Transcript Transcriptomics: A general overview By Todd, Mark, and Tom

Transcriptomics:
A general overview
By Todd, Mark, and Tom
Intro
• Transcriptomics => RNA in a cell
• Either coding or non-coding (ncRNA).
mRNA vs microRNA, siRNA
Also non-functional RNA (pseudo-genes)
Transcriptomics focuses sets on:
how
where
when
why
Also diagnosing :
developmental stages
tissue differentials
viruses
response to stimuli
Microarray
• Used for Biological Assays
– DNA Microarrays
– MMChips
– Protein Microarrays
– Tissue Microarrays
– Antibody Microarrays
DNA Microarray
• Can be used to measure
– Expression levels
– SNPs
– Genotyping
– Comparative Genome Hybridization
Basic DNA Microarray Experiment
http://en.wikipedia.org/
Labeling
Lockhart and Winzeler 2000
Probes and Targets
• Probes
– Known sequence bonded to substrate
• Target
– Sample obtained to wash over chip
• See what and how much is hybridized
Hybridization and Wash
Hybridization and Wash
Basic DNA Microarray Experiment
http://en.wikipedia.org/
Results
Lockhart and Winzeler 2000
Tiling Array
• Genome array consisting of overlapping
probes
• Finer Resolution
• Better at finding RNA in the cell
– mRNA
• Alternative splicing
• Not Polyadenylated
– miRNA
Tiling Arrays
http://en.wikipedia.org/
Tiling Array
http://en.wikipedia.org/
Microarray
Wheelan et al. 2008
Gene expression profiling predicts
clinical outcome of breast cancer
Laura J. van 't Veer1,2, Hongyue Dai2,3, Marc J. van de Vijver1,2, Yudong D. He3, Augustinus A.
M. Hart1, Mao Mao3, Hans L. Peterse1, Karin van der Kooy1, Matthew J. Marton3, Anke T.
Witteveen1, George J. Schreiber3, Ron M. Kerkhoven1, Chris Roberts3, Peter S. Linsley3, René
Bernards1 and Stephen H. Friend3
Divisions of Diagnostic Oncology, Radiotherapy and Molecular Carcinogenesis and Center for Biomedical
Genetics, The Netherlands Cancer Institute, 121 Plesmanlaan, 1066 CX Amsterdam, The Netherlands
Rosetta Inpharmatics, 12040 115th Avenue NE, Kirkland, Washington 98034, USA
These authors contributed equally to this work
Nature, January 2002
• Use DNA microarray analysis and applied supervised classification to identify a
gene expression signature predictive of metastases and BRCA1 carriers.
• Authors predicted that the expression profile would outperform
all currently used clinical parameters in predicting disease outcome.
• Strategy to select patients who would benefit from adjuvant
therapy (chemotherapy).
Metastases – spread of cancer from one area to another; characteristic of malignant tumor cells.
Angiogenesis – process of growing new blood vessels from pre-existing vessels. A normal process in growth and
development, however also a fundamental step in the transition of tumors from a dormant state to a malignant state.
Estrogen Receptor alpha (ERα) – activated by sex hormone estrogen; DNA binding transcription factor which
regulates gene expression; association with cancer known from immunohistochemical data (IHC).
BRCA1 – Human gene, Breast Cancer 1; Mutations associated with significant increase in risk of breast cancer.
• Belongs to a class of genes known as tumor suppressors (DNA damage repair,
transcriptional regulation).
• BRCA1 represses ERα-mediated transcription, with a reduction of BRCA1 activity results in
elevated ERα-mediated transcription and enhanced cell proliferation.
98 primary breast cancers:
34 from patients who developed metastases within 5 years
44 from patients who continued to be disease-free after 5 years
18 from patients with BRCA1 germline mutations
2 from BRCA2 carriers
• Total RNA isolated from patients and used to derive complementary
RNA (cRNA)
• A reference cRNA pool was made by pooling equal amounts of cRNA
from each cancer, for use in quantification of transcript abundance
(fluorescence intensity in relation to reference pool).
• Hybridizations carried out on micoarrays (synthesized by inkjet
technology) containing ~ 25,000 human genes
• ~ 5,000 genes found to be significantly regulated across the group of
samples
Two distinct groups of tumours
apparent on the basis of the set of
~5,000 significant genes.
• In upper group only 34% of
patients were from group
developing metastases within 5
years.
• In lower group 70% of patients
had progressive disease.
• Clustering detects two subgroups of
cancer which differ in ER status and
lymphocytic infiltration
To identify tumours that could reliably represent either a good or
poor prognosis a three-step supervised classification method was
applied:
1) The correlation coefficient of the expression of ~ 5,000 significant
genes was calculated, with 231 genes determined to be significantly
associated with disease outcome.
2) These 231 genes were ranked on basis of magnitude.
3) Number of genes in ‘prognosis classifier’ optimized with the
optimal number of marker genes reached at 70 genes.
Prognosis signature with
prognostic reporter genes
identifying two types of
disease outcome:
• above dashed line good
prognosis
• below dashed line poor
prognosis
Predicted correctly the actual
outcome of disease for 65 out of
78 patients (83%).
To validate prognosis classifier
additional set analyzed (Fig. 2C).
The functional annotation of genes provided insight into the underlying
mechanisms leading to rapid metastases with the following genes
significantly upregulated in the poor prognosis signiture:
• genes involved in cell cycle
• invasion and metastasis
• angiogenesis
• signal transduction
A third classification was performed to look at the expression patterns
associated with ER-positive and ER-negative tumours.
ER clustering has predictive power for prognosis although it does not
reach the level of significance of the prognosis classifier.
Consensus conference developed guidelines for eligibility of
adjuvant chemotherapy based on histological and clinical
characteristics.
Prognosis classifier selects as effectively high-risk patients who would benefit
from therapy, but reduces number to receive unnecessary treatment.
Conclusions:
• Results indicate that breast cancer prognosis can be derived from
gene expression profile of primary tumor.
Recogmendations:
• ER signature - can be used to decide on hormonal therapy
• BRCA1 - knowing status of can improve diagnosis of hereditary
breast cancer.
• Genes overexpressed in tumors with poor prognosis profile
are targets for development of new cancer drugs
MicroRNA expression profiles classify
human cancers
Jun Lu1,4*, Gad Getz1*, Eric A. Miska2*†, Ezequiel Alvarez-Saavedra2,
Justin Lamb1, David Peck1,
Alejandro Sweet-Cordero3,4, Benjamin L. Ebert1,4, Raymond H.
Mak1,4, Adolfo A. Ferrando4, James R. Downing5,
Tyler Jacks2,3, H. Robert Horvitz2 & Todd R. Golub1,4,6
Nature, June 2005
Short size of microRNAs (miRNAs) and sequence similarity between miRNA
family members has resulted in cross-hybridization of related miRNAs on glassslide microarrays.
Development of bead-based flow cytometric expression profiling of miRNAs.
miRNA profiles are informative with a general down regulation of miRNA in
tumors compared with normal tissue
Expression profiles of miRNA are also able to classify poorly differentiated
tumors, highlighting the potential for miRNA profiling in cancer diagnosis
RNA-Seq
Lockhart and Winzeler 2000
Wang et al. 2009
RNA-Seq
• Whole Transcriptome Shotgun Sequencing
– Sequencing cDNA
– Using NexGen technology
• Revolutionary Tool for Transcriptomics
– More precise measurements
– Ability to do large scale experiments with little
starting material
RNA-Seq Experiment
Wang et al. 2009
Mapping
• Place reads onto a known genomic scaffold
– Requires known genome and depends on
accuracy of the reference
http://en.wikipedia.org/
Mapping
• Create unique scaffolds
– Harder algorithms with such short reads
Comparisons
Wang et al. 2009
Comparisons
Wang et al. 2009
Biases
Wang et al. 2009
Directionality
Wang et al. 2009
Coverage Versus Depth
Wang et al. 2009
New Insights
• Mapping Genes and Exon Boundries
– Single Base Resolution
• Transcript Complexity
– Exon Skipping
• Novel Transcription
– More accurate
– No cross hybridization
Transcription Levels
• Can measure Transcript levels more accurately
– Confirmed with qPCR and RNA spike-in
• Can compare measurements with different
cellular states and environmental conditions
– Without sophistication of normalization of data
What does mRNA tell you?
Gene expression
not the same as
phenotypic
expression
Why no line?
Reasons?
1) Noise and bias of sample
2) Lag time of translation
3) Post-translational control
4) RNA/Protein half life
5) ?
Where is the genetics?
• How do you study the transcriptome?
• What are the patterns of expression telling
you?
• Differences between gene expression vs gene
function (i. e. protein code vs concentration)?
1) ‘guilt by association’
2) Change environment, look for patterns;
compare known phenotypic mutants (cancer)
3) Add ‘controlled’ knockout (specific locations/
times/ concentrations)
4)Evolution; diversity of expression across intra
and inter speices
5) Add entire chromosome
Evolution model: neutral vs selection
Mouse with Down syndrome
What happens?
What would Mendel do?
Different environments
Rhythm