Whole Genome Amplification from Single Cell

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Single Cell Whole Genome Amplification

Learn how to get highly uniform whole genome amplification from single cells

https://www.qiagen.com/us/shop/sample-technologies/dna/dna-preparation/repli-g-single-cell-kit/#resources

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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

Single cell genomics by QIAGEN, 2016

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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


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!

single cell genomics by QIAGEN, 2016

Case study

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REPLI-g vs. MALBAC: visualizing mapping results

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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


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



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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)


primer PCR)


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

single cell genomics by QIAGEN, 2016

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Comparison of whole-genome amplification methods


“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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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)


Data taken from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4527218/. Additional file 4: Table S4 (1)



http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4527218/

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4527218/

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Comparison of WGA methods

Comparison of deep-sequenced (~30x) data


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.



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Comparison of single cell WGA methods


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


Download poster: Achieve improved variant detection in single cell sequencing

https://www.qiagen.com/us/resources/resourcedetail?id=7f01640b-264d-48af-9534-f671177211fb&lang=en



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Single cell genomics by QIAGEN

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For in-depth, molecular analysis of single cells

Single cell WGA or WTA

Single cell isolation

Single cell sequencing

• Easily access single cells

• Affordable

• Gentle on cells

QIAscoutQIAseq

FX Single Cell Library Kits

REPLI-g Single Cell Kits

Single cell miRNA analysis

• Create superior-quality NGS libraries directly from single cells

• Provides best-in-class amplification of genomes or transcriptomes from single cells

• Profile miRNA expression using qPCR

miScript Single Cell qPCR Kit


Visit the single cell resource center

https://www.qiagen.com/us/products/life-science-research/single-cell-resource/

Healthcare

Whole Genome Amplification from Single Cell