Whole Genome Amplification from Single Cell
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Transcript of Whole Genome Amplification from Single Cell
Sample to Insight
Single Cell Whole Genome Amplification
Learn how to get highly uniform whole genome amplification from single cells
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You start with only a single cell
• 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
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Technologies for DNA or RNA preamplification
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)
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Comparison of WGA methods for single cell sequencing (1)
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
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Potential challenges observed in WGA or WTA
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Multiple displacement amplification (MDA) by QIAGEN
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
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Overcoming the challenge of gDNA secondary structure
• 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)
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Superior genome coverage
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
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Superior amplification yield and accuracy
• 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.)
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Case Study: comparing REPLI-g and MALBAC
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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
Libraryconstruction
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!
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Case study
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REPLI-g vs. MALBAC: visualizing mapping results
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REPLI-g Single Cell Kit
MALBAC
Typical region of alignment shown: MALBAC introduces higher number of errors
Case study
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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
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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
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Repli-g coverage Max 3,000
MALBAC coverage Max 3,000
REPLI-g Max 153
MALBACMax 4284
REPLI-g produces more uniform coverage than other protocols
REPLI-g vs. MALBAC: coverage uniformity
Case study
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A robust decontamination procedure
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
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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
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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
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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
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Company
Kit name REPLI-g® Single Cell Kit(2) Single Cell WGA Kit(2) Illustra GenomiPhi v2
DNA amplification kitGenomePlex® Single Cell
WGA Kit Ampli1™ WGA Kit PicoPLEX™ WGA Kit
Applied MethodMDA (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 further94%(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
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Comparison of whole-genome amplification methods
(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 CNVdetection
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REPLI-g Single Cell Kit
What are customers saying? Here are a few examples
“Compared to our current method, the REPLI-g SingleCell 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
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Comparison of WGA methods
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/PMC4527218/. 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
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Comparison of WGA methods
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
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Comparison of single cell WGA methods
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
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Download poster: Achieve improved variant detection in single cell sequencing
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For in-depth, molecular analysis of single cells
Single cell WGA or WTA
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