Transcriptomics

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.

• 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.

• 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.

• 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 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.

NGS: next-generation sequencing

• 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.

• 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.

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.

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 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

Transcriptomics

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