RNA Therapeutics Institute
Cell Biology | Cells | Musculoskeletal System | Nucleic Acids, Nucleotides, and Nucleosides
Single-molecule fluorescence in-situ hybridization (smFISH) provides direct access to the spatial relationship between nucleic acids and specific subcellular locations. The ability to precisely localize a messenger RNA can reveal key information about its regulation. Although smFISH is well established in cell culture or thin sections, methods for its accurate application to tissues are lacking. The utility of smFISH in thick tissue sections must overcome several challenges, including probe penetration of fixed tissue, accessibility of target mRNAs for probe hybridization, high fluorescent background, spherical aberration along the optical axis, and image segmentation of organelles. Here we describe how we overcame these obstacles to study mRNA localization in Drosophila larval muscle samples that approach 50 μm thickness. We use sample-specific optimization of smFISH, particle identification based on maximum likelihood testing, and 3-dimensional multiple-organelle segmentation. The latter allows using independent thresholds for different regions of interest within an image stack. Our approach therefore facilitates accurate measurement of mRNA location in thick tissues.
cell biology, single-molecule fluorescence in-situ hybridization, smFISH, messenger RNA, thick tissues, Drosophila
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The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
DOI of Published Version
bioRxiv 156091; doi: https://doi.org/10.1101/156091. Link to preprint on bioRxiv service.
Noma A, Smith C, Huisman M, Martin RM, Moore MJ, Grünwald D. (2017). Single-molecule FISH in Drosophila muscle reveals location dependent mRNA composition of megaRNPs [preprint]. University of Massachusetts Medical School Faculty Publications. https://doi.org/10.1101/156091. Retrieved from https://escholarship.umassmed.edu/faculty_pubs/1556
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.