Performance of a TthPrimPol-based whole genome amplification kit for copy number alteration detection using massively parallel sequencing.
ABSTRACT: Starting from only a few cells, current whole genome amplification (WGA) methods provide enough DNA to perform massively parallel sequencing (MPS). Unfortunately, all current WGA methods introduce representation bias which limits detection of copy number aberrations (CNAs) smaller than 3?Mb. A recent WGA method, called TruePrime single cell WGA, uses a recently discovered DNA primase, TthPrimPol, instead of artificial primers to initiate DNA amplification. This method could lead to a lower representation bias, and consequently to a better detection of CNAs. The enzyme requires no complementarity and thus should generate random primers, equally distributed across the genome. The performance of TruePrime WGA was assessed for aneuploidy screening and CNA analysis after MPS, starting from 1, 3 or 5 cells. Although the method looks promising, the single cell TruePrime WGA kit v1 is not suited for high resolution CNA detection after MPS because too much representation bias is introduced.
Project description:Current whole genome amplification (WGA) methods lead to amplification bias resulting in over- and under-represented regions in the genome. Nevertheless, certain WGA methods, such as SurePlex and subsequent arrayCGH analysis, make it possible to detect copy number alterations (CNAs) at a 10?Mb resolution. A more uniform WGA combined with massive parallel sequencing (MPS), however, could allow detection at higher resolution and lower cost. Recently, MALBAC, a new WGA method, claims unparalleled performance. Here, we compared the well-established SurePlex and MALBAC WGA for their ability to detect CNAs in MPS generated data and, in addition, compared PCR-free MPS library preparation with the standard enrichment PCR library preparation. Results showed that SurePlex amplification led to more uniformity across the genome, allowing for a better CNA detection with less false positives compared to MALBAC amplified samples. An even more uniform coverage was observed in samples following a PCR-free library preparation. In general, the combination of SurePlex and MPS led to the same chromosomal profile compared to a reference arrayCGH from unamplified genomic DNA, underlining the large potential of MPS techniques in CNA detection from a limited number of DNA material.
Project description:Whole genome amplification (WGA) has become an invaluable tool to perform copy number variation (CNV) detection in single, or a limited number of cells. Unfortunately, current WGA methods introduce representation bias that limits the detection of small CNVs. New WGA methods have been introduced that might have the potential to reduce this bias. We compared the performance of PicoPLEX DNA-Seq (Picoseq), DOPlify, REPLI-g and Ampli-1 WGA for aneuploidy screening and copy number analysis using shallow whole genome massively parallel sequencing (MPS), starting from single or a limited number of cells. Although the four WGA methods perform differently, they are all suited for this application.
Project description:DNA copy number aberration (CNA) is very important in the pathogenesis of tumors and other diseases. For example, CNAs may result in suppression of anti-oncogenes and activation of oncogenes, which would cause certain types of cancers. High density single nucleotide polymorphism (SNP) array data is widely used for the CNA detection. However, it is nontrivial to detect the CNA automatically because the signals obtained from high density SNP arrays often have low signal-to-noise ratio (SNR), which might be caused by whole genome amplification, mixtures of normal and tumor cells, experimental noise or other technical limitations. With the reduction in SNR, many false CNA regions are often detected and the true CNA regions are missed. Thus, more sophisticated statistical models are needed to make the CNAs detection, using the low SNR signals, more robust and reliable.This paper presents a conditional random pattern (CRP) model for CNA detection where much contextual cues are explored to suppress the noise and improve CNA detection accuracy. Both simulated and the real data are used to evaluate the proposed model, and the validation results show that the CRP model is more robust and reliable in the presence of noise for CNA detection using high density SNP array data, compared to a number of widely used software packages.The proposed conditional random pattern (CRP) model could effectively detect the CNA regions in the presence of noise.
Project description:BACKGROUND: Some array comparative genomic hybridisation (array CGH) platforms require a minimum of micrograms of DNA for the generation of reliable and reproducible data. For studies where there are limited amounts of genetic material, whole genome amplification (WGA) is an attractive method for generating sufficient quantities of genomic material from miniscule amounts of starting material. A range of WGA methods are available and the multiple displacement amplification (MDA) approach has been shown to be highly accurate, although amplification bias has been reported. In the current study, WGA was used to amplify DNA extracted from whole blood. In total, six array CGH experiments were performed to investigate whether the use of whole genome amplified DNA (wgaDNA) produces reliable and reproducible results. Four experiments were conducted on amplified DNA compared to unamplified DNA and two experiments on unamplified DNA compared to unamplified DNA. FINDINGS: All the experiments involving wgaDNA resulted in a high proportion of losses and gains of genomic material. Previously, amplification bias has been overcome by using amplified DNA in both the test and reference DNA. Our data suggests that this approach may not be effective, as the gains and losses introduced by WGA appears to be random and are not reproducible between different experiments using the same DNA. CONCLUSION: In light of these findings, the use of both amplified test and reference DNA on CGH arrays may not provide an accurate representation of copy number variation in the DNA.
Project description:Whole genome amplification (WGA) is required for single cell genotyping. Effectiveness of currently available WGA technologies in combination with next generation sequencing (NGS) and material preservation is still elusive.In respect to the accuracy of SNP/mutation, indel, and copy number aberrations (CNA) calling, the HiSeq2000 platform outperformed IonProton in all aspects. Furthermore, more accurate SNP/mutation and indel calling was demonstrated using single tumor cells obtained from EDTA-collected blood in respect to CellSave-preserved blood, whereas CNA analysis in our study was not detectably affected by fixation. Although MDA-based WGA yielded the highest DNA amount, DNA quality was not adequate for downstream analysis. PCR-based WGA demonstrates superiority over MDA-PCR combining technique for SNP and indel analysis in single cells. However, SNP calling performance of MDA-PCR WGA improves with increasing amount of input DNA, whereas CNA analysis does not. The performance of PCR-based WGA did not significantly improve with increase of input material. CNA profiles of single cells, amplified with MDA-PCR technique and sequenced on both HiSeq2000 and IonProton platforms, resembled unamplified DNA the most.We analyzed the performance of PCR-based, multiple-displacement amplification (MDA)-based, and MDA-PCR combining WGA techniques (WGA kits Ampli1, REPLI-g, and PicoPlex, respectively) on single and pooled tumor cells obtained from EDTA- and CellSave-preserved blood and archival material. Amplified DNA underwent exome-Seq with the Illumina HiSeq2000 and ThermoFisher IonProton platforms.We demonstrate the feasibility of single cell genotyping of differently preserved material, nevertheless, WGA and NGS approaches have to be chosen carefully depending on the study aims.
Project description:Establishment and subsequent validation of a aCGH protocol for WGA (whole genome amplification) products originating from single cell or low amount of starting material (i.e. microdissected FFPE tissue samples). The establishment of the protocol involved testing of three DNA labeling protocols. Two labeling protocols were designed specifically for Ampli1(TM) WGA products. Additionally random primed isothermal (Klenow-based) labeling approach was tested (Möhlendick et al., PLoS One. 2013 Jun 25;8(6):e67031.). In addition two different types of reference samples were tested and reference based on single-cell WGA products was chosen as most suitable in the end. The validation of the protocol assessed the following aspects: (1) performance of the protocol on primary and reamplified WGA products, (2) accuracy of the protocol in term of sensitivity of the CNA detection, (3) accuracy in terms of recapitulation of complex patterns of CNAs, (4) accuracy in terms of quantitative assessment of the CNAs, (5) ability to detect genomic heterogeneity of single cells (obtained either from in vitro cultures or from clinical patient material), (6) ability to detect minimal regions of aberration within a panel of disseminated cancer cells and corresponding tumor tissues. Establishment and validation of the single-cell aCGH protocol: two condition experiment (i.e. PCR-based labeling technique 1 vs. PCR-based labeling technique 2; PCR-based labeling technique 2 vs. random-primed isothermal (Klenow) labeling approach; reference DNA from cell pool WGA product vs. reference DNA from single-cell WGA products). Validation of the protocol: comparison of the CNA profiles between single-cell WGA products and corresponding bulk DNA. Analysis of the DCC and corresponding FFPE tumor tissue samples: single-condition experiment performed on samples collected at different stages of the disease (DCCs from bone marrow) and/or from different sites (primary tumor-breast; metastasis-lymph nodes; DCCs-bone marrow). Microarray data is corresponding data depicted in the paper manuscript titled: Reliable single cell array CGH for clinical samples
Project description:Sequencing analysis of circulating tumor cells (CTCs) enables "liquid biopsy" to guide precision oncology strategies. However, this requires low-template whole genome amplification (WGA) that is prone to errors and biases from uneven amplifications. Currently, quality control (QC) methods for WGA products, as well as the number of CTCs needed for reliable downstream sequencing, remain poorly defined. We sought to define strategies for selecting and generating optimal WGA products from low-template input as it relates to their potential applications in precision oncology strategies.Single pancreatic cancer cells (HPAF-II) were isolated using laser microdissection. WGA was performed using multiple displacement amplification (MDA), multiple annealing and looping based amplification (MALBAC) and PicoPLEX. Quality of amplified DNA products were assessed using a multiplex/RT-qPCR based method that evaluates for 8-cancer related genes and QC-scores were assigned. We utilized this scoring system to assess the impact of de novo modifications to the WGA protocol. WGA products were subjected to Sanger sequencing, array comparative genomic hybridization (aCGH) and next generation sequencing (NGS) to evaluate their performances in respective downstream analyses providing validation of the QC-score.Single-cell WGA products exhibited a significant sample-to-sample variability in amplified DNA quality as assessed by our 8-gene QC assay. Single-cell WGA products that passed the pre-analysis QC had lower amplification bias and improved aCGH/NGS performance metrics when compared to single-cell WGA products that failed the QC. Increasing the number of cellular input resulted in improved QC-scores overall, but a resultant WGA product that consistently passed the QC step required a starting cellular input of at least 20-cells. Our modified-WGA protocol effectively reduced this number, achieving reproducible high-quality WGA products from ?5-cells as a starting template. A starting cellular input of 5 to 10-cells amplified using the modified-WGA achieved aCGH and NGS results that closely matched that of unamplified, batch genomic DNA.The modified-WGA protocol coupled with the 8-gene QC serve as an effective strategy to enhance the quality of low-template WGA reactions. Furthermore, a threshold number of 5-10 cells are likely needed for a reliable WGA reaction and product with high fidelity to the original starting template.
Project description:Whole genome amplification (WGA) is essential for obtaining genome sequences from single bacterial cells because the quantity of template DNA contained in a single cell is very low. Multiple displacement amplification (MDA), using Phi29 DNA polymerase and random primers, is the most widely used method for single-cell WGA. However, single-cell MDA usually results in uneven genome coverage because of amplification bias, background amplification of contaminating DNA, and formation of chimeras by linking of non-contiguous chromosomal regions. Here, we present a novel MDA method, termed droplet MDA, that minimizes amplification bias and amplification of contaminants by using picoliter-sized droplets for compartmentalized WGA reactions. Extracted DNA fragments from a lysed cell in MDA mixture are divided into 105 droplets (67 pL) within minutes via flow through simple microfluidic channels. Compartmentalized genome fragments can be individually amplified in these droplets without the risk of encounter with reagent-borne or environmental contaminants. Following quality assessment of WGA products from single Escherichia coli cells, we showed that droplet MDA minimized unexpected amplification and improved the percentage of genome recovery from 59% to 89%. Our results demonstrate that microfluidic-generated droplets show potential as an efficient tool for effective amplification of low-input DNA for single-cell genomics and greatly reduce the cost and labor investment required for determination of nearly complete genome sequences of uncultured bacteria from environmental samples.
Project description:Copy number alteration (CNA) is a major contributor to genome instability, a hallmark of cancer. Here, we studied genomic alterations in single primary tumor cells and circulating tumor cells (CTCs) from the same patient. Single-nucleotide variants (SNVs) in single cells from both samples occurred sporadically, whereas CNAs among primary tumor cells emerged accumulatively rather than abruptly, converging toward the CNA in CTCs. Focal CNAs affecting the MYC gene and the PTEN gene were observed only in a minor portion of primary tumor cells but were present in all CTCs, suggesting a strong selection toward metastasis. Single-cell structural variant (SV) analyses revealed a two-step mechanism, a complex rearrangement followed by gene amplification, for the simultaneous formation of anomalous CNAs in multiple chromosome regions. Integrative CNA analyses of 97 CTCs from 23 patients confirmed the convergence of CNAs and revealed single, concurrent, and mutually exclusive CNAs that could be the driving events in cancer metastasis.
Project description:Esophageal adenocarcinoma (EA) is among the leading causes of cancer mortality, especially in developed countries. A high level of somatic copy number alterations (CNAs) accumulates over the decades in the progression from Barrett's esophagus, the precursor lesion, to EA. Accurate identification of somatic CNAs is essential to understand cancer development. Many studies have been conducted for the detection of CNA in EA using microarrays. Next-generation sequencing (NGS) technologies are believed to have advantages in sensitivity and accuracy to detect CNA, yet no NGS-based CNA detection in EA has been reported.In this study, we analyzed whole-exome (WES) and whole-genome sequencing (WGS) data for detecting CNA from a published large-scale genomic study of EA. Two specific comparisons were conducted. First, the recurrent CNAs based on WGS and WES data from 145 EA samples were compared to those found in five previous microarray-based studies. We found that the majority of the previously identified regions were also detected in this study. Interestingly, some novel amplifications and deletions were discovered using the NGS data. In particular, SKI and PRKCZ detected in a deletion region are involved in transforming growth factor-? pathway, suggesting the potential utility of novel biomarkers for EA. Second, we compared CNAs detected in WGS and WES data from the same 15 EA samples. No large-scale CNA was identified statistically more frequently by WES or WGS, while more focal-scale CNAs were detected by WGS than by WES.Our results suggest that NGS can replace microarrays to detect CNA in EA. WGS is superior to WES in that it can offer finer resolution for the detection, though if the interest is on recurrent CNAs, WES can be preferable to WGS for its cost-effectiveness.