Transcriptomics

Introduction

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.

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.

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.

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.

Transcriptomics aims

I. To catalogue all species of transcripts, including mRNAs,

noncoding RNAs and small RNAs.

II. To determine the transcriptional structure of genes, in

terms of their start sites, 5′ and 3′ ends, splicing patterns

and other post-transcriptional modifications.

III. To quantify the changing expression levels of each

transcript during development and under different

conditions.

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)

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.

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.

Hybridization approaches: microarrays and related techniques

• One 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.

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.

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.

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.

Three NGS technologiesNGS: 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.

Three NGS technologiesNGS: next-generation sequencing

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.

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.

Steps in the generation and analysis of microarray data

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.

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.

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)

References

• Liaca V. (2012). Sequencing Technologies and Their Use in PlantBiotechnology and Breeding, DNA Sequencing - Methods andApplications, 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 ofNeuroimmunology.

• Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: arevolutionary tool for transcriptomics. Nature Reviews Genetics,10(1), 57-63.

• Langridge, P., & Fleury, D. (2011). Making the most of ‘omics’ forcrop breeding. Trends in biotechnology, 29(1), 33-40.

• Varshney RK., Graner A. and Sorrells M.(2005), Genomics-assistedbreeding for crop improvement,TRENDS in Plant Science Vol.10No.12