Dataset Information

Vertebrate neural stem cell segmentation, tracking and lineaging with validation and editing.

ABSTRACT: This protocol and the accompanying software program called LEVER (lineage editing and validation) enable quantitative automated analysis of phase-contrast time-lapse images of cultured neural stem cells. Images are captured at 5-min intervals over a period of 5-15 d as the cells proliferate and differentiate. LEVER automatically segments, tracks and generates lineage trees of the stem cells from the image sequence. In addition to generating lineage trees capturing the population dynamics of clonal development, LEVER extracts quantitative phenotypic measurements of cell location, shape, movement and size. When available, the system can include biomolecular markers imaged using fluorescence. It then displays the results to the user for highly efficient inspection and editing to correct any errors in the segmentation, tracking or lineaging. To enable high-throughput inspection, LEVER incorporates features for rapid identification of errors and for learning from user-supplied corrections to automatically identify and correct related errors.

SUBMITTER: Winter M

PROVIDER: S-EPMC3313840 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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Publications

Vertebrate neural stem cell segmentation, tracking and lineaging with validation and editing.

Winter Mark M Wait Eric E Roysam Badrinath B Goderie Susan K SK Ali Rania Ahmed Naguib RA Kokovay Erzsebet E Temple Sally S Cohen Andrew R AR

Nature protocols 20111117 12

This protocol and the accompanying software program called LEVER (lineage editing and validation) enable quantitative automated analysis of phase-contrast time-lapse images of cultured neural stem cells. Images are captured at 5-min intervals over a period of 5-15 d as the cells proliferate and differentiate. LEVER automatically segments, tracks and generates lineage trees of the stem cells from the image sequence. In addition to generating lineage trees capturing the population dynamics of clon ...[more]

PMID: 22094730

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Project description:BackgroundNeural stem cells are motile and proliferative cells that undergo mitosis, dividing to produce daughter cells and ultimately generating differentiated neurons and glia. Understanding the mechanisms controlling neural stem cell proliferation and differentiation will play a key role in the emerging fields of regenerative medicine and cancer therapeutics. Stem cell studies in vitro from 2-D image data are well established. Visualizing and analyzing large three dimensional images of intact tissue is a challenging task. It becomes more difficult as the dimensionality of the image data increases to include time and additional fluorescence channels. There is a pressing need for 5-D image analysis and visualization tools to study cellular dynamics in the intact niche and to quantify the role that environmental factors play in determining cell fate.ResultsWe present an application that integrates visualization and quantitative analysis of 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.ConclusionsBy exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. We combine unsupervised image analysis algorithms with an interactive 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.

Dataset Information

Vertebrate neural stem cell segmentation, tracking and lineaging with validation and editing.

Publications

Vertebrate neural stem cell segmentation, tracking and lineaging with validation and editing.

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