Content-aware image restoration: pushing the limits of fluorescence microscopy.

Fluorescence microscopy is a key driver of discoveries in the life sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how content-aware image restoration based on deep learning extends the range of biological phenomena observable by microscopy. We demonstrate on eight concrete examples how microscopy images can be restored even if 60-fold fewer photons are used during acquisition, how near isotropic resolution can be achieved with up to tenfold under-sampling along the axial direction, and how tubular and granular structures smaller than the diffraction limit can be resolved at 20-times-higher frame rates compared to state-of-the-art methods. All developed image restoration methods are freely available as open source software in Python, FIJI, and KNIME.

Detection and Automated Analysis of Single Transcripts at Subcellular Resolution in Zebrafish Embryos.

Single molecule fluorescence in situ hybridization (smFISH) is a method to visualize single mRNA molecules. When combined with cellular and nuclear segmentation, transcripts can be assigned to different cellular compartments resulting in quantitative information on transcript levels at subcellular resolution. The use of smFISH in zebrafish has been limited by the lack of protocols and an automated image analysis pipeline for samples of multicellular organisms. Here we present a protocol for smFISH on zebrafish cryosections. The protocol includes a method to obtain high-quality sections of zebrafish embryos, an smFISH protocol optimized for zebrafish cryosections, and a user-friendly, automated analysis pipeline for cell segmentation and transcript detection. The software is freely available and can be used to analyze sections of any multicellular organism.

Automated detection and quantification of single RNAs at cellular resolution in zebrafish embryos.

Analysis of differential gene expression is crucial for the study of cell fate and behavior during embryonic development. However, automated methods for the sensitive detection and quantification of RNAs at cellular resolution in embryos are lacking. With the advent of single-molecule fluorescence in situ hybridization (smFISH), gene expression can be analyzed at single-molecule resolution. However, the limited availability of protocols for smFISH in embryos and the lack of efficient image analysis pipelines have hampered quantification at the (sub)cellular level in complex samples such as tissues and embryos. Here, we present a protocol for smFISH on zebrafish embryo sections in combination with an image analysis pipeline for automated transcript detection and cell segmentation. We use this strategy to quantify gene expression differences between different cell types and identify differences in subcellular transcript localization between genes. The combination of our smFISH protocol and custom-made, freely available, analysis pipeline will enable researchers to fully exploit the benefits of quantitative transcript analysis at cellular and subcellular resolution in tissues and embryos.