Single-cell Genome Sequencing Research Articles

Leveraging single-cell sequencing to unravel intratumour heterogeneity and tumour evolution in human cancers.

Intratumour heterogeneity (ITH) and tumour evolution are well‐documented phenomena in human cancers. While the advent of next‐generation sequencing technologies has facilitated the large‐scale capture of genomic data, the field of single‐cell genomics is nascent but rapidly advancing and generating many new insights into the complex molecular mechanisms of tumour biology. In this review, we provide an overview of current single‐cell DNA sequencing technologies, exploring how recent methodological advancements have enumerated new insights into ITH and tumour evolution. Areas highlighted include the potential power of single‐cell genome sequencing studies to explore evolutionary dynamics contributing to tumourigenesis through to progression, metastasis, and therapy resistance. We also explore the use of in situ sequencing technologies to study ITH in a spatial context, as well as examining the use of single‐cell genomics to perform lineage tracing in both normal and malignant tissues. Finally, we consider the use of multimodal single‐cell sequencing technologies. Taken together, it is hoped that these many facets of single‐cell genome sequencing will improve our understanding of tumourigenesis, progression, and lethality in cancer, leading to the development of novel therapies. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Scalable Microbial Strain Inference in Metagenomic Data Using StrainFacts.

While genome databases are nearing a complete catalog of species commonly inhabiting the human gut, their representation of intraspecific diversity is lacking for all but the most abundant and frequently studied taxa. Statistical deconvolution of allele frequencies from shotgun metagenomic data into strain genotypes and relative abundances is a promising approach, but existing methods are limited by computational scalability. Here we introduce StrainFacts, a method for strain deconvolution that enables inference across tens of thousands of metagenomes. We harness a “fuzzy” genotype approximation that makes the underlying graphical model fully differentiable, unlike existing methods. This allows parameter estimates to be optimized with gradient-based methods, speeding up model fitting by two orders of magnitude. A GPU implementation provides additional scalability. Extensive simulations show that StrainFacts can perform strain inference on thousands of metagenomes and has comparable accuracy to more computationally intensive tools. We further validate our strain inferences using single-cell genomic sequencing from a human stool sample. Applying StrainFacts to a collection of more than 10,000 publicly available human stool metagenomes, we quantify patterns of strain diversity, biogeography, and linkage-disequilibrium that agree with and expand on what is known based on existing reference genomes. StrainFacts paves the way for large-scale biogeography and population genetic studies of microbiomes using metagenomic data.

Advances and applications of single-cell omics technologies in plant research.

Single-cell sequencing approaches reveal the intracellular dynamics of individual cells and answer biological questions with high-dimensional catalogs of millions of cells, including genomics, transcriptomics, chromatin accessibility, epigenomics, and proteomics data across species. These emerging yet thriving technologies have been fully embraced by the field of plant biology, with a constantly expanding portfolio of applications. Here, we introduce the current technical advances used for single-cell omics, especially single-cell genome and transcriptome sequencing. Firstly, we overview methods for protoplast and nucleus isolation and genome and transcriptome amplification. Subsequently, we use well-executed benchmarking studies to highlight advances made through the application of single-cell omics techniques. Looking forward, we offer a glimpse of additional hurdles and future opportunities that will introduce broad adoption of single-cell sequencing with revolutionary perspectives in plant biology.

Single cell genome sequencing of laboratory mouse microbiota improves taxonomic and functional resolution of this model microbial community.

Laboratory mice are widely studied as models of mammalian biology, including the microbiota. However, much of the taxonomic and functional diversity of the mouse gut microbiome is missed in current metagenomic studies, because genome databases have not achieved a balanced representation of the diverse members of this ecosystem. Towards solving this problem, we used flow cytometry and low-coverage sequencing to capture the genomes of 764 single cells from the stool of three laboratory mice. From these, we generated 298 high-coverage microbial genome assemblies, which we annotated for open reading frames and phylogenetic placement. These genomes increase the gene catalog and phylogenetic breadth of the mouse microbiota, adding 135 novel species with the greatest increase in diversity to the Muribaculaceae and Bacteroidaceae families. This new diversity also improves the read mapping rate, taxonomic classifier performance, and gene detection rate of mouse stool metagenomes. The novel microbial functions revealed through our single-cell genomes highlight previously invisible pathways that may be important for life in the murine gastrointestinal tract.

Construction of a chromosome-level Japanese stickleback species genome using ultra-dense linkage analysis with single-cell sperm sequencing.

It is still difficult to construct the genomes of higher organisms as their genome sequences must be extended to the length of the chromosome by linkage analysis. In this study, we attempted to provide an innovative alternative to conventional linkage analysis by devising a method to genotype sperm using 10× Genomics single-cell genome sequencing libraries to generate a linkage map without interbreeding individuals. A genome was assembled using sperm from the Japanese stickleback Gasterosteus nipponicus, with single-cell genotyping yielding 1 864 430 very dense hetero-SNPs and an average coverage per sperm cell of 0.13×. In total, 1665 sperm were used, which is an order of magnitude higher than the number of recombinations used for conventional linkage analysis. We then improved the linkage analysis tool scaffold extender with low depth linkage analysis (SELDLA) to analyze the data according to the characteristics of the single-cell genotyping data. Finally, we were able to determine the chromosomal location (97.1%) and orientation (64.4%) of the contigs in the 456 Mb genome of G. nipponicus, sequenced using nanopores. This method promises to be a useful tool for determining the genomes of non-model organisms for which breeding systems have not yet been established by linkage analysis.

Strain-level profiling of viable microbial community by selective single-cell genome sequencing

Culture-independent analysis with high-throughput sequencing has been widely used to characterize bacterial communities. However, signals derived from non-viable bacteria and non-cell DNA may inhibit its characterization. Here, we present a method for viable bacteria-targeted single-cell genome sequencing, called PMA-SAG-gel, to obtain comprehensive whole-genome sequences of surviving uncultured bacteria from microbial communities. PMA-SAG-gel uses gel matrixes that enable sequential enzymatic reactions for cell lysis and genome amplification of viable single cells from the microbial communities. PMA-SAG-gel removed the single-amplified genomes (SAGs) derived from dead bacteria and enabled selective sequencing of viable bacteria in the model samples of Escherichia coli and Bacillus subtilis. Next, we demonstrated the recovery of near-complete SAGs of eight oxygen-tolerant bacteria, including Bacteroides spp. and Phocaeicola spp., from 1331 human feces SAGs. We found the presence of two different strains in each species and identified their specific genes to investigate the metabolic functions. The survival profile of an entire population at the strain level will provide the information for understanding the characteristics of the surviving bacteria under the specific environments or sample processing and insights for quality assessment of live bacterial products or fecal microbiota transplantation and for understanding the effect of antimicrobial treatments.

Recent advances in single-cell sequencing technologies.

Single-cell omics sequencing was first achieved for the transcriptome in 2009, which was followed by fast development of technologies for profiling the genome, DNA methylome, 3D genome architecture, chromatin accessibility, histone modifications, etc., in an individual cell. In this review we mainly focus on the recent progress in four topics in the single-cell omics field: single-cell epigenome sequencing, single-cell genome sequencing for lineage tracing, spatially resolved single-cell transcriptomics and third-generation sequencing platform-based single-cell omics sequencing. We also discuss the potential applications and future directions of these single-cell omics sequencing technologies for different biomedical systems, especially for the human stem cell field.

SCONCE: a method for profiling copy number alterations in cancer evolution using single-cell whole genome sequencing.

MotivationCopy number alterations (CNAs) are a significant driver in cancer growth and development, but remain poorly characterized on the single cell level. Although genome evolution in cancer cells is Markovian through evolutionary time, CNAs are not Markovian along the genome. However, existing methods call copy number profiles with Hidden Markov Models or change point detection algorithms based on changes in observed read depth, corrected by genome content and do not account for the stochastic evolutionary process.ResultsWe present a theoretical framework to use tumor evolutionary history to accurately call CNAs in a principled manner. To model the tumor evolutionary process and account for technical noise from low coverage single-cell whole genome sequencing data, we developed SCONCE, a method based on a Hidden Markov Model to analyze read depth data from tumor cells using matched normal cells as negative controls. Using a combination of public data sets and simulations, we show SCONCE accurately decodes copy number profiles, and provides a useful tool for understanding tumor evolution.Availabilityand implementationSCONCE is implemented in C++11 and is freely available from https://github.com/NielsenBerkeleyLab/sconce.Supplementary information Supplementary data are available at Bioinformatics online.

An Overview on Single-Cell Technology for Hepatocellular Carcinoma Diagnosis.

Hepatocellular carcinoma is a primary liver cancer caused by the accumulation of genetic mutation patterns associated with epidemiological conditions. This lethal malignancy exhibits tumor heterogeneity, which is considered as one of the main reasons for drug resistance development and failure of clinical trials. Recently, single-cell technology (SCT), a new advanced sequencing technique that analyzes every single cell in a tumor tissue specimen, aids complete insight into the genetic heterogeneity of cancer. This helps in identifying and assessing rare cell populations by analyzing the difference in gene expression pattern between individual cells of single biopsy tissue which normally cannot be identified from pooled cell gene expression pattern (traditional sequencing technique). Thus, SCT improves the clinical diagnosis, treatment, and prognosis of hepatocellular carcinoma as the limitations of other techniques impede this cancer research progression. Application of SCT at the genomic, transcriptomic, and epigenomic levels to promote individualized hepatocellular carcinoma diagnosis and therapy. The current review has been divided into ten sections. Herein we deliberated on the SCT, hepatocellular carcinoma diagnosis, tumor microenvironment analysis, single-cell genomic sequencing, single-cell transcriptomics, single-cell omics sequencing for biomarker development, identification of hepatocellular carcinoma origination and evolution, limitations, challenges, conclusions, and future perspectives.

Oceanic Crustal Fluid Single Cell Genomics Complements Metagenomic and Metatranscriptomic Surveys With Orders of Magnitude Less Sample Volume.

Fluids circulating through oceanic crust play important roles in global biogeochemical cycling mediated by their microbial inhabitants, but studying these sites is challenged by sampling logistics and low biomass. Borehole observatories installed at the North Pond study site on the western flank of the Mid-Atlantic Ridge have enabled investigation of the microbial biosphere in cold, oxygenated basaltic oceanic crust. Here we test a methodology that applies redox-sensitive fluorescent molecules for flow cytometric sorting of cells for single cell genomic sequencing from small volumes of low biomass (approximately 103 cells ml–1) crustal fluid. We compare the resulting genomic data to a recently published paired metagenomic and metatranscriptomic analysis from the same site. Even with low coverage genome sequencing, sorting cells from less than one milliliter of crustal fluid results in similar interpretation of dominant taxa and functional profiles as compared to ‘omics analysis that typically filter orders of magnitude more fluid volume. The diverse community dominated by Gammaproteobacteria, Bacteroidetes, Desulfobacterota, Alphaproteobacteria, and Zetaproteobacteria, had evidence of autotrophy and heterotrophy, a variety of nitrogen and sulfur cycling metabolisms, and motility. Together, results indicate fluorescence activated cell sorting methodology is a powerful addition to the toolbox for the study of low biomass systems or at sites where only small sample volumes are available for analysis.

Improving the efficiency of single-cell genome sequencing based on overlapping pooling strategy and CNV analysis.

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.

微生物シングルセルゲノム解析技術bit-MAP®を用いたエンドリシン創薬技術開発

The emergence of antimicrobial-resistant (AMR) bacteria poses an imminent threat to our society and requires urgent actions. We here propose that a bacteriophage-derived protein called endolysin can be exploited as a new antimicrobial agent to tackle this global public heath issue. Endolysins are hydrolytic enzymes that cleave bacterial cell-walls (peptidoglycan layers), allowing for rapid cell lysis in a matter of minutes. Not only this swift response makes a stark contrast with conventional antibiotics, but the protein nature of endolysins makes them amenable to modifications. For example, modular domains of endolysin can be recombined to create new artificial endolysins. Using in-house high-throughput screening system, we show that we can quickly discover multiple endolysins against methicillin-resistant Staphylococcus aureus (MRSA) with improved activity compared to previously reported ones. We also discuss the prospects for the development of antimicrobial endolysins against any potential pathogens using our proprietary microbial single-cell genome sequencing platform, bit-MAP(R).

High throughput single-cell genome sequencing gives insights into the generation and evolution of mosaic aneuploidy in Leishmania donovani.

Leishmania, a unicellular eukaryotic parasite, is a unique model for aneuploidy and cellular heterogeneity, along with their potential role in adaptation to environmental stresses. Somy variation within clonal populations was previously explored in a small subset of chromosomes using fluorescence hybridization methods. This phenomenon, termed mosaic aneuploidy (MA), might have important evolutionary and functional implications but remains under-explored due to technological limitations. Here, we applied and validated a high throughput single-cell genome sequencing method to study for the first time the extent and dynamics of whole karyotype heterogeneity in two clonal populations of Leishmania promastigotes representing different stages of MA evolution in vitro. We found that drastic changes in karyotypes quickly emerge in a population stemming from an almost euploid founder cell. This possibly involves polyploidization/hybridization at an early stage of population expansion, followed by assorted ploidy reduction. During further stages of expansion, MA increases by moderate and gradual karyotypic alterations, affecting a defined subset of chromosomes. Our data provide the first complete characterization of MA in Leishmania and pave the way for further functional studies.

The hidden genomic diversity of ciliated protists revealed by single-cell genome sequencing

BackgroundCiliated protists are a widely distributed, morphologically diverse, and genetically heterogeneous group of unicellular organisms, usually known for containing two types of nuclei: a transcribed polyploid macronucleus involved in gene expression and a silent diploid micronucleus responsible for transmission of genetic material during sexual reproduction and generation of the macronucleus. Although studies in a few species of culturable ciliated protists have revealed the highly dynamic nature of replicative and recombination events relating the micronucleus to the macronucleus, the broader understanding of the genomic diversity of ciliated protists, as well as their phylogenetic relationships and metabolic potential, has been hampered by the inability to culture numerous other species under laboratory conditions, as well as the presence of symbiotic bacteria and microalgae which provide a challenge for current sequencing technologies. Here, we optimized single-cell sequencing methods and associated data analyses, to effectively remove contamination by commensal bacteria, and generated high-quality genomes for a number of Euplotia species.ResultsWe obtained eight high-quality Euplotia genomes by using single-cell genome sequencing techniques. The genomes have high genomic completeness, with sizes between 68 and 125 M and gene numbers between 14K and 25K. Through comparative genomic analysis, we found that there are a large number of gene expansion events in Euplotia genomes, and these expansions are closely related to the phenotypic evolution and specific environmental adaptations of individual species. We further found four distinct subgroups in the genus Euplotes, which exhibited considerable genetic distance and relative lack of conserved genomic syntenies. Comparative genomic analyses of Uronychia and its relatives revealed significant gene expansion associated with the ciliary movement machinery, which may be related to the unique and strong swimming ability.ConclusionsWe employed single-cell genomics to obtain eight ciliate genomes, characterized the underestimated genomic diversity of Euplotia, and determined the divergence time of representative species in this subclass for the first time. We also further investigated the extensive duplication events associated with speciation and environmental adaptation. This study provides a unique and valuable resource for understanding the evolutionary history and genetic diversity of ciliates.

Deciphering intratumoral heterogeneity using integrated clonal tracking and single-cell transcriptome analyses

Cellular heterogeneity is a major cause of treatment resistance in cancer. Despite recent advances in single-cell genomic and transcriptomic sequencing, it remains difficult to relate measured molecular profiles to the cellular activities underlying cancer. Here, we present an integrated experimental system that connects single cell gene expression to heterogeneous cancer cell growth, metastasis, and treatment response. Our system integrates single cell transcriptome profiling with DNA barcode based clonal tracking in patient-derived xenograft models. We show that leukemia cells exhibiting unique gene expression respond to different chemotherapies in distinct but consistent manners across multiple mice. In addition, we uncover a form of leukemia expansion that is spatially confined to the bone marrow of single anatomical sites and driven by cells with distinct gene expression. Our integrated experimental system can interrogate the molecular and cellular basis of the intratumoral heterogeneity underlying disease progression and treatment resistance.

Genomic Heterogeneity and Clonal Evolution in High Hyperdiploid Childhood Acute Lymphoblastic Leukemia

High hyperdiploid (HeH) B-cell precursor acute lymphoblastic leukemia (BCP ALL) is characterized by a very specific nonrandom gain of chromosomes; a feature distinct from other types of aneuploid tumor types that usually display very heterogeneous gains and losses of chromosomes. Gains of chromosomes X, 4, 6, 10, 14, 17, and 18 are seen in more than 75% of cases of HeH childhood ALL, and of chromosome 21 in 100% of cases. In contrast to many aneuploid malignancies, there has been little evidence of chromosomal instability (CIN) in HeH ALL and the mechanisms leading to these chromosomal gains remain unknown. The aim of this project was to determine the level of genomic heterogeneity in HeH ALL. In order to do this, we performed low-pass whole genome sequencing (WGS) of single cells isolated from diagnostic bone marrow samples from HeH ALL patients to investigate cell-to-cell heterogeneity. Single nuclei in G 1 phase from nine diagnostic childhood HeH ALL samples were isolated using fluorescence-activated cell sorting and DNA libraries were constructed for low-pass WGS. Copy number analysis for each individual cell was performed in-house using the software programs AneuFinder and Ginkgo. Homolog inheritance of chromosomes gained or lost was determined by screening for heterozygous variants and calculation of the variant allele frequencies (VAF). Sequencing 2,572 single cells showed that the nine HeH ALL patients were all relatively homogenous at the trunk of their evolution tree and that the bulk of chromosomal gains were stable and unchanging. Structural aberrations, visible as partial chromosome copy number changes, were detected at diagnosis in most cases, with a greater number of structural aberrations detected in patients with greater numbers of sub-clones (defined as three or more cells presenting with the same numerical and structural aberrations). Unique numerical chromosomal aberrations detected in two or fewer cells were relatively random and therefore indicative of nondisjunction events in recent cell divisions rather than being part of the clonal evolution of the leukemia. These results indicate very low heterogeneity in HeH ALL and suggests that the genome of these leukemias is relatively stable. DisclosuresNo relevant conflicts of interest to declare.

Whole chromosome loss and genomic instability in mouse embryos after CRISPR-Cas9 genome editing

Karyotype alterations have emerged as on-target complications from CRISPR-Cas9 genome editing. However, the events that lead to these karyotypic changes in embryos after Cas9-treatment remain unknown. Here, using imaging and single-cell genome sequencing of 8-cell stage embryos, we track both spontaneous and Cas9-induced karyotype aberrations through the first three divisions of embryonic development. We observe the generation of abnormal structures of the nucleus that arise as a consequence of errors in mitosis, including micronuclei and chromosome bridges, and determine their contribution to common karyotype aberrations including whole chromosome loss that has been recently reported after editing in embryos. Together, these data demonstrate that Cas9-mediated germline genome editing can lead to unwanted on-target side effects, including major chromosome structural alterations that can be propagated over several divisions of embryonic development.

Three Draft Single-Cell Genome Sequences of Novel SAR324 Strains Isolated from the Abyssopelagic Southern Ocean.

ABSTRACTSAR324 is a ubiquitous and phylogenetically distinct clade of Deltaproteobacteria in marine environments. Here, we present three single-cell amplified genome sequences from the SAR324 lineage, obtained from the abyssopelagic zone of the Indian sector of the Southern Ocean.

Reconstructing and counting genomic fragments through tagmentation-based haploid phasing

Single-cell sequencing provides a new level of granularity in studying the heterogeneous nature of cancer cells. For some cancers, this heterogeneity is the result of copy number changes of genes within the cellular genomes. The ability to accurately determine such copy number changes is critical in tracing and understanding tumorigenesis. Current single-cell genome sequencing methodologies infer copy numbers based on statistical approaches followed by rounding decimal numbers to integer values. Such methodologies are sample dependent, have varying calling sensitivities which heavily depend on the sample’s ploidy and are sensitive to noise in sequencing data. In this paper we have demonstrated the concept of integer-counting by using a novel bioinformatic algorithm built on our library construction chemistry in order to detect the discrete nature of the genome.

Abstract P214: Isolevuglandins Mediate Inflammatory Gene Expression And Immune Activation In Hypertension And Systemic Lupus Erythematosus

Justification: Hypertension and systemic lupus erythematosus (SLE) share similarities including elevations of blood pressure, proteinuria, inflammation and renal dysfunction and both involve formation of isoLG adducts. We hypothesized that isoLG scavenging would modulate overlapping gene pathways in inflammatory cells in these two conditions. Methods: We performed 10 X genomics single cell sequencing on splenocytes of C57Bl/6 mice with angiotensin II (Ang II)-induced hypertension, Ang II co-treatment with the isoLG scavenger 2HOBA, and 24-week-old B6.SLE123 mice with or without 6-weeks of 2HOBA. Matched C57Bl/6 females were used as controls for the B6.SLE123 mice. Results: Both models exhibited myeloid expansion and genes associated with neutrophil infiltration compared to their respective controls and 2HOBA attenuated this ( Table 1 ). Hypertension was associated with neutrophil expansion whereas SLE was associated an expansion of neutrophils, monocytes, and dendritic cells. In SLE, 2HOBA predominantly modulated gene expression in dendritic cells. Gene ontology revealed 2HOBA downregulated genes governing inflammation including Il1 β (Avglog2(fold change) = -1.6, P adj = 0.002) in both hypertension and SLE. Conclusions: In a mouse model of SLE, scavenging of isoLGs with 2HOBA downregulates inflammatory genes specifically in DCs. In models of both hypertension and SLE, scavenging of isoLG prevents neutrophil expansion. Combined these data describe a shared role of isoLGs in hypertension and SLE and suggest a specific role of DCs and neutrophil activation in the pathogenesis of both conditions. Table 1. Percentage of myeloid derived cells in all groups.