Digital PCR Workshop with the QuantStudioTM 3D

Hervé Pouzoullic
Life Technologies

Digital PCR is an extremely promising method for increasing accuracy, precision and sensitivity of nucleic acid quantification beyond the capabilities of traditional real-time qPCR. This is done by combining PCR assays with single molecule sensitivity with a system to partition a sample into a set of reactions which number close to, or more than, the total number of target molecules in that sample. The combination of these functions makes it possible to calculate the number of target molecules present by counting the number of reactions with or without amplification. Many novel applications are enabled by this new approach, including reference free absolute quantification and high sensitivity rare-allele quantification. We will be presenting a live demonstration of the QuantStudio™ 3D Digital PCR System, a new digital PCR platform from Life Technologies™. This chip based system enables collection of up to 20,000 data points per sample run. The workflow has been optimized for simplicity, minimizing hands-on time, minimizing the risk of sample cross contamination, and minimizing sample loss. In this workshop, we will review the basics of digital PCR theory and practice, discuss the capabilities of the QuantStudio™ 3D Digital PCR System, and demonstrate the steps involved in a full digital PCR experiment, including chip loading, running, imaging and data analysis.

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GenEx – the ultimate tool for qPCR data analysis

Mikael Kubista1,2, Amin Forootan2,3, Björn Sjögren3,4
1TATAA Biocenter, Sweden; 2MultiD Analyses AB; 3Goteborg University; 4Lawrence Livermore National Laboratory

As qPCR is becoming mature technology with widespread use requirements on validation, quality assessment and reporting increases. This is particularly important when data are submitted for approval by regulatory bodies, reported to clients, or published in quality journals. For molecular diagnostic applications this includes determining PCR efficiency with confidence intervals, establishing the linear range of the assay, its limit of quantification, limit of detection and random error. For routine applications also estimates of repeatability and reproducibility may be relevant. For the samples analyzed estimated target concentrations shall be indicated with confidence intervals. These analyses are recommended by the Clinical and Laboratory Standards Institute. In my presentation I will show how these analyses are performed on qPCR using GenEx.
Goal of expression profiling is to explain biological phenomena. Workflow starts by planning, designing and optimizing an experiment, collecting the data, analyzing the data, and extracting biological information. Typically large amounts of data are collected that are batch imported and pre-processed to remove variation between runs, reduce intersubject variation, and minimize technical noise. Missing data are also handled. The data are then analyzed using powerful multivariate statistical methods including hierarchical clustering, principal component analysis (PCA), and self-organized maps. Dynamic PCA is used for variable selection to identify the most relevant genes explaining the observations. Finally, the data are passed from GenEx for cloud based pathway analysis with the Ingenuity iReport. In will present seamless workflow from the collection of data to the extraction of biological information using GenEx.

Download a free GenEx trial version on

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Start Making Sense – NGS Data Analysis with Genomatix

Christian Zinser
Genomatix Software GmbH, Germany

The ongoing evolution of Next Generation Sequencing is revealing ever more of the complexity of genomes, gene expression, and gene regulation. To harness the considerable potential of this technology for creating biologically relevant information, data generation must be matched by analysis tools and strategies which integrate and consolidate the available lines of evidence into scientifically interpretable results.
This talk provides an overview of the solutions offered by Genomatix for the analysis of Next Generation Sequencing data.
The presentation will focus on the following analysis methods:
Assessment of genomic variants; Expression and transcript fusion analysis; Examination of regulatory features: protein-DNA binding and DNA methylation; Data integration employing positional correlation, genome annotation, biological classification, regulatory pathways, and gene networks
Examples will include the application of the above to the study of cancer, the identification of the basis of hereditary diseases, and the elucidation of disease-relevant regulatory networks.

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qbasePLUS to speed up the analysis of your qPCR data and to improve the accuracy of your experiments

Barbara D’haene
Biogazelle, Belgium

Are you struggling to get your qPCR data-analysis right? Do you want to speed up your analysis?
Join Barbara D’haene, PhD, for a lunch talk and get access to qbasePLUS.
During this session Barbara will show how to analyse a qPCR experiment using qbasePLUS. The key points demonstrated will be quality control, normalization and easy biostatistical analysis.
qbasePLUS is based on the proven geNorm and qBase technology. The software is developed at Biogazelle by recognized qPCR experts Jo Vandesompele and Jan Hellemans. Biogazelle is a young and dynamic PCR company, eager to accelerate the discoveries in the PCR community.

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Lunch seminars

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Single-Cell Digital Gene Expression On Up To 800 Unique Transcripts Using Optically-Barcoded Single-Nucleic Acid Counting: Comparison With Microfluidic qPCR And RNA-seq (Whole Transcriptome)

Michael Rhodes
Nanostring Technologies, United Kingdom

True digital (i.e., counting based) multiplexed gene expression can currently only be performed using RNA-seq or optically-barcoded single-nucleic-acid counting (nCounter technology). We demonstrate that a simple modification to the nCounter protocol enables digital quantification of 800 unique transcripts in a single cell, offers several potential advantages relative to single-cell microfluidic PCR, and much better counting statistics than single-cell (or normal input) RNA-seq (when compared to whole transcriptome). The nCounter Single Cell protocol incorporates reverse transcription and linear pre-amplification (10 to 18 cycles) with a highly multiplexed pool of up to 800 gene-specific primer pairs in a single tube, followed by hybridization with optically-barcoded nucleic-acid-labels. Microfluidic qPCR methods require the same pre-amplification step, but must be followed by splitting the amplified sample into 96 separate wells and performing an additional series of up to 40 PCR amplification cycles. nCounter technology requires no sample-splitting (a true multiplex) or additional amplification cycles and (compared to RNA-seq) doesn’t require library generation because single-molecules are counted directly. Gene-expression measurements of flow-sorted single-cells using nCounter, revealed the stochastic “on-off” behavior. The “summed” (aggregate) gene expression profile from multiple individual flow-sorted cells was (essentially) identical to pools of multiple flow-sorted cells (10 per tube and 100 per tube), proving digital linearity of 800-targets at the single-cell level for the first time. When comparing to RNA-seq (whole transcriptome), nCounter (on panels of 100’s-of-genes) resolved a constant ~ 2 million on-target reads per sample (~10,000X coverage), compared with < 100,000 on-target reads for RNA-seq (as expected for a non-targeted approach). Hundreds-to-thousands of single-cells, 800 targets-each, can be examined per-week on an nCounter system: enabling single-cell digital biology.

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Visualizing gene expression at the single cell, single chromosome, single RNA, and single base level

Marshall Levesque, Arjun Raj
Biosearch Technologies Inc., United States of America

We use fluorescence in situ hybridization to provide highly specific and direct detection of individual RNA molecules via fluorescence microscopy in their natural context in cells and tissues. This method enables absolute quantification of gene expression in single cells, and we discuss how to interpret these measurements. We highlight the detection and localization of long non-coding RNAs. We also have extended our assay to detect chromosome structure and gene expression at the same time, enabling per-chromosome transcriptional profiling. We also present a method that allows us to measure single-base differences on individual RNA molecules.

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Quantitative PCR Analysis of DNA, RNAs, and Proteins in the Same Single Cell

Anders Ståhlberg
University of Gothenburg, Sweden

The single cell represents the basic unit of all organisms. Most investigations have been performed on large cell populations, but understanding cell dynamics and heterogeneity requires single-cell analysis. Current methods for single-cell analysis generally can detect only one class of analytes. Reverse transcription and the proximity ligation assay were coupled with quantitative PCR and used to quantify any combination of DNA, mRNAs, microRNAs (miRNAs), noncoding RNAs (ncRNAs), and proteins from the same single cell. The method has been demonstrated on transiently transfected human cells to determine the intracellular concentrations of plasmids, their transcribed mRNAs, translated proteins, and downstream RNA targets. We developed a whole-cell lysis buffer to release unfractionated DNA, RNA, and proteins that would not degrade any detectable analyte or inhibit the assay. The dynamic range, analytical sensitivity, and specificity for quantifying DNA, mRNAs, miRNAs, ncRNAs, and proteins were shown to be accurate down to the single-cell level. Correlation studies revealed that the intracellular concentrations of plasmids and their transcribed mRNAs were correlated only moderately with translated protein concentrations (Spearman correlation coefficient, 0.37 and 0.31, respectively; P < 0.01). In addition, an ectopically expressed gene affected the correlations between analytes and this gene, which is related to gene regulation. This method is compatible with most cell-sampling approaches, and generates output for the same parameter for all measured analytes, a feature facilitating comparative data analysis. This approach should open up new avenues in molecular diagnostics for detailed correlation studies of multiple and different classes of analytes at the single-cell level.We will also discuss how single-cell data can be used to gain detailed information about cell types and cell states.

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Genome Analysis Of Individual Cells

Christian Korfhage
QIAGEN GmbH, Germany

DNA sequence analysis and genotyping of biological samples using next-generation sequencing (NGS), microarrays, or real-time PCR is often limited by the small amount of sample available. A single cell comprises only one to four copies of the genomic DNA, depending on the organism (haploid or diploid organism) and the cell cycle phase. The DNA amount of a single cell ranges from a few femtograms in bacteria to picograms in mammalia. In contrast, a deep analysis of the genome requires a few hundred nanograms up to micrograms of genomic DNA. Consequently, accurate whole genome amplification (WGA) of single cell DNA is required for reliable genetic analysis (e.g., NGS) and is particularly important when genomic DNA is limited, as in single cell WGA. The use of single-cell WGA has enabled the analysis of genomic heterogeneity of individual cells (e.g., somatic genomic variation in tumor cells).
To perform single cell WGA, we used the QIAGEN® REPLI-g® Single Cell Kit, which uses a method based on isothermal multiple displacement amplification (MDA). This technique is capable of accurate in vitro DNA replication of a single whole genome directly from single cells due to innovative lysis and the use of an optimized form of the Phi 29 Polymerase with:
(1) Proofreading activity (up to 1000-fold lower error rates compared to Taq polymerase), (2) Strong processivity (resulting in minimal enzyme dissociation at difficult structures such as GC rich regions), (3) Strong DNA displacement activity (resulting in solving hairpin structures).
Here, we describe the reliability of this single cell WGA method and its application to next-generation sequencing (NGS) and real-time PCR.

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