analysis of single-cell sequencing data by clc/ingenuity: single cell analysis series part 2

Sample to Insight

January 2016 1

Single-Cell Analysis: Sample to Insight Overview, Challenges, Solutions and Case StudiesDr. Anika Joecker, Global Product Manager, QIAGEN Bioinformatics

Sample to Insight

2

Why single-cell analysis?

Single cell analysi s: Sample to Insight , January 2016

• Study genetic heterogeneity between individual cells: copy number alterations, SNPs and differences in gene expression

• Applications:

o Circulating tumor cells (CTCs)

o Cells from small biopsies

o Cells from in vitro fertilized embryos

• Avoid cultivating cells that will change their behavior

• Eliminate result interpretation based on the average behavior of a larger number of cells

Sample to Insight

Single cell analysi s: Sample to Insight , January 2016 3

QIAGEN's Sample to Insight offering for single-cell analysis

Ingenuity Variant Analysis

QIAGEN® Clinical Insight

Ingenuity Pathway Analysis

HGMD®

Biomedical Genomics

Workbench

&

Biomedical Genomics Server

Solution

GeneRead Library Prep Kits

GeneRead DNAseq Panel or custom panels

REPLI-g® Single Cell Kits

rRNA Depletion Kits

QuantiMIZE Kit

Any Sequencer

Sample to Insight

Whole genome amplification (WGA)

Multiple displacement amplification (MDA) technology: • Isothermal amplification (30°C)• 1000-fold higher fidelity than Taq• Long fragments (2–70 kb)• Minimized sequence bias

Start directly from: • Cells, tissue• DNA, RNA Deliver amplified cDNA/DNA for:• All downstream applications• Storage without degradationUnique decontamination process

REPLI-g sc or REPLI-g SensiPhi polymerase

Optimized reagents and buffers

Multiple displacement amplification (MDA) technology

REPLI-g product family:• REPLI-g Single Cell

Kit• REPLI-g WTA Single

Cell Kit• REPLI-g Cell WGA &

WTA Kit• and more…


Sample to Insight


GeneRead DNAseq Targeted Panel V2

• Compatible with multiple sample types

• Requires as little as 10 ng DNA

• Can be used on any sequencing platform

Detecting low frequency variants

Analysis of genetic variants from a focused panel of genes via next-generation sequencing

• Unbiased amplification with an optional, high-fidelity amplification step

• High yields from minimal amounts of starting material

• Single-tube workflow saves time by 50%

Sample to Insight

Biomedical Genomics Workbench – complex tasks, simply done

Streamlined workflows and a rich toolbox to efficiently process data

Customize workflows

6

QC reports

History

Visualization and Validation


Sample to Insight

Specific functionality available in Biomedical Genomics Workbench

Remove Amplicon primers after alignment and remove primer-dimer artifact

7Single cell analysi s: Sample to Insight , January 2016

Sample to Insight


Variant calling with Biomedical Genomics Workbench

Accuracy for calling germline variants using the Genome in a Bottle gold standard dataset

Accuracy for calling 5% low frequency variants using a dilution series

Sample to Insight


QIAGEN Bioinformatics Products Streamline Integration

9

Biomedical Genomics Workbench + Ingenuity Variant Analysis = a strong team!

Biomedical Genomics Workbench & Server

Product bundle available!

] Prepare

] Sequence

] Data Analysis

] Interpretation

Sample to Insight

10

Ingenuity Variant Analysis

Stratification Studies

Personal GenomeTumour-Normal Pair

Trio/Quad StudyGenetic Disease Cohort

Large Cancer Studies

Scal

able

Wor

kflow

s

Biomedical Genomics Workbench


Sample to Insight

11

Case story 1 : REPLI-g vs MALBAC


Sample to Insight

　

E-coli DH10B1 pg

REPLI-g Single Cell Kit

GeneRead Library Prep Kits (I)

MiSeq Sequenc ing(V2, 2X150 nt)


E-coli DH10B 1 pg

MALBAC

GeneRead Library Prep Kits (I)

MiSeq Sequencing(V2, 2X 150nt)


WGA

Libraryconstruction

NGS

Data Analysis

Case story 1: REPLI-g vs. MALBAC


Sample to Insight

REPLI-g vs. MALBAC – visualized mapping result

At randomly differenced Sequence is tend to error

REPLI-g SC WGA

MALBAC



Sample to Insight

MALBAC has a very high coverage around 4.31M (5000 coverage). However, the coverage is not uniform.

REPLI-g 　 coverage Max 3,000

MALBAC 　 coverage Max 3,000

REPLI-g 　Max 153

MALBACMax 4284



Sample to Insight

Number of false positive mutations ： 6 insertions

Number of false positive mutations ： 231 ( 222 SNPs, 6 deletions and 3 insertions)

REPLI-g Single Cell Kit (WGA)

MALBAC



Sample to Insight

16

Case story 2 : Sample to Insight


Sample to Insight

Single cell analysis of colorectal cancer cell lines, HT29 and LoVo

Case study 2: Complete sample to insight workflow

Whole Genome Amplification

Sample Nr. Single/Bulk

1 Bulk

2 Bulk WGA

3 Single

4 Single

5 Single

6 Single

Sample Nr. Single/Bulk

7 Bulk

8 Bulk WGA

9 Single

10 Single

11 Single

12 Single

LoVo HT29


Sample to Insight

• The majority of heterozygous SNP frequency in bulk cell samples was around 50% (as expected)

• No difference between bulk cell DNA and bulk cell DNA that underwent WGA

chr1

2_25

39...

chr3

_178

91...

chr3

_1789

2...

chr5

_5623

3551

chr5

_5624

2774

chr1

1_10

815..

.

chr5

_112

17...

chr1

8_507

3...

chr5

_675

6974

6

chr5

_8007

4604

chr1

8_504

5...

chr5

_799

5239

0

chr3

_3704

8633

chr1

8_507

3...

chr3

_1432

9...

chr4

_463

2972

3

chr1

8_510

5...

chr5

_145

84...

chr1

7_45

19...

chr1

8_453

7...

chr1

8_50

83...

chr7

_140

54...

chr7

_140

44...

chr7

_1404

7...

chr7

_771

6695

20.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

18

Zhong Wu, Katrin Knoll, Christian Korfhage, Frank Narz, Ravi Vijaya Satya, Yexun Wang and Eric Lader. Single cell mutation detection with multiplex PCR-based targeted enrichment sequencing (Poster presentation ASHG 2014)

Allele Frequency for Heterozygous Sites (LoVo)Fr

eque

ncy

(%)


Sample to Insight

Pathogenic variants were detected to 100%

Different amplification of alleles lead to pathogenic variants, which are present in just a very low number of sequencing reads. Therefore low frequency variant detection is necessary to identify all of them.

Single-Cell Mutation Detection – Overcoming Challenges in Single-Cell Analysis 19

Sample to Insight

20

Combining single cell data helps to overcome amplification bias and helps to identify major drivers

Ingenuity Variant Analysis shows a clear separation between LoVo single cells and HT29 cells


Sample to Insight

21

Summary

• Every part in a single cell workflow can introduce bias

• High quality results are important for all steps in the sample to insight workflow

• In two studies we have shown that QIAGEN’s kits and reagents combined with QIAGEN’s Bioinformatics can produce accurate results

• Allele amplification bias introduced in the DNA amplification step leads to low frequency variants, which are normally missed by other pipelines. Biomedical Genomics Workbench can identify these variants

• Variant frequencies between QIAGEN’s NGS sample to insight single cell workflow and PyroMark were strikingly consistent showing the accuracy of the variant identification step

• By looking at many single cells together against a control group, major cancer drivers can be identified and amplification bias can be reduced


Sample to Insight

22

Outlook

Improving amplification bias removal for even better results

• Normalization of variant frequencies across a larger region

• Phasing of variants to longer stretches to identify contamination and help in normalization of variant frequencies


Sample to Insight

23

Thank you!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.

analysis of single-cell sequencing data by clc/ingenuity: single cell analysis series part 2

Healthcare