every cell has a story - university of hong...
TRANSCRIPT
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