Transcript cDNA Micoroarray Data Analysis

cDNA Microarray analysis
of an invasive brain
tumor
OR
More answers than you can
handle
Dominique B Hoelzinger
Overview
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IV.
Introduction
Generating data
Analyzing data
Interpreting data
The biological problem
• Glioblastoma multiforme
– the deadliest brain cancer
• Current treatments:
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Surgery
Chemotherapy
Radiotherapy
Stem cells
Gene therapy
SPREAD OF GLIOBLASTOMA
MULTIFORME
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1) corpus callosum
2) Fornix
3) Optic radiation
4) Association
pathways
5) Anterior
commissure
Glioma motility
• What make
these cells
move?
• What switches
them from
dividing to
motile?
The ones that got away
• Highly invasive
– Surgeon can’t reach them
– Chemotherapy and radiotherapy can’t reach them
– They are not dividing
core
rim
core
rim
Laser Capture Microdissection
1) Prepare
Follow routine protocols for preparing
a tissue on a plain, uncovered microscope slide
2) Locate
Visualize the sample through the video monitor or the
microscope. Position the CapSure™ film carrier over the
cell(s) of interest
3) Capture
Press the button to pulse the low power
infrared laser. The desired cell(s) adhere
to the CapSure ™ film carrier.
4) Microdissect
Lift the CapSure ™ film carrier, with the desired cell(s)
to the film surface. The surrounding tissue remains
intact.
Place the CapSure ™ film carrier directly onto a standard microcentrifuge
tube (Eppendorf) containing the extraction buffer. The cell contents,
DNA, RNA or are ready for subsequent molecular analysis.
5) Analyze
Microdissection of single cells
• Identify invading
glioma cells on
cryostat sections
• Using 20x
magnification,
laser-capture
tumor cells
• Retrieve captured
cells on LCM Cap
• Verify cell capture
by inspection of
Cap
10mm
About RNA
Overview
I.
II.
III.
IV.
Introduction
Generating data
Analyzing data
Interpreting data
Robotic Array Assembly
cDNA microarray technology
http://research.nhgri.nih.gov/microarray/image_analysis.html
Really raw data
Overview
I.
II.
III.
IV.
Introduction
Generating data
Analyzing data
Interpreting data
GeneSpring
• Normalizes the calculated data
• Selects genes more than twofold over or under the ratio of 1
(equally expressed in both
populations)
• Custer analysis
• Principal Components Analysis
Genes down-regulated in migrating
cells
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C/R
Name
Description
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Extracellular
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33
12
11
11
10
7
5
4
4
3
3
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Vascular Involvement/Angiogenesis
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43 FCGR3A
Fc fragment of IgG, low affinity IIIa,
42 PTGER4
prostaglandin E receptor 4 (subtype
17 HLA-DRA
major histocompatibility complex, class II,
6 CD163
CD 163 antigen
5 VEGF vascular endothelial growth factor
5 VCAM1
vascular cell adhesion molecule 1
4 LMO2LIM domain only 2 (rhombotin-like1)
4 CD68 CD68 antigen
Signal Transduction
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6
8
4
3
3
3
3
IGFBP5
insulin-like growth factor binding protein 5
IGFBP2
insulin-like growth factor binding protein 2
DEPP decidual protein induced by progesterone
ABCC3
ATP-binding cassette, C (CFTR/MRP) 3
TNC tenascin C (hexabrachion)
SRPX sushi-repeat-containing protein, X chrom
SFRP4secreted frizzled-related protein 4
SERPINB2
serine (or cystein) proteinase inhibitor, 2 (P
SERPINH2
serine (or cystein) proteinase inhibit
MUC1
mucin 1
EGFR-RS
Likely ortholog of mouse EGF
IQGAP
IQ motiv containing GTPase activating
RDC1
G protein-coupled receptor
RGS16
Regulator of G-protein signaling 16
NFKBIA
NFKB inhibitor, alpha
PLD2 phospholipase D 2
TK2 thymidine kinase 2, mitochondrial
ABL1 abelson murine leukemia viral oncogene homolog 1
Cytoskeleton
12 VIM
vimentin
7 PLEK
plekstrin
5 MSN
moesin
4 CAPG
Capping protein (actin filament), gelsolin-like
3 KANK
kidney ankyrin repeat-containing protein
Apoptosis
4 CASP4
caspase 4
4 PIG3
p53 induced gene 3
Transcription
14 FP36L1
zinc finger protein 36, C3H type-like 1 (ERF-1)
7 ID4
inhibitor of DNA binding 4, dominant neg helix-loop-helix protein
3 BTF3
basic transcription factor 3
6 EYA2
eyes absent (Drosophila) homolog 2
4 EGR1
Early growth response 1
4 JUNB
Jun B proto-oncogene
4 CEBPB
CCAAT/enhancer binding protein (C/EBP), beta
3 NFKBIA
nuclear factor kappa-B inhibitor alpha
3 FOXM1
forkhead box 1M
Proliferation
3 CKS2
CDC28 protein kinase regulatory subunit 2
3 CDC20
cell division cycle 20
Unknown function
5 H47315
EST
7 MT1L
metallothionein 1L
6 CLIC1
chloride intracellular channel 1
6 MT2A
metallothionein 2A
4 HNRPH1
heterogeneous nuclear ribonucleoprotein H1
4 R68464
EST
4 APOE
apolipoprotein E
3 KIAA0630
KIAA0630 protein
3
MSI2
Musashi homolog 2
Overview
I.
II.
III.
IV.
Introduction
Generating data
Analyzing data
Interpreting data
BioHavasu project
Unusual Suspects: Cataloging
Cancer Related Proteins, Genes
using Biomedical Literature
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Pathway involvement (activity of protein): Determine
the cellular pathway(s) during which the protein is
involved : apoptosis, proliferation, or migration
Interaction (protein/protein , protein/nucleic acids or
protein /fatty acids): Determine protein binding.
Swissprot, Entrez protein or Expasy
Disease (protein/disease, protein/tissue type):
Determine the types of cancer that the protein is related
to.
Protein Action (protein/function): Determine the
diverse activation and inhibition relationships between
proteins as well as sub-cellular localization.
Understanding relationships
Laminin a
SERPIN
B2
5
DKK3
Elastin
SFRP4
HGF/SF
VCAM
IGFBP2
TNC
FGF 9
VEGF
KLK6
Ephrin- B2
SERPINH2
Collagen IX
IGFBP5
SPOCK
tenascinC
LPA
ENPP2
PTPRN2
EFNB3
OPCML
2
EGFR-RS
EGFR
a
Eph B6
RGS16
DTR
GPCRs
b
g
GRIA1
integrins
paxillin
G proteins
FAK
STX11
RGS7
Ras
IQGAP
Actin
Guanine exchange factors
PKCB
PKC
Rac
Cdc42
ARHGAP8
Rho
Rho
ROCK
Transcription factors
Pak
ZFP36L1, ID4, BTF3,
EYA2, EGR1, JUNB
WASP
DAP3,BCL2L2
MLCK
AP3M2
Up-regulated during invasion
Apoptosis
MLC
phosphatase
LIM kinase
TRABID, MEF2C,
ETS2, BACH2
Down-regulated during invasion
CAPG
Nucleation
of actin at
membrane
Cofilin
Actin
depolymerization
CASP4, PIG3
myosin
Retrograde
flow of actin
filaments
profilin
Actin
polymerization
stress fibers
Sub-cellular localization
Proposed Ontology-Directed Extraction
Methodology
• Model Medical Terminology: Identify existing medical ontologies
such as UMLS for modeling the domain knowledge.
• Text Classifier Module: Build a classifier for identifying
“interesting” sentences in MEDLINE abstracts.
• Natural Language Processing: Identify pre-processing steps for
structuring free-text. Such steps involve part of speech tagging, noun
and verb phrase chunking and shallow parsing.
• Relationship Extractor Module: Build an extractor system using
machine-learning techniques, such as ILP, for learning rules that
combine the medical ontologies with learned patterns on sentences
to extract relationships among proteins.
• Usability, Performance and Scalability: Determine if the system is
usable by biologists, if it can be easily trained to extract new types of
relationships and its recall and precision is at acceptable levels.
So that I don’t have to spend hours
finding diagrams myself….
Mef 2C
LPA
GCR
G proteins
HB-EGF
Promoter Analysis
• Find the promoter region
– Genome browser
• Find transcription binding site
– TESS
– Genomatix
– Biobase, etc
• Align several promoters to find common
patterns
The ones that got away
• Highly invasive
– Surgeon can’t reach them
– Chemotherapy and radiotherapy can’t reach them
– They are not dividing
core
rim
core
rim
Genetics again!
Transcription
• Core promoter
• Transcription
factors
• Co-activators
• Enhancers
Transcription factors
Consensus binding sites
• Position weighted
matrices
– Define variation in
promoter consensus
sequences
The sequenced human genome
Finding
the
Promoter
Genome Browser
Human Genome Browser Gateway
TESS
TESS Job W0793006061 : Tabulated Results
Promoter structure
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Promoter Alinement
Genomatix
The next step, biological significance
• Proof of transcriptional regulation =
proof of protein
– Cellular specificity
– Subcellular localization
– Activity
TissueInformatics
Tissue micro-array
Conclusion
• cDNA microarray technology has opened a flood gate of
information
• Biologists need HELP
• Expedite the interpretation of data.
•
ideas wanted