A Genomics (R)evolution: Harnessing the Power of Single Cells

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1 A Genomics (R)evolution: Harnessing the Power of Single Cells

2

3 Fundamental Question #1 If Transcriptional Heterogeneity ( Noise ) is so great in single cells What s the Point?

4 Single Cells = True Biology 5 Expression Fold Change Global Population: 1.5x Population A: 1x Population B: -2x Population C: 4x 4 3 Expression Fold Change Cell Number

5 Fundamental Question #2 How can you overcome the noise in single cells?

6 More Cells More Genes

7 The BioMark HD System 4 requirements for Single Cell Gx Excellent Data Quality Single-cell sensitivity High-throughput, low cost Many Cells, Many Genes

8 The BioMark HD System

9

10 Do CTC s look like tumor cell lines?

11 7 Cells from 7 Cell Lines

12 105 CTC s x 31 Genes

13 7 Cell Lines vs. 105 CTC s

14 SCGx Current State of the Art Cell Isolation Cell Preparation Data Acquisition Data Analysis Greatest degree of technical variability human interaction Select & Enrich Isolate Image Verify Extract & Manipulate Detect Analyze* Purify

15 Opportunities for Improvement Cell Selection RT-STA* FACS or Manual Method Single Cell confirmation RNA cdna STA Large number of cells required time and labor intensive No verification. 0, 1, >1 cell? Expensive Chemistry; only 96 gene STA * STA, or Specific Target Amplification, pre-amplifies target cdna

16 C 1 Single-Cell Auto Prep System System Components Fully automated Single-cell processing (capture to STA) Intuitive instrument control software, method scripts & touch-screen interface Integrated Fluidic Circuit (IFC) chips & reagent kits for cell capture and genomic amplification

17 A Simplified, Reliable Workflow Enrich Load & Capture Wash & Stain Isolate Image Lyse, RT, Pre-amp & Harvest Transfer Load Amplify & Detect C 1 Single-Cell AutoPrep System BioMark System

18 The C 1 in Action

19

20 Cell Capture Tests

21 BioMark Heat Map of C 1 sorted K562 cells Genes C t Live Cells Live/Dead Cells No Cells Tube Control RNA spikes

22 QC of cdna libraries by qpcr - BioMark Tube Control RNA dilutions 30 Tube Ct R² = Aggregated Cell Ct

23 Ensemble Averaging Tube Control QC Block of Single- Cell Data * Not all genes are expressed in all cells at all times, but bulk results can be reassembled 1. Convert from C t to copy # 2. Average copy # s across cells 3. Convert back to C t Ensemble Ave Tube Control

24 Data Quality Intra- and Inter-chip Chip / Tube Chip / Chip

25 More Cells : More Genes

26 Single Cell mrna seq Workflow Enrich Load & Capture Wash & Stain Isolate Lyse, RT & Amplify Prepare Library Sequence Analyze C 1 Single-Cell Auto Prep System Any Illumina System

27 Single Cell mrna ds cdna SMARTer (Clontech) Template-Switching Method Works directly from cell lysate No RNA Fragmentation PolyA+ RNA Produces long ds cdna

28 Whole Transcriptome cdna Harvest from C1 Agilent BioAnalyzer Kb 1 cell No cell

29 Assess cdna Quantity and Quality Quantity Quality Picogreen Agilent (12 samples) Qubit qpcr - BioMark (96 samples) * * - Only genes chosen for qpcr analysis can be quantified; requires generation of a standard curve on BioMark chip

30 Library preparation Nextera XT Transposase-based Method cdna Simple workflow Enzymatic fragmentation Integrated barcoding Single- or Paired-end reads

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32 Complete mrnaseq Workflow Capture & prepare cells Reverse transcribe & amplify Prepare sequence library C 1 Reagent Kit & C 1 IFC Clontech SMARTer Kit & C 1 IFC Nextera XT Kit 2 hours 9 hours 2 hours

33 C1 processed Single-cell mrnaseq Data: MiSeq 50 bp single end read Total No. Reads No. reads mapping to Refseq Number of detected transcripts 1.80E E+04 Number of Reads 1.60E E E E E E E E E E E+03 Number of Transcripts 4.00E E E E E Cell 0.00E+00

34 mrna Seq statistics from MiSeq Sequencer Cell (K562) Total Reads* (x1000) Mapped Reads (x1000) % Mapped # of Transcripts (x1000) % % % % % % % % % % % 15 Average % 13 *MiSeq used as a method of QC for each library

35

36 How Does C1 TM RNA-Seq Prep Measure Up?

37 Quantitative assessment of single-cell RNA-sequencing methods Wu et. al. (2013) Nature Methods published online Oct 20

38 Comparison of Bulks to Single Cell RNA-sequencing Single Cell Preps Wu et. al. (2013) Nature Methods published online Oct 20

39 C1 Sample Prep improves RNA-seq Sensitivity Wu et. al. (2013) Nature Methods published online Oct 20

40 How Much Sequencing Is Enough? Wu et. al. (2013) Nature Methods published online Oct 20

41 Gene Detection is nearly Maxed out at 2 Million Reads Wu et. al. (2013) Nature Methods published online Oct 20

42 C1 Sample Prep improves RNA-seq Sensitivity (Zoomed In) Wu et. al. (2013) Nature Methods published online Oct 20

43 C1 Sample Prep improves RNA-seq Sensitivity (Zoomed In) Wu et. al. (2013) Nature Methods published online Oct 20

44 Lower Expression Tends Toward Higher Deviation Wu et. al. (2013) Nature Methods published online Oct 20

45 Data from Broad in review How Much is Enough? # of Transcripts at different read depths ( 10 FPKM reads mapped) M transcripts detected M Reads 18K Transcripts 700K Reads 13K Transcripts 7M E E E E E E+07 reads mapped to RefSeq

46 Data from Broad in review Read Depth Does Not Need to be High for Single-Cell mrnaseq Variability in transcript detection due to shallow read depth is insignificant compared to the inherent variability between single cells

47 How Many Cells Can You Pool? Cells/lane Approximate HiSeq mapped reads/lane (millions) Approximate MiSeq mapped reads K K K K M M

48 Comparison of Individual Cells from Multiple Cell Lines

49 Ribosomal genes show relatively uniform Expression

50 Keratin -5 and Keratin 14 expression is coordinated in Keratinocytes

51 K562 cells show tight coordination of expression of hemoglobin subunits

52 PCA Analysis of single cell mrna seq data discriminates between cell types

53 Fluidigm s Roadmap Early Access Partnerships Single-cell gene expression (RT-STA) mrna-seq prep from Single cells mirna prep from Single cells Available Available Available Whole Genome Amplification from Single cells Available In Development with Partners: Methylation Protein detection

54 Targeted cdna Variant Detection Single Cell Targeted cdna Sequencing and Allele Specific Expression Make cdna Access Array Any Sequencing System SNPs Exon Usage Repetoires

55 Using Sequencing for cdna Variant Detection Sequencing requires minimal a priori knowledge of what SNP might be present in the mrna and could be very useful in hotspots of somatic variation

56 Allelic Bias Seen in >50% of all Heterozygous Bases with RNA-seq Levesque et al 2013 Nature Methods

57 The somatic rearrangement of a complex system of gene segments to form a functional receptor for antigens is an essential step in the development of B and T lymphocytes. Multiple variable (V), diversity (D) and joining (J) gene segments are recombined, a process catalyzed by the prodcuts of the genes Rag1 and Rag2 Geha et al 2003 Nature Reviews Immunology

58

59 Access Array used to Find ASM Paliwal et

60 ASM leads to ASE Paliwal et

61 Input C1 Sample Concentration Vs. AA Product Concentration

62 ASE in Single Cells (via FISH) Allele Specific Expression Varies from Cell to Cell in a single sample Levesque et al 2013 Nature Methods

63 Exon usage varies between cells. Shalek et al., June 2013

64 Single Cell Targeted cdna Sequencing Enrich Load & Capture Wash & Stain Isolate Lyse, RT & Amplify Prepare Targeted Library Sequence Analyze C 1 Single-Cell Auto Prep System Access Array Any Illumina System

65 SMARTer Samples QC ed by the HPP Panel

66 PTCH1 was Detected in Targeted cdna Sequencing despite FPKM of Zero in WTA analysis WTA C03_S1 7 A04_S3 B04_S1 1 C04_S1 8 A05_S4 A01_S1 B05_S1 2 A07_S6 C07_S2 0 B08_S1 3 B10_S1 4 D01_S2 4 A02_S2 B03_S1 0 ARPC ACTB ACTG ASPM PTCH primer 1.523_C 1.753_A 1.509_B 1.175_C 1.606_A 0.389_A 0.703_B 1.683_A 1.273_C 1.356_B 1.196_B 1.106_D 0.904_A 1.627_B ARPC ACTB ACTG ASPM PTCH primer 1.523_C 1.753_A 1.509_B 1.175_C 1.606_A 0.389_A 0.703_B 1.683_A 1.273_C 1.356_B 1.196_B 1.106_D 0.904_A 1.627_B ARPC ACTB ACTG ASPM PTCH

67 Single Cell cdna Variant Call

68 Differential Exon Usage Per Single Cell C1 /AA 0.203/ / /5.55

69 Reveal Hidden Variation: C 1 Single-Cell DNA Sequencing Workflow

70 Fluidigm s Roadmap Early Access Partnerships Single-cell gene expression (RT-STA) mrna-seq prep from Single cells mirna prep from Single cells Available Available Available Whole Genome Amplification from Single cells Available In Development with Partners: Methylation Protein detection

71 Genomes and Genetics DNA is thought to be a complete description of genetic makeup It is not static, but dynamic Germline Variation: genetic variations at conception inherited from parents Somatic Mutations: mutations accumulated over a lifetime due to Smoking UV Viral exposure Others

72 Breast Tumor Single Cell Tree Navin et al. Nature 2011 Published November 13, 2013 in Tracking the Evolution of Cancer, Cell by Cell

73 Mixed Cell Populations Matter in Cancer All cancer tumors contain mixed subpopulations of cells with different mutations If a tumor contains cells with drug resistant mutations, cancer can relapse Tumor characterized based on average mutation status across the population x x x x x x x x Treatment based on average mutation status address a proportion of population Minimal residual disease can cause downstream metastasis or relapse

74 Three Major Single-Cell DNA Applications Discovery Validation Screening Single-Cell Whole Genome Sequencing Single-Cell Whole Exome Sequencing Single-Cell Targeted Resequencing Comprehensive approach to discover all possible somatic mutations in both functional and regulatory regions of the genome. Faster and more cost effective alternate approach to WGS to discover causal variants in protein coding regions of the genome, most biological activity Screen for known mutations or identify signatures that may identify disease susceptibility, progress or therapeutic impact.

75 A Simplified Workflow for Single Cell Library Preparation Enrich Load & Whole Target Genome A Simplified Capture Workflow Amplification Enrichment for Single Cell Library Preparation Sequence Analyze Whole Genome Whole Exome Targeted Sequencing C 1 TM Single-Cell Auto Prep System Next Generation Sequencer

76 C 1 TM DNA Seq: One workflow to discover genetic variants in individual cells Flexible platform: One universal sample prep workflow for targeted & whole genome sequencing Enables SNP, small indel, and translocation detection Sensitive and robust Single-cell sensitivity Broad sample compatibility Efficient workflow: Easy to use Rapid time to results Multiple samples per run Reliable data quality: Good genomic coverage and uniformity with low GC bias

77 The C 1 System for Single-Cell DNA Sequencing C 1 Single-Cell Auto Prep DNA Seq IFC C 1 Single-Cell Auto Prep DNA Seq Reagent Kit C 1 Single-Cell Auto Prep System Compatible Reagents Proprietary IFC for single cell capture Highly-parallel preparation of 96 individual cells Study cells from 5-25 µm in size Pre-formulated reagent kit Supports cell suspension, lysis & purification Compatible with all IFC types Bench-top system that automates single cell processing Perform whole genome amplification for whole exome, genome, and targeted sequencing GE illustra TM GenomiPhi TM V2 DNA Amplification Kit Good chromosomal coverage and uniformity Low GC bias and error rate

78 Whole Genome Amplification (WGA) using Multiple Displacement Amplification Cell Capture Cell Lyse Single- Cell Suspension Genomic DNA Whole Genome Amplification NGS Library Prep

79 Application Workflow WGA in C 1 TM System Single- Cell Suspension Targeted Resequencing Whole Genome or Exome Sequencing Access ArrayTM Library Prep Library Prep in Development NGS

80 WGA implementation on the C 1 TM WGA IFC 4.5nl 9 nl 9 nl 9 nl 135 nl 135 nl Capture & Wash Lysis Neutralization MDA

81 Introducing Single-Cell Targeted Sequencing Enrich Load & Whole Genome Capture Amplification A Simplified Workflow for Single Cell Library Preparation Target Enrichment Sequence Analyze Whole Genome Whole Exome Targeted Sequencing C 1 TM Single-Cell Auto Prep System Access Array TM System D3 TM Assay Design NGS System

82 Single-cell Targeted Resequencing: <24 hours from Cells to Sequence-Ready Libraries Capture, lyse, amplify & harvest Quantify, load & amplify Barcode Sequence Sequence & Analyze & Analyze C 1 TM Reagent Kit & IFC Access Array TM with D3 TM Assay Design Targeted Primers 12 hours 9 hours 1 hour 7 hours < 6 hours hands on time < 4 hours hands on time

83 Benefits of C 1 Single-Cell Targeted DNA Sequencing Tailored for Single-Cells Isolate and prepare sequencing-ready libraries directly from individual cells. Multiple Applications Universal amplification to support whole genome, whole exome and targeted resequencing Identify SNPs, indels, and translocations High throughput 96 single-cells in parallel 480 amplicons per cell (48 amplicons x 10 plex) bp amplicons High Yield Sufficient yield for 2 subsequent sequencing runs & QC Reliable Data Quality Achieve uniform genomic coverage (>90%) Low %GC bias Fast time to results Process, sequence, and analyze 96 single cells in parallel in under 24 hours. <6 hours of hands on time

84 Coming Soon: The C 1 Single-Cell Exome Sequencing Workflow Enrich Load & Whole Genome Capture Amplification A Simplified Workflow for Single Cell Library Preparation Target Enrichment Sequence Analyze Whole Genome Whole Exome Targeted Sequencing C 1 TM Single-Cell Auto Prep System Nextera Rapid Capture Exome Kit Any Illumina System

85 Targeted Sequencing 100% 99% Mapping Rate % PF Reads Aligned 98% 97% 96% 95% 94% 93% 92% 91% Total Reads 90% gdna cell 2 cell 4 cell 6 cell 8 cell 10 cell 12 cell 14 cell 16 cell 18 cell 20 cell 22 cell 24 cell 26 cell 28 cell 30 cell 32 cell 34 cell 36 cell 38 cell 40 cell 42 cell 44 cell 46 0 Data generated on 47 single-cells and unamplified gdna using the C1 Single-Cell Targeted DNA Sequencing workflow with the Access Array Oncogene Panel. Mapping rate above 90% for all single cells (47 total) and unamplified gdna control.

86 Targeted Sequencing High Genome Uniformity Theoretical Unamplified gdna C 1 TM DNA Seq Company A on C 1 TM IFC Company A in tube Company B on C 1 TM IFC The C 1 DNA Seq Reagent Kit maps closely to unamplified genomic DNA and is comparable to tubebased protocols when performance is compared in both tube and C 1 IFC format.

87 C 1 TM DNA Seq WGA is Optimized for High % GC Unamplified gdna C TM 1 DNA Seq Company A on C TM 1 IFC Company A in tube Company B on C TM 1 IFC

88 C 1 TM DNA Seq Offers Comparable Chromosomal Coverage Log 10 (Reads+1) Unamplified genomic DNA C 1 TM DNA Seq Log 10 (Reads+1) Unamplified genomic DNA Company G in tube HiSeq data of a single cell prepared using the C 1 TM DNA Sequencing workflow. At a comparable read depth, both unamplified and single-cell genomic DNA achieve similar coverage with few physical gaps.

89 Low WGA Base Error Rate 0.46% 0.44% 0.42% 0.40% 0.38% 0.36% 0.34% 0.32% Base error rate gdna cell 2 cell 4 cell 6 cell 8 cell 10 cell 12 cell 14 cell 16 cell 18 cell 20 cell 22 cell 24 cell 26 cell 28 cell 30 cell 32 cell 34 cell 36 cell 38 cell 40 cell 42 cell 44 cell 46 Measurement of base error rate using whole genome amplification. Forty-eight single cells were processed on the C 1 DNA Seq protocol using the Access Array Oncogene panel compared to unamplified gdna.

90 Rapid Single-Cell Analysis SINGuLAR TM Analysis Toolset 3.0 An open source R package for automated analysis from your variant call file Windows & Mac OS compatible Includes single & multiple variant analysis Input standard VCF format files, output variant statistics: vcautoanalysis (covering filters, QC distribution, heatmap, NoCall etc) Perform fisher exact test for variants between two groups Display Manhattan plot and select significant variants from plot Generate sample and sample group reports to cover variant QC metrics

91 One workflow to discover genetic variants in individual cells Reveal Hidden Variation with the C 1 TM Single-Cell Auto Prep System One Prep. Many Possibilities Whole genome prep from the C 1 TM System enables genome, exome, and targeted sequencing Single-Cell Sensitivity Isolate and prepare quality, sequencing-ready libraries directly from individual cells Reliable Data Quality Achieve good coverage with low GC bias and error rate

92 For more information, visit:

93 WGA on C1

94 What Does Your Roadmap Look Like?

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