Every Cell has a Story

Igniting a Revolution Through

Single-Cell Systems Biology

Oliver Vasilevski, PhD Senior Manager

Channel Management – Asia Pacific

A Successful Fluidigm Community

Topaz® System 96.96 Dynamic

Array IFC

EP1® System

Access Array™ System

BioMark System

12.765 Digital

Array IFC

48.48 Dynamic Array™ IFC C1 Single-Cell

Auto Prep System

A Decade of Innovation

Fluidigm Technology

1999 2012

BioMark HD

System

BioMark HD: Nanofluidic

Real-Time PCR

High-throughput

qPCR Platform

Nanofluidics

Streamlined, hands-free automated workflow = fast time to results

Small input requirements (down to a single-cell)

Multiple applications

nL Reaction Volumes Enable Discovery from a Single Cell

nL Reaction Volumes Enable Discovery from a Single Cell

nL Reaction Volumes Enable Discovery from a Single Cell

Same qPCR chemistry Saving in cost of reagents Accelerate research through increased throughput

48.48 (2,304dp)

IFCs Available for Gene Expression & Genotyping

96.96 (9,216dp)

192.24 (4,608dp) FLEXsix

Medium-to-High Throughput

Low-to-Medium Throughput

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

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

>9000 reactions had .99 correlation from chip to chip

Run to Run Reproducibility is High

R2 =0.99 R2 =0.99

Chip 1 vs 2 Chip 3 vs 4

An Open Platform

Open to

ANY CHEMISTRY

Flexibility to

CHANGE ASSAYS

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

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

System-level biology requires a more comprehensive

view of biological processes & pathways

The central dogma of molecular and

cellular biology

RNA Protein Phenotype

The simple view

Genomic

DNA

Textbooks teach us

unidirectional pathways

Signal transduction through phosphorylation/kinase cascade

Ras Raf MEK1/2 ERK1/2

Gene B

|

Protein B

Gene C

|

Protein C

Gene D

|

Protein D

Phosphorylation Phosphorylation Phosphorylation

Gene A

|

Protein A

Systems view more closely

describes nature

Gene B

|

Protein B

Gene C

|

Protein C

Gene D

|

Protein D

Gene E

|

Protein F

Gene G

|

Protein H

Gene I

|

Protein J

Gene K

|

Protein L

Gene M

|

Protein N

Gene Q

|

Protein P

Phosphorylation Phosphorylation Phosphorylation

Gene A

|

Protein A

Multidimensional datasets are needed to

define the network architecture and interactome

Systems biology connects the dots

Epigenetics

Chromatin

Methylation

Histone

mRNA

miRNA

lncRNA

Modifications

Phospho-,

Protein-

protein

Stem cells

Differentiation

Apoptosis

Cancer

Temporal element

RNA Protein Phenotype Genomic

DNA

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

Cellular heterogeneity drives biology

Heterogeneity Drives Biology

“The Population Average Does Not Exist”

“The Population Average Does Not Exist”

Actual Pooled Cells Data

Heterogeneity Drives Biology

-3

-2

-1

0

1

2

3

4

5

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

Cell Number

Exp

ressio

n F

old

Ch

an

ge

Expression Fold Change Global Population: 1.5x

Population A: 1x

Population B: -2x

Population C: 4x

Individual cells behave differently

from the average of many cells

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-

• Highly multiplexed, single-cell technologies reveal important heterogeneity within cell

populations

• Single-cell analysis reveals ‘unique subsets of cells’

Cellular heterogeneity exists at

multiple levels

Scientific

integration

Deeper

understanding

of biological

systems

Epigenetic modifications

Alternative gene splicing

Gene expression

DNA translocation

DNA mutation

Protein expression or activity

Cell morphology and function

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

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

• Confidence: Excellent data quality

• Robust: Look at multiple targets to get a robust

signature

Single-cell biology impacts most

areas of research

Stem Cell

Research Cancer Immunology

Developmental

Biology Neurobiology

Applications in single-cell biology

Biological Mechanism

& Pathway

• Explore changes in variability to identify potential

biological mechanisms & pathways

Cell Differentiation • Identify methods to exploit cellular

reprogramming

Cell Lineage

• Characterize cells by development/transition

state, stage of disease progression

• Validate, ensure quality control for cell lines

Biomarker Discovery

• Discover variants and transcriptional signatures

that predict susceptibility, prognosis, and

response

Therapeutics • Identify druggable targets

• Measure drug sensitivity

Single-cell

sequencing

is the new

standard

The era of single-cell biology

Number of publications featuring

Fluidigm single-cell systems

Single-cell

publications on

Fluidigm

technology Over 200 peer-reviewed

publications

… and counting

Single-cell analysis used to be

laborious and inconsistent

Select &

Enrich Isolate Image Verify

Extract &

Purify Manipulate Detect Analyze

Data Collection

& Analysis Cell Preparation Cell Isolation

Cell Selection RT-STA*

RNA

Single Cell

cDNA STA

FACS

or

Manual

Method

Large number of cells required

or labor intensive

No verification. 0, 1, >1 cell?

Expensive Chemistry; “only” 96 gene STA

Opportunities for Improvement

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

A simplified workflow for single-cell genomics

Enrich Load

& Capture

Wash,

Stain, &

Image

Lyse, RT

& Amplify

C1 Single-Cell Auto Prep

System

BioMark HD System

Any Illumina System

C1 IFC architecture

5nl 9 nL 9 nL 9 nL 135 nL 135 nL

Multi-step reaction architecture

Integrated cell capture, lysis, and processing increases

throughput and consistency while reducing costs.

Single-Cell Targeted Gene Expression

Enrich

Load

&

Capture

Wash,

Stain,

Image

Lyse, RT,

Pre-amp

&

Harvest

Transfer Load Amplify

& Detect

A simplified workflow

C1 Single-Cell Auto Prep System BioMark HD System

Analyze

SINGuLAR

Analysis

C1 Capture Plate Workflow

Add Reagents

C1 Capture Plate Workflow

Sample Inlet

A detailed view of cell preparation

Visual QC Confirmation of Single-Cell Capture

C1 Capture Plate Workflow

96x cDNA Sample

Collection Wells

96 cDNA

Samples

96 Genes

C1 Capture Plate Workflow

C1 Capture Plate

96.96 Gene

Expression Plate

• Reprogrammed the transcriptional circuitry of human

cardiac H9F fibroblasts to produce an induced-

cardiomyocyte-like (iCM) cells

• Used the C1™ 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

“Direct reprogramming of human fibroblasts toward a

cardiomyocyte-like state.”

Fu, et al. Stem Cell Reports 1 (2013): 235-247

First C1™ System publication

shows utility of SCGX workflow

Single-Cell MicroRNA

Simplified workflow for single-cell

miRNA analysis

C1 Single-Cell Auto Prep

System

BioMark HD System

Enrich

Load

&

capture

Wash

&

stain

Image

Lyse, RT,

preamp,

&

harvest

Transfer Load Amplify

& detect

TaqMan®

Megaplex™ TaqMan®

Megaplex™

Data

analysis

SINGuLAR

Analysis

• 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

Subpopulations Observed in

Homogeneous BJ Fibroblasts

PCA BJ Fibroblasts Passages 13 and 24

Violin Plot BJ Fibroblasts Passages 13 and 24

Single-Cell mRNA Sequencing

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

Single-cell mRNA sequencing and

library preparation workflow

C1 Single-Cell Auto Prep

System

Any Illumina

system

Enrich Load &

Capture

Wash &

Stain Image

Lyse, RT

&

amplify

Prepare

library Sequence Analyze

SINGuLAR

Analysis

mRNA amplification and library

preparation

SMARTer® (Clontech)

cDNA

Nextera® XT (Illumina) (After cDNA Harvest)

• Examined statistical methods for analyzing single-cell

mRNA Sequencing (mRNA Seq) data generated using

the C1™ 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.

“Accounting for technical noise in single-cell RNA seq

experiments.”

Brennecke, et al. Nat Methods (2013): Epub ahead of print

Quantifying the statistical

significance of cell-to-cell variability

“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)

Single-Cell DNA Sequencing

Etiology of Complex Disease is Still Unknown

Cancer Immunology Neuro-

biology

• 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

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 2nd

autoimmune disease

Aging

Telomerese shorten

with age

Highly associated with

stroke, heart attack

and osteoporesis

Identifical twins, if one

has schizophrenia then

50% chance that other

twin will contract it.

Targeted Sequencing

A simplified workflow for single-

cell targeted resequencing

Access Array System with

D3TM Assay Design

NGS System C1 Single-Cell

Auto Prep System

Whole Genome

Whole Exome

Analyze Sequence Target enrichment

Whole

Genome

Amplification

Load and

capture Enrich

SINGuLAR

™ Analysis

Tooset 3.0

C1 DNA Seq

Unamplified genomic

DNA

Log

10 (

Re

ad

s+

1)

Company A in tube

Unamplified genomic

DNA

Lo

g1

0 (

Re

ad

s+

1)

WGA genome coverage

• Single-cell genome coverage ~75% (@10x average coverage)

• Mapping rate >95%

Human Chr 1, 100kb bins:

Three Major Single-Cell DNA Applications

Single-Cell

Whole Genome

Sequencing

Comprehensive approach to

discover all possible somatic

mutations in both functional

and regulatory regions of the

genome.

Discovery

Single-Cell

Whole Exome

Sequencing

Faster and more cost effective

alternate approach to WGS to

discover protein coding regions

(1%) of the genome, most

biological activity

Validation

Single-Cell

Targeted

Resequencing

Screen for known mutations

or identify signatures that may

identify disease susceptibility,

progress or therapeutic

impact.

Screening

Single-Cell Protein Analysis

30 Panel size

Information per tube

Tubes for wide, deep knowledge

4 11

3 1 10

1

More Parameters, More Biological

Insight

10

20

Combinatorial knowledge Low Medium High

*

CyTOF

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

Cytometry

Fluorophores: signal overlap limits practical panel size

Heavy metal ion tags: mass spectrometry removes the limitation

*

How mass cytometry works

Elemental Reagents Bound to Cells Ionized and Analyzed

CyTOF 2 Mass Cytometer

• 30+ parameter panels made simple

• Breadth and depth in a single tube

• Deep phenotypic and functional profiling

The most comprehensive detection of cell surface

and intracellular protein markers

• 10,000 individual cells in just minutes

Our single-cell leadership comes

from innovation

C1™ 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

Every cell is unique

Tell its story with single-cell biology.

DNA | RNA | Protein