Single-cell Genome Sequencing Research Articles

A single amplified genome catalog reveals the dynamics of mobilome and resistome in the human microbiome

BackgroundThe increase in metagenome-assembled genomes (MAGs) has advanced our understanding of the functional characterization and taxonomic assignment within the human microbiome. However, MAGs, as population consensus genomes, often aggregate heterogeneity among species and strains, thereby obfuscating the precise relationships between microbial hosts and mobile genetic elements (MGEs). In contrast, single amplified genomes (SAGs) derived via single-cell genome sequencing can capture individual genomic content, including MGEs.ResultsWe introduce the first substantial SAG dataset (bbsag20) from the human oral and gut microbiome, comprising 17,202 SAGs above medium-quality without co-assembly. This collection unveils a diversity of bacterial lineages across 312 oral and 647 gut species, demonstrating different taxonomic compositions from MAGs. Moreover, the SAGs showed cellular-level evidence of the translocation of oral bacteria to the gut. We also identified broad-host-range MGEs harboring antibiotic resistance genes (ARGs), which were not detected in the MAGs.ConclusionsThe difference in taxonomic composition between SAGs and MAGs indicates that combining both methods would be effective in expanding the genome catalog. By connecting mobilomes and resistomes in individual samples, SAGs could meticulously chart a dynamic network of ARGs on MGEs, pinpointing potential ARG reservoirs and their spreading patterns in the microbial community.DX-3H-wDoVG35Spx9tFU_UVideo

Inferring replication timing and proliferation dynamics from single-cell DNA sequencing data

Dysregulated DNA replication is a cause and a consequence of aneuploidy in cancer, yet the interplay between copy number alterations (CNAs), replication timing (RT) and cell cycle dynamics remain understudied in aneuploid tumors. We developed a probabilistic method, PERT, for simultaneous inference of cell-specific replication and copy number states from single-cell whole genome sequencing (scWGS) data. We used PERT to investigate clone-specific RT and proliferation dynamics in >50,000 cells obtained from aneuploid and clonally heterogeneous cell lines, xenografts and primary cancers. We observed bidirectional relationships between RT and CNAs, with CNAs affecting X-inactivation producing the largest RT shifts. Additionally, we found that clone-specific S-phase enrichment positively correlated with ground-truth proliferation rates in genomically stable but not unstable cells. Together, these results demonstrate robust computational identification of S-phase cells from scWGS data, and highlight the importance of RT and cell cycle properties in studying the genomic evolution of aneuploid tumors.

Liquid Biopsy-Based Accurate Diagnosis and Genomic Profiling of Hard-to-Biopsy Tumors via Parallel Single-Cell Genomic Sequencing of Exfoliated Tumor Cells.

Liquid biopsy provides a convenient and safer procedure for the diagnosis and genomic profiling of tumors that are inaccessible to biopsy by analyzing exfoliated tumor cells (ETCs) or tumor-derived cell-free DNA (cfDNA). However, its primary challenge lies in its limited accuracy in comparison to tissue-based approaches. We report a parallel single-ETC genomic sequencing (Past-Seq) method for the accurate diagnosis and genomic profiling of hard-to-biopsy tumors such as cholangiocarcinoma (CCA) and upper tract urothelial carcinoma (UTUC). For CCA, a prospective cohort of patients with suspicious biliary strictures (n = 36) was studied. Parallel single-cell whole genome sequencing and whole exome sequencing were performed on bile ETCs for CCA diagnosis and resolving mutational profiles, respectively, along with bile cfDNA sequenced for comparison. Concordant single-cell copy number alteration (CNA) profiles in multiple ETCs provided compelling evidence for generating a malignant diagnosis. Past-Seq yielded bile-based accurate CCA diagnosis (96% sensitivity, 100% specificity, and positive predictive value), surpassing pathological evaluation (56% sensitivity) and bile cfDNA CNA analysis (13% sensitivity), and generated the best performance in the retrieval tissue mutations. To further explore the applicability of Past-Seq, 10 suspicious UTUC patients were investigated with urine specimens, and Past-Seq exhibited 90% sensitivity in diagnosing UTUC, demonstrating its broad applicability across various liquid biopsies and cancer types.

Tracking clonal evolution of drug resistance in ovarian cancer patients by exploiting structural variants in cfDNA.

Drug resistance is the major cause of therapeutic failure in high-grade serous ovarian cancer (HGSOC). Yet, the mechanisms by which tumors evolve to drug resistant states remains largely unknown. To address this, we aimed to exploit clone-specific genomic structural variations by combining scaled single-cell whole genome sequencing with longitudinally collected cell-free DNA (cfDNA), enabling clonal tracking before, during and after treatment. We developed a cfDNA hybrid capture, deep sequencing approach based on leveraging clone-specific structural variants as endogenous barcodes, with orders of magnitude lower error rates than single nucleotide variants in ctDNA (circulating tumor DNA) detection, demonstrated on 19 patients at baseline. We then applied this to monitor and model clonal evolution over several years in ten HGSOC patients treated with systemic therapy from diagnosis through recurrence. We found drug resistance to be polyclonal in most cases, but frequently dominated by a single high-fitness and expanding clone, reducing clonal diversity in the relapsed disease state in most patients. Drug-resistant clones frequently displayed notable genomic features, including high-level amplifications of oncogenes such as CCNE1 , RAB25 , NOTCH3 , and ERBB2 . Using a population genetics Wright-Fisher model, we found evolutionary trajectories of these features were consistent with drug-induced positive selection. In select cases, these alterations impacted selection of secondary lines of therapy with positive patient outcomes. For cases with matched single-cell RNA sequencing data, pre-existing and genomically encoded phenotypic states such as upregulation of EMT and VEGF were linked to drug resistance. Together, our findings indicate that drug resistant states in HGSOC pre-exist at diagnosis and lead to dramatic clonal expansions that alter clonal composition at the time of relapse. We suggest that combining tumor single cell sequencing with cfDNA enables clonal tracking in patients and harbors potential for evolution-informed adaptive treatment decisions.

Uncovering Endolysins against Methicillin-Resistant Staphylococcus aureus Using a Microbial Single-Cell Genome Database.

Endolysins, peptidoglycan hydrolases derived from bacteriophages (phages), are being developed as a promising alternative to conventional antibiotics. To obtain highly active endolysins, a diverse library of these endolysins is vital. We propose here microbial single-cell genome sequencing as an efficient tool to discover dozens of previously unknown endolysins, owing to its culture-independent sequencing method. As a proof of concept, we analyzed and recovered endolysin genes within prophage regions of Staphylococcus single-amplified genomes in human skin microbiome samples. We constructed a library of chimeric endolysins by shuffling domains of the natural endolysins and performed high-throughput screening against Staphylococcus aureus. One of the lead endolysins, bbst1027, exhibited desirable antimicrobial properties, such as rapid bactericidal activity, no detectable resistance development, and in vivo efficacy. We foresee that this endolysin discovery pipeline is in principle applicable to any bacterial target and boost the development of novel antimicrobial agents.

Negative selection allows human primary fibroblasts to tolerate high somatic mutation loads induced by N-ethyl-N-nitrosourea.

Single-cell sequencing has shown that thousands of mutations accumulate with age in most human tissues. While there is ample evidence that some mutations can clonally amplify and lead to disease, the total burden of mutations a cell tolerates without functional decline remains unknown. Here we addressed this question by exposing human primary fibroblasts to multiple, low doses of N-ethyl-N-nitrosourea (ENU) and analyzed somatic mutation burden using single-cell whole genome sequencing. The results indicate that individual cells can sustain ∼60,000 single-nucleotide variants (SNVs) with only a slight adverse effect on growth rate. We provide evidence that such high levels of mutations are only tolerated through negative selection against variants in gene coding regions, and in sequences associated with genetic pathways for maintaining basic cellular function and growth. Since most tissues in adults are non-dividing, these results suggest that somatic mutations in the absence of negative selection may have functionally adverse effects.

Selective pressures of platinum compounds shape the evolution of therapy-related myeloid neoplasms

Therapy-related myeloid neoplasms (t-MN) arise as a complication of chemo- and/or radiotherapy. Although t-MN can occur both in adult and childhood cancer survivors, the mechanisms driving therapy-related leukemogenesis likely vary across different ages. Chemotherapy is thought to induce driver mutations in children, whereas in adults pre-existing mutant clones are selected by the exposure. However, selective pressures induced by chemotherapy early in life are less well studied. Here, we use single-cell whole genome sequencing and phylogenetic inference to show that the founding cell of t-MN in children starts expanding after cessation of platinum exposure. In patients with Li-Fraumeni syndrome, characterized by a germline TP53 mutation, we find that the t-MN already expands during treatment, suggesting that platinum-induced growth inhibition is TP53-dependent. Our results demonstrate that germline aberrations can interact with treatment exposures in inducing t-MN, which is important for the development of more targeted, patient-specific treatment regimens and follow-up.

Mitochondrial genetics through the lens of single-cell multi-omics.

Mitochondria carry their own genetic information encoding for a subset of protein-coding genes and translational machinery essential for cellular respiration and metabolism. Despite its small size, the mitochondrial genome, its natural genetic variation and molecular phenotypes have been challenging to study using bulk sequencing approaches, due to its variation in cellular copy number, non-Mendelian modes of inheritance and propensity for mutations. Here we highlight emerging strategies designed to capture mitochondrial genetic variation across individual cells for lineage tracing and studying mitochondrial genetics in primary human cells and clinical specimens. We review recent advances surrounding single-cell mitochondrial genome sequencing and its integration with functional genomic readouts, including leveraging somatic mitochondrial DNA mutations as clonal markers that can resolve cellular population dynamics in complex human tissues. Finally, we discuss how single-cell whole mitochondrial genome sequencing approaches can be utilized to investigate mitochondrial genetics and its contribution to cellular heterogeneity and disease.

Metabolic heterogeneity in humans

Recent advancements in technology, especially the emergence of single-cell technologies, genomic sequencing, metabolomics, and artificial intelligence, have enabled us to understand the distinct metabolic changes in different cell types, tissues, genders, disease states, ages, and populations. Six scientists whose work intersects with metabolism in various capacities tell us about their vision for human metabolic heterogeneity.

Transient Differentiation-State Plasticity Occurs during Acute Lymphoblastic Leukemia Initiation.

Leukemia is characterized by oncogenic lesions that result in a block of differentiation, whereas phenotypic plasticity is retained. A better understanding of how these two phenomena arise during leukemogenesis in humans could help inform diagnosis and treatment strategies. Here, we leveraged the well-defined differentiation states during T-cell development to pinpoint the initiation of T-cell acute lymphoblastic leukemia (T-ALL), an aggressive form of childhood leukemia, and study the emergence of phenotypic plasticity. Single-cell whole genome sequencing of leukemic blasts was combined with multiparameter flow cytometry to couple cell identity and clonal lineages. Irrespective of genetic events, leukemia-initiating cells altered their phenotypes by differentiation and dedifferentiation. The construction of the phylogenies of individual leukemias using somatic mutations revealed that phenotypic diversity is reflected by the clonal structure of cancer. The analysis also indicated that the acquired phenotypes are heritable and stable. Together, these results demonstrate a transient period of plasticity during leukemia initiation, where phenotypic switches seem unidirectional. Significance: A method merging multicolor flow cytometry with single-cell whole genome sequencing to couple cell identity with clonal lineages uncovers differentiation-state plasticity in leukemia, reconciling blocked differentiation with phenotypic plasticity in cancer.

Integrated single cell analysis reveals co-evolution of malignant B cells and tumor micro-environment in transformed follicular lymphoma

Histological transformation of follicular lymphoma (FL) to aggressive forms is associated with poor outcome. Phenotypic consequences of this evolution and its impact on the tumor microenvironment (TME) remain unknown. We perform single-cell whole genome sequencing (scWGS) and transcriptome sequencing (scWTS) of 11 paired pre/post-transformation patient samples and scWTS of additional samples from patients without transformation. Our analysis reveals evolutionary dynamics of transformation at single-cell resolution, highlighting a shifting TME landscape, with an emerging immune-cell exhaustion signature, co-evolving with the shifting malignant B phenotype in a regulatory ecosystem. Integration of scWGS and scWTS identifies malignant cell pathways upregulated during clonal tumor evolution. Using multi-color immunofluorescence, we transfer these findings to a TME-based transformation biomarker, subsequently validated in two independent pretreatment cohorts. Taken together, our results provide a comprehensive view of the combined genomic and phenotypic evolution of malignant cells during transformation and shifting crosstalk between malignant cells and the TME.

Hybrid sequence-based analysis reveals the distribution of bacterial species and genes in the oral microbiome at a high resolution

Bacteria in the oral microbiome are poorly identified owing to the lack of established culture methods for them. Thus, this study aimed to use culture-free analysis techniques, including bacterial single-cell genome sequencing, to identify bacterial species and investigate gene distribution in saliva. Saliva samples from the same individual were classified as inactivated or viable and then analyzed using 16S rRNA sequencing, metagenomic shotgun sequencing, and bacterial single-cell sequencing. The results of 16S rRNA sequencing revealed similar microbiota structures in both samples, with Streptococcus being the predominant genus. Metagenomic shotgun sequencing showed that approximately 80 % of the DNA in the samples was of non-bacterial origin, whereas single-cell sequencing showed an average contamination rate of 10.4 % per genome. Single-cell sequencing also yielded genome sequences for 43 out of 48 wells for the inactivated samples and 45 out of 48 wells for the viable samples. With respect to resistance genes, four out of 88 isolates carried cfxA, which encodes a β-lactamase, and four isolates carried erythromycin resistance genes. Tetracycline resistance genes were found in nine bacteria. Metagenomic shotgun sequencing provided complete sequences of cfxA, ermF, and ermX, whereas other resistance genes, such as tetQ and tetM, were detected as fragments. In addition, virulence factors from Streptococcus pneumoniae were the most common, with 13 genes detected. Our average nucleotide identity analysis also suggested five single-cell-isolated bacteria as potential novel species. These data would contribute to expanding the oral microbiome data resource.

Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia.

The inherent genetic heterogeneity of acute myeloid leukemia (AML) has challenged the development of precise and effective therapies. The objective of this study was to elucidate the genomic basis of drug resistance or sensitivity, identify signatures for drug response prediction, and provide resources to the research community. We performed targeted sequencing, high-throughput drug screening, and single-cell genomic profiling on leukemia cell samples derived from patients with AML. Statistical approaches and machine learning models were applied to identify signatures for drug response prediction. We also integrated large public datasets to understand the co-occurring mutation patterns and further investigated the mutation profiles in the single cells. The features revealed in the co-occurring or mutual exclusivity pattern were further subjected to machine learning models. We detected genetic signatures associated with sensitivity or resistance to specific agents, and identified five co-occurring mutation groups. The application of single-cell genomic sequencing unveiled the co-occurrence of variants at the individual cell level, highlighting the presence of distinct subclones within patients with AML. Using the mutation pattern for drug response prediction demonstrates high accuracy in predicting sensitivity to some drug classes, such as MEK inhibitors for RAS-mutated leukemia. Our study highlights the importance of considering the gene mutation patterns for the prediction of drug response in AML. It provides a framework for categorizing patients with AML by mutations that enable drug sensitivity prediction.

Abstract 322: Ultraplex genome and exome sequencing of rare single cells

Abstract Single-cell genome analysis enables researchers to gain novel insights into tumor heterogeneity. Conducting single-cell genomic analysis using next-generation sequencing (NGS) methods has traditionally been challenging since the amount of genomic DNA present in a single cell is limited. PCR-based amplification methods normally have high error rates, low coverage uniformity, and extensive allelic drop-outs making interpretation of SNVs and CNVs challenging.Here we introduce a new whole-genome amplification method which utilizes an optimized isothermal DNA amplification reaction with a Hi-fidelity, error correcting polymerase and downstream library preparation protocol to allow pooling of amplified DNA. The key DNA amplification steps use multiple displacement amplification (MDA) technology to amplify gDNA directly from single cells, nuclei or previously isolated genomic DNA. During the MDA reaction, sample-barcodes are incorporated into the newly synthetized DNA, allowing the amplified DNA to be pooled and processed in to one ultraplex library. This method saves library preparation time, reduces DNA library costs and limits plastic consumable usage.Our current workflow allows 24 samples to be multiplexed in one library and 24 libraries to be produced for a total of 576 samples per sequencing flow cell. This method can be used to investigate chromosomal instability with low pass sequencing or SNV and CNV analysis following whole genome, exome-capture workflows. Citation Format: Ioanna Andreou, Samuel Rulli, Katja Heitz, Silke Huebner, Christin Meerschiff, Eric Lader. Ultraplex genome and exome sequencing of rare single cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 322.

Abstract 6937: High throughput single cell simultaneous DNA and RNA sequencing identified cell-of-origin of breast cancer

Abstract Understanding the relationship between genotype and phenotype in breast cancer cells has been challenging at single cell resolution, mainly because existing high-throughput methods are limited to measuring a single modality and data must be integrated indirectly via computational methods. To address this challenge, we developed wellDR-seq, a high-throughput single cell method that can simultaneously measure the single cell whole genome and transcriptome directly from thousands of single cells in parallel. Using this method, we profiled 17,427 single cells in 6 different ER+ breast cancer patients with either premalignant disease (Ductal-carcinoma-in-situ) or invasive ductal carcinoma (IDC). From these data we identified the epithelial cell-of-origin in 3 cases, showing that ER+ breast cancer cells originated from Luminal Hormone Responsive (LumHR) in the normal breast tissues. We also found that autosomal somatic copy number aberrations were exclusively present in LumSec (< 2%) and LumHR epithelial cells, while chrX gain or loss also occurred in stromal cells (eg fibroblast and endothelial) but a low frequency (<2%) in our datasets. Additionally, our data show the impact of subclonal copy number profiles on gene expression programs, which reflects both gene dosage effects in CNA regions and more complex deregulation in copy-neutral regions. wellDR-seq offers a powerful tool for large-scale single cell genome and transcriptome simultaneous sequencing across different clinical samples, opens new avenue to investigate genotype and phenotypes interactions, identify tumor cells origins, reveal somatic copy number and mutation events, quantify the gene dosage effect and discerning differential genes expression between tumor subclones. Citation Format: Kaile Wang, Rui Ye, Shanshan Bai, Zhenna Xiao, Lei Yano, Jianzhuo Li, Yiyun Lin, Emi Sei, Steven Lin, Alastair Thompson, Savitri Krishnamurthy, Nicholas Navin. High throughput single cell simultaneous DNA and RNA sequencing identified cell-of-origin of breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6937.

Abstract 869: Single-cell DNA replication dynamics in genomically unstable cancers

Abstract Background: DNA replication and cell cycle regulation are frequently disrupted as part of a cancer’s progression toward uncontrolled proliferation, generating somatic copy number alterations (CNAs) and producing intratumoral heterogeneity that drives subsequent evolution. Structural variation and CNAs have been shown to impact epigenetic and chromatin states, but our ability to assess their impact on DNA replication timing (RT) and cell proliferation rates remains limited. Single-cell whole genome sequencing (scWGS) is a powerful method for studying clonal heterogeneity and CNAs, and has the potential to provide greater insight into DNA replication dynamics in aneuploid populations. However, computational identification of S-phase cells and distinguishing inherited somatic CNAs from transient DNA replication changes remain challenging. Methods: We present a new method, PERT, which uses a Bayesian framework to model read depth as a combination of somatic copy number, replication, and sequencing bias, enabling estimation of DNA replication profiles and cell cycle phase from scWGS data. Unlike previous approaches, PERT provides unbiased estimates of RT and cell cycle phase which allows for analysis of previously uncharacterized cell types using any scWGS platform. These unique properties enable PERT to perform novel analysis such as estimating clone-specific proliferation rates and studying the interplay between RT and somatic CNAs during tumor evolution. We applied PERT to a cohort of >50,000 scWGS cells obtained from a collection of genomically unstable breast and ovarian cell lines, xenografts and primary cancer tissues. Results: Clone RT profiles correlated with future copy number changes in serially passaged cell lines. Cell type was the strongest determinant of RT heterogeneity, while whole genome doubling (WGD) and mutational signature had weaker RT associations but were associated with accumulation of late S-phase cells. Recurrent CNAs affecting chromosome X had striking impact on RT, with loss of the inactive X allele shifting replication earlier, and loss of inactive Xq resulting in reactivation of Xp. Analysis of time series xenografts illustrated that cell cycle distributions approximate clone proliferation. This relationship enabled us to observe that highly proliferative clones were the most chemosensitive in cisplatin-treated xenografts and, separately, present novel evidence that WGD leads to slower proliferation. Conclusions: Our analysis implicates cell type as the strongest determinant of RT with chrX being the locus of highest RT variation due to X-inactivation. Separately, quantification of S-phase cells enables interrogation of the on- and off-treatment fitness of genetically distinct subclones. This work leads to better understanding of how DNA replication dynamics drive and are further modulated by genomic instability. Citation Format: Adam C. Weiner, Marc Williams, Hongyu Shi, Ignacio Vazquez-Garcia, Sohrab Salehi, Nicole Rusk, Sohrab P. Shah, Andrew McPherson. Single-cell DNA replication dynamics in genomically unstable cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 869.

Abstract 4343: Revealing ovarian cancer copy number variation in single cells

Abstract The mortality rate associated with ovarian cancer (OvCa) is disproportionately high in comparison to its incidence rate. This is partly due to the heterogeneous nature of the disease, which reduces treatment efficacy and contributes to high rates of relapse and chemotherapy resistance. Most OvCa are epithelial in origin and can be classified into four main subtypes: serous, mucinous, endometrioid, and clear cell. Of these, high grade serous ovarian cancer (HGSOC) is the deadliest. Epithelial ovarian carcinomas (EOC) typically exhibit widespread chromosomal and arm-level copy number abnormalities across most of the genome; in HGSOC, focal amplifications and microdeletions are especially prevalent and indicative of high genomic instability. To understand the heterogeneity of aneuploidy in EOC and HGSOC, we performed shallow single-cell whole genome sequencing on four EOC samples: two HGSOC, one clear cell, and one mixed clear cell and endometrioid. All samples were late stage and treatment naïve, and one sample had a known BRCA2 mutation. Sequencing data was processed by two complementary methods to call copy number alterations. First, we used the Cell Ranger DNA pipeline (10x Genomics) to align cell-identified sequencing reads to human reference genome GRCh38 for coverage-based copy number estimation. Resulting copy number calls were cleaned up for mappability, quality, and noisiness. Each sample was then subject to clustering and subclustering analysis using maximum likelihood genetic clustering algorithms. All samples exhibited a high level of aneuploidy, including characteristic alterations known to be associated with EOC. Two tumors contained readily distinguishable clonal populations, and all samples contained main tumor clones that could be further divided by unique subclonal characteristics. Evidence of polyploidy was also seen in all four specimens, with some tumor clusters exhibiting triploid and tetraploid baselines. In parallel, sequencing data was analyzed by the Copy-number Haplotype Inference in Single-cell by Evolutionary Links (CHISEL) algorithm. CHISEL utilizes both binned read depth ratio and B-allele frequency data to determine allele- and haplotype-specific copy numbers in single cells. Results from CHISEL confirmed the copy number calls from Cell Ranger DNA, and revealed widespread loss of heterozygosity in all samples. These findings were corroborated with allele-specific copy number data derived from matched tumor-normal whole exome sequencing. Furthermore, CHISEL detected polyploidy in one-third of the tumor cells with no preference for the A or B alleles. Overall, our findings highlight that the known heterogeneity of ovarian cancer extends to the level of aneuploidy and CNAs, shedding light on factors which pose significant barriers to effective personalized medicine implementation. Citation Format: Yuxin Jin, Rania Bassiouni, Lee D. Gibbs, Jing Qian, Solomon Rotimi, Michelle G. Webb, Seeta Rajpara, David W. Craig, Lynda D. Roman, John D. Carpten. Revealing ovarian cancer copy number variation in single cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4343.

Single-cell sequencing shows mosaic aneuploidy in most human embryos.

Mammalian preimplantation embryos often contain chromosomal defects that arose in the first divisions after fertilization and affect a subpopulation of cells - an event known as mosaic aneuploidy. In this issue of the JCI, Chavli et al. report single-cell genomic sequencing data for rigorous evaluation of the incidence and degree of mosaic aneuploidy in healthy human in vitro fertilization (IVF) embryos. Remarkably, mosaic aneuploidy occurred in at least 80% of human blastocyst-stage embryos, with often less than 20% of cells showing defects. These findings confirm that mosaic aneuploidy is prevalent in human embryos, indicating that the process is a widespread event that rarely has clinical consequences. There are major implications for preimplantation genetic testing of aneuploidy (PGT-A), a test commonly used to screen and select IVF embryos for transfer. The application and benefit of this technology is controversial, and the findings provide more cause for caution on its use.

Mapping crossover events of mouse meiotic recombination by restriction fragment ligation-based Refresh-seq

Single-cell whole-genome sequencing methods have undergone great improvements over the past decade. However, allele dropout, which means the inability to detect both alleles simultaneously in an individual diploid cell, largely restricts the application of these methods particularly for medical applications. Here, we develop a new single-cell whole-genome sequencing method based on third-generation sequencing (TGS) platform named Refresh-seq (restriction fragment ligation-based genome amplification and TGS). It is based on restriction endonuclease cutting and ligation strategy in which two alleles in an individual cell can be cut into equal fragments and tend to be amplified simultaneously. As a new single-cell long-read genome sequencing method, Refresh-seq features much lower allele dropout rate compared with SMOOTH-seq. Furthermore, we apply Refresh-seq to 688 sperm cells and 272 female haploid cells (secondary polar bodies and parthenogenetic oocytes) from F1 hybrid mice. We acquire high-resolution genetic map of mouse meiosis recombination at low sequencing depth and reveal the sexual dimorphism in meiotic crossovers. We also phase the structure variations (deletions and insertions) in sperm cells and female haploid cells with high precision. Refresh-seq shows great performance in screening aneuploid sperm cells and oocytes due to the low allele dropout rate and has great potential for medical applications such as preimplantation genetic diagnosis.

Single-cell low-pass whole genome sequencing accurately detects circulating tumor cells for liquid biopsy-based multi-cancer diagnosis

Accurate detection of circulating tumor cells (CTCs) in blood and non-blood body fluids enables generation of deterministic cancer diagnosis and represent a less invasive and safer liquid biopsy approach. Although genomic alternations have been widely used in circulating tumor DNA (ctDNA) analysis, studies on cell-based genomic alternations profiling for CTC detection are rare due to major technical limitations in single-cell whole genome sequencing (WGS) including low throughput, low accuracy and high cost. We report a single-cell low-pass WGS-based protocol (scMet-Seq) for sensitive and accurate CTC detection by combining a metabolic function-associated marker Hexokinase 2 (HK2) and a Tn5 transposome-based WGS method with improved cell fixation strategy. To explore the clinical use, scMet-Seq has been investigated with blood and non-blood body fluids in diagnosing metastatic diseases, including ascites-based diagnosis of malignant ascites (MA) and blood-based diagnosis of metastatic small-cell lung cancer (SCLC). ScMet-Seq shows high diagnostic sensitivity (MA: 79% in >10 cancer types; metastatic SCLC: 90%) and ~100% of diagnostic specificity and positive predictive value, superior to clinical cytology that exhibits diagnostic sensitivity of 52% in MA diagnosis and could not generate blood-based diagnosis. ScMet-Seq represents a liquid biopsy approach for deterministic cancer diagnosis in different types of cancers and body fluids.