BAIT: Organizing genomes and mapping rearrangements in single cells

Phasing Diploid Genome Assembly Graphs with Single-Cell Strand Sequencing

Haplotype information is crucial for biomedical and population genetics research. However, current strategies to produce de-novo haplotype-resolved assemblies often require either difficult-to-acquire parental data or an intermediate haplotype-collapsed assembly. Here, we present Graphasing, a workflow which synthesizes the global phase signal of Strand-seq with assembly graph topology to produce chromosome-scale de-novo haplotypes for diploid genomes. Graphasing readily integrates with any assembly workflow that both outputs an assembly graph and has a haplotype assembly mode. Graphasing performs comparably to trio-phasing in contiguity, phasing accuracy, and assembly quality, outperforms Hi-C in phasing accuracy, and generates human assemblies with over 18 chromosome-spanning haplotypes.

Quantitative and qualitative mutational impact of ionizing radiation on normal cells

The comprehensive genomic impact of ionizing radiation (IR), a carcinogen, on healthy somatic cells remains unclear. Using large-scale whole-genome sequencing (WGS) of clones expanded from irradiated murine and human single cells, we revealed that IR induces a characteristic spectrum of short insertions or deletions (indels) and structural variations (SVs), including balanced inversions, translocations, composite SVs (deletion-insertion, deletion-inversion, and deletion-translocation composites), and complex genomic rearrangements (CGRs), including chromoplexy, chromothripsis, and SV by breakage-fusion-bridge cycles. Our findings suggest that 1 Gy IR exposure causes an average of 2.33 mutational events per Gb genome, comprising 2.15 indels, 0.17 SVs, and 0.01 CGRs, despite a high level of inter-cellular stochasticity. The mutational burden was dependent on total irradiation dose, regardless of dose rate or cell type. The findings were further validated in IR-induced secondary cancers and single cells without clonalization. Overall, our study highlights a comprehensive and clear picture of IR effects on normal mammalian genomes.

A non-genetic switch triggers alternative telomere lengthening and cellular immortalization in ATRX deficient cells

Alternative Lengthening of Telomeres (ALT) is an aberrant DNA recombination pathway which grants replicative immortality to approximately 10% of all cancers. Despite this high prevalence of ALT in cancer, the mechanism and genetics by which cells activate this pathway remain incompletely understood. A major challenge in dissecting the events that initiate ALT is the extremely low frequency of ALT induction in human cell systems. Guided by the genetic lesions that have been associated with ALT from cancer sequencing studies, we genetically engineered primary human pluripotent stem cells to deterministically induce ALT upon differentiation. Using this genetically defined system, we demonstrate that disruption of the p53 and Rb pathways in combination with ATRX loss-of-function is sufficient to induce all hallmarks of ALT and results in functional immortalization in a cell type-specific manner. We further demonstrate that ALT can be induced in the presence of telomerase, is neither dependent on telomere shortening nor crisis, but is rather driven by continuous telomere instability triggered by the induction of differentiation in ATRX-deficient stem cells.

Progress in Methods for Copy Number Variation Profiling

Copy number variations (CNVs) are the predominant class of structural genomic variations involved in the processes of evolutionary adaptation, genomic disorders, and disease progression. Compared with single-nucleotide variants, there have been challenges associated with the detection of CNVs owing to their diverse sizes. However, the field has seen significant progress in the past 20–30 years. This has been made possible due to the rapid development of molecular diagnostic methods which ensure a more detailed view of the genome structure, further complemented by recent advances in computational methods. Here, we review the major approaches that have been used to routinely detect CNVs, ranging from cytogenetics to the latest sequencing technologies, and then cover their specific features.

Construction of Whole Genomes from Scaffolds Using Single Cell Strand-Seq Data

Accurate reference genome sequences provide the foundation for modern molecular biology and genomics as the interpretation of sequence data to study evolution, gene expression, and epigenetics depends heavily on the quality of the genome assembly used for its alignment. Correctly organising sequenced fragments such as contigs and scaffolds in relation to each other is a critical and often challenging step in the construction of robust genome references. We previously identified misoriented regions in the mouse and human reference assemblies using Strand-seq, a single cell sequencing technique that preserves DNA directionality Here we demonstrate the ability of Strand-seq to build and correct full-length chromosomes by identifying which scaffolds belong to the same chromosome and determining their correct order and orientation, without the need for overlapping sequences. We demonstrate that Strand-seq exquisitely maps assembly fragments into large related groups and chromosome-sized clusters without using new assembly data. Using template strand inheritance as a bi-allelic marker, we employ genetic mapping principles to cluster scaffolds that are derived from the same chromosome and order them within the chromosome based solely on directionality of DNA strand inheritance. We prove the utility of our approach by generating improved genome assemblies for several model organisms including the ferret, pig, Xenopus, zebrafish, Tasmanian devil and the Guinea pig.

Fully phased human genome assembly without parental data using single-cell strand sequencing and long reads

Human genomes are typically assembled as consensus sequences that lack information on parental haplotypes. Here we describe a reference-free workflow for diploid de novo genome assembly that combines the chromosome-wide phasing and scaffolding capabilities of single-cell strand sequencing1,2 with continuous long-read or high-fidelity3 sequencing data. Employing this strategy, we produced a completely phased de novo genome assembly for each haplotype of an individual of Puerto Rican descent (HG00733) in the absence of parental data. The assemblies are accurate (quality value > 40) and highly contiguous (contig N50 > 23 Mbp) with low switch error rates (0.17%), providing fully phased single-nucleotide variants, indels and structural variants. A comparison of Oxford Nanopore Technologies and Pacific Biosciences phased assemblies identified 154 regions that are preferential sites of contig breaks, irrespective of sequencing technology or phasing algorithms.

breakpointR: an R/Bioconductor package to localize strand state changes in Strand-seq data

MOTIVATION

Strand-seq is a specialized single-cell DNA sequencing technique centered around the directionality of single-stranded DNA. Computational tools for Strand-seq analyses must capture the strand-specific information embedded in these data.

RESULTS

Here we introduce breakpointR, an R/Bioconductor package specifically tailored to process and interpret single-cell strand-specific sequencing data obtained from Strand-seq. We developed breakpointR to detect local changes in strand directionality of aligned Strand-seq data, to enable fine-mapping of sister chromatid exchanges, germline inversion and to support global haplotype assembly. Given the broad spectrum of Strand-seq applications we expect breakpointR to be an important addition to currently available tools and extend the accessibility of this novel sequencing technique.

AVAILABILITY AND IMPLEMENTATION

R/Bioconductor package https://bioconductor.org/packages/breakpointR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell analysis of structural variations and complex rearrangements with tri-channel processing

Structural variation (SV), involving deletions, duplications, inversions and translocations of DNA segments, is a major source of genetic variability in somatic cells and can dysregulate cancer-related pathways. However, discovering somatic SVs in single cells has been challenging, with copy-number-neutral and complex variants typically escaping detection. Here we describe single-cell tri-channel processing (scTRIP), a computational framework that integrates read depth, template strand and haplotype phase to comprehensively discover SVs in individual cells. We surveyed SV landscapes of 565 single cells, including transformed epithelial cells and patient-derived leukemic samples, to discover abundant SV classes, including inversions, translocations and complex DNA rearrangements. Analysis of the leukemic samples revealed four times more somatic SVs than cytogenetic karyotyping, submicroscopic copy-number alterations, oncogenic copy-neutral rearrangements and a subclonal chromothripsis event. Advancing current methods, single-cell tri-channel processing can directly measure SV mutational processes in individual cells, such as breakage-fusion-bridge cycles, facilitating studies of clonal evolution, genetic mosaicism and SV formation mechanisms, which could improve disease classification for precision medicine.

Structural variant identification and characterization

Structural variant (SV) differences between human genomes can cause germline and mosaic disease as well as inter-individual variation. De-regulation of accurate DNA repair and genomic surveillance mechanisms results in a large number of SVs in cancer. Analysis of the DNA sequences at SV breakpoints can help identify pathways of mutagenesis and regions of the genome that are more susceptible to rearrangement. Large-scale SV analyses have been enabled by high-throughput genome-level sequencing on humans in the past decade. These studies have shed light on the mechanisms and prevalence of complex genomic rearrangements. Recent advancements in both sequencing and other mapping technologies as well as calling algorithms for detection of genomic rearrangements have helped propel SV detection into population-scale studies, and have begun to elucidate previously inaccessible regions of the genome. Here, we discuss the genomic organization of simple and complex SVs, the molecular mechanisms of their formation, and various ways to detect them. We also introduce methods for characterizing SVs and their consequences on human genomes.

Structural variation in the sequencing era

Identifying structural variation (SV) is essential for genome interpretation but has been historically difficult due to limitations inherent to available genome technologies. Detection methods that use ensemble algorithms and emerging sequencing technologies have enabled the discovery of thousands of SVs, uncovering information about their ubiquity, relationship to disease and possible effects on biological mechanisms. Given the variability in SV type and size, along with unique detection biases of emerging genomic platforms, multiplatform discovery is necessary to resolve the full spectrum of variation. Here, we review modern approaches for investigating SVs and proffer that, moving forwards, studies integrating biological information with detection will be necessary to comprehensively understand the impact of SV in the human genome.

Quantification of Aneuploidy in Mammalian Systems

High-throughput next generation sequencing karyotyping has emerged as a powerful tool for the detection of genomic heterogeneity in normal tissues and cancers. Here we describe a single-cell whole genome sequencing (scWGS) platform to assess whole-chromosome aneuploidy, structural aneuploidies involving only chromosome fragments and more local small copy number alterations in individual cells. We provide a detailed protocol for the isolation, library preparation, low coverage sequencing and data analysis of single cells. Since our approach does not involve a whole-genome preamplification step, our method allows for acquisition of reliable high-resolution single-cell copy number profiles. Moreover, the protocol allows multiplexing of 384 single-cell libraries in one sequencing run, thereby significantly reducing sequencing costs and can be completed in 3-4 days starting from single cell isolation to analysis of sequencing data.

Strand-seq enables reliable separation of long reads by chromosome via expectation maximization

Motivation

Current sequencing technologies are able to produce reads orders of magnitude longer than ever possible before. Such long reads have sparked a new interest in de novo genome assembly, which removes reference biases inherent to re-sequencing approaches and allows for a direct characterization of complex genomic variants. However, even with latest algorithmic advances, assembling a mammalian genome from long error-prone reads incurs a significant computational burden and does not preclude occasional misassemblies. Both problems could potentially be mitigated if assembly could commence for each chromosome separately.

Results

To address this, we show how single-cell template strand sequencing (Strand-seq) data can be leveraged for this purpose. We introduce a novel latent variable model and a corresponding Expectation Maximization algorithm, termed SaaRclust, and demonstrates its ability to reliably cluster long reads by chromosome. For each long read, this approach produces a posterior probability distribution over all chromosomes of origin and read directionalities. In this way, it allows to assess the amount of uncertainty inherent to sparse Strand-seq data on the level of individual reads. Among the reads that our algorithm confidently assigns to a chromosome, we observed more than 99% correct assignments on a subset of Pacific Bioscience reads with 30.1× coverage. To our knowledge, SaaRclust is the first approach for the in silico separation of long reads by chromosome prior to assembly.

Availability and implementation

https://github.com/daewoooo/SaaRclust.

Assembling draft genomes using contiBAIT

Summary

Massively parallel sequencing is now widely used, but data interpretation is only as good as the reference assembly to which it is aligned. While the number of reference assemblies has rapidly expanded, most of these remain at intermediate stages of completion, either as scaffold builds, or as chromosome builds (consisting of correctly ordered, but not necessarily correctly oriented scaffolds separated by gaps). Completion of de novo assemblies remains difficult, as regions that are repetitive or hard to sequence prevent the accumulation of larger scaffolds, and create errors such as misorientations and mislocalizations. Thus, complementary methods for determining the orientation and positioning of fragments are important for finishing assemblies. Strand-seq is a method for determining template strand inheritance in single cells, information that can be used to determine relative genomic distance and orientation between scaffolds, and find errors within them. We present contiBAIT, an R/Bioconductor package which uses Strand-seq data to repair and improve existing assemblies.

Availability and Implementation

contiBAIT is available on Bioconductor. Source files available from GitHub.

Contact

koneill@bcgsc.ca or mark.hills@stemcell.com.

Supplementary information

Supplementary data are available at Bioinformatics online.

BLM helicase suppresses recombination at G-quadruplex motifs in transcribed genes

Bloom syndrome is a cancer predisposition disorder caused by mutations in the BLM helicase gene. Cells from persons with Bloom syndrome exhibit striking genomic instability characterized by excessive sister chromatid exchange events (SCEs). We applied single-cell DNA template strand sequencing (Strand-seq) to map the genomic locations of SCEs. Our results show that in the absence of BLM, SCEs in human and murine cells do not occur randomly throughout the genome but are strikingly enriched at coding regions, specifically at sites of guanine quadruplex (G4) motifs in transcribed genes. We propose that BLM protects against genome instability by suppressing recombination at sites of G4 structures, particularly in transcribed regions of the genome.

Bloom syndrome is characterized by high levels of sister chromatid exchanges (SCEs). Here, the authors use single-cell DNA template strand-sequencing to map SCEs in patient cells, and propose that the BLM helicase protects the genome against unwanted recombination at sites of G-quadruplex structures.

Genome-wide mapping of sister chromatid exchange events in single yeast cells using Strand-seq

Homologous recombination involving sister chromatids is the most accurate, and thus most frequently used, form of recombination-mediated DNA repair. Despite its importance, sister chromatid recombination is not easily studied because it does not result in a change in DNA sequence, making recombination between sister chromatids difficult to detect. We have previously developed a novel DNA template strand sequencing technique, called Strand-seq, that can be used to map sister chromatid exchange (SCE) events genome-wide in single cells. An increase in the rate of SCE is an indicator of elevated recombination activity and of genome instability, which is a hallmark of cancer. In this study, we have adapted Strand-seq to detect SCE in the yeast Saccharomyces cerevisiae. We provide the first quantifiable evidence that most spontaneous SCE events in wild-type cells are not due to the repair of DNA double-strand breaks.

Single-cell template strand sequencing by Strand-seq enables the characterization of individual homologs

The ability to distinguish between genome sequences of homologous chromosomes in single cells is important for studies of copy-neutral genomic rearrangements (such as inversions and translocations), building chromosome-length haplotypes, refining genome assemblies, mapping sister chromatid exchange events and exploring cellular heterogeneity. Strand-seq is a single-cell sequencing technology that resolves the individual homologs within a cell by restricting sequence analysis to the DNA template strands used during DNA replication. This protocol, which takes up to 4 d to complete, relies on the directionality of DNA, in which each single strand of a DNA molecule is distinguished based on its 5'-3' orientation. Culturing cells in a thymidine analog for one round of cell division labels nascent DNA strands, allowing for their selective removal during genomic library construction. To preserve directionality of template strands, genomic preamplification is bypassed and labeled nascent strands are nicked and not amplified during library preparation. Each single-cell library is multiplexed for pooling and sequencing, and the resulting sequence data are aligned, mapping to either the minus or plus strand of the reference genome, to assign template strand states for each chromosome in the cell. The major adaptations to conventional single-cell sequencing protocols include harvesting of daughter cells after a single round of BrdU incorporation, bypassing of whole-genome amplification, and removal of the BrdU⁺ strand during Strand-seq library preparation. By sequencing just template strands, the structure and identity of each homolog are preserved.

Direct chromosome-length haplotyping by single-cell sequencing

Haplotypes are fundamental to fully characterize the diploid genome of an individual, yet methods to directly chart the unique genetic makeup of each parental chromosome are lacking. Here we introduce single-cell DNA template strand sequencing (Strand-seq) as a novel approach to phasing diploid genomes along the entire length of all chromosomes. We demonstrate this by building a complete haplotype for a HapMap individual (NA12878) at high accuracy (concordance 99.3%), without using generational information or statistical inference. By use of this approach, we mapped all meiotic recombination events in a family trio with high resolution (median range ∼14 kb) and phased larger structural variants like deletions, indels, and balanced rearrangements like inversions. Lastly, the single-cell resolution of Strand-seq allowed us to observe loss of heterozygosity regions in a small number of cells, a significant advantage for studies of heterogeneous cell populations, such as cancer cells. We conclude that Strand-seq is a unique and powerful approach to completely phase individual genomes and map inheritance patterns in families, while preserving haplotype differences between single cells.

Characterizing polymorphic inversions in human genomes by single-cell sequencing

Identifying genomic features that differ between individuals and cells can help uncover the functional variants that drive phenotypes and disease susceptibilities. For this, single-cell studies are paramount, as it becomes increasingly clear that the contribution of rare but functional cellular subpopulations is important for disease prognosis, management, and progression. Until now, studying these associations has been challenged by our inability to map structural rearrangements accurately and comprehensively. To overcome this, we coupled single-cell sequencing of DNA template strands (Strand-seq) with custom analysis software to rapidly discover, map, and genotype genomic rearrangements at high resolution. This allowed us to explore the distribution and frequency of inversions in a heterogeneous cell population, identify several polymorphic domains in complex regions of the genome, and locate rare alleles in the reference assembly. We then mapped the entire genomic complement of inversions within two unrelated individuals to characterize their distinct inversion profiles and built a nonredundant global reference of structural rearrangements in the human genome. The work described here provides a powerful new framework to study structural variation and genomic heterogeneity in single-cell samples, whether from individuals for population studies or tissue types for biomarker discovery.

Bromodeoxyuridine does not contribute to sister chromatid exchange events in normal or Bloom syndrome cells

Sister chromatid exchanges (SCEs) are considered sensitive indicators of genome instability. Detection of SCEs typically requires cells to incorporate bromodeoxyuridine (BrdU) during two rounds of DNA synthesis. Previous studies have suggested that SCEs are induced by DNA replication over BrdU-substituted DNA and that BrdU incorporation alone could be responsible for the high number of SCE events observed in cells from patients with Bloom syndrome (BS), a rare genetic disorder characterized by marked genome instability and high SCE frequency. Here we show using Strand-seq, a single cell DNA template strand sequencing technique, that the presence of variable BrdU concentrations in the cell culture medium and in DNA template strands has no effect on SCE frequency in either normal or BS cells. We conclude that BrdU does not induce SCEs and that SCEs detected in either normal or BS cells reflect DNA repair events that occur spontaneously.

The developmental potential of iPSCs is greatly influenced by reprogramming factor selection

Induced pluripotent stem cells (iPSCs) are commonly generated by transduction of Oct4, Sox2, Klf4, and Myc (OSKM) into cells. Although iPSCs are pluripotent, they frequently exhibit high variation in terms of quality, as measured in mice by chimera contribution and tetraploid complementation. Reliably high-quality iPSCs will be needed for future therapeutic applications. Here, we show that one major determinant of iPSC quality is the combination of reprogramming factors used. Based on tetraploid complementation, we found that ectopic expression of Sall4, Nanog, Esrrb, and Lin28 (SNEL) in mouse embryonic fibroblasts (MEFs) generated high-quality iPSCs more efficiently than other combinations of factors including OSKM. Although differentially methylated regions, transcript number of master regulators, establishment of specific superenhancers, and global aneuploidy were comparable between high- and low-quality lines, aberrant gene expression, trisomy of chromosome 8, and abnormal H2A.X deposition were distinguishing features that could potentially also be applicable to human.

How to count chromosomes in a cell: An overview of current and novel technologies

Aneuploidy, an aberrant number of chromosomes in a cell, is a feature of several syndromes associated with cognitive and developmental defects. In addition, aneuploidy is considered a hallmark of cancer cells and has been suggested to play a role in neurodegenerative disease. To better understand the relationship between aneuploidy and disease, various methods to measure the chromosome numbers in cells have been developed, each with their own advantages and limitations. While some methods rely on dividing cells and thus bias aneuploidy rates to that population, other, more unbiased methods can only detect the average aneuploidy rates in a cell population, cloaking cell-to-cell heterogeneity. Furthermore, some techniques are more prone to technical artefacts, which can result in over- or underestimation of aneuploidy rates. In this review, we provide an overview of several "traditional" karyotyping methods as well as the latest high throughput next generation sequencing karyotyping protocols with their respective advantages and disadvantages.

A comprehensive evaluation of assembly scaffolding tools

Background

Genome assembly is typically a two-stage process: contig assembly followed by the use of paired sequencing reads to join contigs into scaffolds. Scaffolds are usually the focus of reported assembly statistics; longer scaffolds greatly facilitate the use of genome sequences in downstream analyses, and it is appealing to present larger numbers as metrics of assembly performance. However, scaffolds are highly prone to errors, especially when generated using short reads, which can directly result in inflated assembly statistics.

Results

Here we provide the first independent evaluation of scaffolding tools for second-generation sequencing data. We find large variations in the quality of results depending on the tool and dataset used. Even extremely simple test cases of perfect input, constructed to elucidate the behaviour of each algorithm, produced some surprising results. We further dissect the performance of the scaffolders using real and simulated sequencing data derived from the genomes of Staphylococcus aureus, Rhodobacter sphaeroides, Plasmodium falciparum and Homo sapiens. The results from simulated data are of high quality, with several of the tools producing perfect output. However, at least 10% of joins remains unidentified when using real data.

Conclusions

The scaffolders vary in their usability, speed and number of correct and missed joins made between contigs. Results from real data highlight opportunities for further improvements of the tools. Overall, SGA, SOPRA and SSPACE generally outperform the other tools on our datasets. However, the quality of the results is highly dependent on the read mapper and genome complexity.

Single cell genomics: advances and future perspectives

Advances in whole-genome and whole-transcriptome amplification have permitted the sequencing of the minute amounts of DNA and RNA present in a single cell, offering a window into the extent and nature of genomic and transcriptomic heterogeneity which occurs in both normal development and disease. Single-cell approaches stand poised to revolutionise our capacity to understand the scale of genomic, epigenomic, and transcriptomic diversity that occurs during the lifetime of an individual organism. Here, we review the major technological and biological breakthroughs achieved, describe the remaining challenges to overcome, and provide a glimpse into the promise of recent and future developments.