Single-cell Genome Sequencing Research Articles

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.

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.

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.

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.

Abstract PR012: Evolutionary dynamics of whole-genome doubling in ovarian cancer

Abstract Chromosomal instability (CIN) is a major driver of tumor progression and treatment resistance in many cancers. CIN is characterized by ongoing chromosome missegregation, segmental aneuploidy and whole-genome doubling (WGD), generating copy number heterogeneity that provides a substrate for natural selection. Although well characterized in model systems, the evolutionary dynamics and genomic impact of WGD is underexplored in primary tumors and metastases. Newly developed single-cell genomics assays now allow for the measurement of the whole genomes of single cells obtained from patient tissues, thus providing a direct view into the evolution of tumor genomes undergoing WGD. We performed multi-region single-cell whole genome sequencing (scWGS) in a cohort of patients with high-grade serous ovarian cancer (HGSOC), an archetype of high CIN cancers. We profiled a total of 70,644 single cells from 63 pre-treatment samples obtained for 36 HGSOC patients. For each patient, we computed total and allele-specific copy number per cell, and inferred the evolutionary history of each patient’s tumor, including clonal and subclonal WGD events and cell-specific whole-chromosome or arm-level copy number changes indicative of missegregations. To determine the role of CIN-associated micronuclei (MN) formation, we used high-resolution cGAS immunofluorescence (IF) microscopy to profile FFPE tissues site-matched to scWGS, and quantify spatially-resolved rates of MN formation and rupture across 120 samples. A majority of tumors were characterized by early clonal WGD (61%, 22/36 cases). The remaining patients exhibited subclonal WGD subpopulations averaging 10% of cancer cells in total. To understand the interplay between recent WGD and CIN across the cohort, we subset all patients cells into non-WGD, subclonal WGD, and clonal WGD and computed mis-segregation rates for these subsets. Non-WGD cells showed the lowest levels of mis-segregation. Subclonal WGD exhibited the highest CIN rates, including mis-segregation and multipolar cells indicative of CIN following a recent WGD event. Clonal WGD exhibited intermediate levels of mis-segregation indicative of stabilization following a historical WGD event. CIN following WGD often resulted in a reduction in ploidy relative to the ancestral WGD cell. Micronucleus rupture was ubiquitously detected in IF across HGSOC tumors, and MN rates varied widely between patients and across primary and metastatic sites. Micronucleus rupture commonly resulted from the impact of WGD, which increased sequestration of missegregated chromosomes into MN. Through multimodal single cell measurements of HGSOC tumors, we were able to characterize genomic complexity from individual cell division errors in HGSOC, and shed light into how the transient processes governing ovarian cancer cell evolution impact the genomes of cell populations. Citation Format: Ignacio Vázquez-García, Matthew Myers, Marc J. Williams, Samuel S. Freeman, Jun Li, Hongyu Shi, Gryte Satas, Nicholas Ceglia, Florian Uhlitz, MSK SPECTRUM Consortium, Britta Weigelt, Dmitriy Zamarin, Andrew McPherson, Samuel F. Bakhoum, Sohrab P. Shah. Evolutionary dynamics of whole-genome doubling in ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR012.

Abstract IA15: Single cell origins and targeting of genomic instability in breast cancers

Abstract Early whole genome sequencing studies have emphasised the different modes by which breast cancers can evolve. However, bulk NGS tissue analysis obscures mutations that are contemporaneous and/or lead to negative selection, and does not resolve clone specific metrics such as replication rate and fitness. We have developed single cell genome sequencing methods (Laks et al., Cell 2019) to identify large scale copy number-structural (CNA-SV) mutational processes that result in genomic shuffling through the duplication or deletion of chromosomal regions, obscured by bulk tissue analysis. This has revealed the existence and activity of distinct “foreground” mutational processes, i.e. mutational events in single mitoses that are observed directly, before the effects of purifying selection. We discovered that foreground processes are preferentially associated with background patterns of genome instability (Funnell et al., Nature 2022). We also discovered that CNA-SV mutational patterns are associated with relative sensitivity or resistance to platinum, a commonly used DNA targeting agent in TNBC and HGSOC. Our second recent discovery has been the association of CNA-SV mutations with clonal resistance to platinum in TNBC (Salehi et al., Nature 2021). Taken together, we now understand that ongoing CNA-SV mutations contribute to the evolution of fitness landscapes for DNA targeting therapeutics. In parallel work (Xu et al Nat. comms 2017) we have discovered stabilisers of folded DNA guanine stacked bases, G-quadruplexes (G4), to be synthetically lethal in contexts including HR and TMEJ that are underpinned by the single cell processes identified through single genome sequencing. To this end we have recently observed G4-ligand clinical activity in BRCA2 and PALB2 mutated cancers in a phase 1 trial (Hilton et al, Nature communication 2022). Citation Format: Samuel Aparicio. Single cell origins and targeting of genomic instability in breast cancers [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr IA15.

A microfluidics-free centrifugally-driven nano-liquid pipetting system (CNPS) for single-cell whole genome amplification

We developed a microfluidics-free centrifugally-driven nano-liquid pipetting system (CNPS) for single-cell whole genome amplification. The capillary tube and the collection vessel (PCR tube or 96-well PCR plate), both of which are easily accessible, make up the basic components of the system. Unlike microfluidic chips that need a clean room and special skills to fabricate, the CNPS can be assembled in the typical biology laboratory without any special training. Centrifugation is used to directly pipette the nanoliter-volume liquid into the collection tube, which eliminates the requirement for extra equipment. The CNPS, which is compatible with the standard handheld pipette, allows for a smooth transition between the micro and macro worlds, thereby overcoming the problem of retrieving nanoliter-volume products. We further demonstrated the feasibility of the CNPS for nanoliter-volume single-cell multiple displacement amplification. Based on the sequencing results, the mapped reads cover on average 95.23%, 95.15%, and 80.94% for Saccharomyces cerevisiae, Escherichia coli, and Staphylococcus epidermidis, respectively. In sum, the CNPS has the potential to become an alternative powerful platform for single-cell genome sequencing with the ability to manipulate ultra-small volumes, as well as to enhance precision and sophistication by significantly minimizing sample losses and reducing amplification bias.

Joint inference of clonal structure using single-cell genome and transcriptome sequencing data.

Latest advancements in the high-throughput single-cell genome (scDNA) and transcriptome (scRNA) sequencing technologies enabled cell-resolved investigation of tissue clones. However, it remains challenging to cluster and couple single cells for heterogeneous scRNA and scDNA data generated from the same specimen. In this study, we present a computational framework called CCNMF, which employs a novel Coupled-Clone Non-negative Matrix Factorization technique to jointly infer clonal structure for matched scDNA and scRNA data. CCNMF couples multi-omics single cells by linking copy number and gene expression profiles through their general concordance. It successfully resolved the underlying coexisting clones with high correlations between the clonal genome and transcriptome from the same specimen. We validated that CCNMF can achieve high accuracy and robustness using both simulated benchmarks and real-world applications, including an ovarian cancer cell lines mixture, a gastric cancer cell line, and a primary gastric cancer. In summary, CCNMF provides a powerful tool for integrating multi-omics single-cell data, enabling simultaneous resolution of genomic and transcriptomic clonal architecture. This computational framework facilitates the understanding of how cellular gene expression changes in conjunction with clonal genome alternations, shedding light on the cellular genomic difference of subclones that contributes to tumor evolution.

Single-cell DNA sequencing reveals a high incidence of chromosomal abnormalities in human blastocysts.

Aneuploidy, a deviation from the normal chromosome copy number, is common in human embryos and is considered a primary cause of implantation failure and early pregnancy loss. Meiotic errors lead to uniformly abnormal karyotypes, while mitotic errors lead to chromosomal mosaicism: the presence of cells with at least two different karyotypes within an embryo. Knowledge about mosaicism in blastocysts mainly derives from bulk DNA sequencing of multicellular trophectoderm (TE) and/or inner cell mass (ICM) samples. However, this can only detect an average net gain or loss of DNA above a detection threshold of 20-30%. To accurately assess mosaicism, we separated the TE and ICM of 55 good quality surplus blastocysts and successfully applied single-cell whole genome sequencing (scKaryo-seq) on 1057 cells. Mosaicism involving numerical and structural chromosome abnormalities was detected in 82% of the embryos, where most abnormalities affected less than 20% of the cells. Structural abnormalities, potentially caused by replication stress and DNA damage, were observed in 69% of the embryos. In conclusion, our findings indicated that mosaicism is prevalent in good-quality blastocysts, while these blastocysts would likely be identified as normal with current bulk DNA sequencing techniques used for preimplantation genetic testing for aneuploidy (PGT-A).

Inherent Genome Instability Underlies Trisomy 21-Associated Myeloid Malignancies

Background Children with Down syndrome (DS) are highly predisposed to pediatric leukemia, particularly to acute myeloid leukemia (AML) with a ~400-fold increased risk before age 5. DS-associated AML is preceded by a unique transient myeloproliferative disorder (TMD) in infancy, which is a pre-leukemic state driven by somatic mutations in exon 2 of GATA1 leading to the expression of a short ‘ GATA1s‘ isoform. Acquisition of secondary mutations in GATA1s-mutant clones causes fulminant AML. It remains a key question as to why children with DS are particularly susceptible to the genetic aberrations promoting TMD and AML so early in life. Objective DS is a condition of aneuploidy, i.e., trisomy 21 (T21). Several critical hematopoietic genes, e.g. RUNX1 and ERG, are located on chromosome 21 (chr21) whose dosage increase is linked to malignancy. In addition, aneuploidy per se exerts various detrimental impacts on cellular physiology, one of them being elevated genome instability. We aim to determine whether genome instability is an inherent property of T21 hematopoietic cells as a contributing factor to TMD and AML in DS. Results We investigated the impacts of T21 on genome stability in primary leukocytes and in a TMD disease model based on human induced pluripotent stem cells (iPSCs). We asked whether chromosomal aberrations are increased in primary leukocytes from individuals with T21 (n=14) compared to euploid/disomy 21 individuals (n=4) by assaying copy number variations (CNVs) using single-cell genomic sequencing. Leukocytes with CNVs were rare in euploid individuals (2.8%; total 178 cells). By contrast, we found that leukocytes from individuals with T21 showed an approximately 2-fold increase in the frequency of single-cells carrying CNVs other than constitutional T21 (6.5%; total 815 cells), particularly the cells with large CNVs (> 10 megabases, chi-square test p=0.011; Fig. 1A-B). We did not detect mutations in the exon 2 of GATA1 in bulk leukocytes, indicating that the background of T21 alone is associated with more frequent chromosomal aberrations. We then examined the effects of GATA1s mutations on genome integrity in an iPSC-based model of TMD. We derived hematopoietic progenitor cells (HPCs) from isogenic euploid and T21 iPSCs harboring CRISPR/Cas9-induced GATA1s mutations. We found that GATA1s mutations in T21 HPCs further augmented the accumulation of CNVs (chi-square test p=0.0007; Fig. 1C). Tetrasomy 21 was also detected in HPCs, recapitulating a frequent karyotypic aberration in DS-AML (Fig. 1D). On the cellular level, GATA1s caused myeloid skewing independently of chr21 status but significantly promoted progenitor cell maintenance only in the T21 context. Transcriptome analysis in HPCs identified altered signatures associated with genome integrity, including downregulation of mitotic spindle and apoptosis gene sets in T21- GATA1s HPCs as well as further upregulation of the chr21 gene DYRK1A, which has been shown to regulate pathways of DNA double-strand break (DSB) repair. We found that increased expression of DYRK1A in hematopoietic cells diminished homologous recombination, thereby impeding error-free repair and disrupting DSB repair pathway choices as a mechanism of genome instability in T21 and associated TMD and AML. Conclusions We conclude that hematopoietic cells carrying T21 display inherent genome instability, and that the acquisition of GATA1s further amplifies genome instability while promoting myeloid skewing and aberrant maintenance of the progenitor state. The inherently unstable nature of T21 blood cells is a likely contributor to the predisposition to leukemia in DS.