advances and applications enabled by single cell technology

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Advances and applications enabled by single cell technologyMiranda Hanson-Baseler, Ph.D.

[email protected]

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Legal disclaimer

QIAGEN products shown here are intended for molecular biology applications. These products are not intended for the diagnosis, prevention or treatment of a disease.

For up-to-date licensing information and product-specific disclaimers, see the respective QIAGEN kit handbook or user manual. QIAGEN kit handbooks and user manuals are available at www.QIAGEN.com or can be requested from QIAGEN Technical Services or your local distributor.

http://www.qiagen.com/

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Agenda

Overview of single cell technology

• Why study single cells?

• Basic parts of a single cell workflow

• QIAGEN solutions for single cell analysis

Advances enabled by single cell technology

• Cancer research

• Reproductive genetics

• Neuroscience

• Metagenomics

• Infectious disease

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• Cancer research


• Neuroscience

• Metagenomics


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• Why study single cells?o Scarce sampleo Genome heterogeneityo Transcriptome heterogeneityo Statistical power

• Basic parts of a single cell workflowo Cell isolationo WGA or WTAo Analytical techniqueso Data analysis

• QIAGEN products for single cell analysis

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You start with only one single cell

• A single mammalian cell contains <0.01% the DNA required by a typical NGS library prep

• A single mammalian cell contains 10–30 pg of total RNA, only 1–5% of the total RNA is mRNA

Standard NGS library prep

input:100–1000ng

Bacterium Mammalian cell

200 µl Blood

1 µg

1 ng

1 pg

1 fg

Average DNA content

This is log scale! In linear scale, you would not even see bars for the bacterial or mammalian cell

Limited availability of DNA or RNA requires a preamplification step

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Cells differ on the genome level

Genome variations occur in health and disease

(1) Iourov, I.Y. et al. (2010) Somatic Genome Variations in Health and Disease, Curr Genomics 11(6)

• Somatic genome variations are:o Aneuploidyo Structural rearrangementso Copy number variationso Gene mutations

• Somatic genome variations o Occur during normal development /

aging

o Contribute to pathogenesis

o Are the cause of diseases like cancer, autoimmune, brain and other diseases

Examples (1):

• Aneuploidy in pre-implantation embryos occurs in 15–91% of samples

• Aneuploidy in skin fibroblasts occurs in adultso Middle age: in 2.2% of cellso Aged: in 4.4% of cells

• Almost all cancers are caused by different types of genome variations including aneuploidy/polyploidy, structural rearrangements, gene amplifications, gene mutations

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Similar cells – unique transcriptional patterns

Cells change their transcription pattern:

• The transcriptome of a cell is not fixed but dynamic

• The transcriptome reflects the o Function of the cello Type of the cello Cell stage

• Gene expression is influenced by intrinsic or extrinsic factors (signaling response, stress response)

• Only on single cell level you get:o Real (not average) transcriptome gene

expression datao Allelic expression datao A deeper understanding of the transcription

dynamics within a cell

Heat map of single cell RNA-seq data for selected pluripotency regulators (1)

(1) Kumar L.M. et al. (2014) Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature 4;516

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Averaging averages

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The basic unit of research we are often interested in is the cell. But we usually analyze populations of cells and this can:• Lead to false positives from underestimating biological variability• Miss important biological divisions

0 0

0 3

0 0

0 0

0 0

0 6

0 6

0 0

0,938

Biological Sample 1

Biological Sample 2Population

Mean 2

1

Single Cell Analysis

Population Mean 1

Mean=0.969Stdev=1.470

Sample Size=32SEM=0.260

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1

1 1

1 1

1 1

1 1

1 1

1 1

1

Mean=0.969Stdev=0.048

Sample Size=2SEM=0.031

Bulk Approach

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Single cell analysis enables new insights

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CTC=Circulating tumor cells, PGD=Pre-implantation genetic diagnosis

Cellular heterogeneity

Detection and analysis of rare cells (example: CTC from liquid biopsy)

Identification of cell subpopulations based on genomic structure or gene expression (tumors, tissues, immune cells, cell cultures)

Limited availability of

cellsAnalysis of limited sample material (example: embryo biopsy for PGD, fine-needle aspirates)

Reasons ApplicationReason

Biological insights instead of average results

No Data

Bulk result Single cell data

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Single cell workflow overview

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In general, single cell molecular biology experiments follow this workflow:• Obtain primary sample• Detect and isolate cell of interest• Lyse cell (often integrated with WGA or

WTA)• Whole genome or whole transcriptome

amplification (for DNA/RNA studies)• Analytical technique of choice (NGS

library prep and sequencing, gene panels, real-time PCR, microarrays, Sanger sequencing)

• Data analysis and interpretation

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Single cell genomics – the workflow

Analysis Data analysis

Whole genome amplification

(WGA)

Whole transcriptome amplification

(WTA)

NGS

qPCR

qRT-PCR

Microarray

Arrays

Data analysis

Biological interpretation

Preamplification

The right preamplification method in your workflow is key

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Technologies for DNA or RNA preamplification

Types of preamplification technologies:

PCR-based

-Degenerative oligo-primer PCR (DOP-PCR)

-Multiple annealing and looping based amplification

cycles (MALBAC)

PCR-free

-Multiple displacement amplification (MDA)

-Single primer isothermal amplification (SPIA)

Whole Genome/Transcriptome Amplification Technologies

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Multiple displacement amplification (MDA) by QIAGEN

QIAGEN’s REPLI-g technology method

• 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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Comparison of WGA methods for single cell WGS(1)

Genome coverage

(0,1x / 30x)

Cumulative depth

distribution(2)

Consensus genotypes detection efficiency

(30x)

Duplication rate in deep-sequencing

30x

Mean depth (x)

CNV detection sensitivity

CNV detection specificity

DOP-PCR(5) 6 % (0,1 x)23 % (30x) 6 % 6 % 39% 3 x 94% (3) 94 %(3)

MALBAC(6) 8 % (0,1x)82 % (30x) 47 % 52 % 13% 21x 85%(4) 85 %(4)

REPLI-g Single Cell

Kit9 % ( 0,1x)98 % (30x) 82 % 85 % 3,6% 34 x 86% (4) 81 %(4)

Best in class for variations calling!

(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

Optimal solution if SNV and CNV are of similar importance, as in tumor heterogeneity or cell evolution research

Best performance

Medium performance

Lowest performance

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Get the most out of a single cell with Sample to Insight solutions

Complete Sample to Insight Solutions for Single Cell Applications

WGA, WTA or both• REPLI-g portfolio

Cell isolation• Coming soon

Analytical techniques• REPLI-g NGS Library Prep kits• GeneRead Panels• RT2 Profiler PCR arrays• Wide variety of available tools

Data Analysis and Interpretation• CLC bioinformatics software• Ingenuity variant and pathway analysis

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Single Cell Multiple Cells

TissueBloodgDNA RNA

single cell DNA Sequencing

single cell RNA sequencing

REPLI-g Single Cell DNA

Library Kit

REPLI-g Single Cell RNA

Library Kit

NGSLibrary

NGS

single cell DNA analysis

single cell RNA analysis

Comparative analysis of DNA and RNA

(25+ cells)

REPLI-g Single Cell

Kit

REPLI-g WTA Single

Cell Kit

REPLI-g Cell WGA & WTA

Kit

Amplified WTA-DNA or WGA-DNA

NGS

Microarray

qPCR

Choosing a REPLI-g Single Cell Kit for your application

Starting material Application Q solution Kit output Analysis

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REPLI-g for WGA, WTA or Both

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Wide array of applications for single cell analysis

WGAor

WTA

Whole Genome Sequencing• Detect variability in genome sequence (SNV, microsatellites, etc.)• Variability in genome structure (CNV, structural rearrangements,

aneuploidy)• De novo sequencing of new, unidentified and unculturable organisms

Targeted Resequencing• Detect variability in a target set of genes or region of the genome

Microarrays• Use SNP-chips to genotype thousands of loci

mRNA-seq• Detect variability in transcript abundance for all expressed genes• Detect variability in isoform structure and abundance

qRT-PCR profiling• Profile gene expression for a targeted set of transcripts• Accurately quantify specific splice-junctions, isoforms or other structural

features

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REPLI-g advantages

• Minimal background• High yield• Integration with PCR-free NGS library prep• Even coverage (manifests as better assembly, fewer drop-outs, better

transcript detection)• Fewer sequence errors

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Lower background with REPLI-g

Bacterial DNA (2000 copies) was spiked into REPLI-g sc Reaction Buffer, which was then decontaminated using the standard procedure for all buffers and reagents provided with the REPLI-g Single Cell Kit. In subsequent real-time PCR, no bacterial DNA was detectable.

The PCR-free REPLI-g kits offer:• Minimal background:

o Kits are produced to exceptionally high standards and reagents undergo a unique manufacturing process which virtually eliminates any chance of contamination

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High yield: wide range of applications

The PCR-free REPLI-g kits offer:• Minimal background• High Yield:

o Kits produce 10 µg or more of amplified cDNA or gDNA from a single cell

o Library prep kits produce 2-4 nM of PCR-free sequencer-ready whole genome or RNAseq library

Starting Material Typical YieldREPLI-g Single Cell RNA Library Prep Single cell or purified total RNA (50 pg-100 ng) 2-4 nM PCR-free NGS Library

REPLI-g Single Cell DNA Library Prep Single cell or purified gDNA (10 pg-10 ng) 2-4 nM PCR-free NGS Library

REPLI-g Single Cell Single cell or purified gDNA (1-10 ng) 40 µg amplified gDNA

REPLI-g WTA Single Cell Single cell or purified total RNA (10 pg-100 ng) 40 µg amplified poly(A+) cDNA

REPLI-g Cell WGA & WTA 25+ cells

WTA: 10-20 µg, depending on protocolWGA: 20 µg

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Completely PCR-free NGS workflows

The PCR-free REPLI-g kits offer:• Minimal background• High Yield• Integration with PCR-free NGS library prep:

o REPLI-g single cell DNA and RNA library kits produce NGS-ready libraries from a single cell in as little as 5.5 hours

REPLI-g Single Cell DNA Library Kit

Cell lysis15 min

WGA3 h

Shearing and purification30-60 min

End-repair50 min

A-addition40 min

Adapter ligation10 min

Cleanup and size selection

15 min

REPLI-g Single Cell RNA Library Kit

Cell lysis15 min Sequencing

Data AnalysisInterpretation

gDNA Removal10 min

Reverse Transciption

1 hLigation35 min

WTA2 h

One-tube

One-tube

One-tube

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Even coverage in whole genome sequencing

The PCR-free REPLI-g kits offer:• Minimal background• High Yield• Integration with PCR-free NGS library prep• Even Coverage:

o Superior genome coverage due to even amplification: fewer drop-outs, missed loci and more accurate quantification

o Important for NGS as well as traditional applications

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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Higher fidelity: fewer sequencing errors

The PCR-free REPLI-g kits offer:• Minimal background• High Yield• Integration with PCR-free NGS

library prep• Even Coverage• Fewer sequence errors:

o Polymerase has ~1000x better proofreading activity than Taq

o Lack of PCR means errors introduced aren’t propagated

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Key for evaluating SNV

The PCR-free REPLI-g kits offer:• Minimal background• High Yield• Integration with PCR-free NGS

library prep• Even Coverage• Fewer sequence errors

o Polymerase has ~1000x better proofreading activity than Taq

o Lack of PCR means errors introduced aren’t propagated

o ~10x better error rate than MALBAC(1); essential for SNV analysis

REPLI-g SC MALBAC

Total Reads 3 187 060 3 327 084

Mapped reads 3 176 341 (99,66%)

3 276 090 (98,47%)

Not mapped 10 719 (0,34%) 50 994 (1,53%)

Broken read pairs 284 017 (8,91% of total reads)

314 550 (9,45% of total reads)

Covered bases in Reference

98,69% 95,82%

Insertions 6 3

Deletions 0 6

Single-nucleotide variation

0 222

(1) Bourcy et al. (2014) PLoS ONE 9(8): e105585. doi:10.1371/journal.pone.0105585

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Summary

Advantages of single cell analysis over bulk data:• Analyze scarce materials• Account for genomic and transcriptomic heterogeneity

Parts of a single cell workflow:• Obtaining primary sample, detecting and isolating cells of interest• Lysis, WGA or WTA, and variety of molecular biology methods• Data analysis and interpretation

QIAGEN products for single cell analysis

REPLI-g enables single cell applications via:• Minimal background• High yield• Integration with PCR-free NGS library prep• Even coverage (manifests as better assembly, fewer drop-outs, better

transcript detection)• Fewer sequence errors

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Agenda






• Cancer research


• Neuroscience

• Metagenomics


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• Cancer research


• Neuroscience

• Metagenomics


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REPLI-g : Accelerating single cell research

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2009 2010 2011 2012 2013 2014 e20150

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Year

Number of publications (1)

(1) http://scholar.google.com, search term „single cell genomics“ (2) http://scholar.google.com, search term „single cell“ replig 2009– e2015e2015: extrapolated total number for 2015 extrapolated from numbers YTD Sep 2015

Over 450 cumulative publications featuring QIAGEN‘s REPLI-g(2)

Number of single cell genomics publications / year (1)

(1) Van Loo, P. and Voet, T. (2014) Single cell analysis of cancer genomes, T. Current Opinion in Genetics and Development,24:(2) Yao, X. et al. (2014) Tumor cells are dislodged into the pulmonary vein during lobectomy., J Thorac Cardiovasc Surg.148(6)(3) Wang, Y.. et al. (2014) Clonal evolution in breast cancer revealed by single nucleus genome sequencing, Nature 512(4) Zhang, C.-Z. et al. (2015) Chromothripsis from DNA damage in micronuclei. Nature, published online 27 May 2015

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REPLI-g advancing research in many areas like:

Cancer research

Neuroscience or stem cell research

Embryo genetic research

Single cell genomics

Superior variant calling, analysis of SNVs and CNVs and genomic rearrangements. Census-based low-pass single cell seq powered by REPLI-g

Identifying clonal and mutational evolution or structural rearrangements in cancer cells

Rare cell identification and characterization towards liquid biopsy research

Circulating tumor cells

Improving aneuploidy analysis, genome-wide SNP typing and advancing NGS-based approaches

Analysis of cellular functions and mechanisms

Metagenomics Sensitive microbial species profiling from environmental samples, overcoming difficult to culture organisms

Infectious disease, microbial research

Resolving multiple genotype infections, reveal information on relatedness and drug resistance genotypes

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Rare cell identification and characterization (2)

Identifying clonal and mutational evolution (3)

Analysis of genomic rearrangements (4)

Advancing cancer research

REPLI-g cited for:

(1) Van Loo, P. and Voet, T. (2014) Single cell analysis of cancer genomes, T. Current Opinion in Genetics and Development,24:(2) Yao, X. et al. (2014) Tumor cells are dislodged into the pulmonary vein during lobectomy., J Thorac Cardiovasc Surg.148(6)(3) Wang, Y.. et al. (2014) Clonal evolution in breast cancer revealed by single nucleus genome sequencing, Nature 512(4) Zhang, C.-Z. et al. (2015) Chromothripsis from DNA damage in micronuclei. Nature, published online 27 May 2015

Figure Van Loo, P. and Voet, T. (1)

single cell analysis of the cancer genome

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Advancing cancer research: REPLI-g in the literature

Yao, X. et al. (2014) Tumor cells are dislodged into the pulmonary vein during lobectomy. J. Thoracic Cardiovasc. Surg. 148(6), 3224–3331

Aim: Determine the contribution of intraopertive tumor shedding to tumor recurrence, using single cell genetic approaches to distinguish between normal and malignant epithelial cells.

Methods: • WGA using REPLI-g Single Cell Kit• Amplicon sequencing• Library prep• Barcoded pools sequenced• Analysis of copy number variation, nested PCR-based

mutation analysis if single cells and targeted sequencing

Findings: Single cell genetic approaches together with patient-matched normal and tumor tissues can accurately quantify the number of shed tumor cells.

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Wang, Y. et al. (2014) Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160.

Aim: Develop a high-coverage method for whole genome and exome single cell sequencing to study the genomic diversity within tumors

Methods: • MDA was performed on FACS-sorted nuclei using REPLI-g

technology• Sequence libraries were first sequenced at low coverage

depth• Libraries pass QC were selected for full genome or exome

sequencing

Findings: The method shows excellent performance, with uniform coverage, low allelic dropout rates and low false positive error rates for point mutations

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Yee, S.S. et al. (2016) A novel approach for next-generation sequencing of circulating tumor cells. Mol. Gen. Genomic Med. 4(1) doi: 10.1002/mgg3.210

Aim: Develop a method for improved noninvasive detection of evolving tumor mutations

Methods: REPLI-g Single Cell Kit was successfully used for WGA when combined with a multiplex targeted resequencing approach

Findings: Proof of concept study for real-time monitoring of patient tumors using noninvasive liquid biopsies

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NGS-based strategies for improved aneuploidy research (1) and mutation analysis (3)

Genome-wide SNP genotyping for high-resolution molecular cytogenetic analysis (2)

Improving reproductive genetics research

REPLI-g cited for:

(1) Wells, D. et al. (2015) Clinical utilisation of a rapid low-pass whole genome sequencing technique for the diagnosis of aneuploidy in human embryos prior to implantation. J Med Genet. 2014 Aug;51(8)

(2) Thornhill, A.R. et al. (2015) Karyomapping—a comprehensive means of simultaneous monogenic and cytogenetic PGD: comparison with standard approaches in real time for Marfan syndrome, Journal of Assisted Reproduction and Genetics, Volume 32

(3) Xu, J. et al. (2015) Embryo Genome Profiling by single cell Sequencing for Preimplantation Genetic Diagnosis in a β-Thalassemia Family Clinical Chemistry 61:4

(4) Wang, L. et al. (2014) Detection of Chromosomal Aneuploidy in Human Pre-implantation Embryos by Next Generation Sequencing, Biology of Reproduction March 19,2014

Sequencing strategy for assessing chromosome copy number change (4)

Figure taken from Wang, L. (4)

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The analysis of functional mechanisms in neurons by single cell qPCR, following single cell WTA of total RNA from neurons (1), (2)

Advances in neuroscience

REPLI-g cited for:

(1) Jeong, J.H. (2015) Cholinergic neurons in the dorsomedial hypothalamus regulate mouse brown adipose tissue metabolism, MOLECULAR METABOLISM 4

(2) Lee, D. et al. (2015) Apelin-13 Enhances Arcuate POMC Neuron Activity via Inhibiting M-Curren. PLoS ONE 10(3): e0119457.doi:10.1371/journal.pone.0119457

Both studies used REPLI-g WTA Single Cell Kit

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Advances in metagenomics

Metagenomics and microbial single cell genomics

• Recently emerged due to advancements in WGA, NGS and bioinformatics• Publicly available metagenomics data is continuously growing

o Portals: IMG/MG, EBI metagenomics, iMicrobe or MG-RAST• Eloe-Fadrosh et al. discovered and described a new bacterial candidate phylum

(Candidatus Kryptonia) from samples collected from 4 geothermal springso Metagenomics data mining and single cell sampling o REPLI-g Single Cell Kit used for WGA of isolated single bacterial cells

Eloe-Fadrosh, E.A. et al. (2016) Global metagenomic survey reveals a new bacterial candidate phylum in geothermal springs. Nat. Commun. 7, Article number: 10476 doi:10.1038/ncomms10476

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Resolving multiple-genotype malaria infections and revealing information on relatedness and drug resistance genotypes (1)

Virome determination directly on clinical samples by multiplexed whole-genome sequencing from low total virus content samples (2)

Whole genome sequencing as a tool in the diagnosis and characterization of norovirus(3)

Infectious disease / microbial research

REPLI-g cited for:

(1) Nair, S. et al. (2014) single cell genomics for dissection of complex malaria infections, Genome Res. 2014. 24:1028-1038(2) Zoll, J. et al. (2015) Direct multiplexed whole genome sequencing of respiratory tract samples reveals full viral genomic information, Journal of Clinical

Virology 66 (2015) 6–11 (3) Bavelaar, H.H. (2015) Whole genome sequencing of fecal samples as a tool for the diagnosis and genetic characterization of norovirus. J. Clin. Virol.

72, 122.

http://www.qiagen.com/

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Dissecting complex malaria infections

Nair, S. et al. (2014) single cell genomics for dissection of complex malaria infections, Genome Res. 2014. 24:1028-1038

• Described an optimization of a single cell genomics approach for malaria parasites that is applicable to both cultivable and noncultivable malaria species to reveal within-host variation

• The approach included isolation of single infected red blood cells, whole genome amplification and then genotyping and sequencing the parasites using next-generation sequencing

• After analysis of >260 single cell assays, the protocol was validated with coverage equivalent to state-of-the-art single cell methods with >99% accuracy o The high success of the method resulted from optimized procedures that included WGA

with the REPLI-g Mini and Midi Kits combined with a simple freeze-thaw step prior to DNA extraction

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Diagnosis and characterization of norovirus

Bavelaar, H.H. (2015) Whole genome sequencing of fecal samples as a tool for the diagnosis and genetic characterization of norovirus. J. Clin. Virol. 72, 122.

• Noroviruses are classified into 5 genogroups, of which only GI, GII and GIV infect humans. Each genogroup is further divided into multiple genotypes. o GII.4 is extremely recombinant and new strains of this genotype replace old strains

approximately every 2–3 years

• Methods: Direct multiplexed whole genome sequencing on fecal samples from patients with gastroenteritiso Sufficient amounts of RNA were isolated from all samples to perform whole

transcriptome sequencing for the detection of RNA viruses using the REPLI-g WTA Single Cell Kit

• The protocol used by the authors to detect and characterize different types of norovirus from clinical specimens was proven reliable, and the results support the utility of NGS in routine diagnostics.

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Summary

Single cell genomics has become a powerful technology for studying small samples and rare cells and for dissecting complex infections

• Vast numbers of uncultivable species and pathogens are now accessible for genomic analysis

Our dedicated solutions powered by REPLI-g enable you to get the most out of your samples• Streamlined PCR-free workflow takes you from one single cell to a high-quality NGS

library in a single day• Bioinformatics solutions for data analysis and interpretation lets you gain meaningful

biological insights from your NGS data

Single cell analysis has made advances in a number of research areas• Cancer• Neuroscience• Infectious disease• Reproductive genetics• Metagenomics

REPLI-g technology has made a large number of these advances possible

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Single-cell genomics by QIAGEN, 2016 43

Single cell resource site: www.qiagen.com/SingleCellAnalysis

Visit our single cell site for application, product information and supportive material

Including a knowledge hub with publications,

webinars, posters, infographics & our

blog posts

http://www.qiagen.com/SingleCellAnalysis

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Single cell Knowledge Hub

Scientific publications, webinars, videos, white papers, posters, infographics

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Agenda






• Cancer research


• Neuroscience

• Metagenomics


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Agenda






• Cancer research


• Neuroscience

• Metagenomics


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Thank you for attending

Thank you for attending today’s webinar!

Contact QIAGENCall: 1-800-426-8157

Email: [email protected]

Miranda Hanson-Baseler, [email protected]@QIAGEN.com

Questions?

mailto:[email protected]



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Potential challenges observed in WGA or WTA

single cell genomics by QIAGEN, 2016

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REPLI-g overcomes challenges in preamplification



(1) J.Liang et al., single cell Sequencing Technologies: Current and FutureJournal of Genetics and Genomics 41 (2014) 513-528

• High enzyme processivity – no dissociation, pausing or slippage – long reads (>70 kb)• Superior proofreading activity with high-fidelity enzyme – 1000-fold higher fidelity than normal

PCR enzymes (1)

• High yields – get sufficient material for your downstream applications, including NGS, PCR or microarrays

• Optimized lysis and DNA denaturation – immediate amplification across all regions

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Amplification yield and accuracy


Advantages of higher yield and lower error rate

• Archive your single cell for future experiments

• More sensitive variant detection

• Higher confidence in your data


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Coverage uniformity


Advantages of coverage uniformity

• Better de novo genome assembly

• Higher transcript detection rate (in WTA/RNAseq experiments)

• Lower total read number required; higher multiplexing

• Advantageous for low-pass sequencing strategy


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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

advances and applications enabled by single cell technology

Healthcare