Every Cell has a Story

Transcription

1 Every Cell has a Story Igniting a Revolution Through Single-Cell Systems Biology Oliver Vasilevski, PhD Senior Manager Channel Management Asia Pacific

2 A Successful Fluidigm Community

3 A Decade of Innovation Fluidigm Technology BioMark System Dynamic Array IFC EP1 System BioMark HD System C 1 Single-Cell Auto Prep System Topaz System Digital Array IFC Dynamic Array IFC Access Array System

4 BioMark HD: Nanofluidic Real-Time PCR High-throughput qpcr Platform Nanofluidics Streamlined, handsfree automated workflow = fast time to results Small input requirements (down to a single-cell) Multiple applications

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11 nl Reaction Volumes Enable Discovery from a Single Cell

12 nl Reaction Volumes Enable Discovery from a Single Cell

13 nl Reaction Volumes Enable Discovery from a Single Cell Same qpcr chemistry Saving in cost of reagents Accelerate research through increased throughput

14 IFCs Available for Gene Expression & Genotyping (2,304dp) (9,216dp) Medium-to-High Throughput (4,608dp) FLEXsix Low-to-Medium Throughput

15 3-hours from sample to data 10-minutes hands on time >9,000 data points

16 High Data Quality Easily Distinguishable Cq Difference Between 1, 10 & 100 Cells Single Tm Peak: 1, 10, and 100 cells, custom EvaGreen Assay Linearity Data

17 Run to Run Reproducibility is High Chip 1 vs 2 Chip 3 vs 4 R 2 =0.99 R 2 =0.99 >9000 reactions had.99 correlation from chip to chip

18 An Open Platform Flexibility to CHANGE ASSAYS Open to ANY CHEMISTRY

19 Complete Assay Flexibility with Fluidigm TaqMan (including fast cycling) DNA Binding Dyes Thermo Solaris SABiosciences Roche UPL Others DELTAgene and SNPtype from Fluidigm Full bioinformatics service Primer synthesis & validation options

20 Applications Single-Cell Gene Expression Profiling Gene Expression Profiling microrna Gene Expression SNP Genotyping Digital PCR: Copy Number Variation Absolute Quantification Haplotyping Rare Mutation Detection

21 System-level biology requires a more comprehensive view of biological processes & pathways

22 The central dogma of molecular and cellular biology The simple view Genomic DNA RNA Protein Phenotype

23 Textbooks teach us unidirectional pathways Signal transduction through phosphorylation/kinase cascade Gene A Gene B Gene C Gene D Ras Raf MEK1/2 ERK1/2 Protein A Protein B Protein C Protein D Phosphorylation Phosphorylation Phosphorylation

24 Systems view more closely describes nature Gene E Protein F Gene I Protein J Gene M Protein N Gene A Gene B Gene C Gene D Multidimensional datasets are needed to define the network architecture and interactome Protein A Protein B Protein C Protein D Gene G Phosphorylation Gene K Phosphorylation Gene Q Phosphorylation Protein H Protein L Protein P

25 Systems biology connects the dots Genomic DNA RNA Protein Phenotype Epigenetics Chromatin Methylation Histone mrna mirna lncrna Modifications Phospho-, Proteinprotein Stem cells Differentiation Apoptosis Cancer Temporal element

26 Hallmarks of systems biology Harvest has many levels of biological data Models network architectures Requires: Cross-disciplinary datasets Integrated temporal and spatial datasets Holistic not reductionist approach Need new approaches to measuring "omic" parameters The goal is to develop better predictive models of biological and disease processes

27 Cellular heterogeneity drives biology

28 Heterogeneity Drives Biology The Population Average Does Not Exist

29 Heterogeneity Drives Biology The Population Average Does Not Exist Actual Pooled Cells Data

30 Expression Fold Change Individual cells behave differently from the average of many cells 5 Expression Fold Change Global Population: 1.5x Population A: 1x Population B: -2x Population C: 4x Cell Number

31 Key questions in single-cell biology Yield: How many cells expressed mrna target? Direction: Is/are the gene(s) up/down regulated? Magnitude: What is the fold change of differential expression? Co-expressed: Which genes are positively/negatively co-regulated? A+/B+ A-/B+ A+/B- A-/B-

32 Highly multiplexed, single-cell technologies reveal important heterogeneity within cell populations Single-cell analysis reveals unique subsets of cells

33 Cellular heterogeneity exists at multiple levels Cell morphology and function Protein expression or activity DNA mutation DNA translocation Gene expression Scientific integration Deeper understanding of biological systems Alternative gene splicing Epigenetic modifications

34 What causes the complexity? Eukaryotic systems demonstrate different rates of DNA, RNA and protein synthesis interspersed with periods of degradation Stochastic variation in duration and intervals of activity Epigenetics changes in active vs. inactive state Chromatin: Open/closed Transcription factor binding: On/off Generates 10 1,000X variation in expression level between cells even in homogeneous populations

35 Requirements for single-cell biology Resolution: Single-cell sensitivity Significance: Process many cells to characterize the population and detect rare cell subpopulations Comprehensive: DNA, RNA, and protein analysis Robust: Look at multiple targets to get a robust signature Confidence: Excellent data quality

36 Single-cell biology impacts most areas of research Stem Cell Research Developmental Biology Immunology Cancer Neurobiology

37 Applications in single-cell biology Biological Mechanism & Pathway Cell Differentiation Cell Lineage Biomarker Discovery Therapeutics Explore changes in variability to identify potential biological mechanisms & pathways Identify methods to exploit cellular reprogramming Characterize cells by development/transition state, stage of disease progression Validate, ensure quality control for cell lines Discover variants and transcriptional signatures that predict susceptibility, prognosis, and response Identify druggable targets Measure drug sensitivity

38 Single-cell sequencing is the new standard

39 The era of single-cell biology Number of publications featuring Fluidigm single-cell systems

40 Single-cell publications on Fluidigm technology Over 200 peer-reviewed publications and counting

41 Single-cell analysis used to be laborious and inconsistent Cell Isolation Cell Preparation Data Collection & Analysis Select & Enrich Isolate Image Verify Extract & Manipulate Detect Analyze Purify

42 Opportunities for Improvement Cell Selection RT-STA* FACS or Manual Method Single Cell RNA cdna STA Large number of cells required or labor intensive No verification. 0, 1, >1 cell? Expensive Chemistry; only 96 gene STA

43 C1 Single-Cell Auto Prep System System Components Single-cell processing instrument platform Intuitive instrument control software, method scripts & touch-screen interface Integrated Fluidic Circuit (IFC) chips & reagent kits for cell capture and genomic amplification

44 A simplified workflow for single-cell genomics Enrich Load & Capture Wash, Stain, & Image Lyse, RT & Amplify BioMark HD System C 1 Single-Cell Auto Prep System Any Illumina System

45 C 1 IFC architecture

46 Multi-step reaction architecture 5nl 9 nl 9 nl 9 nl 135 nl 135 nl Integrated cell capture, lysis, and processing increases throughput and consistency while reducing costs.

47 Single-Cell Targeted Gene Expression

48 A simplified workflow Enrich Load & Capture Wash, Stain, Image Lyse, RT, Pre-amp & Harvest Transfer Load Amplify & Detect Analyze C 1 Single-Cell Auto Prep System BioMark HD System SINGuLAR Analysis

49 C 1 Capture Plate Workflow Add Reagents

50 C 1 Capture Plate Workflow Sample Inlet

51 A detailed view of cell preparation

52 Visual QC Confirmation of Single-Cell Capture

53 C 1 Capture Plate Workflow 96x cdna Sample Collection Wells

54 C 1 Capture Plate Workflow 96 Genes 96 cdna Samples Gene Expression Plate C 1 Capture Plate

55 First C 1 System publication shows utility of SCGX workflow Direct reprogramming of human fibroblasts toward a cardiomyocyte-like state. Fu, et al. Stem Cell Reports 1 (2013): Reprogrammed the transcriptional circuitry of human cardiac H9F fibroblasts to produce an inducedcardiomyocyte-like (icm) cells Used the C 1 Single-Cell Auto Prep and BioMark HD Systems to optimize reprogramming efficiency by monitoring cardiac gene expression. Gene panel show differential expression between fibroblasts, icm at week 4 & 9 post induction & fetal CM

56 Single-Cell MicroRNA

57 Simplified workflow for single-cell mirna analysis Enrich Load & capture Wash & stain Image Lyse, RT, preamp, & harvest Transfer Load Amplify & detect Data analysis TaqMan Megaplex TaqMan Megaplex C 1 Single-Cell Auto Prep System BioMark HD System SINGuLAR Analysis

58 Subpopulations Observed in Homogeneous BJ Fibroblasts PCA BJ Fibroblasts Passages 13 and 24 BJ fibroblast passages 13 and 24. mirna expression profiles were compared for senescence markers (mir-155, mir-17, mir-106a). PCA shows indistinguishable mirna expression between passages. However, violin plot analysis identifies subpopulations Violin Plot BJ Fibroblasts Passages 13 and 24

59 Single-Cell mrna Sequencing

60 A revolutionary tool in transcriptome analysis Offers deep coverage and single base-level to: Measure expression levels of genes, alleles and spliced variants Compare expression profiles between tissues or cell types Mapping transcription start sites Characterize alternative splicing patterns Evaluate post-transcriptional mutations or editing Identify novel transcripts and gene fusions

61 Single-cell mrna sequencing and library preparation workflow Enrich Load & Capture Wash & Stain Image Lyse, RT & amplify Prepare library Sequence Analyze C 1 Single-Cell Auto Prep System Any Illumina system SINGuLAR Analysis

62 mrna amplification and library preparation SMARTer (Clontech) Nextera XT (Illumina) (After cdna Harvest) cdna

63 Quantifying the statistical significance of cell-to-cell variability Accounting for technical noise in single-cell RNA seq experiments. Brennecke, et al. Nat Methods (2013): Epub ahead of print Examined statistical methods for analyzing single-cell mrna Sequencing (mrna Seq) data generated using the C 1 Single-Cell Auto Prep System in conjunction with the Illumina sequencing platforms. Established a quantitative statistical method to distinguish true biological variability in single-cell mrna Seq data from technical noise. We also note that the sequencing coverage of these data was lower than that used in the A. thaliana experiments, thereby illustrating that sequencing deeply is typically unnecessary for drawing biological conclusions from single-cell transcriptomes Philip Brennecke, Wellcome Trust Sanger Institute (Teichmann Lab)

64 Single-Cell DNA Sequencing

65 Etiology of Complex Disease is Still Unknown Cancer Immunology Neurobiology Aging Only 5-10% of cancer is hereditary* Only 1/3 of the risk of developing an autoimmune disease is heritable 2x likely to have a 2 nd autoimmune disease Identifical twins, if one has schizophrenia then 50% chance that other twin will contract it. Telomerese shorten with age Highly associated with stroke, heart attack and osteoporesis Somatic mutations are the basis for mosaic features in complex diseases. Understanding the heterogeneity contributing to the disease helps to establish the etiology and develop effective therapies

66 A simplified workflow for singlecell targeted resequencing Enrich Load and capture Whole Genome Amplification Target enrichment Sequence Analyze Whole Genome Whole Exome Targeted Sequencing C 1 Single-Cell Auto Prep System Access Array System with D3 TM Assay Design NGS System SINGuLAR Analysis Tooset 3.0

67 Log 10 (Reads+1) Log 10 (Reads+1) WGA genome coverage Human Chr 1, 100kb bins: Unamplified genomic DNA C 1 DNA Seq Unamplified genomic DNA Company A in tube Single-cell genome coverage ~75% (@10x average coverage) Mapping rate >95%

68 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 protein coding regions (1%) of the genome, most biological activity Screen for known mutations or identify signatures that may identify disease susceptibility, progress or therapeutic impact.

69 Single-Cell Protein Analysis

70 More Parameters, More Biological Insight * Panel size CyTOF Information per tube Tubes for wide, deep knowledge Combinatorial knowledge Low Medium High

71 Mass Cytometry: The CyTOF Platform The CyTOF platform uniquely enables massively multi-parameter high-throughput analysis of single cells CyTOF 2 MAXPAR reagents Data analysis software

72 Cytometry * Fluorophores: signal overlap limits practical panel size Heavy metal ion tags: mass spectrometry removes the limitation

73 How mass cytometry works Elemental Reagents Bound to Cells Ionized and Analyzed

74 CyTOF 2 Mass Cytometer The most comprehensive detection of cell surface and intracellular protein markers 30+ parameter panels made simple Breadth and depth in a single tube Deep phenotypic and functional profiling 10,000 individual cells in just minutes

75 Our single-cell leadership comes from innovation C 1 Single-Cell Auto Prep System Cell isolation and preparation Supports real-time PCR and NGS Consistent data quality Easy to use Discovery, validation and screening BioMark HD System Flexible genomic applications High throughput Superior data quality Validation and screening CyTOF 2 Mass Cytometer Detect cell surface and intracellular markers Immunophenotype and cell cycle, cytokine signaling Combinatorial with high resolution (~30 targets/cell) Fast and reliable Validation and screening

76 Every cell is unique Tell its story with single-cell biology. DNA RNA Protein