Ttp Lab Tech Talk 051810

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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

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Ttp Lab Tech Talk 051810

  1. 1. High-throughput microRNA functional screening using the Acumen eX3 to identify repressors of a tumorigenic signal transduction pathway<br />Neil Kubica, Janie Zhang, Greg Hoffman and John Blenis<br />Department of Cell Biology<br />Harvard Medical School<br />US Acumen Users Group Meeting (UGM)<br />British Consulate – General<br />Cambridge, MA<br />May 18, 2010<br />
  2. 2. mTORC1 Integrates Multiple Upstream Signals to Determine the Balance Between Cellular Anabolism and Cellular Catabolism<br />Energy<br />Amino<br />Acids<br />Growth<br />Factors<br />mTOR<br />Rapamycin<br />LST8<br />Raptor<br />Ribosomal<br />Biogenesis<br />mRNA<br />Translation<br />Autophagy<br />
  3. 3. The mTORC1 signaling network is populated by a plethora of oncogenes and tumor suppressors<br />Biomarker<br />mTORC1 is hyperactivated in ~80-90% of all human cancers<br />
  4. 4. Phosphatase and Tensin Homolog Deleted on Chromosome 10 (PTEN)Function<br />Cell Membrane<br />Extracellular<br />Cytosol<br />PI3K<br />PDK1<br />PIP2<br />PIP3<br />IRS-1<br />Akt<br />PTEN<br />Cell<br />Survival<br />Cell<br />Division<br />Cell<br />Growth<br />mTOR<br />LST8<br />Raptor<br />
  5. 5. PTEN loss-of-function (LOF) results in constitutive hyperactivation of the PI3K/Akt/mTORC1 signaling axis<br />Cell Membrane<br />Extracellular<br />Cytosol<br />PI3K<br />PDK1<br />PIP3<br />PIP2<br />IRS-1<br />Akt<br />Cell<br />Division<br />Cell<br />Survival<br />Cell<br />Growth<br />Constitutive<br />Hyperactivation<br />mTOR<br />LST8<br />Raptor<br />
  6. 6. PTEN is one of the most frequently mutated tumor suppressors in primary human cancers<br />Endometrial<br />Carcinoma<br />(50-80%)<br />Lung Cancer<br />(30-50%)<br />PTEN <br />LOF<br />Glioblastoma<br />(50-80%)<br />Colon Cancer<br />(30-50%)<br />Prostate Cancer<br />(50-80%)<br />Breast Cancer<br />(30-50%)<br />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)<br />
  7. 7. Molecular Genetics and Prostate Cancer Progression<br />Prostatic<br />Intraepithelial<br />Neoplasia<br />(PIN)<br />Normal<br />Epithelium<br />Invasive<br />Carcinoma<br />Metastasis<br />Time<br />Loss of 8p21<br />NKX3.1<br />Loss of 13q<br />Rb<br />Loss of 17p<br />p53<br />Loss Of<br />Basal Cells<br />Loss Of<br />Basal Lamina<br />Androgen-<br />Independence<br />Loss of 10p<br />PTEN +/-<br />Loss of 10p<br />PTEN -/-<br />Is mTORC1 hyperactivation downstream of PTEN LOF important for <br />prostate cancer formation/progression?<br />Adapted From: Abate-Shen, C. & Shen, MC. (2000) Genes & Dev.14: 2410-34<br />
  8. 8. Genetic inactivation of mTOR suppresses Pten-null-driven prostate cancer (CaP)<br />PTENpc-/-: PTENloxP/loxPxPB-Cre4<br />mTorpc-/-: mTorloxP/loxPxPB-Cre4<br />PB-Cre4 transgenic mice express Crerecombinase<br />under the control of the ARR2-probasin promoter,<br />Which is turned on in the prostate epithelium after<br />puberty<br />Nardella, C. et al. (2009) Sci. Signal. 2: 1-10<br />
  9. 9. What about small regulatory RNAs (e.g. microRNAs)?<br />Biomarker<br />
  10. 10. Kim VN & Siomi MC. (2009) Nat Rev Mol Cell Biol10: 126-39<br />
  11. 11. microRNA (miRNA) expression is dramatically altered in human cancer<br />Normal Tissue vs. 1° Tumor Normal Tissue vs. NCI60 Cell Lines<br /> Lu, J. et al. Nature 435(7043): 834-838 Gaur, A. et al. Cancer Res67: 2456-2468<br />Widespread loss of miRNA expression in cancer suggests most miRNAs function as tumor suppressors, while a minority of overexpressedmiRNAs function as oncogenes<br />
  12. 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)<br />HepG2 Cells:<br />miRNA Mimic<br />Neg. Control <br />let-7 <br />Mimic<br />Human 1° Lung Tumors:<br />Esquela-Kerscher, A &Slack, FJ.(2006) <br />Nat Rev Cancer 6: 259-69<br />Adapted From: Johnson, SM, et al. (2005) Cell 120: 635-47<br />
  13. 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)<br />Mouse Strain: LSL-K-Ras G12D<br />This strain carries a latent point mutant allele of Kras2 (K-RasG12D).<br />Cre-mediated recombination leads to deletion of a transcriptional termination sequence (Lox-Stop-Lox) and expression of the oncogenic protein.<br />Intranasal infection with Cre adenovirus results in very high frequency of lung tumors at baseline.<br />Intranasal infection of a lentivirus encoding let-7 reduces lung tumor burden<br />Adapted From:Trang, P et al. (2010) Oncogene29: 1580-87<br />Jackson, EL et al. (2001) Genes Dev15: 3243-8<br />
  14. 14. Project: Identify and characterize miRNAs and miRNA inhibitors that repress the mTORC1 pathway in cell-based models of PTEN -/- prostate cancer.<br />miRNA<br />Inhibitor 1<br />Positive<br />Regulator<br />Negative<br />Regulator<br />miRNA-Z<br />miRNA-Y<br />miRNA-X<br />mTOR<br />Rapamycin<br />LST8<br />Raptor<br />Ribosomal<br />Biogenesis<br />mRNA<br />Translation<br />Autophagy<br />
  15. 15. Phase 1. Acquire miRNA functional screening capabilities<br />The microRNA Screening Consortium @ the Institute of Chemistry and Cell Biology-Longwood (ICCB-L) Screening Facility (HMS)<br />
  16. 16. The microRNA Screeners Consortium @ the ICCB-L<br />Dana-Farber<br />Cancer Institute<br />Harvard <br />Medical School<br />ICCB-L<br />Chowdhury<br />Lab<br />Blenis Lab<br />(Cell Bio)<br />Struhl Lab<br />(BCMP)<br />Children’s Hospital<br />Boston<br />Ragon Institute<br />of MGH, MIT and Harvard<br />Immune Disease<br />Institute<br />Daley Lab<br />Brass Lab<br />Shimaoka Lab<br />Lieberman Lab<br />
  17. 17. The microRNA Screeners Consortium @ the ICCB-L<br />Consortium model allowed for shared purchase and evaluation of miRNA gain-of-function and loss-of-function libraries.<br />Gain-of-Function Libraries:<br />miScriptmiRNA Mimic Library (Qiagen)<br /> Pre-miRmiRNA Mimic Library (Ambion)<br />Loss-of-Function Library:<br />miRCURY LNA miRNA Knockdown Library (Exiqon)<br />
  18. 18. Phase 2. miRNA 1° Screen Optimization<br />Primary Screen: <br />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.<br />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).<br />Detection with an Alexa 488-conjugated secondary antibody and counterstaining with the DNA intercalating agent propidium iodide (PI). <br />Data is collected using the Acumen eX3 microplatecytometer(TTP LabTech).<br />20X<br />40X<br />Drosha<br />Dicer<br />
  19. 19. Phase 2. miRNA 1° Screen Optimization<br />2A. Validation of the 1° screening assay in PC-3 cells<br />2B. Small RNA transfection protocol for PC-3 cells<br />2C. siRNA/miRNA positive and negative control selection in PC-3 cells <br />
  20. 20. 2A. Validation of 1° Screening Assay in PC-3<br />Small Molecule<br />PC-3 Cells (PTEN -/-)<br />Serum<br />Withdrawal<br />Fix <br />Permeabilize<br />Block<br />&<br />1° Ab<br />Alexa-488<br />2° Ab<br />&<br />PI<br />DNA Stain<br />Image<br />&<br />Data Analysis<br />Plate PC-3 Cells<br />(384-well)<br />Small Molecule Pin Transfer<br />(DMSO vs. Rap)<br />PI3K<br />PTEN<br />N<br />Store<br />@<br />4°C<br />24h<br />48h<br />3h<br />Akt<br />TSC1/2<br />mTORC1<br />Rapamycin<br />Matrix WellMate®<br />Microplate Dispenser<br />(Thermo Scientific)<br />Acumen®eX3<br />MicroplateCytometer<br />(TTP LabTech)<br />Compound Transfer<br />Robot<br />(Epson)<br />S6K1/2<br />S6<br />
  21. 21. 2A. Validation of 1° Screening Assay in PC-3<br />Small Molecule<br />Heat Map<br />Well Scan<br />Plate Map<br />DMSO<br />Rap<br />0 100<br />Green = Active<br />Red = Inactive<br />Mean % p-S6 <br />Active<br />Well Scatter Plot<br />250<br />cells/well<br />500<br />cells/well<br />750<br />cells/well<br />DMSO<br />DMSO<br />% p-S6 Active<br />N = 36<br />Z’=0.852<br />Rapamycin(20 nM)<br />Rap<br />Well #<br />
  22. 22. 2A. Validation of 1° Screening Assay in PC-3<br />Small Molecule<br />Odyssey®<br />Infrared Imaging System<br />(LI-COR Biosciences)<br />Scale to <br />10 cm plate<br />Acumen®eX3<br />MicroplateCytometer<br />(TTP LabTech)<br />384-well plate<br />DMSO<br />DMSO<br />DMSO<br />DMSO<br />Rap<br />Rap<br />Rap<br />Rap<br />a-p-S6 Ser235/236<br />a-S6 Total<br />Merge<br />Mean % p-S6 Active<br />-87%<br />Relative Integrated Intensity<br />p-S6/S6<br />(% Control)<br />-99%<br />250cells<br />500cells<br />750cells<br />Z’ Factor: 0.8310.8520.716<br />
  23. 23. 2C. siRNA/miRNA positive and negative control selection for PC-3 cells.<br />PC-3 Cells (PTEN -/-)<br />Serum<br />Withdrawal<br />LST8<br />&<br />S6K1/2<br />k.d.<br />Fix <br />Permeabilize<br />Block<br />&<br />1° Ab<br />Alexa-488<br />2° Ab<br />&<br />PI<br />DNA Stain<br />Optional:<br />Serum<br />Starve<br />Image<br />&<br />Data Analysis<br />Reverse<br />Transfection<br />(384-well)<br />Feed<br />Cells<br />PI3K<br />PTEN<br />N<br />24h<br />Store<br />@<br />4°C<br />24h<br />24h<br />24h<br />Akt<br />TSC1/2<br />mTOR<br />LST8<br />Raptor<br />Matrix WellMate®<br />Microplate Dispenser<br />(Thermo Scientific)<br />Acumen®eX3<br />MicroplateCytometer<br />(TTP LabTech)<br />Bravo Automated<br />Liquid Handling<br />Platform<br />(Velocity 11)<br />RISC<br />S6K1/2<br />S6<br />
  24. 24. 2C. siRNA/miRNA positive and negative control selection for PC-3 cells.<br />siRNAs<br />Experiment 1<br />NTC siRNA pool vs. siRNA pool positive control panel<br />N = 4/group<br />600 cells/well <br />Asynchronously-growing (+serum)<br />Starve (-serum)<br />LST8:<br />52%/25%<br />S6K1/2:<br />31%/10%<br />Mean % p-S6 Active<br />(% Control)<br />
  25. 25. 2C. siRNA/miRNA positive and negative control selection for PC-3 cells.<br />siRNAs<br />Experiment 2<br />Z’ Factor Calculation Matrix: NTC vs. LST8, S6K1/2 and LST8 + S6K1/2<br />N = 24/group<br />500-1000 cells/well <br />Z’ Factor<br />0.2<br />0.9<br />(+) serum<br />(-) serum<br />*<br />*<br />*<br />*<br />*<br />*<br />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) <br />
  26. 26. 2C. siRNA/miRNA positive and negative control selection for PC-3 cells.<br />miRNAs<br />Experiment 1<br />Mock vs. miRNA negative controls<br />N = 24/group<br />600 cells/well <br />Asynchronously-growing (+serum)<br />Starve (-serum)<br />Mean Cell Number<br />(% Control)<br />Mean % p-S6 Active<br />(% Control)<br />E2<br />Q1<br />M<br />A1<br />A2<br />E1<br />E2<br />Q1<br />M<br />A1<br />A2<br />E1<br />
  27. 27. 2C. siRNA/miRNA positive and negative control selection for PC-3 cells.<br />Odyssey® WB Validation<br />siRNA Pool<br />miRNA<br />Negative <br />Control<br />Sense miR-159 (Exiqon)<br />Sense miR-159 (Exiqon)<br />Pre-miR #2 (Ambion)<br />Pre-miR #1 (Ambion)<br />Pre-miR #2 (Ambion)<br />Pre-miR #1 (Ambion)<br />Scrambled (Exiqon)<br />Scrambled (Exiqon)<br />AllStars (Qiagen)<br />AllStars (Qiagen)<br />LST8 + S6K1/2<br />LST8 + S6K1/2<br />S6K1/2<br />S6K1/2<br />NTC<br />NTC<br />a-LST8 Total<br />Target<br />Knockdown<br />a-S6K1 Total<br />a-b-Actin Total<br />a-p-S6 Ser235/236<br />Biomarker<br />Repression<br />a-S6 Total<br />Merge<br />Serum<br />Starve<br />Condition<br />
  28. 28. Final 384-well library plate layout for 1° screen<br /><ul><li>5 source plates/library
  29. 29. 15 source plates total
  30. 30. Screen in triplicate </li></ul> = 45 plates<br /><ul><li> Screen 2 conditions</li></ul> = 90 plates<br /><ul><li> 50 nM concentration</li></ul>NTC siRNA Pool<br />miRNA Neg. Control 1<br />Empty<br />S6K1/2 siRNA Pool<br />PLK1 siRNA Pool<br />miRNA Library Reagents<br />LST8 & S6K1/2 siRNA Pools<br />miRNA Neg. Control 2<br />
  31. 31. Phase 3. Perform miRNA 1° screen3A. Gain-of-function miRNA mimic libraries (2) <br />
  32. 32. Screening Data Visualization: miScriptmiRNA Mimic Library (Qiagen): Plate-based Heat Map of Raw Mean % p-S6 Active Data<br />Condition<br />Serum<br />Starve<br />Plate ID<br />PL-50684<br />Conclusions:<br />Hits appear to be evenly distributed<br />Serum starvation sensitization<br />Absence of edge effects<br />PL-50685<br />PL-50686<br />PL-50687<br />PL-50688<br />
  33. 33. Screening Data Visualization: miScriptmiRNA Mimic Library (Qiagen): Replicate Correlation Plots of Raw Mean % pS6 Active Data<br />Condition<br />Serum<br />Starve<br />R2=0.949<br />R2=0.939<br />Replicate A<br />Replicate A<br />Conclusions:<br />Experimental replicates highly correlated.<br />Absence of gross outliers<br />Replicate B<br />Replicate B<br />R2=0.934<br />R2=0.929<br />Replicate A<br />Replicate A<br />Replicate C<br />Replicate C<br />R2=0.944<br />R2=0.952<br />Replicate B<br />N1: NTCsiRNA<br />N2: All Stars siRNA<br />P1: S6K1/2siRNA<br />P2: LST8+S6K1/2 siRNA<br />X: miRNA Library<br />Replicate B<br />Replicate C<br />Replicate C<br />
  34. 34. Screening Data Visualization: miScriptmiRNA Mimic Library (Qiagen): Plate/Well-Based Scatter Plot Raw Mean % pS6 Active Data<br />Serum<br />Starve color bySerum<br />Mean % pS6 Active<br />Mean % pS6 Active<br />Plate/Well<br />Plate/Well<br />Starve<br />Conclusions:<br />Qualitative assessment shows many miRNAs with weak or intermediate affect on p-S6 status<br />A few miRNAs with strong affect on p-S6 status (~as strong as siRNA positive controls)<br />Mean % pS6 Active<br />N1: NTCsiRNA<br />N2: All Stars siRNA<br />P1: S6K1/2siRNA<br />P2: LST8+S6K1/2 siRNA<br />X: miRNA Library<br />Plate/Well<br />
  35. 35. Screening Data Analysis: miScriptmiRNA Mimic Library (Qiagen): Hit Selection<br />Data Analysis Workflow:<br />Z-score normalization relative to miRNA negative control (e.g. AllStarssiRNA)<br />One-tailed t-test assuming unequal variance<br />Hit selection: p<0.01<br />“High-confidence” hit selection: Must score in both serum and starve conditions<br />Serum<br />Starve<br />Formula:<br />x - m<br />z = <br />394<br />388<br />229<br />d<br />Where:<br />x = raw % pS6 active value<br />m = miRNA negative control mean<br />d = miRNA negative control s.d.<br />Primary Screen<br />Qiagen<br />“High-confidence” <br />Hits<br />
  36. 36. Screening Data Visualization: Pre-miRmiRNA Mimic Library (Ambion): Plate-based Heat Map of Raw Mean % p-S6 Active Data<br />Condition<br />Serum<br />Starve<br />Plate ID<br />PL-50689<br />Conclusions:<br />Hits appear to be evenly distributed<br />Serum starvation sensitization<br />Absence of edge effects<br />PL-50690<br />PL-50691<br />PL-50692<br />PL-50693<br />
  37. 37. Screening Data Visualization: Pre-miRmiRNA Mimic Library (Ambion): Replicate Correlation Plots of Raw Mean % pS6 Active Data<br />Condition<br />Serum<br />Starve<br />R2=0.962<br />R2=0.974<br />Replicate A<br />Replicate A<br />Conclusions:<br />Experimental replicates highly correlated.<br />Absence of gross outliers<br />Replicate B<br />Replicate B<br />R2=0.964<br />R2=0.969<br />Replicate A<br />Replicate A<br />Replicate C<br />Replicate C<br />R2=0.970<br />R2=0.968<br />Replicate B<br />N1: NTCsiRNA<br />N2: All Stars siRNA<br />P1: S6K1/2siRNA<br />P2: LST8+S6K1/2 siRNA<br />X: miRNA Library<br />Replicate B<br />Replicate C<br />Replicate C<br />
  38. 38. Screening Data Visualization: Pre-miRmiRNA Mimic Library (Ambion): Plate/Well-Based Scatter Plot Raw Mean % pS6 Active Data<br />Serum<br />Starve color bySerum<br />Mean % pS6 Active<br />Mean % pS6 Active<br />Plate/Well<br />Plate/Well<br />Starve<br />Conclusions:<br />Qualitative assessment shows many miRNAs with weak or intermediate affect on p-S6 status<br />A few miRNAs with strong affect on p-S6 status (~as strong as siRNA positive controls)<br />Mean % pS6 Active<br />N1: NTCsiRNA<br />N2: All Stars siRNA<br />P1: S6K1/2siRNA<br />P2: LST8+S6K1/2 siRNA<br />X: miRNA Library<br />Plate/Well<br />
  39. 39. Screening Data Analysis: Pre-miRmiRNA Mimic Library (Ambion): Hit Selection<br />Data Analysis Workflow:<br />Z-score normalization relative to miRNA negative control (e.g. AllStarssiRNA)<br />One-tailed t-test assuming unequal variance<br />Hit selection: p<0.01<br />“High-confidence” hit selection: Must score in both serum and starve conditions<br />Serum<br />Starve<br />Formula:<br />x - m<br />z = <br />d<br />369<br />540<br />243<br />Where:<br />x = raw % pS6 active value<br />m = miRNA negative control mean<br />d = miRNA negative control s.d.<br />Primary Screen<br />Ambion<br />“High-confidence” <br />Hits<br />
  40. 40. Screening Data Analysis: Gain-of-Function Library Hit Selection Summary<br />Pre-miRmiRNA Mimic Library (Ambion)<br />miScriptmiRNA Mimic Library (Qiagen)<br />Serum<br />Starve<br />Serum<br />Starve<br />369<br />540<br />243<br />394<br />388<br />229<br />Primary Screen<br />Qiagen<br />“High-confidence”<br /> Hits<br />Primary Screen<br />Ambion<br />“High-confidence” <br />Hits<br />472miRNA mimics cherry picked for 2° Screen<br />
  41. 41. Phase 3. Perform miRNA 1° screen3B. Loss-of-function miRNA inhibitor library <br />
  42. 42. Screening Data Visualization: miRCURY LNA™miRNA Knockdown Library (Exiqon): Plate-based Heat Map of Raw Mean % p-S6 Active Data<br />Condition<br />Serum<br />Starve<br />Plate ID<br />PL-50694<br />Conclusions:<br />Few hits compared to gain-of-function miRNA mimic libraries<br />Hits appear to be evenly distributed<br />Serum starvation sensitization?<br />Absence of edge effects<br />PL-50695<br />PL-50696<br />PL-50697<br />PL-50698<br />
  43. 43. Screening Data Visualization: miRCURY LNA™miRNA Knockdown Library (Exiqon): Replicate Correlation Plots of Raw Mean % pS6 Active Data<br />Condition<br />Serum<br />Starve<br />R2=0.914<br />R2=0.920<br />Replicate A<br />Replicate A<br />Conclusions:<br />Experimental replicates highly correlated.<br />Absence of gross outliers<br />Replicate B<br />Replicate B<br />R2=0.930<br />R2=0.965<br />Replicate A<br />Replicate A<br />Replicate C<br />Replicate C<br />R2=0.950<br />R2=0.928<br />Replicate B<br />N1: NTCsiRNA<br />N2: All Stars siRNA<br />P1: S6K1/2siRNA<br />P2: LST8+S6K1/2 siRNA<br />X: miRNA Library<br />Replicate B<br />Replicate C<br />Replicate C<br />
  44. 44. Screening Data Visualization: miRCURY LNA™ miRNA Knockdown Library (Exiqon): Plate/Well-Based Scatter Plot Raw Mean % pS6 Active Data<br />Serum<br />Starve color bySerum<br />Mean % pS6 Active<br />Mean % pS6 Active<br />Plate/Well<br />Plate/Well<br />Starve<br />Conclusions:<br />Qualitative assessment shows fewer miRNA inhibitor hits compared to miRNA mimic libraries (as expected).<br />Effect of miRNA inhibitors on p-S6 status tends to be less penetrant.<br />Mean % pS6 Active<br />N1: NTCsiRNA<br />N2: All Stars siRNA<br />P1: S6K1/2siRNA<br />P2: LST8+S6K1/2 siRNA<br />X: miRNA Library<br />Plate/Well<br />
  45. 45. Screening Data Analysis: miRCURY LNA™ miRNA Knockdown Library (Exiqon): miRNA Hit Selection<br />Data Analysis Workflow:<br />Z-score normalization relative to miRNA negative control (e.g. AllStarssiRNA)<br />One-tailed t-test assuming unequal variance<br />Hit selection: p<0.01<br />“High-confidence” hit selection: Must score in both serum and starve conditions<br />Formula:<br />Serum<br />Starve<br />x - m<br />z = <br />118<br />174<br />41<br />d<br />Where:<br />x = raw % pS6 active value<br />m = miRNA negative control mean<br />d = miRNA negative control s.d.<br />Primary Screen<br />Exiqon<br />“High-confidence”<br /> Hits<br />
  46. 46. Screening Data Analysis: Overall Hit Selection Summary<br />Pre-miRmiRNA Mimic Library <br />(Ambion)<br />miScriptmiRNA Mimic Library <br />(Qiagen)<br />miRCURY LNA™ miRNA<br />Knockdown Library<br />(Exiqon)<br />Serum<br />Starve<br />Serum<br />Starve<br />Serum<br />Starve<br />369<br />540<br />243<br />394<br />388<br />229<br />118<br />174<br />41<br />Primary Screen<br />Exiqon<br />“High-confidence”<br /> Hits<br />Primary Screen<br />Qiagen<br />“High-confidence” <br />Hits<br />Primary Screen<br />Ambion<br />“High-confidence” <br />Hits<br />513 total miRNA reagents cherry picked for 2° Screen<br />
  47. 47. Future Directions…<br />Phase 4. Perform secondary screen in LNCaP cells to eliminate cell-type specific hits<br />Phase 5. Further characterization of mTORC1 function for strongest hits<br />Phase 6. Determine mechanism of action for strongest hits<br />
  48. 48. Acknowledgments<br />John Blenis<br />Janie Zhang<br />Greg Hoffman<br />microRNA Screeners Consortium<br />ICCB-L<br />Caroline Shamu<br />Sean Johnston<br />Jen Nale<br />Katrina Rudnicki<br />Stewart Rudnicki<br />Dave Wrobel<br />TTP LabTech<br />Ben Schenker<br />Cell Signaling Technologies (CST)<br />Randy Wetzel<br />EMD Serono<br />Mei Zhang<br />Brian Healey<br />Qiagen<br />Ambion<br />Exiqon<br />Dharmacon/Thermo Scientific<br />

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