Single Cell Gene Expression Analysis
Using qPCR
Linda Strömbom
linda.strombom@tataa.com
Among our activities
• Commissioned research
and method development
• Hands-on training
courses and individual training
• Custom design
QPCR assays and assay validation
• Core facility
6 PCR platforms in Sweden only
• Products and R&D
www.tataa.com
© 2009
Outline
• Why single cell gene expression
profiling
• Gene expression in single cells
• mRNA quantification in single cells
using RT-qPCR
• Single cell gene expression data and its
interpretations
Why study single cells?
Heterogeneous tissue with
multiple cell types
- Example: Islets of
Langerhans
α-cells (20%)
β-cells (75%)
δ-cells (<5%)
100 μm
© 2009
Why study single cells?
• Heterogeneous tissue with multiple cell types
– Islets of Langerhans
• Cell population heterogeneity
– β-cells, human embryonic stem cells, tumour cells
Population heterogeneity
Total # of
© 2009
=7
Total # of
=7
Why single cell analysis?
• Log-normal distribution of expression levels
β-actin
β-actin
30
16
25
14
12
10
15
Count
Count
20
10
8
6
4
5
2
0
0
20
40
60
80
100
120
140
160
180
0
0.0
Gene expression
0.5
1.0
1.5
2.0
2.5
Logarithmic gene expression
M. Bengtsson et al., Genome Research, 15:1388–
15:1388–1392, 2005
“Which mean do you mean?“*
• Gene regulation at single cell level does not
necessarily correlate with cell population level
Geometric
mean
Arithmetic
mean
(population
mean)
20
Cell count
15
The effect of external stimuli
may remain unnoticed in studies
on cell populations, while being
readily recognized from its effect
on the geometric mean of genes’
expression in single cells.
10
5
0
-1.0
-0.5
0.0
0.5
1.0
Relative transcript levels
Lognormal scale
*Research Highlights, Nature Reviews Genetics (2005) 6, 1
© 2009
Why study single cells?
• Heterogeneous tissue with multiple cell types
– Islets of Langerhans
• Cell population heterogeneity / Distribution
– β-cells, human embryonic stem cells, tumour cells
• Single-cell correlations
– Correlated gene expression
– Function correlated with gene expression
Correlation of single cell properties
Total # of
=6
Total # of
=6
Total # of
=4
Total # of
=4
© 2009
Cell-to-cell variation in clonal cells
Elowitz et al ”Stochastic Gene Expression in a Single Cell” (2002)
Transcriptional bursting
Prokaryotic
expression
Eukarytotic
expression
Raj A et al., Cell 2008
© 2009
Single cell expression correlation
Correlation* between genes’ expressions on the cell
level reveals functional relations. Valuable for example
in studies of expression pathways.
Ins1
ins2
Sur1
ActB
Ins1
ins2
Abcc8
ActB
1
0.15
0.12
-0.02
1
0.90
-0.01
1
0.06
1
Kcnj11
0.11
-0.02
0.24
-0.15
Kcnj11
*Expressed as the Pearson’s coefficient
M. Bengtsson, A. Stålberg, P. Rorsman, M. Kubista, Genome Research (2005) 1388-1392
Workflow: RT-qPCR
© 2009
1
Cell collection with glass pipette
Glass capillaries
Flow cytometry
Laser dissection
Workflow: RT-qPCR
© 2009
How to achieve single cell analysis
• Difficulties:
– Conventional RNA isolation procedures are not
suitable because of material losses
– Sample must usually be diluted after reverse
transcription to avoid PCR inhibition
• Solution:
– Eliminate losses
• One tube procedure – just add, no washing
– Maximize reverse transcription yield
– For optimal sensitivity: use all target cDNA to enter the
PCR reaction
Lysis conditions
• Detergents tested:
– NP-40 (Igeal CA630)
– Guanidine thiocyanate
• Proteinase K treatment
• 80˚C, 5 min
• Cell cluster lysis
© 2009
Guanidine thiocyanate in lysis buffer
• Triplicate RT on
single islet cells
• NP-40 vs.
GuSCN
• Higher
reproducibility
with GuSCN
Bengtsson M et al BMC Mol Biol 2008
Workflow: RT-qPCR
© 2009
Optimization of procedure
• Is it possible to use GuSCN lysis in a one-tube protocol,
avoiding sample dilution and purification?
• Goals:
– A method that facilitates small sample qRT-PCR analysis
•
•
•
•
Maximizes lysis efficiency
Inhibits RNases
Eliminates losses
Maximizes the yield of reverse transcription
– A fast and simple method with minimal hands-on time
– A method amenable to automation
• Facilitates high throughput analysis
Optimized protocol for small sample amounts/single cells
• A novel reagent and one-tube protocol has been
developed to facilitate all the requirements of small
sample qRT-PCR
• Easy, fast and amenable to automation
• A robust and efficient RT enzyme is required for
compatibility with lysis buffer and optimal yield
– Procedure optimized with Transcriptor RT reagent
(Roche)
Cell Collection
Lysis, 5 minutes at Add reagents
room temperature and run RT
© 2009
Ready to use
cDNA
Analysis of FACS sorted THP1 monocytes
H2O
Optimized lysis buffer
16
14
Count
12
10
8
6
4
2
0
28,028,5
28,529,0
29,029,5
29,530,0
30,030,5
30,531,0
31,031,5
31,532,0
32,032,5
32,533,0
33,033,5
33,534,0
34,034,5
34,535,0
35,035,5
35,536,0
Ct
• Expression of ACTB in individual cells collected in either 4µl
optimized lysis buffer or in H2O
• The entire lysate was reverse transcribed in a 20µl reaction using
the Transcriptor First Strand cDNA Synthesis Kit (Roche)
Selected applications
– siRNA analysis in manually collected
single mouse oocytes
– Gene expression analysis of single
FACS sorted monocytes
– Gene expression analysis of single
THP1 macrophages using the
AVISO CellCelectorTM
– Gene expression analysis in primary
astrocytes (~1000 cells)
– Gene expression analysis in HeLa
cells (~10000 cells)
© 2009
140
R e la tiv e e x p re s s io n (M o s )
• Single cells and cell populations
120
100
80
60
40
20
0
WT1
WT2
TG1
TG2
RT
TG3
TG4
TG5
No RT
TG6
Workflow: RT-qPCR
Noise versus expression levels
© 2009
Sampling strategy
high RT efficiency
low RT efficiency
Dilution effect
Replicates
Bengtsson M et al BMC Mol Biol 2008
Optimized single cell analysis - PCR
Cell
Collection
Lysis,
5min
Add
reagents
and run RT
Ready to
use cDNA
• Two options
– Aliquote cDNA to separate qPCR
mixes
• Several reactions per cell – more
information
– Add qPCR reagents and start PCR
• One reaction per cell – optimal
sensitivity
© 2009
Excess of cDNA in PCR is inhibitory
M. Bengtsson et al, BMC Mol. Biol., 2008, 9:63,
9:63, add. file 1
One-tube qRT-PCR
• Optimized procedure allows all cDNA to enter the
PCR
– 20µl reverse transcription reaction using optimized
protocol and Transcriptor RT reagents (Roche)
– 50µl PCR using LightCycler 480 qPCR mastermixes
(Roche)
© 2009
One-tube qRT-PCR, Example
Count
ACTB expression in single THP1 monocytes
8
7
6
5
4
3
2
1
0
27,0- 27,5- 28,0- 28,5- 29,0- 29,5- 30,0- 30,5- 31,0- 31,5- 32,0- 32,5- 33,027,5 28,0 28,5 29,0 29,5 30,0 30,5 31,0 31,5 32,0 32,5 33,0 33,5
Ct
• Analysis of ACTB in individual FACS sorted monocytes collected
in 4µl optimized lysis buffer.
• The entire lysate was reverse transcribed in a 20µl reaction using
optimized protocol and Transcriptor RT reagents (Roche).
• The entire RT reaction was analyzed in a 50µl PCR using
LightCycler 480 Master reagents (Roche).
Pre-amplification
• Aliquoting a single cell to measure multiple targets
increases noise
• Multiplex qPCR is limited to 4-5 targets, and accurate
quantification is difficult to achieve
• The issue may be resolved with pre-amplification
– amplification of multiple targets in the same tube
• Pre-amplification is often PCR-based (multiplex PCR)
– Low concentrations of primers
– Few cycles of amplification
• After pre-amplification the sample is aliqoted to single
qPCR reactions
© 2009
Pre-amplification
• Bias between genes may occur
– Only compare samples that are treated the same way, or
– Verify that bias is not an issue
ABI TaqMan pre-amplification sysyem
• Bias is gene dependent
• If the initial mRNA concentration is too low data will
be noisy
Workflow: RT-qPCR
© 2009
Single cell data
Insulin I mRNA expression levels in 125 β-cells:
From Ct to copies
• Absolute standard curves
• ssDNA to dsDNA compensation
– Compensate Ct values with (1+E)
– Ståhlberg A et al. Clin Chem 2004
• True number of mRNA > estimated number of cDNA
– RT efficiency < 100%
© 2009
Transcript levels are lognormally distibuted
Lognormal distribution confirmed by
other (non-PCR-based) methods
• Geometric mean (Ins1): 19 000 copies/cell
• Arithmetic mean (Ins1): 37 000 copies/cell
Transcriptional bursting
• Possible
explanation
to
lognormality
• Use of
reference
genes are
not valid
Chubb et al. Current Biology 2006
© 2009
Cell stimulation studies at single cell level
Paper I
Fold change in trancript
levels going from 5 mM to
20 mM glucose:
Arithmetic
Geometric
ActB
3.3
4.9
Ins1
4.6
17
Ins2
3.0
9.5
Abcc8
1.4
1.5
Kcnj11 1.1
1.3
Bengtsson M et al. Genome Res 2005
Single cell correlations
Chr 19 Chr 7
© 2009
Correlation implies common transcription factors
Ins1
Ins2
Actb
Summary – single cell gene expression analysis
• Single cell gene expression studies reveal:
– Hetereogeneity within tissues
– Hetereogeneity within cell types
• Transcriptional bursting
– Lognormallity
– Traditional use of reference genes not valid
• Careful optimization and design of single cell
experiments
• Gene expression correlations
– Common transcriptional regulation
– Cell function
© 2009
Summary – experimental set-up
• A novel streamlined approach has been developed for
real-time PCR based RNA analysis of few-cell samples of
lower complexity
• Robust down to a single cell but also applicable to
samples of several thousands of cells
• No material losses due to material transfer – One tube
format
• 100% of RNA can be used in reverse transcription and
subsequent PCR
• Quick and easy procedure
• Minimal hands-on time
• No special equipment necessary
• Large reduction in hands-on time compared to columnbased kits
Thank you for your attention!
Acknowledgements
• Anders Stålberg
• Martin Bengtsson
• TATAA Biocenter R&D team
• Radek Sindelka
• David Svec
• Greg Shipley
• Roche Diagnostics
© 2009
www.tataa.com