Development · 2016 · 227 citations · 21 references
EngineeringSingle-molecule Rna DetectionMolecular BiologyBiomedical EngineeringRobust DetectionRobust MethodTissue ImagingTranslational Molecular ImagingBioimagingSingle MoleculeMolecular ImagingHybridization Chain ReactionNovel Imaging MethodHigh SensitivityTissue Hydrogel EmbeddingRna Structure PredictionRna BiologyDna ReplicationSingle-cell AnalysisCell BiologySingle-molecule DetectionBiomedical DiagnosticsBiomedical ImagingMedicineCell Imaging
Accurate and robust detection of mRNA molecules in thick tissue samples can reveal gene expression patterns in single cells within their native environment. Preserving spatial relationships while accessing the transcriptome of selected cells is a crucial feature for advancing many biological areas - from developmental biology to neuroscience. However, because of the high autofluorescence background of many tissue samples, it is difficult to detect single-molecule fluorescence in situ hybridization (smFISH) signals robustly in opaque thick samples. Here, we draw on principles from the emerging discipline of dynamic nucleic acid nanotechnology to develop a robust method for multi-color, multi-RNA imaging in deep tissues using single-molecule hybridization chain reaction (smHCR). Using this approach, single transcripts can be imaged using epifluorescence, confocal or selective plane illumination microscopy (SPIM) depending on the imaging depth required. We show that smHCR has high sensitivity in detecting mRNAs in cell culture and whole-mount zebrafish embryos, and that combined with SPIM and PACT (passive CLARITY technique) tissue hydrogel embedding and clearing, smHCR can detect single mRNAs deep within thick (0.5 mm) brain slices. By simultaneously achieving ∼20-fold signal amplification and diffraction-limited spatial resolution, smHCR offers a robust and versatile approach for detecting single mRNAs in situ, including in thick tissues where high background undermines the performance of unamplified smFISH.
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Homogeneous Populations, Systems Biology, Functional Genomics +15