Advanced 3D Analysis and Optimization of Single‐Molecule FISH in Drosophila Muscle. Issue 9 (27th May 2018)

• Record Type: Journal Article
• Authors: Noma, Akiko; Smith, Carlas S.; Huisman, Maximiliaan; Martin, Robert M.; Moore, Melissa J.; Grunwald, David
• Is Part Of: Small methods. Volume 2:Issue 9(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-05-27
• Subjects: image processing -- segmentation -- smFISH; Nanotechnology -- Methodology -- Periodicals; Nanotechnology -- Periodicals; Periodicals; 620.5028
• DOI: 10.1002/smtd.201700324
• Languages: English
• ISSNs: 2366-9608

Abstract

Abstract: 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, the utility of smFISH is hindered in thick tissue sections due to the poor probe penetration of fixed tissue, the inaccessibility of target mRNAs for probe hybridization, high background fluorescence, spherical aberration along the optical axis, and the lack of methods for image segmentation of organelles. Studying mRNA localization in 50 µm thick Drosophila larval muscle sections, these obstacles are overcome using sample‐specific optimization of smFISH, particle identification based on maximum likelihood testing, and 3D multiple‐organelle segmentation. The latter allows independent thresholds to be assigned to different regions of interest within an image stack. This approach therefore facilitates accurate measurement of mRNA location in thick tissues. Abstract : The ability to precisely localize a messenger RNA can reveal key information about its regulation. Single‐molecule fluorescence in situ hybridization (smFISH) is well established, but its use has been hindered in thick tissue sections. Sample‐specific optimization of smFISH, particle identification based on maximum likelihood testing, and 3D multiple‐organelle segmentation are developed to overcome these obstacles.