Analyzing Expression Profiles from Single Stem Cells Using the Single Cell-to-Ct™ kit
Richard Fekete, Ph.D.Life TechnologiesAustin, Texas
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Outline
• Background
• Why Single Cell Analysis?
• Single Cell Workflow
• Describe hESC-to-hNSC Differentiation Expt.
• Single Cell Data Analysis/Normalization
• Conclusions
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Analysis of “populations”• Analyzing gene expression en masse gives an average profile (obscuring
populations)
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Analysis of “populations”• Analyzing gene expression en masse gives an average profile (obscuring
populations)
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Analysis of “populations”• Analyzing gene expression en masse gives an average profile (obscuring
populations)• Small populations are lost• Example finding Cancer Stem cells in a population of normal cells or finding
undifferentiated Embryonic Stem Cells in a population of differentiated (important for transplantation)
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Analysis of “populations”• Analyzing gene expression en masse gives an average profile (obscuring
populations)
• These issues are important for analysis of any analyte• This type of “digital” analysis is not new, flow cytometry looks at individual
cells, but number of analytes per cell is limited and need affinity reagents/dyes
• Small populations are lost• Example finding Cancer Stem cells in a population of normal cells or finding
undifferentiated Embryonic Stem Cells in a population of differentiated (important for transplantation)
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Key Emerging Trend – Study of Single Cells• Nearly all tools look at populations of cells
• Everyone realizes there are cell-to-cell differences
Rare cell or event• Circulating metastatic cells• Fetal cell in maternal blood• Event within a library
Scarce, precious sample• Archival tissue (FFPE)• Clinical sample (fresh tumor)• Biomarker discovery
Single cell precision in populations
• Drug candidate screening• Cell differentiation (eg stem cell)• Stochastic responses to stimuli
Single cell applications span basic research, clinical research, diagnostics, and therapeutics
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• 4 functional steps: cell lysis – Cells-to-Ct based, reverse transcription – SuperScriptIII, cDNA pre-amplification – PreAmp MasterMix and Real-Time PCR
• gDNA removal occur at the same time as cell lysis - 5 minutes at room temperature• All done in same tube (no loss due to tube transfers)• Enable use of entire lysate sample in subsequent RT/PreAmp rxns• Performance benefit with smaller volumes and allows use of 384-well plates• Increased sensitivity (SuperScript III)• Optimized volumes in workflow minimize errors in reaction assembly (no water
addition in any step)• Multiple stopping points• microRNA and DNA protocols
Single Cell-to-Ct: a complete workflow to obtain statistically relevant single cell data
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Single Cell-to-Ct: a complete workflow to obtain statistically relevant single cell data
Dynabeads®
CD3/CD28
Naive orresting T cell
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•500 μL Single Cell Lysis Solution (store at 4ºC)•50 μL Single Cell Stop Solution (store at -20ºC)•50 μL Single Cell DNase I (store at -20ºC)•150 μL Single Cell VILO™ RT Mix (store at -20ºC)•75 μL Single Cell SuperScript® RT (store at -20ºC)•265 μL Single Cell PreAmp Mix (store at -20ºC)
Kit Components 50 and 400 reaction kits
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Starting Material
• Samples can be obtained through− FACS− Dilution− Laser capture microdisection (not tested internally)− Physical selection (eg bead based)− Laser Ablation (Cyntellect Leap System; not tested internally)− Mouth pipetting
• Up to 10 cells can be used
• Input volume of cell (s) should be less than 1 ul
• Validated multiple cell lines (including hESC) for this kit and >20 for the parent kit
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Product Performance
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CT
1 Cell Equivalents
Single Cells 100 cells
20.4
21.6
13.6
Single Cell Detection Occurs with expected sensitivity (6.6 Cts difference from 100 cells). Technical reproducibility of the 100 cell samples and single cell equivalents is tight (CV of Cell Equivalents is small). Variability of single cells is due to biological variability in single cells
N=84 single cells
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Ten-cell samples were lysed in the presence of Single Cell Lysis buffer (5 min at 25°C), frozen and thawed five times in PBS, or boiled for 10 min in PBS or RT buffer at 95°C.
Product Performance
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Potential applications of hESCs• Pluripotency make embryonic stem cells a means to new cell
replacement strategies• Applications require pure populations of differentiated cells
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Neural lineage differentiation markers/pathway
Immature Neuron
Mature Neuron
β3 tubulin+NeuN+
NF160+Synapsin+
Cell death
Quiescence
Maturing stem cells
Neural Stem CellNestin+Sox1+Pax6+CD133+Sox2+
Embryonic Stem Cell
Oct4+Nanog+Sox2+
Neuronal precursor
PSA-NCAM+ β3 tubulin+ CD133-Nestin+ Sox1-, Sox2-
Glial Precursor
A2B5+Olig2+NG2+Nestin+Sox1-
Oligodendrocyte Precursor Late Oligodendrocyte
PrecursorMature
Oligodendrocyte
Olig2+NG2+PDGFRα+Nkx2.2+Sox10+
O4+GalC+Olig2+Nkx2.2+
CNP+PLP+MBP+
Mature Astrocyte
Astrocyte precursor
CD44+GFAP-
GFAP+
UTF1+ZFP42+CD133-
Nestin-Sox1-
• Identifying new markers becomes difficult due to heterogeneity during differentiation
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SV25 (Olig2-EGFP), a derivative of BG01
• Platform line maintains expression of hESC markers
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Grow ESCs on Geltrex plates in CM until 80-90% confluent
Change media to SFM for 24 hours.
Culture in NAA media until confluent, changing daily.
Split cells 1:2 using TrypLE, culture on Geltrex plates.
Once confluent use collagenase, create neurospheres 150 – 250 um in Ultra Low Attachment plates.
When cells start to attach to the plate, split 1:2 onto Geltrex plates.
Continue culturing on Geltrex plates, splitting 1 2 2 3 d
Day 0 ESC
Day 1 ESC
Day 2 Differentiating ESC
Day 6 Neurospheres
Day 13 Neurospheres
Day 17 AttachedNeurospheres
Day 20 Rosette formation
Day 22 NSC derived
ESC to NSC Workflow 1575 μm
Day 0 Day 3
Day 7 Day 11
Day 17 Day 18
Day 21 Day 21157 μmDay 24 GFP Overlay
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Single Cell Analysis Workflow• Look at single cells to more closely define NSC profiles, and identify
profiles of cells not differentiating in the NSC pathway (markers not detectable due to low cell numbers)
Day 0 - ESC
Day 7 -Neurospheres
Day 17 –attached Neurospheres Day 18
Day 21 -Rosettes
Day 11 -Neurospheres
Day 3 –diff ESC
0 cells 100 cells
10 genes/cell10 plates/time point
•30 “0” cell samples•30 “100” cell samples•900 “1” cell samples
Cells-to-CT™ TaqMan®PreAmp MMx
Gene Expression Master Mix
VILO RT Superscript®
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Analyzed genes• Initial 10 genes for analysis• Genes for ESC and NSC, and for cells differentiating into other types
Gene Symbol
Description / Alternate Name Group Description
POU5F1 OCT4 Stem Cell - slow off
UTF1 UTF1 Stem Cell
ZFP42 REX1 Stem Cell - fast off
SOX1 Homo sapiens SRY (sex determining region Y)-box 1 (SOX1).
Proneural and Neural stem cell - late on
NES NESTIN Proneural and Neural stem cell
PAX6 Proneural and Neural stem cell - early on
TUBB3 Beta 3 Tubulin` Motor Neuron
T BRACHYURY Mesoderm - cardiac
GFAP GLIAL FIBRILLARY ACIDIC PROTEIN
astrocyte
GAPDH Control Kit Control gene
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100 cells (average CT): 13.7 + 0.2
1 cell low cluster (36 cells): 19.2 + 1.3
1 cell high cluster (48 cells): 27.4 + 1.0
Average CT (84 cells): 21.4 + 4.2
• Analyzing gene expression profiles en masse gives an average profile• Obscures or potentially obliterates any differences in single cells
100 cells 1 cellACTB
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• Massive amount of cell-to-cell variation -5,000 fold differences in 84 single cells in an endogenous control gene!
Single cell analysis or Embryonic Stem Cells
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Single cell analysis or Embryonic Stem Cells• Gene variability - large expression range for one gene; size variations do
not account for this, but cell cycle dependent regulation may
100 cells1 cell
ACTB
100 cells1 cell
OCT4
• Cell-to-cell variability - expression profiles are not the same in every cell• See small sub-populations (OCT4 low expressers)
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Single cell analysis or Embryonic Stem Cells• Gene variability - large expression range for one gene; size variations do
not account for this, but cell cycle dependent regulation may
100 cells1 cell
ACTB
100 cells1 cell
OCT4
• Cell-to-cell variability - expression profiles are not the same in every cell• See small sub-populations (OCT4 low expressers)
“technical” variability
22.8±0.3 (6.2 from 100 cell samples)
• Technical variability (from method of detection) needs to be identified (low here)
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10
20
30
40
CT
10
20
30
40
CT
CT
Day 0
Day 14
Day 24
Variation in expression level in single cells
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20
30
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GA
PD
GFA
P
NES
PAX6
POU
5F1 T
TUB
B3
UTF
1
ZFP4
2
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Single cell dynamics (cell-to-cell differences)• 100 cell samples show progression of 0, 7, and 14 days
100 Cell Samples
Day 0 7 14
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Single cell dynamics (cell-to-cell differences)• 100 cell samples show progression of 0, 7, and 14 days
100 Cell Samples
Day 0 7 14
Single Cell Samples
• Clustering of 1 cell samples shows clustering of 0 and 14 day samples but the 7 day samples show 2 populations – one similar to day 0, the other to day 14
7 14 7 14 0 7
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qPCR results from 1 and 100 cell samples1 Cell 100 Cell
Day 0
Day 14
Day 24
1014182226303438
CT
1014182226303438
CT
1014182226303438
CT
GAPD NESOCT4
TUBB3ZFP42
1014182226303438
1014182226303438
1014182226303438
GAPD NESOCT4
TUBB3ZFP42
CT
CT
CT
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Normalization - 100 Cell Data From Day 0
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10
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GAPDH NES POU5F1 TUBB3 ZFP42
CT
Thirty 100 cell samples show similar expression levels as demonstrated by small center quantiles (left). Normalized expression levels of each gene to GAPDH expression levels remove some of the sample to sample variability as shown by smaller box and whisker (right) and show that the gene “profiles” of each sample are very similar.
•100 cell samples have similar expression levels which tighten when normalized
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Normalization - 100 Cell Data From Day 0
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10
15
20
25
30
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GAPDH NES POU5F1 TUBB3 ZFP42
CT
Thirty 100 cell samples show similar expression levels as demonstrated by small center quantiles (left). Normalized expression levels of each gene to GAPDH expression levels remove some of the sample to sample variability as shown by smaller box and whisker (right) and show that the gene “profiles” of each sample are very similar.
•100 cell samples have similar expression levels which tighten when normalized
-10
-5
0
5
10
15
20
ΔC
TNES-
GAPDHPOU5F1-GAPDH
TUBB3-GAPDH
ZFP42-GAPDH
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Normalization - Single Cell Data From Day 0•Single cell samples give wide range of expression levels which spreads out further when normalized
900 single cell samples show a wide range of expression levels shown by the large box and whiskers (left). After normalization (right), box and whisker sizes increase as does the number of outliers
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30
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GAPDH NES POU5F1 TUBB3 ZFP42
CT
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Normalization - Single Cell Data From Day 0•Single cell samples give wide range of expression levels which spreads out further when normalized
900 single cell samples show a wide range of expression levels shown by the large box and whiskers (left). After normalization (right), box and whisker sizes increase as does the number of outliers
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25
30
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40
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GAPDH NES POU5F1 TUBB3 ZFP42
CT
-10
-5
0
5
10
15
20
NES-GAPDH
POU5F1-GAPDH
TUBB3-GAPDH
ZFP42-GAPDH
ΔC
T
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15
20
25
30
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40
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GAPD GFAP NES PAX6 POU5F1 T TUBB3 UTF1 ZFP42
Avg.
Ct V
alue
Three cells shows different expression patterns
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Conclusions and Impacts
• There are significant differences from cell-to-cell
• Single cells have a large range of gene expression levels.
• Analyzing gene expression en masse gives an average profile and masks differences and variability (gene-to-gene and cell-to-cell)
• Small populations are lost when large populations are averaged
• Average expression patterns of single cell populations are similar to 100 cells.
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Normalization Conclusions
•When analyzed en masse, variation in expression level and sample size is reduced when results are normalized to reference genes.
•In a single cell expression levels of each gene vary independently
•In single cells normalization increases the variation in calculated expression level.
•These normalized values are not the same within each cell and vary depending on the genes compared.
•These results suggest that normalizing single cell data is not an accurate method of analysis.
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The TaqMan Single Cell-to-Ct kit
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• Optimized reagents provide a simplified workflow for expression analysis of single cells by qRT-PCR
• Enables transfer of entire cell into each step
• No sample is lost during the reaction which occurs in a single tube
• Sensitivity is maximized by use of reagents such as SuperScript III and PreAmp Master Mix
• Variation introduced by the protocol is very low
• Enables the acquisition of statistically significant data sets
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Thank you
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100 cells1 cell
Life Technologies, Applied Biosystems and Ambion products are for Research Use Only. Not for use in diagnostic procedures.
Life Technologies, Applied Biosystems, and Ambion are registered trademarks of Life Technologies Corporation or its subsidiaries in the US and/or certain other countries.
Cells-to-Ct is a trademark of Ambion, Inc. in the U.S. and/or certain other countries. TaqMan is registered trademark of Roche Molecular Systems, Inc.
Practice of the patented 5’ Nuclease Process requires a license from Applied Biosystems. The purchase of the TaqMan® Gene Expression Cells-to-Ct™ Kit includes an immunity from suit under patents specified in the product insert to use only the amount purchased for the purchaser's own internal research when used with the separate purchase of a Licensed Probe. No other patent rights are conveyed expressly, by implication, or by estoppel.
Purchase of the TaqMan® Gene Expression Cells-to-Ct™ Kit is accompanied by a limited license under U.S. Patent 5,035,996 and foreign equivalents to use for research.
© 2011 Life Technologies Corporation. All rights reserved.
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Questions?