Techniques of RNA analysis for target identification

Hi. Welcome to come back. In this video. We‘ll talk about RNA profiling techniques for target identification. Currently, RNA expression profiling is usually carried

out with three methods: Real time-quantitative PCR, cDNA microarrays, and RNA-Sequencing (or called RNA-Seq). Real time-quantitative PCR provides the fastest and the most cost effective solution, but it is most suitable for investigating a relatively small number of transcripts. Both microarrays and RNA-sequencing could provide comprehensive whole-genome coverage. Microarrays offer rapid data acquisition at a low price, whereas RNA-Seq provides the most comprehensive data with the requirement of the most time and the highest investment.

Next, we will talk more about microarrays, RNA-sequencing and their differences.

In general, a microarray is a small silica or glass slide that contains thousands of spots. Each spot contains a specific DNA sequence, known as DNA probes, corresponding to a specific gene.

The DNA probes are placed in a specific pattern so that each spot on the slide corresponds to a particular gene. To study RNA expression, RNAs are extracted and prepared from each of the samples. The mRNA is then reverse transcribed into more stable complementary DNA (or called cDNA).

During the reverse transcription, the cDNA of each sample are labelled with different fluorescent colored-tags. For example, the TEST Sample cDNA is labelled red, whereas the Control Sample cDNA is labelled green. Usually, the two labelled cDNA populations are combined. The combined sample of fluorescently labelled cDNA is then applied to the microarray chip.

During the hybridization step, cDNAs fromthe sample binds to its complementary DNA probes fixed on the chip and stick tightly in the corresponding spot. Those unbound cDNA will be washed away after hybridization step. Then, the chip is put into a scanner. In the scanner, laser passed over the slide, activating fluorescence signal and reading the signal intensity emitted by each fluorescence. The red and green signals are merged to generate image for analysis. Black-colored spots means no detectable signal, which signifies no hybridization. Red color indicate greater binding of the TEST sample than the control sample, which means up-regulation. Green color indicate less binding of the TEST sample than the control sample, which means down-regulation.

Yellow color indicate equal binding between two samples, which means constant regulation.

The computer captures these information and calculates the ratio of red and green signals on each spot. Finally, the scanned images are quantified and analyzed by specialized software. Because microarray provides an efficient way to snapshot the entire transcriptome, it is becoming an essential tool for research and drug discovery, and may play a central role in disease diagnosis in the near future.

However, microarrays still have several limitations,

including: Dependence on prior knowledge about genetic sequence; High background levels owing to cross-hybridization; and a limited dynamic detection range due to both background and saturation of signals.

Moreover, it is often difficult to compare expression levels across different experiments.

In contrast to microarray, RNA-Sequencing directly determine the exact order of nucleotides present in a given RNA (or its cDNA) molecule.

In general, the RNA sample of interest are converted to a library of cDNA fragments with adaptors attached.

Each molecule is then sequenced in a high-throughput manner, which is called massively parallel sequencing, to obtain short sequences.

Bio-informatic analysis of these sequence reads could identify both known and novel RNAs in the data sets and allow abundance estimation with a digital approach.

In summary, both microarrays and RNA-seq are high throughput and highly reproducible. However, the design of microarray is based on the hybridization between DNA probes on microarray and target cDNAs in biological samples. These probes are designed based on existing genome annotation. Unlike microarray, RNA-Seq is not limited to detecting known transcripts, which makes RNA-Seq more attractive for the discovery of noncoding RNAs and novel gene transcripts.

In addition, RNA-Seq is superior in resolution. RNA-seq can distinguish different RNAs by a single nucleotide, which can help identify novel transcript, the isoforms and single nucleotide polymorphisms (SNPs) in the transcribed regions. Furthermore, RNA-Seq has a broader dynamic detection range than microarray and has been shown to be highly accurate for quantifying expression levels.

Currently, the cost of RNA-seq and the computational infrastructure required for data analysis is still too expensive for many labs. As the sequencing cost continues to drop, RNA-Seq is expected to replace microarray as the main approach for transcriptome study.

Thanks for watching, everyone. We hope the information provided here will help you to better understand these two RNA profiling techniques.