Analysis of Single-Cell Sequencing Data by CLC/Ingenuity: Single Cell Analysis Series Part 2
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Single-cell analysis is useful to study genetic heterogeneity between individual cells and can help in result interpretation by looking at the average behavior of a large number of cells. Applications include circulating tumor cells, cells from small biopsies and cells from in vitro fertilized embryos. In this slidedeck, we show how single cell next-generation sequencing data can be analyzed and what challenges needs to be overcome. One of the examples we use is single cell data from two colorectal cancer cell lines.
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Similar a Analysis of Single-Cell Sequencing Data by CLC/Ingenuity: Single Cell Analysis Series Part 2
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Analysis of Single-Cell Sequencing Data by CLC/Ingenuity: Single Cell Analysis Series Part 2
- 1. Sample to Insight Single-Cell Analysis: Sample to Insight Overview, Challenges, Solutions and Case Studies Dr. Anika Joecker, Global Product Manager, QIAGEN Bioinformatics January 2016 1
- 2. Sample to Insight Why single-cell analysis? Single cell analysi s: Sampleto Insight , January 2016 2 • 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
- 3. Sample to Insight QIAGEN's Sample to Insight offering for single-cell analysis Single cell analysi s: Sampleto Insight , January 2016 3 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
- 4. 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/DNAfor: • All downstreamapplications • Storage without degradation Unique decontaminationprocess 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… Single cell analysi s: Sampleto Insight , January 2016 4
- 5. Sample to Insight Single cell analysi s: Sampleto Insight , January 2016 5 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%
- 6. Sample to Insight BiomedicalGenomics Workbench – complex tasks, simply done Streamlined workflows and a rich toolbox to efficiently process data Customize workflows 6 QC reports History Visualization and Validation Single cell analysi s: Sampleto Insight , January 2016
- 7. Sample to Insight Specific functionalityavailable in Biomedical Genomics Workbench Remove Amplicon primers after alignment and remove primer-dimer artifact 7Single cell analysi s: Sampleto Insight , January 2016
- 8. Sample to Insight Variant calling with Biomedical Genomics Workbench Single cell analysi s: Sampleto Insight , January 2016 8 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
- 9. Sample to Insight QIAGEN Bioinformatics Products Streamline Integration Single cell analysi s: Sampleto Insight , January 2016 9 Biomedical Genomics Workbench + Ingenuity Variant Analysis = a strong team! Biomedical Genomics Workbench & Server Product bundle available! ] Prepare ] Sequence ] Data Analysis ] Interpretation
- 10. Sample to Insight Ingenuity Variant Analysis 10 ✓ ✓ ✓ ✓ ✓ Stratification Studies Personal Genome Tumour-Normal Pair Trio/Quad Study GeneticDisease Cohort Large Cancer Studies ✓ ScalableWorkflows Biomedical Genomics Workbench Single cell analysi s: Sampleto Insight , January 2016
- 11. Sample to Insight Case story 1 : REPLI-g vs MALBAC 11Single cell analysi s: Sampleto Insight , January 2016
- 12. Sample to Insight E-coli DH10B 1 pg REPLI-g Single Cell Kit GeneRead Library Prep Kits (I) MiSeq Sequencing (V2, 2X150 nt) Biomedical Genomics Workbench E-coli DH10B 1 pg MALBAC GeneRead Library Prep Kits (I) MiSeq Sequencing (V2, 2X 150nt) Biomedical Genomics Workbench WGA Library construction NGS Data Analysis Case story 1: REPLI-g vs. MALBAC 12Single cell analysi s: Sampleto Insight , January 2016
- 13. Sample to Insight REPLI-g vs. MALBAC – visualized mapping result At randomly differenced Sequence is tend to error REPLI-g SC WGA MALBAC Case story 1: REPLI-g vs. MALBAC 13Single cell analysi s: Sampleto Insight , January 2016
- 14. 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 MALBAC Max 4284 Case story 1: REPLI-g vs. MALBAC 14Single cell analysi s: Sampleto Insight , January 2016
- 15. 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 Case story 1: REPLI-g vs. MALBAC 15Single cell analysi s: Sampleto Insight , January 2016
- 16. Sample to Insight Case story 2 : Sample to Insight 16Single cell analysi s: Sampleto Insight , January 2016
- 17. 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 17Single cell analysi s: Sampleto Insight , January 2016
- 18. 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 0,0 10,0 20,0 30,0 40,0 50,0 60,0 70,0 80,0 chr12_253… chr3_1789… chr5_1121… chr3_1789… chr5_7996… chr11_108… chr5_5624… chr2_2021… chr5_7996… chr5_1121… chr18_509… chr3_3705… chr5_6756… chr5_5622… chr18_509… chr18_504… chr18_509… chr3_3706… chr3_3704… chr18_509… chr18_504… chr3_1432… chr12_523… chr3_1429… chr18_510… chr17_452… chr20_512… chr17_451… chr3_3708… chr5_6758… chr18_508… chr3_3709… chr7_1404… chr7_1404… chr7_1404… chr18_505… chr7_7716… chr11_108… 18 Zhong Wu, Katrin Knoll, Christian Korfhage, Frank Narz, Ravi VijayaSatya, Yexun Wang and Eric Lader. Single cell mutation detection with multiplex PCR-basedtargeted enrichment sequencing (Poster presentationASHG 2014) Allele Frequency for Heterozygous Sites (LoVo)Frequency(%) Single cell analysi s: Sampleto Insight , January 2016
- 19. 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
- 20. Sample to Insight Combining single cell data helps to overcome amplification bias and helps to identify major drivers 20 Ingenuity Variant Analysis shows a clear separation between LoVo single cells and HT29 cells Single cell analysi s: Sampleto Insight , January 2016
- 21. Sample to Insight Summary 21 • 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 Single cell analysi s: Sampleto Insight , January 2016
- 22. Sample to Insight Outlook 22 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 Single cell analysi s: Sampleto Insight , January 2016
- 23. 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.
Notas del editor
- Is part of QIAGEN’s sample to insight workflow! Analyze QIAGEN GeneRead DNASeq Amplicon Panel data with one click! Streamlined in a solution with Ingenuity Variant Analysis (IVA) & Ingenuity Pathway Analysis (IPA) Fast, intuitive and easy-in-use Includes comprehensive end-to-end analysis workflows for single samples or cohort studies Accurate and trustworthy results Fast and easy analysis of Whole Genome, Whole Exome, Targeted Amplicon, Whole Transcriptome Sequencing, Chip-Seq data and the combination of these kinds of data Flexible & customizable All ready-to-use workflows can be customized Build you own workflows! Validation and Visualization of results Visualization of Variants in protein 3D structure Genome Browser style output & QC reports Specific functionalities for human disease data analysis Sample genotyping with a list of known variants CNV as well as insertion and deletion detection