| 1. |
- Wählby, Carolina, et al.
(författare)
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High- and low-throughput scoring of fat mass and body fat distribution in C. elegans
- 2014
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Ingår i: Methods. - : Elsevier BV. - 1046-2023 .- 1095-9130. ; 68:3, s. 492-499
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Tidskriftsartikel (refereegranskat)abstract
- Fat accumulation is a complex phenotype affected by factors such as neuroendocrine signaling, feeding, activity, and reproductive output. Accordingly, the most informative screens for genes and compounds affecting fat accumulation would be those carried out in whole living animals. Caenorhabditis elegans is a well-established and effective model organism, especially for biological processes that involve organ systems and multicellular interactions, such as metabolism. Every cell in the transparent body of C. elegans is visible under a light microscope. Consequently, an accessible and reliable method to visualize worm lipid-droplet fat depots would make C. elegans the only metazoan in which genes affecting not only fat mass but also body fat distribution could be assessed at a genome-wide scale. Here we present a radical improvement in oil red O worm staining together with high-throughput image-based phenotyping. The three-step sample preparation method is robust, formaldehyde-free, and inexpensive, and requires only 15 min of hands-on time to process a 96-well plate. Together with our free and user-friendly automated image analysis package, this method enables C. elegans sample preparation and phenotype scoring at a scale that is compatible with genome-wide screens. Thus we present a feasible approach to small-scale phenotyping and large-scale screening for genetic and/or chemical perturbations that lead to alterations in fat quantity and distribution in whole animals.
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| 2. |
- Arganda-Carreras, Ignacio, et al.
(författare)
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Crowdsourcing the creation of image segmentation algorithms for connectomics
- 2015
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Ingår i: Frontiers in Neuroanatomy. - : Frontiers Media S.A.. - 1662-5129. ; 9:142
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Tidskriftsartikel (refereegranskat)abstract
- To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This "deep learning" approach has since become accepted as a standard for segmentation of FM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.
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| 3. |
- Wählby, Carolina, 1974-, et al.
(författare)
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An image analysis toolbox for high-throughput C. elegans assays
- 2012
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Ingår i: Nature Methods. - : Springer Science and Business Media LLC. - 1548-7091 .- 1548-7105. ; 9:7, s. 714-716
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Tidskriftsartikel (refereegranskat)abstract
- We present a toolbox for high-throughput screening of image-based Caenorhabditis elegans phenotypes. The image analysis algorithms measure morphological phenotypes in individual worms and are effective for a variety of assays and imaging systems from different laboratories. The toolbox is available via the open-source CellProfiler project and enables objective scoring of whole-animal high-throughput image-based assays using this unique model organism for the study of diverse biological pathways relevant to human disease.
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