The goal of this research is to develop a new class of computational tools to analyze the spatiotemporal dynamics of clonal (family tree) development of neural stem cells (NSCs) by analyzing time lapse microscopy images. These computational tools will automatically segment or delineate individual cells in each image frame. The cells will be tracked to establish temporal correspondences. Lineaging will establish parent-child relationships among the cells and generate the lineage tree, a powerful representation that captures many of the important properties of clonal development. A user interface will be developed for viewing and editing the results of the automatic segmentation, tracking and lineaging. A new immunostaining multiplexing tool will provide detailed information on the types of neurons produced after all of the stem cells have terminally differentiated. Computational analyses of the segmentation, tracking, lineaging, and immunostaining results have the potential to enable a variety of important new experiments in developmental and cancer biology. In previous research, we developed uniquely sensitive tools based on algorithmic information theory that use a multivariate, multiresolution approach to analyzing biological image sequence data. In one exciting discovery, these software tools established that the fate of retinal stem cells can be accurately predicted from their dynamic behaviors. Here, we will apply these novel "computational sensing" approaches to studying stem cell clonal development. The project will proceed by capturing image sequence data showing clonal development from a single neural stem cell through multiple rounds of cell division to the ultimate differentiated progeny. The software tools will be developed in the context of three important biological questions. First, by imaging stem cells from different regions of the mouse cerebral cortex, we will identify whether region specification is encoded in lineage trees. Second, we will consider the role of FGF 10, an environmental factor related to regional specification, in imparting area specific developmental patterns to neurons from the cerebral cortex. Finally, we will analyze the role that syndecan1, an in vivo niche factor that causes lineage progression of NSCs and is upregulated in brain tumors, plays on NSC clonal development. These are fundamental questions in neurobiology that cannot be addressed without these new computational tools. The ultimate goal of this project is to develop a set of tools that is widely and generally applicable and that enables a new high throughput analysis approach for studying stem and tumorigenic cell clonal development. These tools will be developed in the context of three important questions from developmental biology. These questions concerning the fundamental dynamic behavior of NSCs and the influence of specific exogenous factors on this behavior require analysis of hundreds of lineage trees to define statistically meaningful differences. This was not possible in the past using manually constructed lineage trees. With the development of the computational tools described here, such analyses will now be within reach.
The goal of this research is to develop computational tools for analyzing how neural stem cells develop into brain tissue. By analyzing image sequence data obtained from live cell time-lapse microscopy, we will follow individual stem cells until they complete the process of differentiating into neurons and analyze their patterns of motion, shape, association and reproduction under different conditions of disease and development. Understanding how cell diversity is generated during development has important implications in developmental disorders such as autism and mental retardation and will be critical to produce specific types of neurons or glia for disease modeling and for future transplantation therapies.
|De La Hoz, Edgar Cardenas; Winter, Mark R; Apostolopoulou, Maria et al. (2016) Measuring Process Dynamics and Nuclear Migration for Clones of Neural Progenitor Cells. Comput Vis ECCV 9913:291-305|
|Cohen, Andrew R; VitÃ¡nyi, Paul M B (2015) Normalized Compression Distance of Multisets with Applications. IEEE Trans Pattern Anal Mach Intell 37:1602-14|
|Winter, Mark R; Liu, Mo; Monteleone, David et al. (2015) Computational Image Analysis Reveals Intrinsic Multigenerational Differences between Anterior and Posterior Cerebral Cortex Neural Progenitor Cells. Stem Cell Reports 5:609-20|
|Cohen, Andrew R (2014) Extracting meaning from biological imaging data. Mol Biol Cell 25:3470-3|
|Mankowski, Walter C; Winter, Mark R; Wait, Eric et al. (2014) Segmentation of occluded hematopoietic stem cells from tracking. Conf Proc IEEE Eng Med Biol Soc 2014:5510-3|
|Wait, Eric; Winter, Mark; Bjornsson, Chris et al. (2014) Visualization and correction of automated segmentation, tracking and lineaging from 5-D stem cell image sequences. BMC Bioinformatics 15:328|
|Chenouard, Nicolas; Smal, Ihor; de Chaumont, Fabrice et al. (2014) Objective comparison of particle tracking methods. Nat Methods 11:281-9|
|Winter, Mark R; Fang, Cheng; Banker, Gary et al. (2012) Axonal transport analysis using Multitemporal Association Tracking. Int J Comput Biol Drug Des 5:35-48|
|Clark, Brian S; Winter, Mark; Cohen, Andrew R et al. (2011) Generation of Rab-based transgenic lines for in vivo studies of endosome biology in zebrafish. Dev Dyn 240:2452-65|
|Winter, Mark; Wait, Eric; Roysam, Badrinath et al. (2011) Vertebrate neural stem cell segmentation, tracking and lineaging with validation and editing. Nat Protoc 6:1942-52|