Advances and Applications Enabled by Single Cell Technology

Sample to Insight
Advances and applications enabled by single cell
technology
1
Miranda Hanson-Baseler, Ph.D.
Miranda.Hanson-Baseler@qiagen.com

Sample to Insight
Legal disclaimer
2
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.

Sample to Insight
Agenda
3
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
Questions
1
2
3

Sample to Insight
Agenda
4
• Cancer research
• Neuroscience
• Metagenomics
Questions
1
2
3

Sample to Insight
5
o Scarce sample
o Genome heterogeneity
o Transcriptome heterogeneity
o Statistical power
o Cell isolation
o WGA or WTA
o Analytical techniques
o Data analysis
• QIAGEN products for single cell analysis

Sample to Insight
You start with only one single cell
6
• 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

Sample to Insight
Cells differ on the genome level
7
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 Aneuploidy
o Structural rearrangements
o Copy number variations
o 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
adults
o Middle age: in 2.2% of cells
o 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

Sample to Insight
Similar cells – unique transcriptional patterns
8
Cells change their transcription pattern:
• The transcriptome of a cell is not fixed but
dynamic
• The transcriptome reflects the
o Function of the cell
o Type of the cell
o 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 data
o Allelic expression data
o 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

Sample to Insight
Averaging averages
9
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 2
Population
Mean 2
1
Single Cell
Analysis
Population
Mean 1
Mean=0.969
Stdev=1.470
Sample Size=32
SEM=0.260
1 1
1
1 1
1 1
1 1
1 1
1 1
1 1
1
Mean=0.969
Stdev=0.048
Sample Size=2
SEM=0.031
Bulk
Approach

Sample to Insight
Single cell analysis enables new insights
10
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
cells
Analysis 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

Sample to Insight
Single cell workflow overview
11
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

Sample to Insight
Single cell genomics – the workflow
12
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

Sample to Insight
Technologies for DNA or RNA preamplification
13
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

Sample to Insight
Multiple displacement amplification (MDA) by QIAGEN
14
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

Sample to Insight
Comparison of WGA methods for single cell WGS(1)
15
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
Kit
9 % ( 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

Sample to Insight
Get the most out of a single cell with Sample to Insight solutions
16
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

Sample to Insight
Single Cell
Multiple Cells
Tissue
Blood
gDNA
RNA
single cell DNA
Sequencing
single cell RNA
sequencing
REPLI-g Single
Cell DNA
Library Kit
REPLI-g Single
Cell RNA
Library Kit
NGS
Library
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
17
Choosing a REPLI-g Single Cell Kit for your application
Starting material Application Q solution Kit output Analysis

Sample to Insight
REPLI-g for WGA, WTA or Both
18

Sample to Insight
Wide array of applications for single cell analysis
19
WGA
or
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

Sample to Insight
REPLI-g advantages
20
• 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

Sample to Insight
Lower background with REPLI-g
21
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

Sample to Insight
High yield: wide range of applications
22
• 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 Yield
REPLI-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
protocol
WGA: 20 µg

Sample to Insight
Completely PCR-free NGS workflows
23
• 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 lysis
15 min
WGA
3 h
Shearing and
purification
30-60 min
End-repair
50 min
A-addition
40 min
Adapter
ligation
10 min
Cleanup and
size selection
15 min
REPLI-g Single Cell RNA Library Kit
Cell lysis
15 min
Sequencing
Data Analysis
Interpretation
gDNA
Removal
10 min
Reverse
Transciption
1 h
Ligation
35 min
WTA
2 h
One-tube
One-tube
One-tube

Sample to Insight
Even coverage in whole genome sequencing
24
• High Yield
• 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.)

Sample to Insight
Higher fidelity: fewer sequencing errors
25
• 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

Sample to Insight
Key for evaluating SNV
26
• High Yield
• Integration with PCR-free NGS
library prep
• Even Coverage
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

Sample to Insight
Summary
27
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:
• High yield
• Even coverage (manifests as better assembly, fewer drop-outs,
better transcript detection)

Sample to Insight
Agenda
28
• Cancer research
• Neuroscience
• Metagenomics
Questions
1
2
3

Sample to Insight
Agenda
29
• Cancer research
• Neuroscience
• Metagenomics
Questions
2
1
3

Sample to Insight
REPLI-g : Accelerating single cell research
30
0
100
200
300
400
500
600
700
800
2009 2010 2011 2012 2013 2014 e2015
Number of
publications (1)
Year
(1) http://scholar.google.com, search term „single cell genomics“
(2) http://scholar.google.com, search term „single cell“ replig 2009– e2015
e2015: 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

Sample to Insight
REPLI-g advancing research in many areas like:
31
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

Sample to Insight
Rare cell identification and characterization (2)
Identifying clonal and mutational evolution (3)
Analysis of genomic rearrangements (4)
Advancing cancer research
32
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

Sample to Insight
Advancing cancer research: REPLI-g in the literature
33
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.

Sample to Insight
34
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

Sample to Insight
35
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

Sample to Insight
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
36
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)

Sample to Insight
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
37
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

Sample to Insight
Advances in metagenomics
38
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 springs
o 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

Sample to Insight
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
39
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.

Sample to Insight
Dissecting complex malaria infections
40
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

Sample to Insight
Diagnosis and characterization of norovirus
41
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
gastroenteritis
o 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.

Sample to Insight
Summary
42
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
• Metagenomics
REPLI-g technology has made a large number of these advances possible

Sample to Insight
Single cell resource site: www.qiagen.com/SingleCellAnalysis
Single-cell genomics by QIAGEN, 2016 43
Visit our single cell site for application, product information and supportive material
Including a knowledge
hub with publications,
webinars, posters,
infographics & our
blog posts

Sample to Insight
Single cell Knowledge Hub
44
Scientific publications, webinars, videos, white papers, posters, infographics

Sample to Insight
Agenda
45
• Cancer research
• Neuroscience
• Metagenomics
Questions
2
1
3

Sample to Insight
Agenda
46
• Cancer research
• Neuroscience
• Metagenomics
Questions3
1
2

Sample to Insight
Thank you for attending
47
Thank you for attending today’s webinar!
Contact QIAGEN
Call: 1-800-426-8157
Email: BRCsupport@QIAGEN.com
Miranda Hanson-Baseler, PhD
Miranda.Hanson-Baseler@QIAGEN.com
QIAwebinars@QIAGEN.com
Questions?

Sample to Insight
Potential challenges observed in WGA or WTA
single cell genomics by QIAGEN, 2016 48

Sample to Insight
REPLI-g overcomes challenges in preamplification
1 pg DH10B DNA, amplified with either REPLI-g Single Cell Kit or by MALBAC, sequenced on MiSeq
Illumina (V2, 2x150nt.)
(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

Sample to Insight
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
1 pg DH10B DNA, amplified with either REPLI-g Single Cell Kit or by MALBAC,
sequenced on MiSeq Illumina (V2, 2x150nt.)

Sample to Insight
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
1 pg DH10B DNA, amplified with either REPLI-g Single Cell Kit or by
MALBAC, sequenced on MiSeq Illumina (V2, 2x150nt.)

Sample to Insight
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

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (11)

Similar to Advances and Applications Enabled by Single Cell Technology

Similar to Advances and Applications Enabled by Single Cell Technology (20)

More from QIAGEN

More from QIAGEN (20)

Recently uploaded

Recently uploaded (20)

Advances and Applications Enabled by Single Cell Technology

Editor's Notes