Dataset Information

Global profiling of the circadian transcriptome using microarrays.

ABSTRACT: Circadian rhythms are biological cycles with a period length of approximately 24 h that are generated by endogenous clocks. The application of microarrays for high-throughput transcriptome analysis has led to the insight that substantial portions of the transcriptomes of both humans and many model organisms are clock-regulated. In a typical circadian time course microarray experiment, samples are collected from organisms maintained in constant environmental conditions, gene expression at each time point is determined using microarrays, and finally clock-regulated transcripts are identified using statistical algorithms. Here, we describe how to design the experiment, process RNA, determine expression profiles using ATH1 microarrays, and use a nonparametric statistical algorithm named JTK_CYCLE in order to identify circadian-regulated transcripts in Arabidopsis. This basic procedure can be modified to identify clock-regulated transcripts in different organisms or using different expression analysis platforms.

SUBMITTER: Hsu PY

PROVIDER: S-EPMC4346219 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Global profiling of the circadian transcriptome using microarrays.

Hsu Polly Yingshan PY Harmer Stacey L SL

Methods in molecular biology (Clifton, N.J.) 20140101

Circadian rhythms are biological cycles with a period length of approximately 24 h that are generated by endogenous clocks. The application of microarrays for high-throughput transcriptome analysis has led to the insight that substantial portions of the transcriptomes of both humans and many model organisms are clock-regulated. In a typical circadian time course microarray experiment, samples are collected from organisms maintained in constant environmental conditions, gene expression at each ti ...[more]

PMID: 24792043

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Project description:With lower manufacturing cost, high spot density, and flexible probe design, genomic tiling microarrays are ideal for comprehensive transcriptome studies. Typically, transcriptome profiling using microarrays involves reverse transcription, which converts RNA to cDNA. The cDNA is then labeled and hybridized to the probes on the arrays, thus the RNA signals are detected indirectly. Reverse transcription is known to generate artifactual cDNA, in particular the synthesis of second-strand cDNA, leading to false discovery of antisense RNA. To address this issue, we have developed an effective method using RNA that is directly labeled, thus by-passing the cDNA generation. This paper describes this method and its application to the mapping of transcriptome profiles.RNA extracted from laboratory cultures of Porphyromonas gingivalis was fluorescently labeled with an alkylation reagent and hybridized directly to probes on genomic tiling microarrays specifically designed for this periodontal pathogen. The generated transcriptome profile was strand-specific and produced signals close to background level in most antisense regions of the genome. In contrast, high levels of signal were detected in the antisense regions when the hybridization was done with cDNA. Five antisense areas were tested with independent strand-specific RT-PCR and none to negligible amplification was detected, indicating that the strong antisense cDNA signals were experimental artifacts.An efficient method was developed for mapping transcriptome profiles specific to both coding strands of a bacterial genome. This method chemically labels and uses extracted RNA directly in microarray hybridization. The generated transcriptome profile was free of cDNA artifactual signals. In addition, this method requires fewer processing steps and is potentially more sensitive in detecting small amount of RNA compared to conventional end-labeling methods due to the incorporation of more fluorescent molecules per RNA fragment.

Dataset Information

Global profiling of the circadian transcriptome using microarrays.

Publications

Global profiling of the circadian transcriptome using microarrays.

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