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

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  • Additional Information
    • Source:
      Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE
    • Publication Information:
      Original Publication: [London] : BioMed Central, 2000-
    • Subject Terms:
      High-Throughput Screening Assays* ; Software*; Caenorhabditis elegans/*growth & development ; Image Processing, Computer-Assisted/*methods ; Imaging, Three-Dimensional/*methods ; Olfactory Mucosa/*cytology ; Single-Cell Analysis/*methods; Algorithms ; Animals ; Blastocyst/cytology ; Blastocyst/metabolism ; Caenorhabditis elegans/metabolism ; Cell Cycle/physiology ; Cells, Cultured ; Computational Biology/methods ; Female ; Gonads/cytology ; Gonads/metabolism ; Humans ; Male ; Mice ; Microscopy, Confocal/methods ; Olfactory Mucosa/metabolism
    • Abstract:
      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.
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    • Grant Information:
      R01-GM102635 United States GM NIGMS NIH HHS; T32 EB009418 United States EB NIBIB NIH HHS; T32-EB009418 United States EB NIBIB NIH HHS; T15-LM007443 United States LM NLM NIH HHS; T32-HD060555 United States HD NICHD NIH HHS; T32 HD060555 United States HD NICHD NIH HHS; CA-62203 United States CA NCI NIH HHS; P50 GM076516 United States GM NIGMS NIH HHS; P30 CA062203 United States CA NCI NIH HHS; R21 AG042125 United States AG NIA NIH HHS; R01 GM102635 United States GM NIGMS NIH HHS; T15 LM007443 United States LM NLM NIH HHS; P50-GM076516 United States GM NIGMS NIH HHS; R21-AG042125 United States AG NIA NIH HHS
    • Publication Date:
      Date Created: 20151127 Date Completed: 20160621 Latest Revision: 20200306
    • Publication Date:
      20221213
    • Accession Number:
      PMC4659165
    • Accession Number:
      10.1186/s12859-015-0814-7
    • Accession Number:
      26607933