Cell and Gene Therapy Catapult

12th Floor, Tower Wing, Guy’s Hospital, Great Maze Pond, London SE1 9RT

+44 (0) 203 728 9500 | info@ct.catapult.org.uk | 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.

PROPOSED SOLUTION

We developed a novel analytical workflow to analyse vector copy number at a single cell level and

demonstrated that this is representative of the population of transduced cells. The readout of this assay also

provides label-free, PCR-based transduction efficiency, overcoming the challenge of measuring this

specification when the standard flow cytometry approach is not applicable.

METHOD

• Lentiviral vector expressing ZsGreen used to transduce CD3+ T cells purified from healthy donors.

• Single cell isolation and processing performed on the Fluidigm C1 system.

• Droplet digital PCR (ddPCR) used as readout.

• An in-house analysis framework based on Bayesian statistics to convert measured data to integer copy

number predictions.

Conclusions

5. scVCN assay optimisation for gene-modified

cell therapy products

4. Accuracy of the scVCN assay is verified

on a larger sample set3. Development of the single cell VCN (scVCN) workflow

1. Multiplex ddPCR can be used on gDNA to measure

population Vector Copy Number (pVCN)

Single cell analysis of lentiviral integration to support ex-vivo gene modified cell therapy developmentIlaria Santeramo, Marta Bagnati, Enas Hassan, Beata Surmacz-Cordle, Damian Marshall, Vincenzo Di Cerbo*

Cell and Gene Therapy Catapult, London – *presenting and corresponding contact: vincenzo.dicerbo@ct.catapult.org.uk

0 1 2 3 4 5 6 7 8

Vector copies

Dis

trib

ution

0 5000 10000150002000025000

RG2

RG1

VG3

VG2

VG1

Non-amplified gDNA

Absolute number of copies

Targ

et gene

0 50000 100000 150000

RG2

RG1

VG3

VG2

VG1

Targeted pre-amplification

Absolute number of copies

Targ

et gene

0 20000 40000 60000

RG2

RG1

VG3

VG2

VG1

Whole Genome Amplification

Absolute number of copies

Targ

et gene

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80

0

2

4

6

8

10

Single Cells

Mean s

ingle

cell

VC

N

95% confidence interval

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80

0

2

4

6

8

10

Single Cells

Mean s

ingle

cell

VC

N

95% confidence interval

Vector copies

40%

18%

5%27%

9%

Mean of scVCN predictions= 2.35Bulk gDNA VCN/cell= 2.93 ± 0.12

N= 77 cells

1234567

Fluidigm C1 system

Single cellisolation

gDNAextraction

Targetspre-amplification

ddPCRanalysis

In-housestatistical

model

Quality control of cells

isolation and number

Single cell isolation in a

closed automated system

Automated droplet

digital PCR analysis

Absolute quantification of

viral and human targets

0 20 40 60 80 100

Flow cytometry

Imaging

PCR-based

Number of cells (%)

Non-transducedTransduced

0 1 2 3 4 5 6 7 8 9

0

10

20

30

Vector copies

Num

ber

of cells Chip2

Chip1

Human Reference

Gene 1Vector Gene 1

Vector Gene 2

Vector Gene 3

Multiplex TaqMan ddPCR reactions

Human Reference

Gene 2

Ch1 A

mplit

ude (

VG

)

5000

0

5000

10000

RG1+/VG1-

RG1+/VG1+RG1-/VG1+

RG1-/VG1-

Ch2 Amplitude (RG)

9000

Exemplar 2D ddPCR amplitude plot

pVCN1

pVCN2

pVCN3

pVCN4

pVCN5

pVCN6

0

1

2

3

Popula

tion V

CN

(bulk

gD

NA

)

MOI 1MOI 0.3

pVCN measured on two samples transduced

at differing MOI levels

• All six combinations of multiplex ddPCR reactions

provide consistent vector copy number results.

• pVCN analysis by ddPCR is precise and robust on

gDNA from transduced cells.

2. A targeted approach is required for linear pre-

amplification of the target gene sequences

• Three commercial whole genome amplification (WGA) kits and two pre-amplification master mixes tested.

• High variability in targets’ amplification observed in all WGA kits.

• Uniform amplification of targets observed with a targeted approach, comparable to non-amplified gDNA

quantification.

Representative graphs comparing pre-amplification strategies on gDNA as analysed by ddPCR

scVCN analysis on “low” pVCN sample scVCN analysis on “high” pVCN sample

• Single cell VCN analysis can estimate the number of vector units in each analysed single cell.

• Bayesian statistics is applied to convert measured scVCN values by ddPCR into integer values

corresponding to vector units per cell.

• Comparison of scVCN mean with the population VCN from gDNA analysis indicates that the assay can be

predictive of the cell population specifications.

• Comparison of scVCN predictions mean with pVCN is used to estimate the accuracy of the assay.

• The absolute difference between these values ranges from 0 to 0.6 copies, corresponding to an accuracy ≥ 80%.

• Linear regression test shows positive correlation between population and single cell VCN.

scVCN distribution across nine samples

compared to the population VCN and assay biasLinear regression comparing

scVCN mean with population VCN

1

N= 161 cells

0

234

18%

8%

22%

6%

17%

Viral copies

Mean of scVCN predictions= 1.93

Bulk gDNA VCN/cell= 2.31 ± 0.24

5679

29%

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160

0

2

4

6

8

10

12

Single Cells

Me

an

sin

gle

ce

ll V

CN 95% confidence interval

• The scVCN assay can be applied to non-sorted cell sample used as a

proxy for a typical cell therapy product.

• The sample size can be increased by analysing multiple microfluidics chips

for the same population. No statistical difference between scVCN

distributions is observed here (p = 0.4297, two-tail Wilcoxon test).

• PCR-based transduction efficiency can be measured with this assay and is

comparable to conventional flow cytometry or imaging analysis.

• We designed a novel analytical method for advanced analysis of vector copy number and label-free transduction

efficiency at a single cell level to improve the characterisation of gene-modified cell therapy products.

• The scVCN assay can improve the control over the safety of gene-modified products by indicating the proportion of

single cell with a high number of vector integrations, which are generally associated with higher risk of insertional

mutagenesis, before therapy delivery and during follow-up studies.

• We envisage a broad applicability of the method to monitor product’s variability due to process, donor-to-donor, and

operational variations.

• Although it was developed on a lentiviral-based cell therapy model system, we anticipate this to be amenable to VCN

analysis of any transgene from viral or non-viral vectors, by simple modification of the primer sequences.

BACKGROUND

Ex-vivo gene-modified cell therapies are increasingly being developed for the treatment of monogenic

diseases and cancers. Product safety and consistency of manufacturing are key to the success of the cell

therapy field. The generation of genetically-modified cells often entails the use of lentiviral or retroviral vectors,

which randomly integrate into the host genome posing a risk for insertional mutagenesis, while product

variability is one of the main burdens to successful product generation.

CHALLENGE

Analytical characterisation is fundamental to monitor product variability

and safety, but current analytical methods are often inadequate or

laborious and only partially aid the evaluation of product’s attributes. In

particular, population analysis is the standard approach, yet this does

not provides insights on the underlying cell-to-cell variability.

Vector copies

64%

23%

5%

Mean of scVCN predictions= 1.36Bulk gDNA VCN/cell= 1.46 ± 0.32

N= 78 cells

3%

01234

5%

1.0 1.5 2.0 2.5 3.0

1.0

1.5

2.0

2.5

3.0

pVCN (bulk gDNA)M

ean s

cV

CN

pre

dic

tions

R2 = 0.7156

1 2 3 4 5 6 7

Vector copies

Analy

sed S

am

ple

s

0%20%40%60%80%

VCN

0

0.2

0.4

0.6

Bias

0%

5%

10%

15%

20%pVCN

1.0

1.5

2.0

2.5

3.0

scVCN analysis on a non-FACS sorted sample

Brightfield ZsGreen