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This is the Powerpoint presentation from my recent presentation at the TTP LabTech US Acumen Users Group Meeting (UGM) held at the British Consulate-General in Cambridge, MA on May 18, 2010

This is the Powerpoint presentation from my recent presentation at the TTP LabTech US Acumen Users Group Meeting (UGM) held at the British Consulate-General in Cambridge, MA on May 18, 2010

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  • 1. 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
  • 2. mTORC1 Integrates Multiple Upstream Signals to Determine the Balance Between Cellular Anabolism and Cellular Catabolism
    Energy
    Amino
    Acids
    Growth
    Factors
    mTOR
    Rapamycin
    LST8
    Raptor
    Ribosomal
    Biogenesis
    mRNA
    Translation
    Autophagy
  • 3. The mTORC1 signaling network is populated by a plethora of oncogenes and tumor suppressors
    Biomarker
    mTORC1 is hyperactivated in ~80-90% of all human cancers
  • 4. Phosphatase and Tensin Homolog Deleted on Chromosome 10 (PTEN)Function
    Cell Membrane
    Extracellular
    Cytosol
    PI3K
    PDK1
    PIP2
    PIP3
    IRS-1
    Akt
    PTEN
    Cell
    Survival
    Cell
    Division
    Cell
    Growth
    mTOR
    LST8
    Raptor
  • 5. PTEN loss-of-function (LOF) results in constitutive hyperactivation of the PI3K/Akt/mTORC1 signaling axis
    Cell Membrane
    Extracellular
    Cytosol
    PI3K
    PDK1
    PIP3
    PIP2
    IRS-1
    Akt
    Cell
    Division
    Cell
    Survival
    Cell
    Growth
    Constitutive
    Hyperactivation
    mTOR
    LST8
    Raptor
  • 6. PTEN is one of the most frequently mutated tumor suppressors in primary human cancers
    Endometrial
    Carcinoma
    (50-80%)
    Lung Cancer
    (30-50%)
    PTEN
    LOF
    Glioblastoma
    (50-80%)
    Colon Cancer
    (30-50%)
    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)
  • 7. Molecular Genetics and Prostate Cancer Progression
    Prostatic
    Intraepithelial
    Neoplasia
    (PIN)
    Normal
    Epithelium
    Invasive
    Carcinoma
    Metastasis
    Time
    Loss of 8p21
    NKX3.1
    Loss of 13q
    Rb
    Loss of 17p
    p53
    Loss Of
    Basal Cells
    Loss Of
    Basal Lamina
    Androgen-
    Independence
    Loss of 10p
    PTEN +/-
    Loss of 10p
    PTEN -/-
    Is mTORC1 hyperactivation downstream of PTEN LOF important for
    prostate cancer formation/progression?
    Adapted From: Abate-Shen, C. & Shen, MC. (2000) Genes & Dev.14: 2410-34
  • 8. Genetic inactivation of mTOR suppresses Pten-null-driven prostate cancer (CaP)
    PTENpc-/-: PTENloxP/loxPxPB-Cre4
    mTorpc-/-: mTorloxP/loxPxPB-Cre4
    PB-Cre4 transgenic mice express Crerecombinase
    under the control of the ARR2-probasin promoter,
    Which is turned on in the prostate epithelium after
    puberty
    Nardella, C. et al. (2009) Sci. Signal. 2: 1-10
  • 9. What about small regulatory RNAs (e.g. microRNAs)?
    Biomarker
  • 10. Kim VN & Siomi MC. (2009) Nat Rev Mol Cell Biol10: 126-39
  • 11. microRNA (miRNA) expression is dramatically altered in human cancer
    Normal Tissue vs. 1° Tumor Normal Tissue vs. NCI60 Cell Lines
    Lu, J. et al. Nature 435(7043): 834-838 Gaur, A. et al. Cancer Res67: 2456-2468
    Widespread loss of miRNA expression in cancer suggests most miRNAs function as tumor suppressors, while a minority of overexpressedmiRNAs function as oncogenes
  • 12. miRNAs can act as tumor suppressorsby repressing the expression of signal transduction proteins that serve as powerful oncogenes(e.g.Ras and let-7)
    HepG2 Cells:
    miRNA Mimic
    Neg. Control
    let-7
    Mimic
    Human 1° Lung Tumors:
    Esquela-Kerscher, A &Slack, FJ.(2006)
    Nat Rev Cancer 6: 259-69
    Adapted From: Johnson, SM, et al. (2005) Cell 120: 635-47
  • 13. 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)
    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
    Adapted From:Trang, P et al. (2010) Oncogene29: 1580-87
    Jackson, EL et al. (2001) Genes Dev15: 3243-8
  • 14. Project: Identify and characterize miRNAs and miRNA inhibitors that repress the mTORC1 pathway in cell-based models of PTEN -/- prostate cancer.
    miRNA
    Inhibitor 1
    Positive
    Regulator
    Negative
    Regulator
    miRNA-Z
    miRNA-Y
    miRNA-X
    mTOR
    Rapamycin
    LST8
    Raptor
    Ribosomal
    Biogenesis
    mRNA
    Translation
    Autophagy
  • 15. Phase 1. Acquire miRNA functional screening capabilities
    The microRNA Screening Consortium @ the Institute of Chemistry and Cell Biology-Longwood (ICCB-L) Screening Facility (HMS)
  • 16. The microRNA Screeners Consortium @ the ICCB-L
    Dana-Farber
    Cancer Institute
    Harvard
    Medical School
    ICCB-L
    Chowdhury
    Lab
    Blenis Lab
    (Cell Bio)
    Struhl Lab
    (BCMP)
    Children’s Hospital
    Boston
    Ragon Institute
    of MGH, MIT and Harvard
    Immune Disease
    Institute
    Daley Lab
    Brass Lab
    Shimaoka Lab
    Lieberman Lab
  • 17. 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:
    miScriptmiRNA Mimic Library (Qiagen)
    Pre-miRmiRNA Mimic Library (Ambion)
    Loss-of-Function Library:
    miRCURY LNA miRNA Knockdown Library (Exiqon)
  • 18. Phase 2. miRNA 1° Screen Optimization
    Primary Screen:
    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.
    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).
    Detection with an Alexa 488-conjugated secondary antibody and counterstaining with the DNA intercalating agent propidium iodide (PI).
    Data is collected using the Acumen eX3 microplatecytometer(TTP LabTech).
    20X
    40X
    Drosha
    Dicer
  • 19. 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
  • 20. 2A. Validation of 1° Screening Assay in PC-3
    Small Molecule
    PC-3 Cells (PTEN -/-)
    Serum
    Withdrawal
    Fix
    Permeabilize
    Block
    &
    1° Ab
    Alexa-488
    2° Ab
    &
    PI
    DNA Stain
    Image
    &
    Data Analysis
    Plate PC-3 Cells
    (384-well)
    Small Molecule Pin Transfer
    (DMSO vs. Rap)
    PI3K
    PTEN
    N
    Store
    @
    4°C
    24h
    48h
    3h
    Akt
    TSC1/2
    mTORC1
    Rapamycin
    Matrix WellMate®
    Microplate Dispenser
    (Thermo Scientific)
    Acumen®eX3
    MicroplateCytometer
    (TTP LabTech)
    Compound Transfer
    Robot
    (Epson)
    S6K1/2
    S6
  • 21. 2A. Validation of 1° Screening Assay in PC-3
    Small Molecule
    Heat Map
    Well Scan
    Plate Map
    DMSO
    Rap
    0 100
    Green = Active
    Red = Inactive
    Mean % p-S6
    Active
    Well Scatter Plot
    250
    cells/well
    500
    cells/well
    750
    cells/well
    DMSO
    DMSO
    % p-S6 Active
    N = 36
    Z’=0.852
    Rapamycin(20 nM)
    Rap
    Well #
  • 22. 2A. Validation of 1° Screening Assay in PC-3
    Small Molecule
    Odyssey®
    Infrared Imaging System
    (LI-COR Biosciences)
    Scale to
    10 cm plate
    Acumen®eX3
    MicroplateCytometer
    (TTP LabTech)
    384-well plate
    DMSO
    DMSO
    DMSO
    DMSO
    Rap
    Rap
    Rap
    Rap
    a-p-S6 Ser235/236
    a-S6 Total
    Merge
    Mean % p-S6 Active
    -87%
    Relative Integrated Intensity
    p-S6/S6
    (% Control)
    -99%
    250cells
    500cells
    750cells
    Z’ Factor: 0.8310.8520.716
  • 23. 2C. siRNA/miRNA positive and negative control selection for PC-3 cells.
    PC-3 Cells (PTEN -/-)
    Serum
    Withdrawal
    LST8
    &
    S6K1/2
    k.d.
    Fix
    Permeabilize
    Block
    &
    1° Ab
    Alexa-488
    2° Ab
    &
    PI
    DNA Stain
    Optional:
    Serum
    Starve
    Image
    &
    Data Analysis
    Reverse
    Transfection
    (384-well)
    Feed
    Cells
    PI3K
    PTEN
    N
    24h
    Store
    @
    4°C
    24h
    24h
    24h
    Akt
    TSC1/2
    mTOR
    LST8
    Raptor
    Matrix WellMate®
    Microplate Dispenser
    (Thermo Scientific)
    Acumen®eX3
    MicroplateCytometer
    (TTP LabTech)
    Bravo Automated
    Liquid Handling
    Platform
    (Velocity 11)
    RISC
    S6K1/2
    S6
  • 24. 2C. siRNA/miRNA positive and negative control selection for PC-3 cells.
    siRNAs
    Experiment 1
    NTC siRNA pool vs. siRNA pool positive control panel
    N = 4/group
    600 cells/well
    Asynchronously-growing (+serum)
    Starve (-serum)
    LST8:
    52%/25%
    S6K1/2:
    31%/10%
    Mean % p-S6 Active
    (% Control)
  • 25. 2C. siRNA/miRNA positive and negative control selection for PC-3 cells.
    siRNAs
    Experiment 2
    Z’ Factor Calculation Matrix: NTC vs. LST8, S6K1/2 and LST8 + S6K1/2
    N = 24/group
    500-1000 cells/well
    Z’ Factor
    0.2
    0.9
    (+) serum
    (-) serum
    *
    *
    *
    *
    *
    *
    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)
  • 26. 2C. siRNA/miRNA positive and negative control selection for PC-3 cells.
    miRNAs
    Experiment 1
    Mock vs. miRNA negative controls
    N = 24/group
    600 cells/well
    Asynchronously-growing (+serum)
    Starve (-serum)
    Mean Cell Number
    (% Control)
    Mean % p-S6 Active
    (% Control)
    E2
    Q1
    M
    A1
    A2
    E1
    E2
    Q1
    M
    A1
    A2
    E1
  • 27. 2C. siRNA/miRNA positive and negative control selection for PC-3 cells.
    Odyssey® WB Validation
    siRNA Pool
    miRNA
    Negative
    Control
    Sense miR-159 (Exiqon)
    Sense miR-159 (Exiqon)
    Pre-miR #2 (Ambion)
    Pre-miR #1 (Ambion)
    Pre-miR #2 (Ambion)
    Pre-miR #1 (Ambion)
    Scrambled (Exiqon)
    Scrambled (Exiqon)
    AllStars (Qiagen)
    AllStars (Qiagen)
    LST8 + S6K1/2
    LST8 + S6K1/2
    S6K1/2
    S6K1/2
    NTC
    NTC
    a-LST8 Total
    Target
    Knockdown
    a-S6K1 Total
    a-b-Actin Total
    a-p-S6 Ser235/236
    Biomarker
    Repression
    a-S6 Total
    Merge
    Serum
    Starve
    Condition
  • 28. Final 384-well library plate layout for 1° screen
    • 5 source plates/library
    • 29. 15 source plates total
    • 30. Screen in triplicate
    = 45 plates
    • Screen 2 conditions
    = 90 plates
    • 50 nM concentration
    NTC siRNA Pool
    miRNA Neg. Control 1
    Empty
    S6K1/2 siRNA Pool
    PLK1 siRNA Pool
    miRNA Library Reagents
    LST8 & S6K1/2 siRNA Pools
    miRNA Neg. Control 2
  • 31. Phase 3. Perform miRNA 1° screen3A. Gain-of-function miRNA mimic libraries (2)
  • 32. Screening Data Visualization: miScriptmiRNA Mimic Library (Qiagen): Plate-based Heat Map of Raw Mean % p-S6 Active Data
    Condition
    Serum
    Starve
    Plate ID
    PL-50684
    Conclusions:
    Hits appear to be evenly distributed
    Serum starvation sensitization
    Absence of edge effects
    PL-50685
    PL-50686
    PL-50687
    PL-50688
  • 33. Screening Data Visualization: miScriptmiRNA Mimic Library (Qiagen): Replicate Correlation Plots of Raw Mean % pS6 Active Data
    Condition
    Serum
    Starve
    R2=0.949
    R2=0.939
    Replicate A
    Replicate A
    Conclusions:
    Experimental replicates highly correlated.
    Absence of gross outliers
    Replicate B
    Replicate B
    R2=0.934
    R2=0.929
    Replicate A
    Replicate A
    Replicate C
    Replicate C
    R2=0.944
    R2=0.952
    Replicate B
    N1: NTCsiRNA
    N2: All Stars siRNA
    P1: S6K1/2siRNA
    P2: LST8+S6K1/2 siRNA
    X: miRNA Library
    Replicate B
    Replicate C
    Replicate C
  • 34. Screening Data Visualization: miScriptmiRNA Mimic Library (Qiagen): Plate/Well-Based Scatter Plot Raw Mean % pS6 Active Data
    Serum
    Starve color bySerum
    Mean % pS6 Active
    Mean % pS6 Active
    Plate/Well
    Plate/Well
    Starve
    Conclusions:
    Qualitative assessment shows many miRNAs with weak or intermediate affect on p-S6 status
    A few miRNAs with strong affect on p-S6 status (~as strong as siRNA positive controls)
    Mean % pS6 Active
    N1: NTCsiRNA
    N2: All Stars siRNA
    P1: S6K1/2siRNA
    P2: LST8+S6K1/2 siRNA
    X: miRNA Library
    Plate/Well
  • 35. Screening Data Analysis: miScriptmiRNA Mimic Library (Qiagen): Hit Selection
    Data Analysis Workflow:
    Z-score normalization relative to miRNA negative control (e.g. AllStarssiRNA)
    One-tailed t-test assuming unequal variance
    Hit selection: p<0.01
    “High-confidence” hit selection: Must score in both serum and starve conditions
    Serum
    Starve
    Formula:
    x - m
    z =
    394
    388
    229
    d
    Where:
    x = raw % pS6 active value
    m = miRNA negative control mean
    d = miRNA negative control s.d.
    Primary Screen
    Qiagen
    “High-confidence”
    Hits
  • 36. Screening Data Visualization: Pre-miRmiRNA Mimic Library (Ambion): Plate-based Heat Map of Raw Mean % p-S6 Active Data
    Condition
    Serum
    Starve
    Plate ID
    PL-50689
    Conclusions:
    Hits appear to be evenly distributed
    Serum starvation sensitization
    Absence of edge effects
    PL-50690
    PL-50691
    PL-50692
    PL-50693
  • 37. Screening Data Visualization: Pre-miRmiRNA Mimic Library (Ambion): Replicate Correlation Plots of Raw Mean % pS6 Active Data
    Condition
    Serum
    Starve
    R2=0.962
    R2=0.974
    Replicate A
    Replicate A
    Conclusions:
    Experimental replicates highly correlated.
    Absence of gross outliers
    Replicate B
    Replicate B
    R2=0.964
    R2=0.969
    Replicate A
    Replicate A
    Replicate C
    Replicate C
    R2=0.970
    R2=0.968
    Replicate B
    N1: NTCsiRNA
    N2: All Stars siRNA
    P1: S6K1/2siRNA
    P2: LST8+S6K1/2 siRNA
    X: miRNA Library
    Replicate B
    Replicate C
    Replicate C
  • 38. Screening Data Visualization: Pre-miRmiRNA Mimic Library (Ambion): Plate/Well-Based Scatter Plot Raw Mean % pS6 Active Data
    Serum
    Starve color bySerum
    Mean % pS6 Active
    Mean % pS6 Active
    Plate/Well
    Plate/Well
    Starve
    Conclusions:
    Qualitative assessment shows many miRNAs with weak or intermediate affect on p-S6 status
    A few miRNAs with strong affect on p-S6 status (~as strong as siRNA positive controls)
    Mean % pS6 Active
    N1: NTCsiRNA
    N2: All Stars siRNA
    P1: S6K1/2siRNA
    P2: LST8+S6K1/2 siRNA
    X: miRNA Library
    Plate/Well
  • 39. Screening Data Analysis: Pre-miRmiRNA Mimic Library (Ambion): Hit Selection
    Data Analysis Workflow:
    Z-score normalization relative to miRNA negative control (e.g. AllStarssiRNA)
    One-tailed t-test assuming unequal variance
    Hit selection: p<0.01
    “High-confidence” hit selection: Must score in both serum and starve conditions
    Serum
    Starve
    Formula:
    x - m
    z =
    d
    369
    540
    243
    Where:
    x = raw % pS6 active value
    m = miRNA negative control mean
    d = miRNA negative control s.d.
    Primary Screen
    Ambion
    “High-confidence”
    Hits
  • 40. Screening Data Analysis: Gain-of-Function Library Hit Selection Summary
    Pre-miRmiRNA Mimic Library (Ambion)
    miScriptmiRNA Mimic Library (Qiagen)
    Serum
    Starve
    Serum
    Starve
    369
    540
    243
    394
    388
    229
    Primary Screen
    Qiagen
    “High-confidence”
    Hits
    Primary Screen
    Ambion
    “High-confidence”
    Hits
    472miRNA mimics cherry picked for 2° Screen
  • 41. Phase 3. Perform miRNA 1° screen3B. Loss-of-function miRNA inhibitor library
  • 42. Screening Data Visualization: miRCURY LNA™miRNA Knockdown Library (Exiqon): Plate-based Heat Map of Raw Mean % p-S6 Active Data
    Condition
    Serum
    Starve
    Plate ID
    PL-50694
    Conclusions:
    Few hits compared to gain-of-function miRNA mimic libraries
    Hits appear to be evenly distributed
    Serum starvation sensitization?
    Absence of edge effects
    PL-50695
    PL-50696
    PL-50697
    PL-50698
  • 43. Screening Data Visualization: miRCURY LNA™miRNA Knockdown Library (Exiqon): Replicate Correlation Plots of Raw Mean % pS6 Active Data
    Condition
    Serum
    Starve
    R2=0.914
    R2=0.920
    Replicate A
    Replicate A
    Conclusions:
    Experimental replicates highly correlated.
    Absence of gross outliers
    Replicate B
    Replicate B
    R2=0.930
    R2=0.965
    Replicate A
    Replicate A
    Replicate C
    Replicate C
    R2=0.950
    R2=0.928
    Replicate B
    N1: NTCsiRNA
    N2: All Stars siRNA
    P1: S6K1/2siRNA
    P2: LST8+S6K1/2 siRNA
    X: miRNA Library
    Replicate B
    Replicate C
    Replicate C
  • 44. Screening Data Visualization: miRCURY LNA™ miRNA Knockdown Library (Exiqon): Plate/Well-Based Scatter Plot Raw Mean % pS6 Active Data
    Serum
    Starve color bySerum
    Mean % pS6 Active
    Mean % pS6 Active
    Plate/Well
    Plate/Well
    Starve
    Conclusions:
    Qualitative assessment shows fewer miRNA inhibitor hits compared to miRNA mimic libraries (as expected).
    Effect of miRNA inhibitors on p-S6 status tends to be less penetrant.
    Mean % pS6 Active
    N1: NTCsiRNA
    N2: All Stars siRNA
    P1: S6K1/2siRNA
    P2: LST8+S6K1/2 siRNA
    X: miRNA Library
    Plate/Well
  • 45. Screening Data Analysis: miRCURY LNA™ miRNA Knockdown Library (Exiqon): miRNA Hit Selection
    Data Analysis Workflow:
    Z-score normalization relative to miRNA negative control (e.g. AllStarssiRNA)
    One-tailed t-test assuming unequal variance
    Hit selection: p<0.01
    “High-confidence” hit selection: Must score in both serum and starve conditions
    Formula:
    Serum
    Starve
    x - m
    z =
    118
    174
    41
    d
    Where:
    x = raw % pS6 active value
    m = miRNA negative control mean
    d = miRNA negative control s.d.
    Primary Screen
    Exiqon
    “High-confidence”
    Hits
  • 46. Screening Data Analysis: Overall Hit Selection Summary
    Pre-miRmiRNA Mimic Library
    (Ambion)
    miScriptmiRNA Mimic Library
    (Qiagen)
    miRCURY LNA™ miRNA
    Knockdown Library
    (Exiqon)
    Serum
    Starve
    Serum
    Starve
    Serum
    Starve
    369
    540
    243
    394
    388
    229
    118
    174
    41
    Primary Screen
    Exiqon
    “High-confidence”
    Hits
    Primary Screen
    Qiagen
    “High-confidence”
    Hits
    Primary Screen
    Ambion
    “High-confidence”
    Hits
    513 total miRNA reagents cherry picked for 2° Screen
  • 47. 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
  • 48. Acknowledgments
    John Blenis
    Janie Zhang
    Greg Hoffman
    microRNA Screeners Consortium
    ICCB-L
    Caroline Shamu
    Sean Johnston
    Jen Nale
    Katrina Rudnicki
    Stewart Rudnicki
    Dave Wrobel
    TTP LabTech
    Ben Schenker
    Cell Signaling Technologies (CST)
    Randy Wetzel
    EMD Serono
    Mei Zhang
    Brian Healey
    Qiagen
    Ambion
    Exiqon
    Dharmacon/Thermo Scientific

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