formulation of the cell matching problem incorporating topology changes and acknowledging the fact that segmentation errors do occur. A matched filter for detection of mitotic candidates is constructed to ensure that cell division is only allowed in the model when relevant. Potentially the combination of these robust methods can simplify the initiation of cell lineage construction and extraction of statistics