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

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TranscriptomicsA general overviewBy Todd,
Mark, and Tom
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Intro

• Transcriptomics gt RNA in a cell
• Either coding or non-coding (ncRNA).
• mRNA vs microRNA, siRNA
• Also non-functional RNA (pseudo-genes)

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Transcriptomics focuses sets onhowwherewhenwh
yAlso diagnosing developmental stagestissue
differentialsvirusesresponse to stimuli
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Microarray

• Used for Biological Assays
• DNA Microarrays
• MMChips
• Protein Microarrays
• Tissue Microarrays
• Antibody Microarrays

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DNA Microarray

• Can be used to measure
• Expression levels
• SNPs
• Genotyping
• Comparative Genome Hybridization

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Basic DNA Microarray Experiment
http//en.wikipedia.org/
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Labeling
Lockhart and Winzeler 2000
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Probes and Targets

• Probes
• Known sequence bonded to substrate
• Target
• Sample obtained to wash over chip
• See what and how much is hybridized

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Hybridization and Wash
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Hybridization and Wash
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Basic DNA Microarray Experiment
http//en.wikipedia.org/
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Results
Lockhart and Winzeler 2000
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Tiling Array

• Genome array consisting of overlapping probes
• Finer Resolution
• Better at finding RNA in the cell
• mRNA
• Alternative splicing
• Not Polyadenylated
• miRNA

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Tiling Arrays
http//en.wikipedia.org/
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Tiling Array
http//en.wikipedia.org/
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Microarray
Wheelan et al. 2008
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Gene expression profiling predicts clinical
outcome of breast cancerLaura 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 workNature, January 2002
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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).
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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 (ERa) 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 ERa-mediated transcription, with
  a reduction of BRCA1 activity results in elevated
  ERa-mediated transcription and enhanced cell
  proliferation.

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98 primary breast cancers 34 from patients who
developed metastases within 5 years44 from
patients who continued to be disease-free after 5
years18 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

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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
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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.
To identify tumours that could reliably represent
either a good or poor prognosis a three-step
supervised classification method was applied
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• 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).
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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
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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.
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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.
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Results indicate that breast cancer prognosis
can be derived from gene expression profile of
primary tumor.
Conclusions
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
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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
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Short size of microRNAs (miRNAs) and sequence
similarity between miRNA family members has
resulted in cross-hybridization of related miRNAs
on glass-slide microarrays. Development of
bead-based flow cytometric expression profiling
of miRNAs.
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miRNA profiles are informative with a general
down regulation of miRNA in tumors compared with
normal tissueExpression profiles of miRNA
are also able to classify poorly differentiated
tumors, highlighting the potential for miRNA
profiling in cancer diagnosis
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RNA-Seq
Lockhart and Winzeler 2000
Wang et al. 2009
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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

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RNA-Seq Experiment
Wang et al. 2009
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Mapping

• Place reads onto a known genomic scaffold
• Requires known genome and depends on accuracy of
  the reference

http//en.wikipedia.org/
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Mapping

• Create unique scaffolds
• Harder algorithms with such short reads

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Comparisons
Wang et al. 2009
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Comparisons
Wang et al. 2009
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Biases
Wang et al. 2009
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Directionality
Wang et al. 2009
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Coverage Versus Depth
Wang et al. 2009
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New Insights

• Mapping Genes and Exon Boundries
• Single Base Resolution
• Transcript Complexity
• Exon Skipping
• Novel Transcription
• More accurate
• No cross hybridization

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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

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What does mRNA tell you?
Gene expression not the same as phenotypic
expression
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Why no line?

• Reasons?
• Noise and bias of sample
• Lag time of translation
• Post-translational control
• RNA/Protein half life
• ?

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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)?

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• 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

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Evolution model neutral vs selection
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Mouse with Down syndromeWhat happens? What
would Mendel do?
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Different environments
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Rhythm