Dataset Information

A Versatile Optical Clearing Protocol for Deep Tissue Imaging of Fluorescent Proteins in Arabidopsis thaliana.

ABSTRACT: Confocal microscopy is widely used to visualize gene expression patterns and developmental processes in plants. However, the imaging of plant tissue can be challenging due to its opacity, which often makes previous immersion in a clearing agent necessary. Many commonly-used chemicals suffer either from their incompatibility with fluorescent proteins or their complex and lengthy application. 2,2'-thiodiethanol (TDE) has recently been described as a clearing agent with an emphasis on high resolution microscopy due to its potential to adjust the refractive index. Here, we evaluate the use of TDE-based clearing for confocal as well as two-photon microscopy in various Arabidopsis thaliana tissue types. We demonstrate that tissue fixation is a mandatory prerequisite for the use of TDE, in order to preserve tissue integrity and fluorescent protein activity. TDE concentrations between 50-70% are a good compromise for imaging of technically challenging tissue to achieve good clearing without affecting fluorescent protein activity. TDE-based clearing is simple and rapid to use and allows for a flexible experimental setup while facilitating high quality imaging.

SUBMITTER: Musielak TJ

PROVIDER: S-EPMC4982668 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

A Versatile Optical Clearing Protocol for Deep Tissue Imaging of Fluorescent Proteins in Arabidopsis thaliana.

Musielak Thomas J TJ Slane Daniel D Liebig Christian C Bayer Martin M

PloS one 20160812 8

Confocal microscopy is widely used to visualize gene expression patterns and developmental processes in plants. However, the imaging of plant tissue can be challenging due to its opacity, which often makes previous immersion in a clearing agent necessary. Many commonly-used chemicals suffer either from their incompatibility with fluorescent proteins or their complex and lengthy application. 2,2'-thiodiethanol (TDE) has recently been described as a clearing agent with an emphasis on high resoluti ...[more]

PMID: 27517463

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Project description:Knowledge of protein subcellular localization assists in the elucidation of protein function and understanding of different biological mechanisms that occur at discrete subcellular niches. Organelle-centric proteomics enables localization of thousands of proteins simultaneously. Although such techniques have successfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along density gradients are able to assign proteins of unknown localization based on comigration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled to sophisticated computational tools. Here we apply and compare multiple approaches to establish a high-confidence data set of Arabidopsis root tissue trans-Golgi network (TGN) proteins. The method employed involves immunoisolations of the TGN, coupled to probability-based organelle proteomics techniques. Specifically, the technique known as LOPIT (localization of organelle protein by isotope tagging), couples density centrifugation with quantitative mass-spectometry-based proteomics using isobaric labeling and targeted methods with semisupervised machine learning methods. We demonstrate that while the immunoisolation method gives rise to a significant data set, the approach is unable to distinguish cargo proteins and persistent contaminants from full-time residents of the TGN. The LOPIT approach, however, returns information about many subcellular niches simultaneously and the steady-state location of proteins. Importantly, therefore, it is able to dissect proteins present in more than one organelle and cargo proteins en route to other cellular destinations from proteins whose steady-state location favors the TGN. Using this approach, we present a robust list of Arabidopsis TGN proteins.

Dataset Information

A Versatile Optical Clearing Protocol for Deep Tissue Imaging of Fluorescent Proteins in Arabidopsis thaliana.

Publications

A Versatile Optical Clearing Protocol for Deep Tissue Imaging of Fluorescent Proteins in Arabidopsis thaliana.

Similar Datasets

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