Cellecta Overview Started Operations in 2007 Headquarters: Mountain View, CA Focus: Development of flexible, scalable, and broadly parallel genetic screening assays to expedite the discovery and characterization of novel therapeutic targets for drug discovery.
Library Production and Screening Workflow For sgrna screens, cells must express Cas9. For shrna screens, cells do not need pre-engineering Each Dot = # reads of shrna or sgrna (measured by deep sequencing) Dot on diagonal not toxic for cells Target gene not essential Dot under diagonal Lethal to cells Target gene essential
Some Citations for Cellecta s Libraries Hoffman GR, et al (2014) "Functional epigenetics approach identifies BRM/SMARCA2 as a critical synthetic lethal target in BRG1-deficient cancers." PNAS. PMID: 24520176. (shrna library) Mounir Z., et al (2016) "ERG signaling in prostate cancer is driven through PRMT5-dependent methylation of the Androgen Receptor" elife. 2016;5:e13964. (shrna library) Munoz DM, et al (2016) "CRISPR Screens Provide a Comprehensive Assessment of Cancer Vulnerabilities but Generate False-Positive Hits for Highly Amplified Genomic Regions" Cancer Discovery. 2016 6; 900. (CRISPR library) Bossi D, et al (2016) "In Vivo Genetic Screens of Patient-Derived Tumors Revealed Unexpected Frailty of the Transformed Phenotype" Cancer Discovery. 2016 6; 650. (custom shrna library; in vivo screen) Carugo A, et al (2016) "In Vivo Functional Platform Targeting Patient-Derived Xenografts Identifies WDR5-Myc Association as a Critical Determinant of Pancreatic Cancer" Cell Reports. 2016 16; 133. (custom shrna library) Wagenaar, T, R, et al. (2014), Identification of the endosomal sorting complex required for transport-i (ESCRT-I) as an important modulator of anti-mir uptake by cancer cells. Nucleic Acids Res. 2015 Jan 30;43 (Custom shrna Library, in vivo screen) Wolf J, et al. (2013) "An in vivo RNAi screen identifies SALL1 as a tumor suppressor in human breast cancer with a role in CDH1 regulation." Oncogene. December 2013; doi: 10.1038/onc.2013.515. (Decipher shrna Library) Wolf J, et al. (2013) "A mammosphere formation RNAi screen reveals that ATG4A promotes a breast cancer stemlike phenotype." Breast Cancer Research. 15:R109. (Decipher shrna Library) Fredebohm J, et al. (2013) "Depletion of RAD17 sensitizes pancreatic cancer cells to gemcitabine." J Cell Sci. Aug 1;126(Pt 15):3380-9. (Decipher shrna Library) Sancho P, et al (2015) "MYC/PGC-1α Balance Determines the Metabolic Phenotype and Plasticity of Pancreatic Cancer Stem Cells" Cell Metabolism. 2015 22; 590. (CellTracker barcode library) Nolan-Steveau O, et al (2015) "Measurement of Cancer Cell Growth Heterogeneity through Lentiviral Barcoding Identifies Clonal Dominance as a Characteristic of In Vivo Tumor Engraftment PLOS One 2013 dx.doi.org/10.1371/journal.pone.0067316. (barcode library cell tracking collaboration) Bjorklund, CC, et al. (2015), Rate of CRL4CRBN substrate Ikaros and Aiolos degradation underlies differential activity of lenalidomide and pomalidomide in multiple myeloma cells by regulation of c-myc and IRF4 Blood Cancer Journal (2015) 5, e354; doi:10.1038/bcj.2015.66 (shrna Constructs)
Gene Expression Analysis Techniques qrt-pcr Microarrays RNA-Seq Easy to run Sensitive Low Input Amounts Good Dynamic Range Easy to Analyze Not Genome-Wide Easy to run High Thoughput High Input Amounts Limited Dynamic Range Low Sensitivity Complex Analysis Less Quantitative Complex Protocol Genome-Wide Analysis Digital NGS Readout Better Dynamic Range Limited Sensitivity Complex Data Analysis
Driver-Map Workflow: Single-Tube Profiling
Primer Optimization Is Key to Functionality Most Commercially Available Primer not suitable for multiplex reactions (e.g, strong primer dimer). PCR primers targeting each protein-coding gene were selected experimentally for high on-target and low off-target activity through multiple sequential rounds of multiplex RT- PCR/NGS experiments. As a results, we have developed a set of genome-wide PCR primers that reliably measure at least 95% of mrnas
Select Primers Optimal Multiplex PCR Quantitative amplification and specificity to the targets were essential. In addition, for highabundance genes, primers with lower efficacy were required to enable adequate detection of low abundance transcripts within in the 100,000-fold dynamic range (100-fold more than RNA-Seq or microarrays).
RNA vs. Driver-Map
Correlation RNAseq vs. Driver-Map Driver-Map shows 100 fold increase in sensitivity over RNAseq. Note the differences in the axis values. 25 Reads (log2) Driver-Map 1740 gene panel 20 99% concordance with mid-to-high abundance genes. 15 10 More than half of the low abundance genes are not detected at all with RNAseq (bottom out on Y-axis) 5 0 0 2 4 6 8 RPKM (log2) RNA-Seq (5x more reads) 10
10 pg 50 pg 200 pg 780 pg 3.1 ng 12.5 ng 50 ng 10 pg 50 pg 200 pg 780 pg 3.1 ng 12.5 ng 50 ng Reproducible and Robust Human Universal Human Brain 10 pg Universal 1.00 0.93 0.93 0.94 0.93 0.94 0.93 0.62 0.63 0.63 0.63 0.63 0.63 0.63 10 pg Brain 0.62 0.62 0.62 0.62 0.62 0.63 0.62 1.00 0.93 0.93 0.93 0.93 0.94 0.93 The correlation (R-squared values) of detected genes between human universal RNA and total brain RNA remains highly consistent across a 10 pg to 50 ng of starting total RNA. Each dot above represents the number of NGS reads for a gene in duplicate runs of a model system with T-cyt RNA spiked at 0.625%, 2.5%, and 10% into 100ng of MDA231 RNA. Even at the lowest spikein level, read levels for each of the detected gene remains highly reproducible.
Tumor Component Profiling Above is an H&E stained triple-negative breast cancer tumor section with significant immune cell infiltration Driver-Map analysis of a section from the same tumor block shows the degree of immune infiltration of various cellular subtypes as determined based on indicative gene expression signatures present in the Driver-Map data.
Sensitivity of T Cell Detection in Tumor Detection Limit 0.5%
Primary and Metastatic Xenograft Profiling Fat Pad Injection Site Tumor Metastatic Tumor in Lung Proteolysis Migration Adhesion Human breast adenocarcinoma cells (MDA-MB-231) were implanted into mouse mammary fat pads of three animals. Primary tumors formed in the mammary tissue at the injection sites, and later secondary metastasized tumors formed in the lungs. Pre-Inject Primary Metastatic To the left is the full Driver- Map profile of the preimplanted cells, implanted primary tumors, and lung tissue with the metastasized tumors. On the right, clear changes in the genes in signature pathways associated with metastasis are apparent as compared with the primary tumors.
Blood Marker Analysis for Fibromyalgia Panel of 25 candidate pain biomarkers up-regulated in individuals with fibromyalgia (FM) vs. healthy control cases. Driver-Map expression data from 50 ng of RNA isolated from whole blood from each of 6 negative controls (no pain) and 7 FM clinical specimens with a grade 9 pain assessment. No beta-globin depletion or mrna enrichment Identified a set of 25 candidate pain-associated markers that are significantly up-regulated in the FM cohort.
Driver-Map Provides Bridge to Clinical PCR 18 16 14 12 10 8 6 4 2 0 18 16 14 12 10 8 6 4 2 0 18 16 14 12 10 8 6 4 2 0 High Abundant genes Medium Low Driver-Map assay Same Primers Targeted Gene NGS Expression Assay or RT-PCR assay 31 26 21 16 31 26 21 16 31 26 21 16 High Abundant genes Medium Low
Driver-Map Features and Benefits Key Features Multiplex RT-PCR RNA Input Sensitivity/Dynamic Range Specificity Single-Tube Protocol Benefits No rrna or globin depletion required Use directly with total RNA 10 pg total RNA (single-cell) minimum 1 ng - 50 ng (optimal) Linear data in 10 5 -fold dynamic range 100-fold more sensitive than RNA-Seq or GeneChip No background (sequencing data) Low cross-amplification of mouse cdnas 96 samples per day (2 hours hands-on time) Result Validation Conventional qrt-pcr using the same PCR primers
Driver-Map Profiling Service Genome-Wide RNA Expression Profiling Send us: Total RNA (50-150 ng) Cells or Tissue Service (3-4 weeks): Sample Preparation, PCR, Sequencing, Deconvolution, and Report Deliverables: Driver-Map Report: Statistics on sequencing run Gene levels for each sample (raw and normalized counts) Differential expression analysis sample-to-sample or treated v. non-treated Custom Assays to Targeted Gene Sets Want to profile specific transcripts in large numbers of samples? Have us formulate a targeted assay with our validated primers. Ask about Custom Driver-Map Assay Development ** Coming Soon ** Driver-Map Genome-Wide Profiling Kit Run complete assay in your lab Launch Beginning 2017
Thank You! Paul Diehl Tel: 650-938-4050 E-mail: pauld@cellecta.com Cellecta, Inc. 320 Logue Ave. Mountain View, CA, USA 1-877-938-3910 info@cellecta.com www.cellecta.com