Dataset Information

EPro-ClearSee: a simple immunohistochemical method that does not require sectioning of plant samples.

ABSTRACT: Investigations into the epigenetic status of individual cells within tissues can produce both epigenetic data for different cell types and positional information of the cells. Thus, these investigations are important for understanding the intra- and inter-cellular control systems of developmental and environmental responses in plants. However, a simple method to detect epigenetic modifications of individual cells in plant tissues is not yet available because detection of the modifications requires immunohistochemistry using specific antibodies. In this study, we developed a simple immunohistochemical method that does not require sectioning to investigate epigenetic modifications. This method uses a clearing system to detect methylated histones, acetylated histones, methylated DNA and/or centromeric histone H3 variants. Analyses of four dicots and five monocots indicated that this method provides a universal technique to investigate epigenetic modifications in diverse plant species.

SUBMITTER: Nagaki K

PROVIDER: S-EPMC5297246 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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ePro-ClearSee: a simple immunohistochemical method that does not require sectioning of plant samples.

Nagaki Kiyotaka K Yamaji Naoki N Murata Minoru M

Scientific reports 20170208

Investigations into the epigenetic status of individual cells within tissues can produce both epigenetic data for different cell types and positional information of the cells. Thus, these investigations are important for understanding the intra- and inter-cellular control systems of developmental and environmental responses in plants. However, a simple method to detect epigenetic modifications of individual cells in plant tissues is not yet available because detection of the modifications requir ...[more]

PMID: 28176832

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Project description:Background: As costs decline, the size and scope of microarray experiments have increased. In multi-centre studies there is a need to ensure consistency of data pre-processing across centres. Similarly, in smaller scale studies the evolution of microarray platforms means that there is often a need to compare data generated on earlier microarrays to that generated on newer ones. It is important in such studies to ensure that platform-dependent biases are removed so that meta-analysis of different datasets can be performed reliably. In both these cases the optimal scenario is to have a small subset of samples repeated at each site or on each platform. These replicates can then be used to learn a relationship between probe intensities on the two platforms. Results: I introduce here a simple, linear-modelling-based method for normalizing data from multiple-platforms by using replicate hybridizations. A dataset of 20 rat liver samples is used as a benchmark. Eight samples are hybridized to two separate versions of Affymetrix microarrays, while the other 12 are hybridized to one, for a total of 28 arrays. Our linear modelling method removes platform bias as assessed using both unsupervised machine-learning and two-group statistical analyses. The method is computationally efficient and works well for data pre-processed by the GCRMA, RMA and MAS5 algorithms and using either default or alternative probe-mappings. The method is very stable towards the number of replicate samples used, with even two replicates greatly reducing platform-specific bias. Conclusions: A simple linear-modelling method can remove platform-specific bias independent of the pre-processing algorithm and ProbeSet-mapping used. This technique can readily be extended to multi-site experiments, and suggests the benefits of including a small number of replicate hybridizations in each new study as a normalization control. Twenty rats livers were processed, eight on both RAE230-A and RAE230-2 arrays, 8 on only RAE230-A arrays, and 4 on RAE230-2 arrays only.

Dataset Information

EPro-ClearSee: a simple immunohistochemical method that does not require sectioning of plant samples.

Publications

ePro-ClearSee: a simple immunohistochemical method that does not require sectioning of plant samples.

Similar Datasets

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