Intermittent Stem Cell Cycling Balances Self-Renewal and Senescence of the C. elegans Germ Line.

Self-renewing organs often experience a decline in function in the course of aging. It is unclear whether chronological age or external factors control this decline, or whether it is driven by stem cell self-renewal-for example, because cycling cells exhaust their replicative capacity and become senescent. Here we assay the relationship between stem cell cycling and senescence in the Caenorhabditis elegans reproductive system, defining this senescence as the progressive decline in "reproductive capacity," i.e. in the number of progeny that can be produced until cessation of reproduction. We show that stem cell cycling diminishes remaining reproductive capacity, at least in part through the DNA damage response. Paradoxically, gonads kept under conditions that preclude reproduction keep cycling and producing cells that undergo apoptosis or are laid as unfertilized gametes, thus squandering reproductive capacity. We show that continued activity is in fact beneficial inasmuch as gonads that are active when reproduction is initiated have more sustained early progeny production. Intriguingly, continued cycling is intermittent-gonads switch between active and dormant states-and in all likelihood stochastic. Other organs face tradeoffs whereby stem cell cycling has the beneficial effect of providing freshly-differentiated cells and the detrimental effect of increasing the likelihood of cancer or senescence; stochastic stem cell cycling may allow for a subset of cells to preserve proliferative potential in old age, which may implement a strategy to deal with uncertainty as to the total amount of proliferation to be undergone over an organism's lifespan.

Analysis of in vivo single cell behavior by high throughput, human-in-the-loop segmentation of three-dimensional images.

BACKGROUND: Analysis of single cells in their native environment is a powerful method to address key questions in developmental systems biology. Confocal microscopy imaging of intact tissues, followed by automatic image segmentation, provides a means to conduct cytometric studies while at the same time preserving crucial information about the spatial organization of the tissue and morphological features of the cells. This technique is rapidly evolving but is still not in widespread use among research groups that do not specialize in technique development, perhaps in part for lack of tools that automate repetitive tasks while allowing experts to make the best use of their time in injecting their domain-specific knowledge. RESULTS: Here we focus on a well-established stem cell model system, the C. elegans gonad, as well as on two other model systems widely used to study cell fate specification and morphogenesis: the pre-implantation mouse embryo and the developing mouse olfactory epithelium. We report a pipeline that integrates machine-learning-based cell detection, fast human-in-the-loop curation of these detections, and running of active contours seeded from detections to segment cells. The procedure can be bootstrapped by a small number of manual detections, and outperforms alternative pieces of software we benchmarked on C. elegans gonad datasets. Using cell segmentations to quantify fluorescence contents, we report previously-uncharacterized cell behaviors in the model systems we used. We further show how cell morphological features can be used to identify cell cycle phase; this provides a basis for future tools that will streamline cell cycle experiments by minimizing the need for exogenous cell cycle phase labels. CONCLUSIONS: High-throughput 3D segmentation makes it possible to extract rich information from images that are routinely acquired by biologists, and provides insights - in particular with respect to the cell cycle - that would be difficult to derive otherwise.

Layered object models for image segmentation.

We formulate a layered model for object detection and image segmentation. We describe a generative probabilistic model that composites the output of a bank of object detectors in order to define shape masks and explain the appearance, depth ordering, and labels of all pixels in an image. Notably, our system estimates both class labels and object instance labels. Building on previous benchmark criteria for object detection and image segmentation, we define a novel score that evaluates both class and instance segmentation. We evaluate our system on the PASCAL 2009 and 2010 segmentation challenge data sets and show good test results with state-of-the-art performance in several categories, including segmenting humans.