Microarray analysis of copy number variation in single cells

We present a protocol for reliably detecting DNA copy number aberrations in a single human cell. Multiple displacement-amplified DNAs of a cell are hybridized to a 3,000-bacterial artificial chromosome (BAC) array and to an Affymetrix 250,000 (250K)-SNP array. Subsequent copy number calling is based on the integration of BAC probe-specific copy number probabilities that are estimated by comparing probe intensities with a single-cell whole-genome amplification (WGA) reference model for diploid chromosomes, as well as SNP copy number and loss-of-heterozygosity states estimated by hidden Markov models (HMM). All methods for detecting DNA copy number aberrations in single human cells have difficulty in confidently discriminating WGA artifacts from true genetic variants. Furthermore, some methods lack thorough validation for segmental DNA imbalance detection. Our protocol minimizes false-positive variant calling and enables uniparental isodisomy detection in single cells. Additionally, it provides quality assessment, allowing the exclusion of uninterpretable single-cell WGA samples. The protocol takes 5-7 d.     

Optimized amplification and fluorescent labeling of small cell samples for genomic array-CGH.

BACKGROUND: Whole genome amplification (WGA) is usually needed in the genetic analysis of samples containing a low number of cells. In genome-wide analysis of DNA copy numbers by array comparative genomic hybridization (array-CGH) it is very important that the genome is evenly represented throughout the amplified product. All currently available WGA techniques are generating some degree of bias. METHODS: A way to compensate for this is using a reference sample which is similarly amplified, as the introduced amplification bias will be leveled out. Additionally, direct labeling of the amplified DNA is performed to bypass the currently widely applied random primed labeling, which involves an additional amplification of the product and is introducing extra bias. RESULTS: In this article it is shown that equal processing of the test and reference sample is indeed crucial to generate an optimal array-CGH profile of amplified DNA samples. Also presented here is that the labeling method may significantly effect the array-CGH result, it is shown that with direct chemical labeling using platinum derivates (ULS labeling) optimal array-CGH results are obtained. CONCLUSIONS: We show that an optimized WGA strategy for both test and reference sample in combination with direct chemical labeling results in a reliable array-CGH profile of samples as low as a 30 cell equivalent.     

Use of whole genome amplification and comparative genomic hybridisation to detect chromosomal copy number alterations in cell line material and tumour tissue

We have established that whole genome amplification (WGA), in conjunction with genomic DNA array comparative genomic hybridisation (gaCGH) allows for the identification of genome-wide copy number abnormalities (CNAs) in DNA extracted from both cell line and patient material. To determine the fidelity and reproducibility of WGA to detect copy number imbalances using gaCGH, well characterized cell line genomic DNA was analysed. The gaCGH data obtained from non-amplified DNA and amplified DNA for the neuroblastoma cell line NUB7 and a paediatric medulloblastoma patient was almost identical. In addition, laser capture microdissection (LCM) of prostate tumour cells and subsequent WGA allowed for the detection of a number of CNAs that may not have been identified if DNA had been extracted in bulk from heterogeneous tissue. The results presented here demonstrate the use of WGA for generating sufficient DNA for gaCGH analysis without the introduction of significant sequence representation bias. The combination of amplification and gaCGH using DNA extracted from archival patient material has the potential for permitting the studying of DNA from small cancerous or pre-cancerous foci, which may help to identify potential genomic markers for early diagnosis.     

Allele-specific disparity in breast cancer

Background

Molecular alterations critical to development of cancer include mutations, copy number alterations (amplifications and deletions) as well as genomic rearrangements resulting in gene fusions. Massively parallel next generation sequencing, which enables the discovery of such changes, uses considerable quantities of genomic DNA (> 5 ug), a serious limitation in ever smaller clinical samples. However, a commonly available microarray platforms such as array comparative genomic hybridization (array CGH) allows the characterization of gene copy number at a single gene resolution using much smaller amounts of genomic DNA. In this study we evaluate the sensitivity of ultra-dense array CGH platforms developed by Agilent, especially that of the 1 million probe array (1 M array), and their application when whole genome amplification is required because of limited sample quantities.

Methods

We performed array CGH on whole genome amplified and not amplified genomic DNA from MCF-7 breast cancer cells, using 244 K and 1 M Agilent arrays. The ADM-2 algorithm was used to identify micro-copy number alterations that measured less than 1 Mb in genomic length.

Results

DNA from MCF-7 breast cancer cells was analyzed for micro-copy number alterations, defined as measuring less than 1 Mb in genomic length. The 4-fold extra resolution of the 1 M array platform relative to the less dense 244 K array platform, led to the improved detection of copy number variations (CNVs) and micro-CNAs. The identification of intra-genic breakpoints in areas of DNA copy number gain signaled the possible presence of gene fusion events. However, the ultra-dense platforms, especially the densest 1 M array, detect artifacts inherent to whole genome amplification and should be used only with non-amplified DNA samples.

Conclusions

This is a first report using 1 M array CGH for the discovery of cancer genes and biomarkers. We show the remarkable capacity of this technology to discover CNVs, micro-copy number alterations and even gene fusions. However, these platforms require excellent genomic DNA quality and do not tolerate relatively small imperfections related to the whole genome amplification.     

不同单细胞全基因组扩增方法的比较及MALBAC在辅助生殖中的应用

单细胞全基因组扩增(whole genome amplification, WGA)是指在单细胞水平对全基因组进行扩增的新技术,其原理是将分离的单个细胞的微量全基因组DNA进行扩增,获得高覆盖率的完整的基因组后进行高通量测序,用于揭示细胞异质性。目前,WGA方法主要包括引物延伸预扩增(primer extension preamplification PCR, PEP-PCR)、简并寡核苷酸引物PCR (degenerate oligonucleotide primed PCR, DOP-PCR)、多重置换扩增(multiple displacement amplification, MDA)、多次退火环状循环扩增(multiple annealing and looping-based amplification cycles, MALBAC)等。本文对不同的单细胞WGA方法的原理及应用情况分别进行了阐述,并对其扩增效率进行评价和比较,包括基因组覆盖度、均一性、重现性、SNV (single-nucleotide variants)和CNV (copy number variants)检测力等。综合对比不同单细胞WGA方法后发现,MALBAC的扩增均一性最高、等位基因脱扣率最低、重现性最好,且对于CNV和SNV的检测效果最好。本文还阐述了MALBAC技术在人类单精子减数重组、非整倍体分析以及人类卵细胞基因组研究中的应用。     

A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing

Copy number variations (CNVs), a common genomic mutation associated with various diseases, are important in research and clinical applications. Whole genome amplification (WGA) and massively parallel sequencing have been applied to single cell CNVs analysis, which provides new insight for the fields of biology and medicine. However, the WGA-induced bias significantly limits sensitivity and specificity for CNVs detection. Addressing these limitations, we developed a practical bioinformatic methodology for CNVs detection at the single cell level using low coverage massively parallel sequencing. This method consists of GC correction for WGA-induced bias removal, binary segmentation algorithm for locating CNVs breakpoints, and dynamic threshold determination for final signals filtering. Afterwards, we evaluated our method with seven test samples using low coverage sequencing (4∼9.5%). Four single-cell samples from peripheral blood, whose karyotypes were confirmed by whole genome sequencing analysis, were acquired. Three other test samples derived from blastocysts whose karyotypes were confirmed by SNP-array analysis were also recruited. The detection results for CNVs of larger than 1 Mb were highly consistent with confirmed results reaching 99.63% sensitivity and 97.71% specificity at base-pair level. Our study demonstrates the potential to overcome WGA-bias and to detect CNVs (>1 Mb) at the single cell level through low coverage massively parallel sequencing. It highlights the potential for CNVs research on single cells or limited DNA samples and may prove as a promising tool for research and clinical applications, such as pre-implantation genetic diagnosis/screening, fetal nucleated red blood cells research and cancer heterogeneity analysis.     

Implications of copy number variation in people with chromosomal abnormalities: potential for greater variation in copy number state may contribute to variability of phenotype

Copy number variation is common in the human genome with many regions, overlapping thousands of genes, now known to be deleted or amplified. Aneuploidies and other forms of chromosomal imbalance have a wide range of adverse phenotypes and are a common cause of birth defects resulting in significant morbidity and mortality. “Normal” copy number variants (CNVs) embedded within the regions of chromosome imbalance may affect the clinical outcomes by altering the local copy number of important genes or regulatory regions: this could alleviate or exacerbate certain phenotypes. In this way CNVs may contribute to the clinical variability seen in many disorders caused by chromosomal abnormalities, such as the congenital heart defects (CHD) seen in ~40% of Down’s syndrome (DS) patients. Investigation of CNVs may therefore help to pinpoint critical genes or regulatory elements, elucidating the molecular mechanisms underlying these conditions, also shedding light on the aetiology of such phenotypes in people without major chromosome imbalances, and ultimately leading to their improved detection and treatment.     

SNP-based chromosomal copy number ascertainment following multiple displacement whole-genome amplification

Whole genome amplification by multiple displacement amplification (MDA) offers investigators using precious genomic DNA samples a high fidelity method for amplifying nanogram quantities of DNA several thousandfold. This becomes especially important for the modemrn day genomics researcher who more and more commonly is applying today's genome scanning technologies to patient cohort samples collected years ago that are irrecoverable and invariably in short supply. We present evidence here that MDA-prepared genomic DNA includes artifacts of chromosomal copy number that resemble copy number polymorphisms (CNPs) upon analysis of the DNA on the Affymetrix 10K GeneChip. The study of CNPs in both health and disease is a rapidly growing area of research, however our current understanding of the relevance of CNPs is incomplete. Our data indicate that utilization of whole genome-amplified samples for analysis heavily reliant on accurate copy number retention could be confounded if the genomic DNA sample was subjected to MDA. We recommend that small amounts of patient cohort DNA stocks be set aside and not subjected to whole genome amplification in order to facilitate the unbiased determination of chromosomal copy numbers when desired.     

Exploring the role of copy number variants in human adaptation

Over the past decade, the ubiquity of copy number variants (CNVs, the gain or loss of genomic material) in the genomes of healthy humans has become apparent. Although some of these variants are associated with disorders, a handful of studies documented an adaptive advantage conferred by CNVs. In this review, we propose that CNVs are substrates for human evolution and adaptation. We discuss the possible mechanisms and evolutionary processes in which CNVs are selected, outline the current challenges in identifying these loci, and highlight that copy number variable regions allow for the creation of novel genes that may diversify the repertoire of such genes in response to rapidly changing environments. We expect that many more adaptive CNVs will be discovered in the coming years, and we believe that these new findings will contribute to our understanding of human-specific phenotypes.     

Assessment of Whole Genome Amplification for Sequence Capture and Massively Parallel Sequencing

Exome sequence capture and massively parallel sequencing can be combined to achieve inexpensive and rapid global analyses of the functional sections of the genome. The difficulties of working with relatively small quantities of genetic material, as may be necessary when sharing tumor biopsies between collaborators for instance, can be overcome using whole genome amplification. However, the potential drawbacks of using a whole genome amplification technology based on random primers in combination with sequence capture followed by massively parallel sequencing have not yet been examined in detail, especially in the context of mutation discovery in tumor material. In this work, we compare mutations detected in sequence data for unamplified DNA, whole genome amplified DNA, and RNA originating from the same tumor tissue samples from 16 patients diagnosed with non-small cell lung cancer. The results obtained provide a comprehensive overview of the merits of these techniques for mutation analysis. We evaluated the identified genetic variants, and found that most (74%) of them were observed in both the amplified and the unamplified sequence data. Eighty-nine percent of the variations found by WGA were shared with unamplified DNA. We demonstrate a strategy for avoiding allelic bias by including RNA-sequencing information.     

The Performance of Whole Genome Amplification Methods and Next-Generation Sequencing for Pre-Implantation Genetic Diagnosis of Chromosomal Abnormalities

Reliable and accurate pre-implantation genetic diagnosis(PGD) of patient’s embryos by next-generation sequencing(NGS) is dependent on efficient whole genome amplification(WGA) of a representative biopsy sample. However, the performance of the current state of the art WGA methods has not been evaluated for sequencing. Using low template DNA(15 pg) and single cells, we showed that the two PCR-based WGA systems Sure Plex and MALBAC are superior to the REPLI-g WGA multiple displacement amplification(MDA) system in terms of consistent and reproducible genome coverage and sequence bias across the 24 chromosomes, allowing better normalization of test to reference sequencing data. When copy number variation sequencing(CNV-Seq) was applied to single cell WGA products derived by either Sure Plex or MALBAC amplification, we showed that known disease CNVs in the range of 3e15 Mb could be reliably and accurately detected at the correct genomic positions. These findings indicate that our CNV-Seq pipeline incorporating either Sure Plex or MALBAC as the key initial WGA step is a powerful methodology for clinical PGD to identify euploid embryos in a patient’s cohort for uterine transplantation.     

Genome-wide analysis of copy number variation in type 1 diabetes

Type 1 diabetes (T1D) tends to cluster in families, suggesting there may be a genetic component predisposing to disease. However, a recent large-scale genome-wide association study concluded that identified genetic factors, single nucleotide polymorphisms, do not account for overall familiality. Another class of genetic variation is the amplification or deletion of >1 kilobase segments of the genome, also termed copy number variations (CNVs). We performed genome-wide CNV analysis on a cohort of 20 unrelated adults with T1D and a control (Ctrl) cohort of 20 subjects using the Affymetrix SNP Array 6.0 in combination with the Birdsuite copy number calling software. We identified 39 CNVs as enriched or depleted in T1D versus Ctrl. Additionally, we performed CNV analysis in a group of 10 monozygotic twin pairs discordant for T1D. Eleven of these 39 CNVs were also respectively enriched or depleted in the Twin cohort, suggesting that these variants may be involved in the development of islet autoimmunity, as the presently unaffected twin is at high risk for developing islet autoimmunity and T1D in his or her lifetime. These CNVs include a deletion on chromosome 6p21, near an HLA-DQ allele. CNVs were found that were both enriched or depleted in patients with or at high risk for developing T1D. These regions may represent genetic variants contributing to development of islet autoimmunity in T1D.     

A high-resolution map of segmental DNA copy number variation in the mouse genome

Submicroscopic (less than 2 Mb) segmental DNA copy number changes are a recently recognized source of genetic variability between individuals. The biological consequences of copy number variants (CNVs) are largely undefined. In some cases, CNVs that cause gene dosage effects have been implicated in phenotypic variation. CNVs have been detected in diverse species, including mice and humans. Published studies in mice have been limited by resolution and strain selection. We chose to study 21 well-characterized inbred mouse strains that are the focus of an international effort to measure, catalog, and disseminate phenotype data. We performed comparative genomic hybridization using long oligomer arrays to characterize CNVs in these strains. This technique increased the resolution of CNV detection by more than an order of magnitude over previous methodologies. The CNVs range in size from 21 to 2,002 kb. Clustering strains by CNV profile recapitulates aspects of the known ancestry of these strains. Most of the CNVs (77.5%) contain annotated genes, and many (47.5%) colocalize with previously mapped segmental duplications in the mouse genome. We demonstrate that this technique can identify copy number differences associated with known polymorphic traits. The phenotype of previously uncharacterized strains can be predicted based on their copy number at these loci. Annotation of CNVs in the mouse genome combined with sequence-based analysis provides an important resource that will help define the genetic basis of complex traits.     

Elusive Copy Number Variation in the Mouse Genome

Background

Array comparative genomic hybridization (aCGH) to detect copy number variants (CNVs) in mammalian genomes has led to a growing awareness of the potential importance of this category of sequence variation as a cause of phenotypic variation. Yet there are large discrepancies between studies, so that the extent of the genome affected by CNVs is unknown. We combined molecular and aCGH analyses of CNVs in inbred mouse strains to investigate this question.

Principal Findings

Using a 2.1 million probe array we identified 1,477 deletions and 499 gains in 7 inbred mouse strains. Molecular characterization indicated that approximately one third of the CNVs detected by the array were false positives and we estimate the false negative rate to be more than 50%. We show that low concordance between studies is largely due to the molecular nature of CNVs, many of which consist of a series of smaller deletions and gains interspersed by regions where the DNA copy number is normal.

Conclusions

Our results indicate that CNVs detected by arrays may be the coincidental co-localization of smaller CNVs, whose presence is more likely to perturb an aCGH hybridization profile than the effect of an isolated, small, copy number alteration. Our findings help explain the hitherto unexplored discrepancies between array-based studies of copy number variation in the mouse genome.     

Segmentation and Estimation for SNP Microarrays: A Bayesian Multiple Change‐Point Approach

Summary High‐density single‐nucleotide polymorphism (SNP) microarrays provide a useful tool for the detection of copy number variants (CNVs). The analysis of such large amounts of data is complicated, especially with regard to determining where copy numbers change and their corresponding values. In this article, we propose a Bayesian multiple change‐point model (BMCP) for segmentation and estimation of SNP microarray data. Segmentation concerns separating a chromosome into regions of equal copy number differences between the sample of interest and some reference, and involves the detection of locations of copy number difference changes. Estimation concerns determining true copy number for each segment. Our approach not only gives posterior estimates for the parameters of interest, namely locations for copy number difference changes and true copy number estimates, but also useful confidence measures. In addition, our algorithm can segment multiple samples simultaneously, and infer both common and rare CNVs across individuals. Finally, for studies of CNVs in tumors, we incorporate an adjustment factor for signal attenuation due to tumor heterogeneity or normal contamination that can improve copy number estimates.     

Origins and breakpoint analyses of copy number variations: up close and personal

Array-based methods have enabled the detection of many genomic gains and losses. These are stated as copy number variants (CNVs) and comprise up to 13% of the human genome. Based on their breakpoints and modes of formation CNVs are termed recurrent or nonrecurrent. Recurrent CNVs are flanked by low copy repeats and are of a fixed size. They arise as a result of misalignment during meiosis by a mechanism named nonallelic homologous recombination. Several of such recurrent CNVs have been linked to human diseases. Nonrecurrent CNVs, which are not flanked by low copy repeats, are of variable size and may arise via mechanisms like nonhomologous end joining and replication-based mechanisms described by the fork stalling and template switching and microhomology-mediated break-induced replication models. It is becoming clear that most disease-causing CNVs are nonrecurrent and generally arise via replication-based mechanisms. Furthermore, it is now appreciated that genomic features other than low copy repeats play a role in the formation of nonrecurrent CNVs. This review will discuss the different mechanisms of CNV formation and how high resolution analyses of CNV breakpoints have added to our knowledge of their precise structure.     

Accurate detection of subclonal single nucleotide variants in whole genome amplified and pooled cancer samples using HaloPlex target enrichment

Background

Target enrichment and resequencing is a widely used approach for identification of cancer genes and genetic variants associated with diseases. Although cost effective compared to whole genome sequencing, analysis of many samples constitutes a significant cost, which could be reduced by pooling samples before capture. Another limitation to the number of cancer samples that can be analyzed is often the amount of available tumor DNA. We evaluated the performance of whole genome amplified DNA and the power to detect subclonal somatic single nucleotide variants in non-indexed pools of cancer samples using the HaloPlex technology for target enrichment and next generation sequencing.

Results

We captured a set of 1528 putative somatic single nucleotide variants and germline SNPs, which were identified by whole genome sequencing, with the HaloPlex technology and sequenced to a depth of 792–1752. We found that the allele fractions of the analyzed variants are well preserved during whole genome amplification and that capture specificity or variant calling is not affected. We detected a large majority of the known single nucleotide variants present uniquely in one sample with allele fractions as low as 0.1 in non-indexed pools of up to ten samples. We also identified and experimentally validated six novel variants in the samples included in the pools.

Conclusion

Our work demonstrates that whole genome amplified DNA can be used for target enrichment equally well as genomic DNA and that accurate variant detection is possible in non-indexed pools of cancer samples. These findings show that analysis of a large number of samples is feasible at low cost, even when only small amounts of DNA is available, and thereby significantly increases the chances of indentifying recurrent mutations in cancer samples.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-14-856) contains supplementary material, which is available to authorized users.     

On the frequency of copy number variants

MOTIVATION: Estimating the frequency distribution of copy number variants (CNVs) is an important aspect of the effort to characterize this new type of genetic variation. Currently, most studies report a strong skew toward low-frequency CNVs. In this article, our goal is to investigate the frequencies of CNVs. We employ a two-step procedure for the CNV frequency estimation process. We use family information a posteriori to select only the most reliable CNV regions, i.e. those showing high rates of Mendelian transmission. RESULTS: Our results suggest that the current skew toward low-frequency CNVs may not be representative of the true frequency distribution, but may be due, among other reasons, to the non-negligible false negative rates that characterize CNV detection methods. Moreover, false positives are also likely, as low-frequency CNVs are hard to detect with small sample sizes and technologies that are not ideally suited for their detection. Without appropriate validation methods, such as incorporation of biologically relevant information (for example, in our case, the transmission of heritable CNVs from parents to offspring), it is difficult to assess the validity of specific CNVs, and even harder to obtain reliable frequency estimates.     

Allele-specific amplification in cancer revealed by SNP array analysis

Amplification, deletion, and loss of heterozygosity of genomic DNA are hallmarks of cancer. In recent years a variety of studies have emerged measuring total chromosomal copy number at increasingly high resolution. Similarly, loss-of-heterozygosity events have been finely mapped using high-throughput genotyping technologies. We have developed a probe-level allele-specific quantitation procedure that extracts both copy number and allelotype information from single nucleotide polymorphism (SNP) array data to arrive at allele-specific copy number across the genome. Our approach applies an expectation-maximization algorithm to a model derived from a novel classification of SNP array probes. This method is the first to our knowledge that is able to (a) determine the generalized genotype of aberrant samples at each SNP site (e.g., CCCCT at an amplified site), and (b) infer the copy number of each parental chromosome across the genome. With this method, we are able to determine not just where amplifications and deletions occur, but also the haplotype of the region being amplified or deleted. The merit of our model and general approach is demonstrated by very precise genotyping of normal samples, and our allele-specific copy number inferences are validated using PCR experiments. Applying our method to a collection of lung cancer samples, we are able to conclude that amplification is essentially monoallelic, as would be expected under the mechanisms currently believed responsible for gene amplification. This suggests that a specific parental chromosome may be targeted for amplification, whether because of germ line or somatic variation. An R software package containing the methods described in this paper is freely available at http://genome.dfci.harvard.edu/~tlaframb/PLASQ.     

Single-cell paired-end genome sequencing reveals structural variation per cell cycle

The nature and pace of genome mutation is largely unknown. Because standard methods sequence DNA from populations of cells, the genetic composition of individual cells is lost, de novo mutations in cells are concealed within the bulk signal and per cell cycle mutation rates and mechanisms remain elusive. Although single-cell genome analyses could resolve these problems, such analyses are error-prone because of whole-genome amplification (WGA) artefacts and are limited in the types of DNA mutation that can be discerned. We developed methods for paired-end sequence analysis of single-cell WGA products that enable (i) detecting multiple classes of DNA mutation, (ii) distinguishing DNA copy number changes from allelic WGA-amplification artefacts by the discovery of matching aberrantly mapping read pairs among the surfeit of paired-end WGA and mapping artefacts and (iii) delineating the break points and architecture of structural variants. By applying the methods, we capture DNA copy number changes acquired over one cell cycle in breast cancer cells and in blastomeres derived from a human zygote after in vitro fertilization. Furthermore, we were able to discover and fine-map a heritable inter-chromosomal rearrangement t(1;16)(p36;p12) by sequencing a single blastomere. The methods will expedite applications in basic genome research and provide a stepping stone to novel approaches for clinical genetic diagnosis.