The effort involved in accurately tracing cell lineages, even with computer assistance, has limited the application of in vivo imaging in the task of tying together genetic, molecular and anatomical level understandings of cell migration and proliferation. Quantitative image analysis approaches are required for this. Specifically, this proposal describes a software tool for accurately tracing cell lineages in fluorescence labeled time lapse microscopy datasets. This will allow lineaging of C. elegans through the 10th and final round of cell division with less than 1% error throughout the whole lineage. Comparable levels of accuracy will be achievable in zebrafish at least up until the 12,000 cell stage. This approach is novel in not simply seeking greater accuracy, but in also encompassing a probabilistic framework for handling cell lineages that allows useful quantitative analysis even when errors are present. Multiple lineages can be compared, and where differences between lineages exist, these will be classified as most likely to be (1) errors, (2) evidence of a mutant phenotype, or (3) natural variability within the lineage. This requires the completion of three tasks:
(Aim 1) Design and implement a reliable method for nuclear detection.
(Aim 2) Build a global tracking system that can create an accurate lineage tree from detected cells in spite of occasional detection errors.
(Aim 3) Create a system for comparing multiple lineages from different embryos, flagging discrepancies and provisionally classifying them as errors, mutant phenotypes or natural variability. To accomplish this, nuclei are detected and their boundaries traced. Detected nuclei are then linked through time to create a globally coherent lineage using a model of cell division to quantify and manage ambiguity in matching. Probabilistic confidences are associated with each nucleus detected, and each link between nuclei. Differences between lineages can be classified based on confidence values and consensus among lineages as errors or genuine. This lineaging tool will enable novel in vivo high throughput research methodologies in C. elegans and zebrafish. In the long run, this set of tools can be adapted, with relatively minor effort, for any of the models in which in vivo imaging is becoming more prominent such as drosophila, or mouse. In doing so, it will advance basic research efforts to understand the mechanisms underlying a variety of developmental and cell behaviors. As many mechanisms are conserved between these models organisms and humans, this will ultimately have a significant impact on areas of public health such as aging and cancer research.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Postdoctoral Individual National Research Service Award (F32)
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Special Emphasis Panel (ZRG1-F14-C (20))
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Flicker, Paula F
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Sloan-Kettering Institute for Cancer Research
New York
United States
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Santella, Anthony; Du, Zhuo; Nowotschin, Sonja et al. (2010) A hybrid blob-slice model for accurate and efficient detection of fluorescence labeled nuclei in 3D. BMC Bioinformatics 11:580