Dynamic-array based method for high throughout and ... · 48.48 dynamic arrays were run on Biomark...
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Cell and Gene Therapy Catapult 12 th Floor, Tower Wing, Guy’s Hospital, Great Maze Pond, London SE1 9RT +44 (0) 203 728 9500 | [email protected] | ct.catapult.org.uk Cell and Gene Therapy Catapult is a trading name of Cell Therapy Catapult Limited, registered in England and Wales under company number 07964711. Background and Objectives: The mechanisms responsible for maintaining pluripotency during expansion of pluripotent stem cells (PSCs) to date remain unclear. Continuous propagation of PSCs is associated with varying levels of spontaneous differentiation as well as genomic aberrations. Any changes to the quality of the cells have to be detected in order to pass or fail produced batches of cells (e.g. cell banks). Differentiation of pluripotent cells can be detected by gene expression analysis, where reduction of expression of self-renewal genes and increased levels of expression of differentiation markers is observed. Current methods such as hPSC Scorecards™, can detect changes by comparing samples to reference material, however, scorecards also require high quantity of RNA for analysis. We therefore aimed to develop a method which would allow more flexible, high throughput analysis for pluripotent gene expression analysis by applying dynamic array methodology. Dynamic-array based method for high throughout and flexible assessment of pluripotency in PSCs Shai Senderovich , Mark Bell, Evangelia Rologi, Rhys Macown, Damian Marshall, Ricardo P. Baptista and Beata Surmacz-Cordle Cell and Gene Therapy Catapult 12th Floor Tower Wing, Guy’s Hospital, Great Maze Pond, London SE1 9RT Contact: [email protected] Scorecard (96 genes) Dynamic array (48 genes) 33 genes Figure 2: Genes shared by the two platforms Hierarchical clustering Scorecard™ Control Ectoderm Endoderm Mesoderm ACTB CDH9 AFP FGF4 ACTB COL2A1 CABP7 ABCA4 ACTB DMBX1 CDH20 ALOX15 ACTB DRD4 CLDN1 BMP10 CTCF EN1 CPLX2 CDH5 EP300 LMX1A ELAVL3 CDX2 SMAD1 MAP2 EOMES COLEC10 MYO3B FOXA1 ESM1 Self renewal NOS2 FOXA2 FCN3 CXCL5 NR2F1/NR2F2 FOXP2 FOXF1 DNMT3B NR2F2 GATA4 GDF3 HESX1 OLFM3 GATA6 HAND1 IDO1 PAPLN HHEX HAND2 LCK PAX3 HMP19 HEY1 NANOG PAX6 HNF1B HOPX POU5F1 POU4F1 HNF4A IL6ST SOX2 PRKCA KLF5 NKX2-5 TRIM22 SDC2 LEFTY1 NPPB SOX1 LEFTY2 NR5A2 TRPM8 NODAL ODAM WNT1 PHOX2B PDGFRA ZBTB16 POU3F3 PLVAP PRDM1 PTHLH RXRG RGS4 SOX17 SNAI2 SST T TBX3 TM4SF1 Table 1: Scorecard™ panel of genes Hierarchical clustering dynamic array Design: Cells from a CGT-RCiB10 Master Cell Bank (MCB) were profiled by gene expression using hPSC Scorecard™ (Life Technology; Table 1) and by dynamic array using a CGT panel of Taqman™ gene expression assays (Figure 1) . Cell potency was assessed by differentiation into the 3-germ layers of embryoid bodies (EBs) cultured in serum-containing medium. Cells were cultured for 3 passages, then divided into 6 well plates. Non-differentiated cells were harvested on day 0. Ectoderm, endoderm and mesoderm differentiated cells were harvested after 6 days of directed differentiation (Figure 1) RNA was extracted using Qiagen mini Rneasy kits, and reverse transcriptase was performed using Quantitect RT (Qiagen). RNA (8μg) was used for each scorecard sample and the equivalent of 0.5μg RNA was used for dynamic array samples. Scorecards were run on Quantstudio 7 (life technologies) and 48.48 dynamic arrays were run on Biomark HD (Fluidigm). Statistical analysis was performed using Genex 6 (MultiD) Comparison of a dynamic array gene expression panel against Scorecards TM during PSCs culture optimisation Non- differentiated Mesoderm Ectoderm Endoderm Dynamic array data Scorecards data Dynamic array and Scorecard™ Trend Analysis Seeding Concentration Self renewal Meso Endo Ecto Hierarchical clustering dynamic array Hierarchical clustering Scorecard™ Figure 4: Multi-component bioreactor optimisation Gene expression trend Gene expression correlation 8 conditions 2 cell lines 48 samples N=3 Self renewal Meso Endo Ecto ES cells iPS cells ES cells iPS cells Comparison of a dynamic array gene expression panel against Scorecards TM in PSCs directed differentiation assay Endo Ecto Meso Non diff Harvest and extract RNA RCiB10 iPS Day 0 Day 6 Day 6 Day 6 cDNA Scorecards™ Dynamic arrays N=3 Conclusions : • Both dynamic arrays and Scorecards give comparable results (Figures 3-6); • Dynamic array can be applied as in process control for testing multiple culture conditions of PSCs; • Dynamic array assay is flexible and adaptable; • The tested panel will be further extended and we are currently testing additional markers; Design: Culture conditions were optimised on AMBR 15 bioreactor (Figure 4). The effect of culture conditions such as impeller speed, media type and seeding concentration on gene expression was analysed by hPSC Scorecards™ (Life Technologies) and dynamic arrays and the two platforms were compared (Figures 5 and 6). Results : Gene expression trend was compared according to normalised cycle threshold values read by each of the platforms. The trend of gene expression shows correlation (Figure 5 A). Both dynamic arrays and Scorecards™ show high level of similarity in gene expression, however the raw data reads on dynamic arrays show overall lower Ct values compared to Scorecards due to differences in the methods (Figure 5 B) however the trend between different genes remains similar. A B Self renewal Meso Endo Ecto Results : In-order to compare Scorecards and dynamic arrays, gene expression data was analysed by hierarchical clustering according to normalised Ct values of dynamic array (Figure 6 A) and Scorecards™ (Figure 6 B). Data obtained from both platforms created informative clusters that differentiated between inducible pluripotent (iPS) cell line and Embryonic Stem cell line (ES). In the analysis of both platforms self-renewal genes were highly expressed and there was no differentiation. A B Results : Gene expression trend was compared according to normalised cycle threshold values read by each of the platforms. Both dynamic arrays and Scorecards™ show high level of similarity, differences between the platform are seen only in non-expressing genes, where there is a different read-out t0 each of the platforms (Figure 3 A). Hierarchical clustering was used to identify groups of genes that are expressed by differentiated and non-differentiated cells showing using both platforms achieve the same clustering of samples, in accord with their differentiation (Figure 3 B and C). A B C Figure 1: Differentiation analysis by Scorecard™ and dynamic arrays Figure 3: A comparison of differentiation analysis by Scorecard™ and dynamic arrays Figure 6: CGT dynamic array panel vs Scorecard™ comparison Figure 5: CGT dynamic array panel vs Scorecard™ correlation analysis Genes Ct Genes Ct Genes Ct Genes Ct Genes Ct Methods Methods
Transcript of Dynamic-array based method for high throughout and ... · 48.48 dynamic arrays were run on Biomark...
Cell and Gene Therapy Catapult
12th Floor, Tower Wing, Guy’s Hospital, Great Maze Pond, London SE1 9RT
+44 (0) 203 728 9500 | [email protected] | ct.catapult.org.uk
Cell and Gene Therapy Catapult is a trading name of Cell Therapy Catapult Limited, registered in England and Wales under company number 07964711.
Background and Objectives: The mechanisms responsible for maintaining pluripotency during expansion of pluripotent stem cells (PSCs) to date remainunclear. Continuous propagation of PSCs is associated with varying levels of spontaneous differentiation as well as genomic aberrations. Any changes to the qualityof the cells have to be detected in order to pass or fail produced batches of cells (e.g. cell banks). Differentiation of pluripotent cells can be detected by geneexpression analysis, where reduction of expression of self-renewal genes and increased levels of expression of differentiation markers is observed. Current methodssuch as hPSC Scorecards™, can detect changes by comparing samples to reference material, however, scorecards also require high quantity of RNA for analysis. Wetherefore aimed to develop a method which would allow more flexible, high throughput analysis for pluripotent gene expression analysis by applying dynamic arraymethodology.
Dynamic-array based method for high throughout and flexible assessment of pluripotency in PSCs
Shai Senderovich, Mark Bell, Evangelia Rologi, Rhys Macown, Damian Marshall, Ricardo P. Baptista and Beata Surmacz-Cordle
Cell and Gene Therapy Catapult 12th Floor Tower Wing, Guy’s Hospital, Great Maze Pond, London SE1 9RTContact: [email protected]
Scorecard
(96 genes)
Dynamic
array (48
genes)33 g
en
es
Figure 2: Genes shared by the two
platforms
Hierarchical clustering Scorecard™Control Ectoderm Endoderm Mesoderm
ACTB CDH9 AFP FGF4
ACTB COL2A1 CABP7 ABCA4
ACTB DMBX1 CDH20 ALOX15ACTB DRD4 CLDN1 BMP10CTCF EN1 CPLX2 CDH5
EP300 LMX1A ELAVL3 CDX2SMAD1 MAP2 EOMES COLEC10
MYO3B FOXA1 ESM1
Self renewal NOS2 FOXA2 FCN3
CXCL5 NR2F1/NR2F2 FOXP2 FOXF1DNMT3B NR2F2 GATA4 GDF3
HESX1 OLFM3 GATA6 HAND1IDO1 PAPLN HHEX HAND2LCK PAX3 HMP19 HEY1
NANOG PAX6 HNF1B HOPXPOU5F1 POU4F1 HNF4A IL6ST
SOX2 PRKCA KLF5 NKX2-5
TRIM22 SDC2 LEFTY1 NPPB
SOX1 LEFTY2 NR5A2
TRPM8 NODAL ODAM
WNT1 PHOX2B PDGFRA
ZBTB16 POU3F3 PLVAP
PRDM1 PTHLH
RXRG RGS4
SOX17 SNAI2
SST T
TBX3
TM4SF1
Table 1: Scorecard™ panel of genes Hierarchical clustering dynamic array
Design: Cells from a CGT-RCiB10 Master Cell Bank (MCB)
were profiled by gene expression using hPSC Scorecard™ (LifeTechnology; Table 1) and by dynamic array using a CGT panelof Taqman™ gene expression assays (Figure 1) . Cell potencywas assessed by differentiation into the 3-germ layers ofembryoid bodies (EBs) cultured in serum-containing medium.Cells were cultured for 3 passages, then divided into 6 wellplates. Non-differentiated cells were harvested on day 0.Ectoderm, endoderm and mesoderm differentiated cells wereharvested after 6 days of directed differentiation (Figure 1)RNA was extracted using Qiagen mini Rneasy kits, and reversetranscriptase was performed using Quantitect RT (Qiagen).RNA (8µg) was used for each scorecard sample and theequivalent of 0.5µg RNA was used for dynamic array samples.Scorecards were run on Quantstudio 7 (life technologies) and48.48 dynamic arrays were run on Biomark HD (Fluidigm).Statistical analysis was performed using Genex 6 (MultiD)
Comparison of a dynamic array gene expression panel against ScorecardsTM during PSCs culture optimisation
Non-differentiated
Mesoderm
Ectoderm
Endoderm
Dynamic array data
Scorecards data
Dynamic array and Scorecard™ Trend Analysis
See
din
g
Co
nce
ntr
ati
on
Self renewal
Meso
Endo
Ecto
Hierarchical clustering dynamic array Hierarchical clustering Scorecard™ Figure 4: Multi-component bioreactor optimisation
Gene expression trendGene expression correlation
8 conditions
2 cell lines
48 samplesN=3
Self renewal
Meso
Endo
Ecto
ES
cel
ls
iPS
cel
ls
ES
cel
ls
iPS
cel
ls
Comparison of a dynamic array gene expression panel against ScorecardsTM in PSCs directed differentiation assay
Endo
Ecto
Meso
Non diff
Harvest and extract RNA
RCiB10 iPS
Day 0
Day 6
Day 6
Day 6
cDNA
Scorecards™
Dynamic arrays
N=3
Conclusions:• Both dynamic arrays and Scorecards give
comparable results (Figures 3-6);• Dynamic array can be applied as in process control
for testing multiple culture conditions of PSCs;• Dynamic array assay is flexible and adaptable;• The tested panel will be further extended and we are
currently testing additional markers;
Design: Culture conditions were optimised on AMBR 15 bioreactor (Figure 4). The effect of culture
conditions such as impeller speed, media type and seeding concentration on gene expression was analysedby hPSC Scorecards™ (Life Technologies) and dynamic arrays and the two platforms were compared(Figures 5 and 6).
Results: Gene expression trend was compared according to normalised cycle threshold values read by
each of the platforms. The trend of gene expression shows correlation (Figure 5 A). Both dynamic arraysand Scorecards™ show high level of similarity in gene expression, however the raw data reads on dynamicarrays show overall lower Ct values compared to Scorecards due to differences in the methods (Figure 5 B)however the trend between different genes remains similar.
A B
Self renewal
Meso
Endo
Ecto
Results: In-order to compare Scorecards and dynamic
arrays, gene expression data was analysed byhierarchical clustering according to normalised Ctvalues of dynamic array (Figure 6 A) and Scorecards™(Figure 6 B). Data obtained from both platformscreated informative clusters that differentiated betweeninducible pluripotent (iPS) cell line and EmbryonicStem cell line (ES). In the analysis of both platformsself-renewal genes were highly expressed and there wasno differentiation.
A B
Results:Gene expression trend was compared according to normalised cyclethreshold values read by each of the platforms. Both dynamic arrays andScorecards™ show high level of similarity, differences between theplatform are seen only in non-expressing genes, where there is a differentread-out t0 each of the platforms (Figure 3 A). Hierarchical clusteringwas used to identify groups of genes that are expressed by differentiatedand non-differentiated cells showing using both platforms achieve thesame clustering of samples, in accord with their differentiation (Figure 3B and C).
A B C
Figure 1: Differentiation analysis by Scorecard™ and dynamic arrays
Figure 3: A comparison of differentiation analysis by Scorecard™ and dynamic arrays
Figure 6: CGT dynamic array panel vs Scorecard™ comparison
Figure 5: CGT dynamic array panel vs Scorecard™ correlation analysis
Genes
Ct
Genes
Ct
Genes
Ct
Genes
Ct
Genes
Ct
Methods
Methods
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