Single-cell Genome Sequencing Research Articles

Abstract LB-310: Single cell genome sequencing reveals clonal stability and diversity in breast cancer

Abstract Human breast cancers often display intratumor genomic heterogeneity. This clonal diversity confounds the clinical diagnosis and basic research of cancer, because single samples may not represent that tumor as a whole. Sequencing breast tumor cohorts en masse has identified many prevalent mutations, but has limited ability for resolving subclonal diversity. Here, we developed a whole-genome and exome single-cell sequencing approach (Nuc-Seq) using G2/M cells. To validate our method, we applied Nuc-Seq to sequence the whole genomes of two single cells from a genetically monoclonal breast cancer cell line (SK-BR-3) at high coverage depth (61X ± 5 sem, n=2) and breadth (83.70% ± 3.40 sem, n=2) to detect somatic mutations. Our analysis suggests that Nuc-Seq generates low allelic dropout rates (9.73% ± 2.19%) and low false positive error rates for point mutations (FPR = 1.24e-6). We then applied this method to sequence single normal and tumor cells from an estrogen-receptor positive breast cancer and a triple-negative ductal carcinoma at base-pair resolution. In parallel, we performed single cell copy number profiling. In both tumors, we observed a large number of rare variants that were not detected by sequencing the bulk tumor en masse. In contrast, we find that single cell copy number profiles are highly similar. Our data suggest that aneuploid rearrangements occurred early in tumor evolution and remained highly stable as the tumor mass expanded. In contrast we find that point mutations evolved gradually, generating extensive clonal diversity. Many of the diverse mutations were shown to occur at low frequencies (0.03 -10%) in the tumor mass by targeted duplex sequencing. Mathematical modeling suggests that the triple-negative tumor cells have an increased mutation rate (13.3X), while the ER+ tumor cells do not. These findings have important implications for the diagnosis, therapeutic treatment and evolution of chemoresistance in breast cancer. Citation Format: Yong Wang, Nicholas Navin, Jill Waters, Marco Leung, Anna Unruh, Xiuqing Shi, Whijae Roh, Ken Chen, Paul Scheet, Selina Vattathil, Han Liang, Asha Multani, Hong Zhang, Funda Meric-Bernstam, Franziska Michor, Rui Zhao. Single cell genome sequencing reveals clonal stability and diversity in breast cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-310. doi:10.1158/1538-7445.AM2014-LB-310

Illuminating microbial dark matter in meromictic Sakinaw Lake.

Despite recent advances in metagenomic and single-cell genomic sequencing to investigate uncultivated microbial diversity and metabolic potential, fundamental questions related to population structure, interactions, and biogeochemical roles of candidate divisions remain. Numerous molecular surveys suggest that stratified ecosystems manifesting anoxic, sulfidic, and/or methane-rich conditions are enriched in these enigmatic microbes. Here we describe diversity, abundance, and cooccurrence patterns of uncultivated microbial communities inhabiting the permanently stratified waters of meromictic Sakinaw Lake, British Columbia, Canada, using 454 sequencing of the small-subunit rRNA gene with three-domain resolution. Operational taxonomic units (OTUs) were affiliated with 64 phyla, including more than 25 candidate divisions. Pronounced trends in community structure were observed for all three domains with eukaryotic sequences vanishing almost completely below the mixolimnion, followed by a rapid and sustained increase in methanogen-affiliated (∼10%) and unassigned (∼60%) archaeal sequences as well as bacterial OTUs affiliated with Chloroflexi (∼22%) and candidate divisions (∼28%). Network analysis revealed highly correlated, depth-dependent cooccurrence patterns between Chloroflexi, candidate divisions WWE1, OP9/JS1, OP8, and OD1, methanogens, and unassigned archaeal OTUs indicating niche partitioning and putative syntrophic growth modes. Indeed, pathway reconstruction using recently published Sakinaw Lake single-cell genomes affiliated with OP9/JS1 and OP8 revealed complete coverage of the Wood-Ljungdahl pathway with potential to drive syntrophic acetate oxidation to hydrogen and carbon dioxide under methanogenic conditions. Taken together, these observations point to previously unrecognized syntrophic networks in meromictic lake ecosystems with the potential to inform design and operation of anaerobic methanogenic bioreactors.

High-coverage, single-cell genome and exome sequencing at base-pair resolution to aid biomarker identification

High-coverage, single-cell genome and exome sequencing at base-pair resolution to aid biomarker identification

Cancer at single-cell resolution

Cancer Genomics Single-cell sequencing can illuminate the genetic properties of brain cancers and reveal heterogeneity within a tumor. Patel et al. examined the genome sequence of single cells isolated from brain glioblastomas. The findings revealed shared chromosomal changes but also extensive transcription variation, including genes related to signaling, which represent potential therapeutic targets. The authors suggest that the variation in tumor cells reflects neural development and that such variation among cancer cells may prove to have clinical significance. Science , this issue p. [1396][1] [1]: /lookup/doi/10.1126/science.1254257

Current Challenges in the Bioinformatics of Single Cell Genomics

Single cell genomics is a rapidly growing field with many new techniques emerging in the past few years. However, few bioinformatics tools specific for single cell genomics analysis are available. Single cell DNA/RNA sequencing data usually have low genome coverage and high amplification bias, which makes bioinformatics analysis challenging. Many current bioinformatics tools developed for bulk cell sequencing do not work well with single cell sequencing data. Here, we summarize current challenges in the bioinformatics analysis of single cell genomic DNA sequencing and single cell transcriptomes. These challenges include calling copy number variations, identifying mutated genes in tumor samples, reconstructing cell lineages, recovering low abundant transcripts, and improving the accuracy of quantitative analysis of transcripts. Development in single cell genomics bioinformatics analysis will promote the application of this technology to basic biology and medical research.

Singled out for sequencing

Single-cell genome and transcriptome sequencing methods are generating a fresh wave of biological insights into development, cancer and neuroscience. Kelly Rae Chi reports.

Dissecting genomic diversity, one cell at a time

Emerging technologies are bringing single-cell genome sequencing into the mainstream; this field has already yielded insights into the genetic architecture and variability between cells that highlight the dynamic nature of the genome.

Massively parallel polymerase cloning and genome sequencing of single cells using nanoliter microwells

Genome sequencing of single cells has a variety of applications, including characterizing difficult-to-culture microorganisms and identifying somatic mutations in single cells from mammalian tissues. A major hurdle in this process is the bias in amplifying the genetic material from a single cell, a procedure known as polymerase cloning. Here we describe the microwell displacement amplification system (MIDAS), a massively parallel polymerase cloning method in which single cells are randomly distributed into hundreds to thousands of nanoliter wells and simultaneously amplified for shotgun sequencing. MIDAS reduces amplification bias because polymerase cloning occurs in physically separated nanoliter-scale reactors, facilitating the de novo assembly of near-complete microbial genomes from single E. coli cells. In addition, MIDAS allowed us to detect single-copy number changes in primary human adult neurons at 1–2 Mb resolution. MIDAS will further the characterization of genomic diversity in many heterogeneous cell populations.

Single-cell genomics: An overview

The newly developed next-generation sequencing platforms, in combination with genome-scale amplification methods, provide a powerful tool to study genomics from a single cell. This mini-review summarizes the technologies of single cell genomics and their applications in several areas of biomedical research including stem cells, cancer biology and reproductive medicine. Particularly, it highlights recent advances in single cell exome sequencing, RNA-seq, and genome sequencing. The application of these powerful techniques will shed new light on the fundamental principles of gene transcription and genome organization at single-cell level and improve our understanding of cellular heterogeneity and diversity in multicellular organisms.

Microwell displacement amplification system (MIDAS), a parallel polymerase cloning system for single-cell genome sequencing

Microwell displacement amplification system (MIDAS), a parallel polymerase cloning system for single-cell genome sequencing

The Genetic Landscape Of FPD/AML Revealed CDC25C Mutation As a Driver That Promotes Malignant Transformation

Familial platelet disorder with predisposition to acute myelogenous leukemia (FPD/AML) is an autosomal dominant disorder and is characterized by inherited thrombocytopenia and a lifelong risk of development of hematological malignancies. Although inherited RUNX1 mutation is the cause of thrombocytopenia, additional genetic events may be responsible for the tumor development as only 40 % of FPD/AML patients develop leukemia. Because of the rarity of this disorder, underlying mechanisms for malignant transformation in FPD/AML have not been elucidated. Thus we conducted a nationwide survey in Japan to collect samples and make a diagnosis of patients with familial thrombocytopenia or hematological malignancies. As a result, 56 pedigrees were extracted, and seven pedigrees with RUNX1 mutation were diagnosed as having FPD/AML, in which eight out of 14 patients had developed hematological malignancies. To systematically identify additional genetic alterations, we utilized whole exome sequencing in two patients with FPD/AML who had RUNX1_F303fsX566 mutation and developed MDS (Patient 1) or myelofibrosis (Patient 2) followed by AML. We identified 12 and 10 somatically acquired nonsynonymous mutations in these patients, respectively. Intriguingly, the two patients had common CDC25C mutation at codon 234 (D234G). Further genomic screening of other pedigrees revealed that four out of eight FPD/AML patients who developed hematological malignancy harbored somatic mutations in CDC25C (CDC25C_D234G in three patients and CDC25C_H437N in one patient). Recurrent CDC25C mutation in FPD/AML with subsequent hematological malignancy implies that it forms common genetic foundation of transformation in this disease. CDC25C is a phosphatase that prevents premature mitosis in response to DNA damage at the G2/M checkpoint, while it is constitutively phosphorylated at Ser216 throughout the interphase by c-TAK1. When phosphorylated at Ser216, CDC25C binds to 14-3-3 protein, which sequestrates CDC25C in the cytoplasm and inactivates it. In all of the mutated forms of CDC25C that we found in FPD/AML, their binding capacity with c-TAK1 and 14-3-3 protein was reduced, resulting in decreased phosphorylation status of CDC25C at Ser216. As a consequence, those CDC25C mutants led to enhanced mitosis entry, which was exaggerated by radiation-induced DNA damage. These results demonstrate that CDC25C mutation results in disruption of DNA checkpoint machinery. It is known that FPD/AML-associated RUNX1 mutations evokes DNA damage and induces cell cycle arrest in hematopoietic cells, suggesting that the DNA checkpoint mechanism is activated in the presence of those types of RUNX1 mutation. We found, however, that introduction of CDC25C mutation results in the marked enhancement of mitosis entry in spite of co-expression of RUNX1 mutation in Ba/F3 cells. Thus, premature mitosis by loss of DNA checkpoint mechanisms in the presence of mutated CDC25C may contribute to malignant transformation of RUNX1-mutated cells. Interestingly, analysis of clonal evolution during leukemic transformation revealed that a clone defined by CDC25C mutation was dominant in the early phase of disease progression in both patients, which supports the idea that CDC25C mutation is associated with establishment of the founder clone during the leukemic progression of FPD/AML. In Patient 1, the founder clone with CDC25C mutation acquired FAM22G and COL9A1 (group 1) mutations, followed by occurrence of GATA2 and LPP (group 2) mutations to become a dominant clone in the AML phase, whereas another subclone of the founder defined by CHEK2 and DTX2 (group 3) mutations regressed. Similar hierarchical progression was observed in Patient 2. An additional single cell genomic sequencing of bone marrow cells from Patient 1 in the AML phase revealed that group 1/2 mutations and group 3 mutations were mutually exclusive, which supports our predicted model. Collectively, these results indicate that somatic mutation in CDC25C is a recurrent event in the early phase of leukemic progression of FPD/AML, which induces premature mitosis and genetic instability in hematopoietic cells with germline RUNX1 mutation. Disclosures:Usuki:Alexion Pharmaceuticals, Inc.: Speakers Bureau. Kurokawa:Novartis: Consultancy, Research Funding; Bristol-Myers Squibb: Research Funding; Celgene: Consultancy, Research Funding.

Single-cell genome and metatranscriptome sequencing reveal metabolic interactions of an alkane-degrading methanogenic community

Microbial interactions have a key role in global geochemical cycles. Although we possess significant knowledge about the general biochemical processes occurring in microbial communities, we are often unable to decipher key functions of individual microorganisms within the environment in part owing to the inability to cultivate or study them in isolation. Here, we circumvent this shortcoming through the use of single-cell genome sequencing and a novel low-input metatranscriptomics protocol to reveal the intricate metabolic capabilities and microbial interactions of an alkane-degrading methanogenic community. This methanogenic consortium oxidizes saturated hydrocarbons under anoxic conditions through a thus-far-uncharacterized biochemical process. The genome sequence of a dominant bacterial member of this community, belonging to the genus Smithella, was sequenced and served as the basis for subsequent analysis through metabolic reconstruction. Metatranscriptomic data generated from less than 500 pg of mRNA highlighted metabolically active genes during anaerobic alkane oxidation in comparison with growth on fatty acids. These data sets suggest that Smithella is not activating hexadecane by fumarate addition. Differential expression assisted in the identification of hypothetical proteins with no known homology that may be involved in hexadecane activation. Additionally, the combination of 16S rDNA sequence and metatranscriptomic data enabled the study of other prevalent organisms within the consortium and their interactions with Smithella, thus yielding a comprehensive characterization of individual constituents at the genome scale during methanogenic alkane oxidation.

From Cellular Characteristics to Disease Diagnosis: Uncovering Phenotypes with Supercells

Cell heterogeneity and the inherent complexity due to the interplay of multiple molecular processes within the cell pose difficult challenges for current single-cell biology. We introduce an approach that identifies a disease phenotype from multiparameter single-cell measurements, which is based on the concept of “supercell statistics”, a single-cell-based averaging procedure followed by a machine learning classification scheme. We are able to assess the optimal tradeoff between the number of single cells averaged and the number of measurements needed to capture phenotypic differences between healthy and diseased patients, as well as between different diseases that are difficult to diagnose otherwise. We apply our approach to two kinds of single-cell datasets, addressing the diagnosis of a premature aging disorder using images of cell nuclei, as well as the phenotypes of two non-infectious uveitides (the ocular manifestations of Behçet's disease and sarcoidosis) based on multicolor flow cytometry. In the former case, one nuclear shape measurement taken over a group of 30 cells is sufficient to classify samples as healthy or diseased, in agreement with usual laboratory practice. In the latter, our method is able to identify a minimal set of 5 markers that accurately predict Behçet's disease and sarcoidosis. This is the first time that a quantitative phenotypic distinction between these two diseases has been achieved. To obtain this clear phenotypic signature, about one hundred CD8+ T cells need to be measured. Although the molecular markers identified have been reported to be important players in autoimmune disorders, this is the first report pointing out that CD8+ T cells can be used to distinguish two systemic inflammatory diseases. Beyond these specific cases, the approach proposed here is applicable to datasets generated by other kinds of state-of-the-art and forthcoming single-cell technologies, such as multidimensional mass cytometry, single-cell gene expression, and single-cell full genome sequencing techniques.

Reduced cost single cell genome sequencing and de novo assembly of microbial communities

DISTINCT GENOME SEQUENCING AND ASSEMBLY GOAL: Identification of every distinct genome present in a microbial sample. ASSUMPTION: Automated microfluidic devices are envisioned, that will be capable of high throughput • isolation of every individual cell in the sample, • DNA extraction, • DNA amplification using multiple displacement amplification (MDA) method, • grouping and barcoding of cells. METHOD 1: SINGLE-CELL SEQUENCING AND CO-ASSEMBLY

Distilled single-cell genome sequencing and de novo assembly for sparse microbial communities

Identification of every single genome present in a microbial sample is an important and challenging task with crucial applications. It is challenging because there are typically millions of cells in a microbial sample, the vast majority of which elude cultivation. The most accurate method to date is exhaustive single-cell sequencing using multiple displacement amplification, which is simply intractable for a large number of cells. However, there is hope for breaking this barrier, as the number of different cell types with distinct genome sequences is usually much smaller than the number of cells. Here, we present a novel divide and conquer method to sequence and de novo assemble all distinct genomes present in a microbial sample with a sequencing cost and computational complexity proportional to the number of genome types, rather than the number of cells. The method is implemented in a tool called Squeezambler. We evaluated Squeezambler on simulated data. The proposed divide and conquer method successfully reduces the cost of sequencing in comparison with the naïve exhaustive approach. Squeezambler and datasets are available at http://compbio.cs.wayne.edu/software/squeezambler/.

Isolation and Genome Analysis of Single Virions using 'Single Virus Genomics'

Whole genome amplification and sequencing of single microbial cells enables genomic characterization without the need of cultivation (1-3). Viruses, which are ubiquitous and the most numerous entities on our planet (4) and important in all environments (5), have yet to be revealed via similar approaches. Here we describe an approach for isolating and characterizing the genomes of single virions called 'Single Virus Genomics' (SVG). SVG utilizes flow cytometry to isolate individual viruses and whole genome amplification to obtain high molecular weight genomic DNA (gDNA) that can be used in subsequent sequencing reactions.

The future is now: single-cell genomics of bacteria and archaea.

Interest in the expanding catalog of uncultivated microorganisms, increasing recognition of heterogeneity among seemingly similar cells, and technological advances in whole-genome amplification and single-cell manipulation are driving considerable progress in single-cell genomics. Here, the spectrum of applications for single-cell genomics, key advances in the development of the field, and emerging methodology for single-cell genome sequencing are reviewed by example with attention to the diversity of approaches and their unique characteristics. Experimental strategies transcending specific methodologies are identified and organized as a road map for future studies in single-cell genomics of environmental microorganisms. Over the next decade, increasingly powerful tools for single-cell genome sequencing and analysis will play key roles in accessing the genomes of uncultivated organisms, determining the basis of microbial community functions, and fundamental aspects of microbial population biology.

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.

Genome of the pathogen Porphyromonas gingivalis recovered from a biofilm in a hospital sink using a high-throughput single-cell genomics platform

Although biofilms have been shown to be reservoirs of pathogens, our knowledge of the microbial diversity in biofilms within critical areas, such as health care facilities, is limited. Available methods for pathogen identification and strain typing have some inherent restrictions. In particular, culturing will yield only a fraction of the species present, PCR of virulence or marker genes is mainly focused on a handful of known species, and shotgun metagenomics is limited in the ability to detect strain variations. In this study, we present a single-cell genome sequencing approach to address these limitations and demonstrate it by specifically targeting bacterial cells within a complex biofilm from a hospital bathroom sink drain. A newly developed, automated platform was used to generate genomic DNA by the multiple displacement amplification (MDA) technique from hundreds of single cells in parallel. MDA reactions were screened and classified by 16S rRNA gene PCR sequence, which revealed a broad range of bacteria covering 25 different genera representing environmental species, human commensals, and opportunistic human pathogens. Here we focus on the recovery of a nearly complete genome representing a novel strain of the periodontal pathogen Porphyromonas gingivalis (P. gingivalis JCVI SC001) using the single-cell assembly tool SPAdes. Single-cell genomics is becoming an accepted method to capture novel genomes, primarily in the marine and soil environments. Here we show for the first time that it also enables comparative genomic analysis of strain variation in a pathogen captured from complex biofilm samples in a healthcare facility.

Predominant archaea in marine sediments degrade detrital proteins

Half of the microbial cells in the Earth's oceans are found in sediments. Many of these cells are members of the Archaea, single-celled prokaryotes in a domain of life separate from Bacteria and Eukaryota. However, most of these archaea lack cultured representatives, leaving their physiologies and placement on the tree of life uncertain. Here we show that the uncultured miscellaneous crenarchaeotal group (MCG) and marine benthic group-D (MBG-D) are among the most numerous archaea in the marine sub-sea floor. Single-cell genomic sequencing of one cell of MCG and three cells of MBG-D indicated that they form new branches basal to the archaeal phyla Thaumarchaeota and Aigarchaeota, for MCG, and the order Thermoplasmatales, for MBG-D. All four cells encoded extracellular protein-degrading enzymes such as gingipain and clostripain that are known to be effective in environments chemically similar to marine sediments. Furthermore, we found these two types of peptidase to be abundant and active in marine sediments, indicating that uncultured archaea may have a previously undiscovered role in protein remineralization in anoxic marine sediments.