1) The document discusses different whole genome amplification techniques for obtaining DNA from single cells, including PCR-based and PCR-free methods. 2) It provides comparisons of different whole genome amplification kits, finding that QIAGEN's REPLI-g Single Cell Kit has the highest genome coverage, lowest duplication rates, and best performance for detecting copy number variations and single nucleotide variations, making it optimal for single cell sequencing applications. 3) Case studies demonstrate that the REPLI-g Single Cell Kit provides more uniform coverage and significantly fewer sequencing errors compared to the MALBAC method.

1. Sample to Insight
Single Cell Whole Genome Amplification
Learn how to get highly uniform whole genome amplification from single cells

2. Sample to Insight
You start with only a single cell
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• One mammalian cell
contains an average of
6 pg DNA
• Bacterial cells typcially
contain DNA in the
femtogram (10-3 pg) range
• A single mammalian cell
contains 10–30 pg of total
RNA but only 1–5% of the
total RNA is mRNA
◦ Much less than
required by a typical
NGS library prep
Bacterium Mammalian cell 200 µl Blood
1 µg
1 ng
1 pg
1 fg
Average DNA content
This chart is on
a log scale. On
a linear scale,
we would not be
able to see the
bars for the
bacterial or
mammalian cell!
Limited availability of DNA or RNA requires a preamplification step
Single cell genomics by QIAGEN, 2016

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Technologies for DNA or RNA preamplification
3
Types of preamplification technologies
Whole genome/transcriptome amplification technologies
PCR-based PCR-free
• Degenerative oligo-primer PCR
(DOP-PCR)
• Multiple annealing and looping
based amplification cycles
(MALBAC)
• Multiple displacement amplification
(MDA)
• Single primer isothermal amplification
(SPIA)
Single cell genomics by QIAGEN, 2016

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Comparison of WGA methods for single cell sequencing (1)
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Genome
coverage
(0.1x / 30x)
Cumulative
depth
distribution
(2)
Consensus
genotypes
detection
efficiency (30x)
Duplication rate in
deep-sequencing
(30x)
CNV
detection
sensitivity
CNV
detection
specificity
DOP-PCR
(5) 6% (0.1 x)
23% (30x)
6% 6% 39% 94%
(3)
94%
(3)
MALBAC
(6) 8% (0.1x)
82% (30x)
47% 52% 13% 85%
(4)
85%
(4)
REPLI-g
Single Cell Kit
9% ( 0.1x)
98% (30x)
82% 85% 3.6% 86%
(4)
81%
(4)
QIAGEN’s WGA technology: best in class for variations calling!
Optimal solution if SNV and CNV are of similar importance, as in tumor heterogeneity or cell
evolution research
(1) Hou, Y. et al. (2015) Comparison of variations detection between whole-genome
amplification methods used in single cell resequencing. GigaScience 4:37
(2) Deep-sequencing (30x) to evaluate amp bias
(3) Simulated data
(4) Real data
(5) DOP-PCR2: degenerate-oligonucleotide-primed PCR
(6) MALBAC: multiple annealing and looping-based amplification cycles
Single cell genomics by QIAGEN, 2016

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Potential challenges observed in WGA or WTA
5Single cell genomics by QIAGEN, 2016

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Multiple displacement amplification (MDA) by QIAGEN
6
QIAGEN’s REPLI-g technology
• Primers (arrows) anneal to the
template
• Primers are extended at 30°C as
the polymerase moves along the
gDNA or cDNA strand displacing
the complementary strand while
becoming a template itself for
replication
• In contrast to PCR amplification,
MDA:
◦ Does not require different
temperatures
◦ Ends in very long fragments with
low mutation rates
Single cell genomics by QIAGEN, 2016

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Overcoming the challenge of gDNA secondary structure
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• Denatured gDNA has a complex
secondary structure
• Consists of regions of ssDNA and
dsDNA that can form complicated
hairpins and loops
QIAGEN’s MDA enzyme handles complex DNA structures
generating extremely long amplicons (up to 70 kb)
Single cell genomics by QIAGEN, 2016

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Superior genome coverage
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1 pg DH10B DNA; amplified with either REPLI-g Single Cell Kit or by
MALBAC; sequenced on MiSeq Illumina (V2, 2x150nt.)
• More uniform genome coverage
◦ Lower total read number required; higher multiplexing
◦ Better de novo genome assembly
◦ Advantageous for low-pass sequencing strategy
Single cell genomics by QIAGEN, 2016

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Superior amplification yield and accuracy
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• 10X higher yield and significantly higher accuracy
◦ More sensitive variant detection
◦ Allows archiving of single cells for future experiments
◦ Provides higher confidence in your data
1 pg DH10B DNA; amplified with either REPLI-g Single Cell Kit or by MALBAC; sequenced
on MiSeq Illumina (V2, 2x150nt.)
Single cell genomics by QIAGEN, 2016

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Case Study: comparing REPLI-g and MALBAC
83
Study outline*
E-coli DH10B
1 pg
REPLI-g Single Cell Kit
GeneRead Library Prep (I)
MiSeq Sequencing
(V2, 2X150 nt)
Data Analysis: CLC
Workbench
E-coli DH10B
1 pg
MALBAC
GeneRead Library Prep (I)
MiSeq Sequencing
(V2, 2X 150nt)
Data Analysis: CLC
Workbench
Needs trimming (first 35 nts)
WGA
Library
construction
NGS
Data Analysis
*Note: experiment had to be started with bacterial gDNA because MALBAC cannot be
used to start directly from the bacterium as the REPLI-g Single Cell Kit can!
single cell genomics by QIAGEN, 2016
Case
study

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REPLI-g vs. MALBAC: visualizing mapping results
84
REPLI-g Single
Cell Kit
MALBAC
Typical region of alignment shown: MALBAC introduces higher number of errors
Case
study
Single cell genomics by QIAGEN, 2016

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Variant calling result: number of mutations: total 6 (insertions)
Variant calling results: number of mutations: total 231
(222 are SNV, 6 are deletions, 3 are insertions
REPLI-g vs. MALBAC: variant calling
85
MALBAC
REPLI-g Single
Cell Kit
MALBAC introduced ~40x more single-nucleotide errors than REPLI-g in this
experiment, represents a huge increase in background when evaluating SNPs
Case
study
Single cell genomics by QIAGEN, 2016

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86
Repli-g coverage Max 3,000
MALBAC coverage Max 3,000
REPLI-g
Max 153
MALBAC
Max 4284
REPLI-g produces more uniform coverage than other protocols
REPLI-g vs. MALBAC: coverage uniformity
Case
study
Single cell genomics by QIAGEN, 2016

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A robust decontamination procedure
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Dedicated buffers and reagents undergo a unique, robust decontamination
procedure to avoid amplification of contaminating DNA, ensuring high reliability
Bacterial DNA (2000 copies) was spiked
into REPLI-g SC Reaction Buffer, which
was then decontaminated using standard
procedure for all buffers and reagents
provided with the REPLI-g Single Cell Kit.
In subsequent real-time PCR, no bacterial
DNA was detected.
All REPLI-g single cell products ensure high reliability
Single cell genomics by QIAGEN, 2016

15. Sample to Insight
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Discover the REPLI-g Single Cell Kit
• Highest sequence fidelity
◦ Best-in-class for variation calling
• Superior and highly uniform coverage
◦ Less GC bias
◦ Optimal solution if SNV and CNV are of equal importance
• Proven publication record
◦ Multiple citations across various research areas
• For downstream NGS, arrays, aCGH and PCR
◦ Free choice of downstream analysis
• Enables Bio-Banking
◦ Amplified DNA can be stored for follow-up studies or confirmatory testing.
MDA*
instead of
PCR
phi29 enzyme
with high fidelity
& processivity
Robust
decontamination
procedure
*MDA = multiple displacement amplification
Single cell genomics by QIAGEN, 2016

16. Sample to Insight
For single cell WGA
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Ideally suited for
• Whole genome amplification from eukaryotic or bacterial
single cells
• Analyzing aneuploidy and sub-chromosomal copy number
variations
• Sequence variation analysis (SNV, structural variants) in
single cells
• Sensitive microbial applications
• Downstream analysis using aCGH, PCR or NGS
• Multiple analyses from a single cell
• Bio-banking the genomic content of a single cell
REPLI-g
Single Cell Kit
Single cell genomics by QIAGEN, 2016

17. Sample to Insight
REPLI-g Single Cell Kit
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The gold standard in WGA for sensitive applications
Primary
sample
isolation
Single cell
isolation WGASample
• Single eukaryotic cells
• Single bacterial cells
• Picogram levels of
purified DNA
• Free choice of downstream analysis
◦ NGS
◦ SNP array, aCGH
◦ PCR
• Multiple analyses from a single cell
InsightPCR
Data
analysis
Interpretation
NGS
aCGH,
SNP array
Single cell genomics by QIAGEN, 2016

18. Sample to Insight
Company
Kit name
REPLI-g® Single Cell
Kit(2) Single Cell WGA Kit(2) Illustra GenomiPhi v2
DNA amplification kit
GenomePlex® Single Cell
WGA Kit
Ampli1™ WGA Kit
PicoPLEX™ WGA
Kit
Applied Method
MDA (Multiple
Displacement
Amplification)
MALBAC (Multiple
annealing and looping
based amplification
cycles)
MDA (Multiple
Displacement
Amplification)
DOP-PCR
(Degenerative oligo-
primer PCR)
DOP-PCR
(Degenerative oligo-
primer PCR)
DOP-PCR
(Degenerative oligo-
primer PCR)
Genome coverage
(0.1x/30x)
9% ( 0.1x)
98% (30x)
8% (0.1x)
82% (30x)
~7 (0,1 x)
94% (30x)
6% (0.1x)
23% (30x)
Not evaluated after low-coverage whole
genome sequencing, because theese kits
had less genome recovery sensitivity and
less sequence evenness than the other kits
Cumulative depth
distribution(43 82% 47% 59% 6%
Consensus
genotypes detection
efficiency (30x)
85% 52% 67% 6%
Duplication rate in
deep-sequencing
(30x)
3.6% 13% 6% 39%
CNV detection
sensitivity
86%(4)
85%(4)
Not evaluated further
94%(5)
CNV detection
specificity
81%(4)
85%(4) 94%(5)
Comparison of WGA methods for single cell sequencing(1)
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A comparative study by Hou et al. (2015) (1)
Lowest performance
Medium performance
Best performance
Comparable performance
Note: REPLI-g Single Cell Kit is the best-in-class for variations calling. It is also the optimal solution if
SNV and CNV are of similar importance, as in tumor heterogeneity or cell evolution research
(1) Hou, Y. et al. (2015), Comparison of variations detection between whole-genome amplification methods used in single cell resequencing, GigaScience 4:37
(2) Data are mean from 3 to 5 single cells
(3) Deep sequencing (30x) to evaluate amplification bias
(4) Real data
(5) Simulated data
single cell genomics by QIAGEN, 2016

19. Sample to Insight
Comparison of whole-genome amplification methods
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(1) Hou, Y. et al. (2015) Comparison of variations detection between whole-genome
amplification methods used in single cell resequencing. GigaScience 4:37
“The results indicated that SCRS (single cell resequencing) data generated by MDA-2 (MDA using the QIAGEN REPLI-g
Single Cell Kit) presented higher genome recovery sensitivity than those generated by MALBAC and DOP-PCR with the
same sequencing depth.” (1)
“A previous study showed that MALBAC was advantageous for SNVs and CNVs detection in SCRS data compared with
MDA … However, when we compared the SNVs and CNVs detection performance of the MDA-2 kit [REPLI-g Single
Cell Kit] (an optimized version of the MDA-1 kit), we found that the MDA-2 data had higher genome recovery than the
MALBAC data with the same sequencing depth … More importantly, we found that the MDA-2 (REPLI-g Single Cell Kit)
data had a comparable SNVs detection accuracy and CNVs detection accuracy with those of the MALBAC data; and
this accuracy was greater than that indicated by a previous report for MDA-1. Taken together, these data suggest that
optimization of MDA experimental protocols may significantly improve SNVs and CNVs detection in SCRS data.” (1)
REPLI-g Single Cell
Kit has higher genome
recovery sensitivity
than those generated
by MALBAC
and DOP-PCR
REPLI-g Single Cell
Kit significantly
improves SNV and CNV
detection
Single cell genomics by QIAGEN, 2016

20. Sample to Insight
REPLI-g Single Cell Kit
20
What are customers saying? Here are a few examples
“Compared to our current
method, the REPLI-g Single
Cell Kit greatly reduced the
amplification bias and
delivered more uniform
whole genome amplification
(comparable to non-amplified
genomic DNA) of single
lymphocytes, making it
possible to detect SNPs,
CNVs and SVs
simultaneously. No significant
differences were observed for
next-generation sequencing
parameters, such as the mean
mapping quality, read mapping
ratio, and read duplication ratio
when compared to the high-
quality results obtained using
the REPLI-g Mini Kit.“
Luting Song,
Staff Scientist, Oncology
Research, Beijing Genome
Institute (BGI), China
“….we achieved the best overall coverage
uniformity with this latest version of REPLI-
g from QIAGEN REPLI-g Single Cell Kit)” (1)
“MDA gives better overall genome
coverage than PCR-based methods ….” (1)
“Phi-29 polymerase has the highest
processivity and the lowest error rate
among existing polymerases” (1)
“…the high processivity of Phi-29
polymerase consistently generates large
amplicons above 10 kb” (1)
(1) Zhang, C.-Z. et al. (2015) Chromothripsis from DNA damage in micronuclei. Nature, published online 27 May 2015. 6, 6822
Single cell genomics by QIAGEN, 2016

21. Sample to Insight
Comparison of WGA methods
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Comparison of recovery sensitivity using randomly extracted 0.1X data
“We found that MDA-2 (REPLI-g Single
Cell Kit) amplified data had the highest
mean genome …coverage, even
higher than that of MALBAC...” (1)
REPLI-g Single Cell Kit
Data taken from
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4
527218/. Additional file 4: Table S4 (1)
(1) Hou, Y. et al. (2015) Comparison of variations detection between whole-genome
amplification methods used in single cell resequencing. GigaScience 4:37
Single cell genomics by QIAGEN, 2016

22. Sample to Insight
Comparison of WGA methods
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Comparison of deep-sequenced (~30x) data
REPLI-g Single Cell Kit
MALBAC, Yikon Genomics
Bulk control
Bulk control
“… but MDA-2 (REPLI-g Single Cell Kit)
showed the highest effective covered
sequencing depth that may best suited for
variations calling.” (1)
The cumulative distribution of sequencing fold depth of deep WGS
data amplified by DOP-1, MDA-2, MDA-3 and MALBAC,
respectively. The standard Poisson Cumulative Distribution (λ = 30)
is plotted (dashed), and YH-mix and SW480 bulk data are
presented as a control.
(1) Hou, Y. et al. (2015) Comparison of variations detection between whole-genome
amplification methods used in single cell resequencing. GigaScience 4:37
Single cell genomics by QIAGEN, 2016

23. Sample to Insight
Comparison of single cell WGA methods
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REPLI-g Single Cell Kit
Combined single-nucleotide error rates (1)
Experimental error rates D versus gain G, for low
gains. Here, D is the fraction of bases differing from
the reference in the mapped reads. Linear fits for D as
a function of log2G/2: their slope approximately
indicates the per-base per-cycle replication error rate.
Inset: D versus G over the entire gain range. Filled
symbols signify bulk experiments, open symbols
single cell experiments.
The REPLI-g MDA method exhibits high fidelity
(1) de Bourcy CFA, De Vlaminck I, Kanbar JN, Wang J, Gawad C, et al. (2014) A
Quantitative Comparison of single cell Whole Genome Amplification, Methods. PLoS
ONE 9(8): e105585. doi:10.1371/journal.pone.0105585
Single cell genomics by QIAGEN, 2016
Download poster: Achieve improved variant
detection in single cell sequencing

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Single cell genomics by QIAGEN
24
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WGA or WTA
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miScript
Single Cell
qPCR Kit
Single cell genomics by QIAGEN, 2016
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