SureSelect XT HS2 DNA to prepare libraries for single-cell Whole Genome Sequencing (scWGS) after single-cell Whole Genome Amplification (scWGA) v1

Single microbial cell genome sequencing is becoming a powerful tool for the discovery of the hidden genetic information valuable for many medical applications. One of the critical steps in single-cell genome sequencing is the physical isolation of individual cells from a highly diverse heterogeneous population. Amplifying the genome of a single microbial cell is another challenge due to the minute amount of DNA. Efforts have been directed in developing an optofluidic platform integrating advanced microscopy, optical tweezers and microfluidic technology for single cell isolation and genome amplification. Here, we investigate and evaluate the validity of this platform for single microbial cell genome amplification. The successful validation of this approach allows us to perform various single cell studies using this platform.

Single-cell sequencing is a novel approach to genome sequencing of clinical samples. However, there are only few studies using single-cell sequencing of genomes for bacterial infections. A 71-year-old woman presented to the emergency department with epigastric pain, 38.5°C fever, and a history of hypertension and hyperuricemia. From blood test results, acute cholecystitis was suspected. The surgery went well and bilirubin calcium stones were found in the gallbladder. Single-cell sequencing was used to investigate a bile sample from a patient with acute cholecystitis. The sample, cultured on a MacConkey agar plate, produced four colonies, all identified as Escherichia coli by bacteriological and biochemical properties. Whole genome sequences of the four strains were determined using the single-cell amplified genome (SAG) sequencing technique. The average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) values of all four were 99.98-100% and 100%, respectively, indicating that they were the same bacterial species. Compared with type strains, these four strains were closest to Shigella sonnei (ANI 98.65-98.66%; dDDH 88.5%) than E. coli (ANI 96.79-96.80%; dDDH 74.2%), despite lacking stx1, stx2 and ipaH, which Shigella species harbor. 16S metagenome analysis identified E. coli as the predominant bacterial genome in the sample, comprising 93.15%. SAG raw data had a relatively high level of quality, with 98.4-98.7% of the read numbers used after quality trimming. However, the genome sequencing coverage was only 9.45-42.88% when compared to a complete genome of an isolate with a mapping quality set above 99%, resulting in gaps compared to conventional whole genome sequence data of these isolates. The procedures of the SAG sequencing technique should be revised to improve the sequencing coverage and reduce gaps in the sequence data. Nonetheless, single-cell genome sequencing can provide novel information for bacterial infections.

The content of the 13th Mutation Detection meeting (Leiden, April 2015) is summarized in this report. Topics discussed at the meeting included current challenges of clinical NGS, advances in bioinformatics, data quality control, single cell analysis and RNA sequencing, among others. Social, ethical and regulatory challenges of genomic data handling and data sharing were the focus of an expert panel debate. The 14th International Symposium on Variants in the Genome will take place in Santiago de Compostela, June 5-8, 2017. http://isv.variome.org.

<p dir="ltr">Cancer is a highly heterogeneous disease, both at the genomic and phenotypic level. Single-cell methods have allowed exploration of phenotypic cell states and genomic evolution, but the association between genotype and phenotype remains largely elusive. Activating or deleterious single nucleotide variants (SNVs) can have very clear effects on a phenotype, but copy number variations (CNVs), which are ubiquitous in cancers, are not well understood functionally. Recent advances within ultra-low coverage, single-cell whole genome sequencing (WGS) have enabled high throughput analysis of CNV. However, downstream analysis is still largely dependent on tools developed for high coverage bulk data.</p><p dir="ltr">In <b>paper I</b> we developed a computational method, ASCENT, which enables accurate breakpoint detection, absolute copy number calling, and haplotyping of clonal segments. We showed that by using ASCENT we could find minor subclones not detected in bulk WGS data, and produce accurate high-resolution copy number profiles from ultra-low coverage single-cell WGS.</p><p dir="ltr">By running ASCENT on joint mRNA/WGS data from 57 patients representing six cancer types, we were able to make generalizable inferences about how subclonal genetics affect cell phenotypes (<b>paper III</b>). We found that highly amplified CNVs constrain the phenotype to a much greater degree than lowly amplified whole chromosome aneuploidies. We found that gene dosage is largely tissue-dependent and that oncogenes, such as MYC, are often not sensitive to dosage. We identified a previously underappreciated group of tumors that lack a clear clonal structure, where each cell division leads to two distinct genotypes.</p><p dir="ltr">In <b>paper IV</b> we analyzed the relationship between genotype and phenotype during induction treatment in pediatric acute lymphoblastic leukemia (ALL). We found that while some genotypes have specific phenotypes, during induction treatment cell states shifted toward a more mature B-cell-like state, independent of genomic background. We contrast this to the mechanism which leads to relapse, which always includes additional genomic aberrations, selection on the genetic level, and inter-patient heterogeneity in cell states.</p><p dir="ltr">Single-cell sequencing methods are often used to determine which cell states are responsible for phenotypes found in bulk sequencing. In <b>paper II</b> we investigated the complicated relationship between shear stress and vascular disease in the aorta. We used single-cell sequencing to deconvolve which cell type caused an immune-pro phenotype in AmotL2-depleted mice and found a subset of endothelial cells to be responsible.</p><p dir="ltr">In conclusion, we have charted the transcriptional effect of different genetic aberrations across human cancer. We conclude that specific genomic aberrations can affect the functional phenotype of a cell. We found that dosage effects are largely tissue-dependent and that the amplification of oncogenes is often compensated for on the mRNA-level. We additionally found that induction treatment confers a common cell state in ALL, while relapsed samples had evolved divergent phenotypes during low intensity treatment.</p><h3 dir="ltr">List of scientific papers</h3><p dir="ltr">I. <b>Solrun Kolbeinsdottir</b>*, Vasilios Zachariadis*, Christian Sommerauer, Olli Lohi, Merja Heinäniemi, Martin Enge. Absolute copy number aware CNV calling of sub megabase segments in ultra-low coverage single-cell DNA sequencing data. Nucleic Acids Research, Volume 53, Issue 17, 23 September 2025. <a href="https://doi.org/10.1093/nar/gkaf919" rel="noreferrer" target="_blank">https://doi.org/10.1093/nar/gkaf919</a></p><p dir="ltr">II. Yuanyuan Zhang, Yumeng Zhang, Evelyn Hutterer, Sara Hultin, Otto Bergman, <b>Solrun Kolbeinsdottir</b>, Hong Jin, Maria J Forteza, Daniel F J Ketelhuth, Joy Roy, Ulf Hedin, Martin Enge, Ljubica Matic, Per Eriksson, Lars Holmgren. The VE-cadherin/AmotL2 mechanosensory pathway suppresses aortic inflammation and the formation of abdominal aortic aneurysms. Nature Cardiovascular Research, 2023, 7, 629-644. <a href="https://doi.org/10.1038/s44161-023-00298-8" rel="noreferrer" target="_blank">https://doi.org/10.1038/s44161-023-00298-8</a></p><p dir="ltr">III. <b>Solrun Kolbeinsdottir</b>, Vasilios Zachariadis, Muyi Yang, Luuk Broeils, Christian Sommerauer, Huaitao Cheng, Xinsong Chen, Yingbo Lin, Sampsa Hautaniemi, Johanna Hynninen, Suzanne Egyhazi Brage, Dhifaf Sarhan, Anna Vähärautio, Nikolas Herold, Johan Hartman, Hildur Helgadóttir, Felix Haglund de Flon, Martin Enge. Subclonal copy number alterations and their transcriptional impacts across human cancers using joint single-cell genome and transcriptome sequencing. [Manuscript]</p><p dir="ltr">IV. Vasilios Zachariadis, <b>Solrun Kolbeinsdottir</b>, Jessica Hacheney, Huaitao Cheng, Laura Oksa, Aonghus Naughton, Arghavan Alizadeh, Sanni Moisio, Olli Lohi, Merja Heinäniemi, Martin Enge. Persister states and relapse in childhood leukemia. [Manuscript]</p><p dir="ltr">*These authors contributed equally</p>

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and microbial populations. Traditional methods for assessing genetic heterogeneity have been limited by low resolution, low sensitivity, and/or low specificity. Single cell sequencing has emerged as a powerful tool for detecting genetic heterogeneity with high resolution, high sensitivity and, when appropriately analyzed, high specificity. Here we provide a protocol for the isolation, whole genome amplification, sequencing, and analysis of single cells. Our approach allows for the reliable identification of megabase-scale copy number variants in single cells. However, aspects of this protocol can also be applied to investigate other types of genetic alterations in single cells.

Accurate detection of variants and long-range haplotypes in genomes of single human cells remains very challenging. Common approaches require extensive in vitro amplification of genomes of individual cells using DNA polymerases and high-throughput short-read DNA sequencing. These approaches have two notable drawbacks. First, polymerase replication errors could generate tens of thousands of false-positive calls per genome. Second, relatively short sequence reads contain little to no haplotype information. Here we report a method, which is dubbed SISSOR (single-stranded sequencing using microfluidic reactors), for accurate single-cell genome sequencing and haplotyping. A microfluidic processor is used to separate the Watson and Crick strands of the double-stranded chromosomal DNA in a single cell and to randomly partition megabase-size DNA strands into multiple nanoliter compartments for amplification and construction of barcoded libraries for sequencing. The separation and partitioning of large single-stranded DNA fragments of the homologous chromosome pairs allows for the independent sequencing of each of the complementary and homologous strands. This enables the assembly of long haplotypes and reduction of sequence errors by using the redundant sequence information and haplotype-based error removal. We demonstrated the ability to sequence single-cell genomes with error rates as low as 10-8 and average 500-kb-long DNA fragments that can be assembled into haplotype contigs with N50 greater than 7 Mb. The performance could be further improved with more uniform amplification and more accurate sequence alignment. The ability to obtain accurate genome sequences and haplotype information from single cells will enable applications of genome sequencing for diverse clinical needs.

Single-Cell Whole Genome Amplification and Sequencing to Study Neuronal Mosaicism and Diversity

Single-cell genome sequencing has become a useful tool in medicine and biology studies. However, an independent library is required for each cell in single-cell genome sequencing, so that the cost grows with the number of cells. In this study, we report a study which efficiently analyses single-cell copy number variation (CNV) using overlapping pooling strategy and branch and bound (B&B) algorithm. Single cells were overlapped pooled before sequencing, and later were assorted into specific types by estimating their CNV patterns by B&B algorithm. Instead of constructing libraries for each cell, a library is required only for each pool. As the number of pools is smaller than the cells, fewer libraries are required, which means lower cost. Through computer simulations, we overlapped pooled 80 cells into 40 or 27 pools and classified them into cell types based on CNV pattern. The results showed that 84% cells in 40 pools and 76.5% cells in 27 pools were correctly classified on average, while only half or one-third of the sequencing libraries were required. Combining with traditional approaches, our method is expected to significantly improve the efficiency of single-cell genome sequencing.

Comparative analysis of single-cell genome sequencing techniques toward the characterization of germline and somatic genomes in ciliated protists

MotivationAllele dropout (ADO) and unbalanced amplification of alleles are main technical issues of single-cell sequencing (SCS), and effectively emulating these issues is necessary for reliably benchmarking SCS-based bioinformatics tools. Unfortunately, currently available sequencing simulators are free of whole-genome amplification involved in SCS technique and therefore not suited for generating SCS datasets. We develop a new software package (SCSsim) that can efficiently simulate SCS datasets in a parallel fashion with minimal user intervention. SCSsim first constructs the genome sequence of single cell by mimicking a complement of genomic variations under user-controlled manner, and then amplifies the genome according to MALBAC technique and finally yields sequencing reads from the amplified products based on inferred sequencing profiles. Comprehensive evaluation in simulating different ADO rates, variation detection efficiency and genome coverage demonstrates that SCSsim is a very useful tool in mimicking single-cell sequencing data with high efficiency.Availability and implementationSCSsim is freely available at https://github.com/qasimyu/scssim.Supplementary information Supplementary data are available at Bioinformatics online.

Next-generation sequencing: challenges in reproductive genetics

Single-cell sequencing for analyzing DNA mutations across the genome in somatic tissues is critically important for studying development, cancer and aging. However, current procedures are prone to artifacts and to date a reliable protocol for single-cell somatic mutation analysis remains to be developed. We address the two largest sources of artifacts, i.e., DNA denaturation-related cytosine deamination and allelic bias-driven whole genome amplification errors. We first reconfigured multiple displacement amplification (MDA) into an efficient protocol for whole genome amplification of single cells without cytosine deamination artifacts, i.e., Single Cell MDA (SCMDA). We then developed a new single-cell SNV caller (SCcaller) that distinguishes real somatic mutations and amplification errors by utilizing a SNP-based localized estimate of allelic amplification bias. The procedure was validated by comparing SCMDA-amplified single cells with unamplified clones derived from single cells from the same population. Using this highly accurate single-cell whole-genome sequencing method we analyzed human B lymphocytes from donors varying in age from birth to over 100 year, studying both genome distribution and functional impact of base substitution mutations. Mutations per cell were found to increase with age from less than 500 in cord blood to over 3,000 in cells from individuals over 100. While overall mutations were randomly distributed across the genome with all chromosomes equally affected, 24 hotspot regions were identified, five of which were part of immunoglobulin variable regions subject to somatic hypermutation. Age-related mutation accumulation was found to be significantly slower in genomic sequences directly involved in cellular function, such as exons and gene regulatory sequences. Still, on average, B lymphocytes from aged individuals contained 3-15 damaging mutations in the transcribed part of the exome identified by RNA-seq, plus 15-50 mutations in transcription factor binding sites identified by ATAC-seq. Analysis of the spontaneous mutation spectra in these normal cells revealed signatures similar to those previously found associated with B cell leukemia and other cancers. These results indicate that cancer risk is already part of age-related mutation accumulation in normal cells. Taken together, our single-cell sequencing method provides a firm foundation for analyzing cellular genetic heterogeneity in normal human tissues. Citation Format: Xiao Dong, Lei Zhang, Moonsook Lee, Alexander Y. Maslov, Jan Vijg. Single-cell whole-genome sequencing reveals somatic mutation signatures in normal somatic cells predictive of cancer later in life [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 440.

Single-cell genome sequencing of protozoan parasites

Single cell sequencing (SCS) was chosen as Method of the Year in 2013 by Nature Methods. It refers to the deep sequencing of DNA or RNA molecules of single cells for the study of genome, transcriptome, noncoding RNA, and protein sequences encompassed within the single cells of interest. SCS possesses a strong potential to exert a great impact on biological research due to its unprecedented opportunity and capability of resolving sequence composition and genetic variations on single cell basis, which can be further integrated to explain the biological phenomena at the population or organismal level. During the past few years, SCS has produced promising results in a number of fields, especially cancer research. Prior to SCS, tissue sequencing could only analyze a tumor mass or a cancer cell population as a whole. With methods for single cell isolation, further powered by molecular level resolution made possible by minute DNA amplification and nucleotide level resolution made possible by next-generation sequencing, SCS technologies are becoming robust approaches for the study of cancer genomics and transcriptomics, and the cancer–normal cell interactions in the microenvironment. SCS analyses of cancer evolution, for example, have shown the capability of being able to empirically infer the driver mutations and map the sequential mutation events during cancer development. Undoubtedly, these novel approaches will produce profound health benefits. SCS will continue to attract more attention, and further expansion of its applications in many biological fields is inevitable. This chapter focuses on SCS methodologies, its existing applications, especially its capability in reconstructing the evolutionary history of cancer progression and in profiling cancer transcriptome, and the potential applications expected to come.

Objective To apply multiple displacement amplification (MDA)to improve primer extension preamplification (IPEP) for whole genome amplification (WGA) and to compare their effects on forensic DNA analysis. Methods DNA samples of varying amounts were for WGA based on MDA and IPEP.WGA products yield was evaluated by real-time quantitative PCR and STR genotyping performance was determined with AmpFLSTR? Identifiler(R) Kit. Results The DNA quantity was increased about 103～106 folds by MDA and 25～310 folds by IPEP. The least genome DNA amount is 1ng for MDA and 0.05ng for IPEP to obtain accurate genotypes of all loci. IPEP products and MDA products of 0.01 ng～0.1 ng DNA exhibited more loci observed than original DNA that was not for WGA,and IPEP products exhibited more loci observed than MDA products. Conclusion Both MDA and IPEP can improve STR genotyping of forensic minute DNA. The yield of MDA is higher than IPEP but the sensitivity of IPEP is higher than MDA and the STR genotyping effect of IPEP products is better than MDA products so IPEP is better for forensic trace DNA analysis. Key words: Multiple displacement amplification; Improved primer extension preamplification; Whole genome amplification; STR genotyping