Ttp Lab Tech Talk 051810
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Transcript of Ttp Lab Tech Talk 051810
High-throughput microRNA functional screening using the Acumen eX3 to identify repressors of a tumorigenic signal
transduction pathway
Neil Kubica, Janie Zhang, Greg Hoffman and John Blenis
Department of Cell Biology
Harvard Medical School
US Acumen Users Group Meeting (UGM)
British Consulate – General
Cambridge, MA
May 18, 2010
mTORC1 Integrates Multiple Upstream Signals to Determine the Balance Between Cellular Anabolism and Cellular Catabolism
mTOR
RaptorLST8
AminoAcids
GrowthFactors
Energy
RibosomalBiogenesis
mRNATranslationAutophagy
Rapamycin
The mTORC1 signaling network is populated by a plethora of oncogenes and tumor suppressors
mTORC1 is hyperactivated in ~80-90% of all human cancers
Biomarker
Phosphatase and Tensin Homolog Deleted on Chromosome 10 (PTEN)Function
IRS-1
PI3K
PTEN
PIP2 PIP3
PDK1
Akt
CellSurvival Cell
Growth
CellDivision
Cell Membrane Extracellular
Cytosol
mTOR
RaptorLST8
PTEN loss-of-function (LOF) results in constitutive hyperactivation of the PI3K/Akt/mTORC1 signaling axis
IRS-1
PI3K
PIP2 PIP3
PDK1
Akt
CellGrowth
CellDivision
Cell Membrane Extracellular
Cytosol
mTOR
RaptorLST8
CellSurvival
ConstitutiveHyperactivation
PTEN is one of the most frequently mutated tumor suppressors in primary human cancers
PTEN LOF
EndometrialCarcinoma(50-80%)
Glioblastoma(50-80%)
Prostate Cancer(50-80%)
Breast Cancer(30-50%)
Generally, PTEN +/- is associated with early-stage disease (e.g. formation/progression), while complete LOF (PTEN -/-) is associated with advanced
stages of cancer (e.g. metastatic disease)
Colon Cancer(30-50%)
Lung Cancer(30-50%)
Molecular Genetics and Prostate Cancer Progression
NormalEpithelium
ProstaticIntraepithelial
Neoplasia(PIN)
InvasiveCarcinoma Metastasis
Loss of 8p21NKX3.1
Loss of 10pPTEN +/-
Loss of 13qRb Loss of 17p
p53Loss Of
Basal CellsLoss Of
Basal LaminaAndrogen-
Independence
Adapted From: Abate-Shen, C. & Shen, MC. (2000) Genes & Dev. 14: 2410-34
Time
Loss of 10pPTEN -/-
Is mTORC1 hyperactivation downstream of PTEN LOF important for prostate cancer formation/progression?
Genetic inactivation of mTOR suppresses Pten-null-driven prostate cancer (CaP)
Nardella, C. et al. (2009) Sci. Signal. 2: 1-10
PTENpc-/-: PTENloxP/loxP x PB-Cre4
mTorpc-/-: mTorloxP/loxP x PB-Cre4
PB-Cre4 transgenic mice express Cre recombinaseunder the control of the ARR2-probasin promoter,Which is turned on in the prostate epithelium afterpuberty
What about small regulatory RNAs (e.g. microRNAs)?
Biomarker
Kim VN & Siomi MC. (2009) Nat Rev Mol Cell Biol 10: 126-39
microRNA (miRNA) expression is dramatically altered in human cancer
Widespread loss of miRNA expression in cancer suggests most miRNAs function as tumor suppressors, while a minority of overexpressed miRNAs function as oncogenes
Lu, J. et al. Nature 435 (7043): 834-838 Gaur, A. et al. Cancer Res 67: 2456-2468
Normal Tissue vs. 1° Tumor Normal Tissue vs. NCI60 Cell Lines
miRNAs can act as tumor suppressors by repressing the expression of signal transduction proteins that serve as powerful oncogenes
(e.g. Ras and let-7)
Esquela-Kerscher, A & Slack, FJ.(2006) Nat Rev Cancer 6: 259-69
HepG2 Cells:
Human 1° Lung Tumors:
miRNA MimicNeg. Control
let-7 Mimic
Adapted From: Johnson, SM, et al. (2005) Cell 120: 635-47
miRNAs can act as tumor suppressors by repressing the expression of signal transduction proteins that serve as powerful oncogenes
(e.g. Ras and let-7)
Adapted From: Trang, P et al. (2010) Oncogene 29: 1580-87
Mouse Strain: LSL-K-Ras G12D
This strain carries a latent point mutant allele of Kras2 (K-RasG12D).
Cre-mediated recombination leads to deletion of a transcriptional termination sequence (Lox-Stop-Lox) and expression of the oncogenic protein.
Intranasal infection with Cre adenovirus results in very high frequency of lung tumors at baseline.
Intranasal infection of a lentivirus encoding let-7 reduces lung tumor burden
Jackson, EL et al. (2001) Genes Dev 15: 3243-8
Project: Identify and characterize miRNAs and miRNA inhibitors that repress
the mTORC1 pathway in cell-based models of PTEN -/- prostate cancer.
mTOR
RaptorLST8
PositiveRegulator
RibosomalBiogenesis
mRNATranslationAutophagy
Rapamycin
miRNA-X
NegativeRegulator miRNA-YmiRNA-Z
miRNAInhibitor 1
Phase 1. Acquire miRNA functional screening capabilities
The microRNA Screening Consortium @ the Institute of Chemistry and Cell Biology-Longwood (ICCB-L) Screening
Facility (HMS)
The microRNA Screeners Consortium @ the ICCB-L
ICCB-LHarvard
Medical School
Ragon Instituteof MGH, MIT and Harvard Immune Disease
Institute
Dana-FarberCancer Institute
Children’s HospitalBoston
Blenis Lab(Cell Bio)
Struhl Lab(BCMP)
Brass LabLieberman Lab
Daley Lab
Chowdhury Lab
Shimaoka Lab
The microRNA Screeners Consortium @ the ICCB-L
• Consortium model allowed for shared purchase and evaluation of miRNA gain-of-function and loss-of-function libraries.
Gain-of-Function Libraries:
miScript miRNA Mimic Library (Qiagen)
Pre-miR miRNA Mimic Library (Ambion)
Loss-of-Function Library:
miRCURY LNA miRNA Knockdown Library (Exiqon)
Phase 2. miRNA 1° Screen Optimization
Primary Screen:
1. Transfection of miRNA gain-of-function and miRNA loss-of-function reagents into PC-3 cells (PTEN -/- human prostate cancer cell line) in a 384-well format.
2. Monitoring of mTORC1 function using an In-Cell Western (ICW) fluorescence-based assay. The screening assay involves antibody-based detection of endogenous ribosomal protein S6 Ser-235/236 phosphorylation (Cell Signaling Technology).
3. Detection with an Alexa 488-conjugated secondary antibody and counterstaining with the DNA intercalating agent propidium iodide (PI).
4. Data is collected using the Acumen eX3 microplate cytometer (TTP LabTech).
20X
40X DroshaDicer
Phase 2. miRNA 1° Screen Optimization
2A. Validation of the 1° screening assay in PC-3 cells
2B. Small RNA transfection protocol for PC-3 cells
2C. siRNA/miRNA positive and negative control selection in PC-3 cells
Matrix WellMate® Microplate Dispenser(Thermo Scientific)
Acumen® eX3 Microplate Cytometer
(TTP LabTech)
PI3K
N
Akt
TSC1/2
mTORC1
S6K1/2
S6
PC-3 Cells (PTEN -/-)
PTEN
Rapamycin
2A. Validation of 1° Screening Assay in PC-3Small Molecule
Plate PC-3 Cells
(384-well)
Small Molecule Pin Transfer
(DMSO vs. Rap)
Fix Permeabilize
Block&
1° Ab
Alexa-488 2° Ab
&PI
DNA Stain
Image&
Data Analysis
Compound TransferRobot
(Epson)
48h 3h 24h Store@
4°C
SerumWithdrawal
2A. Validation of 1° Screening Assay in PC-3Small Molecule
Plate Map1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
250cells/well
500cells/well
750cells/well
DMSO
Rapamycin (20 nM)
DMSO
Rap
Heat Map
Mean % p-S6 Active
0 100
Green = ActiveRed = Inactive
Well Scan
Z’=0.852
0102030405060708090
100110
DMSO
Rap
% p
-S6
Ac
tiv
e
Well #
Well Scatter Plot
N = 36
2A. Validation of 1° Screening Assay in PC-3Small Molecule
a-p-S6 Ser235/236
a-S6 Total
Merge
Odyssey® Infrared Imaging System
(LI-COR Biosciences)
DMSO
Rap DMSO
Rap DMSO
Rap DMSO
Rap
Rel
ativ
e In
teg
rate
d I
nte
nsi
typ
-S6/
S6
(% C
on
tro
l)
DMSO Rap0
10
20
30
40
50
60
70
80
90
100
110
-99%
Scale to 10 cm plate
Acumen® eX3 Microplate Cytometer
(TTP LabTech)
384-well plate
DMSO Rap DMSO Rap DMSO Rap0
10
20
30
40
50
60
70
80
90
100
Me
an
% p
-S6
Ac
tiv
e
250 cells
Z’ Factor: 0.831 0.852 0.716
500 cells 750 cells
-87%
Matrix WellMate® Microplate Dispenser(Thermo Scientific)
Acumen® eX3 Microplate Cytometer
(TTP LabTech)
2C. siRNA/miRNA positive and negative control selection for PC-3 cells.
ReverseTransfection
(384-well)
FeedCells
Fix Permeabilize
Block&
1° Ab
Alexa-488 2° Ab
&PI
DNA Stain
Image&
Data Analysis
Bravo AutomatedLiquid Handling
Platform(Velocity 11)
24h 24h 24h Store@
4°C
Optional:SerumStarve
24h
PC-3 Cells (PTEN -/-)
LST8
PI3K
N
Akt
TSC1/2
mTOR
S6K1/2
S6
PTEN
SerumWithdrawal
RISC
Raptor
LST8&
S6K1/2k.d.
2C. siRNA/miRNA positive and negative control selection for PC-3 cells.siRNAs
NTC
IGF-1
RIR
S1IR
S2
IRS-1
/2
P13K p
110α
PDK1Rheb
RagA
RagB
RagA/B
RagC
RagD
RagC/D
mTOR
Rapto
r
LST8
Ricto
r
mSin
1S6K
1S6K
2
S6K1/
2
PTENTSC1
TSC2
PRAS400
20
40
60
80
100
120
140
Mea
n %
p-S
6 A
ctiv
e(%
Co
ntr
ol)
Experiment 1NTC siRNA pool vs. siRNA pool positive control panelN = 4/group600 cells/well
Asynchronously-growing (+serum)
Starve (-serum)
LST8: 52%/25%
S6K1/2: 31%/10%
2C. siRNA/miRNA positive and negative control selection for PC-3 cells.siRNAs
Experiment 2Z’ Factor Calculation Matrix: NTC vs. LST8, S6K1/2 and LST8 + S6K1/2N = 24/group500-1000 cells/well
Cell siRNA Pool(s)
Number LST8 S6K1/2LST8 + S6K1/2
500 0.310 0.219 0.632
600 0.343 0.471 0.673
700 0.519 0.693 0.780
800 0.666 0.738 0.797
900 0.418 0.673 0.752
1000 0.337 0.702 0.749
Cell siRNA Pool(s)
Number LST8 S6K1/2LST8 + S6K1/2
500 0.671 0.785 0.879
600 0.646 0.763 0.832
700 0.631 0.768 0.820
800 0.709 0.812 0.841
900 0.700 0.803 0.846
1000 0.673 0.755 0.808
(+) serum (-) serum
Z’ Factor0.2 0.9
*
* *
*
* *
Under optimal conditions the Z’-factor values obtained from our siRNA positive control optimization rival those achieved in our small molecule validation study
(Z’ = 0.852)
2C. siRNA/miRNA positive and negative control selection for PC-3 cells.miRNAs
Experiment 1Mock vs. miRNA negative controlsN = 24/group600 cells/well
Asynchronously-growing (+serum)
Starve (-serum)
0
20
40
60
80
100
120
Mea
n %
p-S
6 A
ctiv
e(%
Co
ntr
ol)
Mea
n C
ell N
um
ber
(% C
on
tro
l)0
20
40
60
80
100
120
Q1M A1 A2 E1 E2 Q1M A1 A2 E1 E2
2C. siRNA/miRNA positive and negative control selection for PC-3 cells.Odyssey® WB Validation
siRNA PoolmiRNA
Negative Control
NT
C
S6K
1/2
LS
T8
+ S
6K1/
2
All
Sta
rs (
Qia
gen
)
Pre
-miR
#1
(Am
bio
n)
Pre
-miR
#2
(Am
bio
n)
Scr
amb
led
(E
xiq
on
)
Sen
se m
iR-1
59 (
Exi
qo
n)
NT
C
S6K
1/2
LS
T8
+ S
6K1/
2
All
Sta
rs (
Qia
gen
)
Pre
-miR
#1
(Am
bio
n)
Pre
-miR
#2
(Am
bio
n)
Scr
amb
led
(E
xiq
on
)
Sen
se m
iR-1
59 (
Exi
qo
n)
a-p-S6 Ser235/236
a-S6 Total
Merge
a-S6K1 Total
a-LST8 Total
a-b-Actin Total
TargetKnockdown
BiomarkerRepression
Serum StarveCondition
Final 384-well library plate layout for 1° screen
• 5 source plates/library
• 15 source plates total
• Screen in triplicate = 45 plates
• Screen 2 conditions = 90 plates
• 50 nM concentration
NTC siRNA Pool
S6K1/2 siRNA Pool
LST8 & S6K1/2 siRNA Pools
PLK1 siRNA Pool
miRNA Neg. Control 1
miRNA Neg. Control 2
Empty
miRNA Library Reagents
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Phase 3. Perform miRNA 1° screen
3A. Gain-of-function miRNA mimic libraries (2)
Screening Data Visualization: miScript miRNA Mimic Library (Qiagen): Plate-based Heat Map of Raw Mean % p-S6
Active Data
ConditionSerum StarvePlate ID
PL-50684
PL-50685
PL-50686
PL-50687
PL-50688
Conclusions:
1. Hits appear to be evenly distributed
2. Serum starvation sensitization
3. Absence of edge effects
Screening Data Visualization: miScript miRNA Mimic Library (Qiagen): Replicate Correlation Plots of Raw
Mean % pS6 Active DataR
ep
lic
ate
A
Replicate B
R2=0.949
Re
pli
ca
te A
Replicate C
R2=0.934
Replicate C
Re
pli
ca
te B
R2=0.944
ConditionSerum Starve
Re
pli
ca
te A
Replicate B
R2=0.939
Re
pli
ca
te A
Replicate C
R2=0.929
Replicate C
Re
pli
ca
te B
R2=0.952
N1: NTC siRNAN2: All Stars siRNAP1: S6K1/2 siRNAP2: LST8+S6K1/2 siRNAX: miRNA Library
Conclusions:
1. Experimental replicates highly correlated.
2. Absence of gross outliers
Screening Data Visualization: miScript miRNA Mimic Library (Qiagen): Plate/Well-Based Scatter Plot Raw
Mean % pS6 Active Data
Serum Starve color by Serum
N1: NTC siRNAN2: All Stars siRNAP1: S6K1/2 siRNAP2: LST8+S6K1/2 siRNAX: miRNA Library
Conclusions:
1. Qualitative assessment shows many miRNAs with weak or intermediate affect on p-S6 status
2. A few miRNAs with strong affect on p-S6 status (~as strong as siRNA positive controls)
Me
an
% p
S6
Ac
tiv
e
Plate/Well
Starve
Me
an
% p
S6
Ac
tiv
e
Plate/Well
Plate/Well
Me
an
% p
S6
Ac
tiv
e
Screening Data Analysis: miScript miRNA Mimic Library (Qiagen): Hit Selection
Serum Starve
Data Analysis Workflow:
1. Z-score normalization relative to miRNA negative control (e.g. AllStars siRNA)
2. One-tailed t-test assuming unequal variance
3. Hit selection: p<0.01
4. “High-confidence” hit selection: Must score in both serum and starve conditions
z = x - m
d
Formula:
Where:
x = raw % pS6 active valuem = miRNA negative control meand = miRNA negative control s.d.
394 388229
Primary ScreenQiagen
“High-confidence” Hits
Screening Data Visualization: Pre-miR miRNA Mimic Library (Ambion): Plate-based Heat Map of Raw Mean % p-S6
Active Data
ConditionSerum StarvePlate ID
PL-50689
PL-50690
PL-50691
PL-50692
PL-50693
Conclusions:
1. Hits appear to be evenly distributed
2. Serum starvation sensitization
3. Absence of edge effects
Screening Data Visualization: Pre-miR miRNA Mimic Library (Ambion): Replicate Correlation Plots of Raw
Mean % pS6 Active DataR
ep
lic
ate
A
Replicate B
R2=0.962
Re
pli
ca
te A
Replicate C
R2=0.964
Replicate C
Re
pli
ca
te B
R2=0.970
ConditionSerum Starve
Re
pli
ca
te A
Replicate B
R2=0.974
Re
pli
ca
te A
Replicate C
R2=0.969
Replicate C
Re
pli
ca
te B
R2=0.968
N1: NTC siRNAN2: All Stars siRNAP1: S6K1/2 siRNAP2: LST8+S6K1/2 siRNAX: miRNA Library
Conclusions:
1. Experimental replicates highly correlated.
2. Absence of gross outliers
Screening Data Visualization: Pre-miR miRNA Mimic Library (Ambion): Plate/Well-Based Scatter Plot Raw
Mean % pS6 Active Data
Serum Starve color by Serum
N1: NTC siRNAN2: All Stars siRNAP1: S6K1/2 siRNAP2: LST8+S6K1/2 siRNAX: miRNA Library
Conclusions:
1. Qualitative assessment shows many miRNAs with weak or intermediate affect on p-S6 status
2. A few miRNAs with strong affect on p-S6 status (~as strong as siRNA positive controls)
Me
an
% p
S6
Ac
tiv
e
Plate/Well
Starve
Me
an
% p
S6
Ac
tiv
e
Plate/Well
Plate/Well
Me
an
% p
S6
Ac
tiv
e
Screening Data Analysis: Pre-miR miRNA Mimic Library (Ambion): Hit Selection
Serum Starve
Data Analysis Workflow:
1. Z-score normalization relative to miRNA negative control (e.g. AllStars siRNA)
2. One-tailed t-test assuming unequal variance
3. Hit selection: p<0.01
4. “High-confidence” hit selection: Must score in both serum and starve conditions
z = x - m
d
Formula:
Where:
x = raw % pS6 active valuem = miRNA negative control meand = miRNA negative control s.d.
369 540243
Primary ScreenAmbion
“High-confidence” Hits
Screening Data Analysis: Gain-of-Function Library Hit Selection Summary
Serum Starve
369 540243
Primary ScreenAmbion
“High-confidence” Hits
Pre-miR miRNA Mimic Library (Ambion)
Serum Starve
394 388229
Primary ScreenQiagen
“High-confidence” Hits
miScript miRNA Mimic Library (Qiagen)
472 miRNA mimics cherry picked for 2° Screen
Phase 3. Perform miRNA 1° screen
3B. Loss-of-function miRNA inhibitor library
Screening Data Visualization: miRCURY LNA™ miRNA Knockdown Library (Exiqon): Plate-based Heat Map of Raw
Mean % p-S6 Active Data
ConditionSerum StarvePlate ID
PL-50694
PL-50695
PL-50696
PL-50697
PL-50698
Conclusions:
1. Few hits compared to gain-of-function miRNA mimic libraries
2. Hits appear to be evenly distributed
3. Serum starvation sensitization?
4. Absence of edge effects
Screening Data Visualization: miRCURY LNA™ miRNA Knockdown Library (Exiqon): Replicate Correlation
Plots of Raw Mean % pS6 Active DataR
ep
lic
ate
A
Replicate B
R2=0.914
Re
pli
ca
te A
Replicate C
R2=0.930
Replicate C
Re
pli
ca
te B
R2=0.950
ConditionSerum Starve
Re
pli
ca
te A
Replicate B
R2=0.920
Re
pli
ca
te A
Replicate C
R2=0.965
Replicate C
Re
pli
ca
te B
R2=0.928
N1: NTC siRNAN2: All Stars siRNAP1: S6K1/2 siRNAP2: LST8+S6K1/2 siRNAX: miRNA Library
Conclusions:
1. Experimental replicates highly correlated.
2. Absence of gross outliers
Screening Data Visualization: miRCURY LNA™ miRNA Knockdown Library (Exiqon): Plate/Well-Based
Scatter Plot Raw Mean % pS6 Active Data
Serum Starve color by Serum
N1: NTC siRNAN2: All Stars siRNAP1: S6K1/2 siRNAP2: LST8+S6K1/2 siRNAX: miRNA Library
Conclusions:
1. Qualitative assessment shows fewer miRNA inhibitor hits compared to miRNA mimic libraries (as expected).
2. Effect of miRNA inhibitors on p-S6 status tends to be less penetrant.
Me
an
% p
S6
Ac
tiv
e
Plate/Well
Starve
Me
an
% p
S6
Ac
tiv
e
Plate/Well
Plate/Well
Me
an
% p
S6
Ac
tiv
e
Screening Data Analysis: miRCURY LNA™ miRNA Knockdown Library (Exiqon): miRNA Hit Selection
Data Analysis Workflow:
1. Z-score normalization relative to miRNA negative control (e.g. AllStars siRNA)
2. One-tailed t-test assuming unequal variance
3. Hit selection: p<0.01
4. “High-confidence” hit selection: Must score in both serum and starve conditions
z = x - m
d
Formula:
Where:
x = raw % pS6 active valuem = miRNA negative control meand = miRNA negative control s.d.
Serum Starve
118 17441
Primary ScreenExiqon
“High-confidence” Hits
Screening Data Analysis: Overall Hit Selection Summary
Serum Starve
369 540243
Primary ScreenAmbion
“High-confidence” Hits
Pre-miR miRNA Mimic Library (Ambion)
Serum Starve
394 388229
Primary ScreenQiagen
“High-confidence” Hits
miScript miRNA Mimic Library (Qiagen)
513 total miRNA reagents cherry picked for 2° Screen
Serum Starve
118 17441
Primary ScreenExiqon
“High-confidence” Hits
miRCURY LNA™ miRNA Knockdown Library
(Exiqon)
Future Directions…
• Phase 4. Perform secondary screen in LNCaP cells to eliminate cell-type specific hits
• Phase 5. Further characterization of mTORC1 function for strongest hits
• Phase 6. Determine mechanism of action for strongest hits
Acknowledgments
John BlenisJanie Zhang
Greg Hoffman
microRNA Screeners Consortium
ICCB-LCaroline Shamu
Sean Johnston
Jen Nale
Katrina Rudnicki
Stewart Rudnicki
Dave Wrobel
TTP LabTechBen Schenker
Cell Signaling Technologies (CST)
Randy Wetzel
EMD Serono
Mei Zhang
Brian Healey
Qiagen
Ambion
Exiqon
Dharmacon/Thermo Scientific