Transcriptomics

1. Transcriptomics Introduction

2. Transcriptome The transcriptome is the complete set of transcripts in a cell and their quantity, for a specific developmental stage or physiological condition. Understanding the transcriptome is essential for interpreting the functional elements of the genome and revealing the molecular constituents of cells and tissues, and also for understanding development and disease.

3. Transcriptomics Transcriptomics , the study of RNA in any of its forms. The transcriptome is the set of all RNA molecules, including mRNA, rRNA , tRNA , and other non-coding RNA produced in one or a population of cells.

4. Transcriptomics scope The term can be applied to the total set of transcripts in a given organism, or to the specific subset of transcripts present in a particular cell type. Unlike the genome, which is roughly fixed for a given cell line (excluding mutations), the transcriptome can vary with external environmental conditions.

5. Transcriptomics scope Because it includes all mRNA transcripts in the cell, the transcriptome reflects the genes that are being actively expressed at any given time, with the exception of mRNA degradation phenomena such as transcriptional attenuation. The study of transcriptomics , also referred to as expression profiling , examines the expression level of mRNAs in a given cell population, often using high-throughput techniques based on DNA microarray technology.

6. Transcriptomics aims To catalogue all species of transcripts, including mRNAs, noncoding RNAs and small RNAs. To determine the transcriptional structure of genes, in terms of their start sites, 5′ and 3′ ends, splicing patterns and other post-transcriptional modifications. To quantify the changing expression levels of each transcript during development and under different conditions.

7. Technologies Hybridization-based approaches fluorescently labelled cDNA with custom-made microarrays commercial high-density oligo microarrays Sequence-based approaches Sanger sequencing of cDNA or EST libraries serial analysis of gene expression (SAGE) cap analysis of gene expression (CAGE) massively parallel signature sequencing (MPSS)

8. Hybridization approaches: microarrays and related techniques The technology has been developed in several variants but in the following we only discuss the two most popular : “ two colour ” (or cDNA or two-channel) microarrays and “ one colour ” ( or oligonucleotides or one-channel) microarrays.

9. Hybridization approaches: microarrays and related techniques Two colour microarrays are based on the competitive hybridization of two samples each of which has been labeled with a different fluorescent dye (e.g. red or green ). After hybridization, the array is exposed to red and green laser light the array emits fluorescence proportional to the quantity of RNA the image produced is scanned yielding after some corrections a value which represents the expression of one sample relative to the other.

10. Hybridization approaches: microarrays and related techniques O ne channel microarrays are based on RNA of one sample which has been labeled with a fluorescent dye and hybridized to a single array where millions of copies of short (around 24 base pairs) oligonucleotide probes representing all known genes (several probes for gene form a “ probeset ”) have been synthesized. After exposition to laser light and scanner the intensity of each location is measured yielding a value which represents an absolute measure of expression.

11. Hybridization approaches: microarrays and related techniques Gene expression microarrays have been very useful to provide an overall view of how gene expression changes between two or more biological conditions. However , as the understanding of expression has evolved it has become apparent that more complex events than transcription and splicing actually occur within individual genes in a sample .

12. RNA- seq : sequencing approaches to study the transcriptome RNA- Seq transcriptomics replaces the hybridization of nucleotide probes with sequencing individual cDNAs produced from the target RNA. Emerging methods for these fully quantitative transcriptomic analyses have the potential to overcome the limitations of microarray technology and there are ongoing discussions about whether sequencing approaches may replace microarrays in the middle or even short term.

13. RNA- seq : sequencing approaches to study the transcriptome As a massively parallel process , next-generation sequencing (NGS) generates hundreds of megabases to gigabases of nucleotide sequence output in a single instrument run , depending on the platform.

14. Three NGS technologies NGS: next-generation sequencing Roche 454: A template DNA is fragmented and the fragments are end-repaired and ligated to adapters. These are clonally amplified by emulsion PCR inside microscopic “beads”. After amplification , the beads are deposited into picotiter -plate wells with sequencing enzymes where iterative pyrosequencing is performed. Every time a nucleotide is incorporated a pyrophosphate ( PPi ) is released and well-localized luminescence is emitted and recorded .

15. Three NGS technologies NGS: next-generation sequencing

16. Three NGS technologies Illummina Genome Analyzer sequencing: adapter-modified, singlestranded DNA is added to the flow cell and immobilized by hybridization. Amplification generates clonally amplified clusters which are then denatured and cleaved. Sequencing is initiated with addition of primer, polymerase and 4 reversible dye terminators. At incorporation each nucleotide generates fluorescence which is recorded.

17. Three NGS technologies Applied Biosystems SOLID sequencing technology employs sequencing by ligation. Here, a pool of all possible oligonucleotides a fixed length is labeled according to the sequenced position. Oligonucleotides are annealed and ligated ; the preferential ligation by DNA ligase for matching sequences results in a signal informative of the nucleotide at that position. Before sequencing, the DNA is amplified by emulsion PCR. The resulting bead, each containing only copies of the same DNA molecule are deposited on a glass slide.

18. Steps in the generation and analysis of microarray data

19. Application of transcriptomics in plant breeding 1- Transcriptome assembly and profiling: the widespread use of transcriptome sampling strategies is a complementary approach to genome sequencing, and results in a large collection of expressed sequence tags (ESTs) for almost all the important plant species ( http://www.ncbi.nlm.nih.gov/dbEST/dbEST_summary.html ). The plant EST database has recently passed the five million sequence landmark. More than 50 plant species, each with >5000 ESTs, are represented.

20. Application of transcriptomics in plant breeding 2- Small RNA characterization : Small RNAs ( sRNA ) are non-protein-coding small RNA molecules ranging from 20 to 30 nt that have a role in development, genome maintenance and plant responses to environmental stresses. Most sRNAs belong to two major groups: 1 ) microRNAs ( miRNA ) are about 21 nt and usually have a post-transcriptional regulatory role by directing cleavage of a specific transcript 2 ) short interfering RNAs ( siRNA ) are usually 24 nt -long and influence de novo methylation or other modifications to silence genes The finding of their prevalence in low-molecular-weight fractions of total RNA in animals and plants predated the development of NGS.

21. Application of transcriptomics in plant breeding 3 - eQTL : Metabolite, protein and transcript profiles can also be directly mapped onto a segregating population to provide information on loci that control gene expression levels, protein modification or levels of a particular secondary metabolite. The QTLs associated with such traits are known as expression ( eQTL ), protein ( pQTL ) or metabolite ( mQTL )

22. References Liaca V. (2012). Sequencing Technologies and Their Use in Plant Biotechnology and Breeding, DNA Sequencing - Methods and Applications, Dr. Anjana Munshi (Ed.), ISBN: 978-953-51-0564-0, InTech . Sánchez-Pla , A., Reverter , F., Ruíz de Villa, M. C., & Comabella , M. (2012). Transcriptomics : mRNA and alternative splicing. Journal of Neuroimmunology . Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA- Seq : a revolutionary tool for transcriptomics . Nature Reviews Genetics , 10 (1), 57-63. Langridge , P., & Fleury , D. (2011). Making the most of ‘ omics ’ for crop breeding. Trends in biotechnology , 29 (1), 33-40. Varshney RK., Graner A. and Sorrells M.(2005), Genomics-assisted breeding for crop improvement,TRENDS in Plant Science Vol.10 No.12