High-throughput Single-cell DNA Research Articles

SciMET-cap: high-throughput single-cell methylation analysis with a reduced sequencing burden

DNA methylation is a key component of the mammalian epigenome, playing a regulatory role in development, disease, and other processes. Robust, high-throughput single-cell DNA methylation assays are now possible (sciMET); however, the genome-wide nature of DNA methylation results in a high sequencing burden per cell. Here, we leverage target enrichment with sciMET to capture sufficient information per cell for cell type assignment using substantially fewer sequence reads (sciMET-cap). Accumulated off-target coverage enables genome-wide differentially methylated region (DMR) calling for clusters with as few as 115 cells. We characterize sciMET-cap on human PBMCs and brain (middle frontal gyrus).

Potential value of high-throughput single-cell DNA sequencing of Juvenile myelomonocytic leukemia: report of two cases

Juvenile myelomonocytic leukemia (JMML) is a rare myeloproliferative disease of early childhood that develops due to mutations in the genes of the RAS-signaling pathway. Next-generation high throughput sequencing (NGS) enables identification of various secondary molecular genetic events that can facilitate JMML progression and transformation into secondary acute myeloid leukemia (sAML). The methods of single-cell DNA sequencing (scDNA-seq) enable overcoming limitations of bulk NGS and exploring genetic heterogeneity at the level of individual cells, which can help in a better understanding of the mechanisms leading to JMML progression and provide an opportunity to evaluate the response of leukemia to therapy. In the present work, we applied a two-step droplet microfluidics approach to detect DNA alterations among thousands of single cells and to analyze clonal dynamics in two JMML patients with sAML transformation before and after hematopoietic stem cell transplantation (HSCT). At the time of diagnosis both of our patients harbored only “canonical” mutations in the RAS signaling pathway genes detected by targeted DNA sequencing. Analysis of samples from the time of transformation JMML to sAML revealed additional genetic events that are potential drivers for disease progression in both patients. ScDNA-seq was able to measure of chimerism level and detect a residual tumor clone in the second patient after HSCT (sensitivity of less than 0.1% tumor cells). The data obtained demonstrate the value of scDNA-seq to assess the clonal evolution of JMML to sAML, response to therapy and engraftment monitoring.

Arc-well Was Developed and Reveals Persistent Subclones in DCIS Progression.

The method Arc-well was developed for high-throughput single-cell DNA sequencing of archival FFPE material.

Application of high-throughput single-nucleus DNA sequencing in pancreatic cancer

Despite insights gained by bulk DNA sequencing of cancer it remains challenging to resolve the admixture of normal and tumor cells, and/or of distinct tumor subclones; high-throughput single-cell DNA sequencing circumvents these and brings cancer genomic studies to higher resolution. However, its application has been limited to liquid tumors or a small batch of solid tumors, mainly because of the lack of a scalable workflow to process solid tumor samples. Here we optimize a highly automated nuclei extraction workflow that achieves fast and reliable targeted single-nucleus DNA library preparation of 38 samples from 16 pancreatic ductal adenocarcinoma patients, with an average library yield per sample of 2867 single nuclei. We demonstrate that this workflow not only performs well using low cellularity or low tumor purity samples but reveals genomic evolution patterns of pancreatic ductal adenocarcinoma as well.

Sgcocaller and comapr: personalised haplotype assembly and comparative crossover map analysis using single-gamete sequencing data.

Profiling gametes of an individual enables the construction of personalised haplotypes and meiotic crossover landscapes, now achievable at larger scale than ever through the availability of high-throughput single-cell sequencing technologies. However, high-throughput single-gamete data commonly have low depth of coverage per gamete, which challenges existing gamete-based haplotype phasing methods. In addition, haplotyping a large number of single gametes from high-throughput single-cell DNA sequencing data and constructing meiotic crossover profiles using existing methods requires intensive processing. Here, we introduce efficient software tools for the essential tasks of generating personalised haplotypes and calling crossovers in gametes from single-gamete DNA sequencing data (sgcocaller), and constructing, visualising, and comparing individualised crossover landscapes from single gametes (comapr). With additional data pre-possessing, the tools can also be applied to bulk-sequenced samples. We demonstrate that sgcocaller is able to generate impeccable phasing results for high-coverage datasets, on which it is more accurate and stable than existing methods, and also performs well on low-coverage single-gamete sequencing datasets for which current methods fail. Our tools achieve highly accurate results with user-friendly installation, comprehensive documentation, efficient computation times and minimal memory usage.

Abstract 6171: A single-cell solution for solid tumors to detect mutations and quantify copy number variations

Abstract Cancer is a highly heterogeneous disease characterized by the presence of chromosomal structural copy number variation (CNV) and driver gene mutation events, which appear somatically at the early stages of oncogenesis and can drive tumor progression. Previously, we have developed a high throughput single cell DNA analysis platform that leverages droplet microfluidics and a multiplex-PCR based targeted DNA sequencing approach. It demonstrates high sensitivity detection of SNVs and indels in the same cells and generation of high resolution maps of clonal architecture based on mutational profiling. Here we expand upon the same approach and create an algorithm to accurately quantify CNVs along with genetic mutations from the same single cells. To estimate the CNV the amplicon read counts were corrected for artefacts related to cell to cell variability due sequencing efficiencies along with amplicon to amplicon variability that occur due to amplification efficiency differences. We used a spiked in diploid cell line as a reference to normalize all the cells. The spiked cell line was identified using its mutational profile. We validated the system using cell lines with known CNVs and found that we could at least accurately estimate copy numbers ranging from 0 through 5. Some of the regions were also validated orthogonally using droplet digital PCR. Another set of validation was performed using well characterized cell lines from NIST. We enable multiple visualizations of the copy number estimates in karyotype plots and line plots projected on SNV clones. With improved biochemistry, panel design and novel data analysis algorithms we develop a complete solution to detect amplification or loss of function in oncogenes and tumor suppressors reliably. Integration of CNVs and SNVs facilitates more accurate reconstruction of tumor evolution to better understand cancer progression mechanisms as well for quality control of gene edited cells, to further advance cancer research and therapy. Citation Format: Saurabh Parikh, Khushali Patel, Alex Li, Anup Parikh. A single-cell solution for solid tumors to detect mutations and quantify copy number variations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6171.

High-throughput single-cell epigenomic profiling by targeted insertion of promoters (TIP-seq).

Chromatin profiling in single cells has been extremely challenging and almost exclusively limited to histone proteins. In cases where single-cell methods have shown promise, many require highly specialized equipment or cell type-specific protocols and are relatively low throughput. Here, we combine the advantages of tagmentation, linear amplification, and combinatorial indexing to produce a high-throughput single-cell DNA binding site mapping method that is simple, inexpensive, and capable of multiplexing several independent samples per experiment. Targeted insertion of promoters sequencing (TIP-seq) uses Tn5 fused to proteinA to insert a T7 RNA polymerase promoter adjacent to a chromatin protein of interest. Linear amplification of flanking DNA with T7 polymerase before sequencing library preparation provides ∼10-fold higher unique reads per single cell compared with other methods. We applied TIP-seq to map histone modifications, RNA polymerase II (RNAPII), and transcription factor CTCF binding sites in single human and mouse cells.

High-Throughput Single-Cell DNA Sequencing of AML Tumors with Droplet Microfluidics

Single cell analysis tools are crucial to understand the role that rare or heterogeneous cancer cells play in tumor progression. To enable the characterization of genetic variation within cancer cell populations, we developed a novel approach that barcodes amplified genomic DNA of individual cells confined to microfluidic droplets. The barcodes are used to reassemble the genetic profiles of individual cells from next-generation sequencing data. A key feature of our approach is the “two-step” microfluidic workflow. The microfluidic workflow first encapsulates individual cells in droplets, lyses the cells and prepares the genomic DNA for amplification with proteases. Following this lysate preparation step, the proteases are inactivated and droplets containing the genomes of individual cells are then paired with molecular barcodes and PCR reagents. We demonstrate that the two-step microfluidic approach is superior to workflows without the two-step process for efficient DNA amplification on thousands of individual cells per run with high coverage uniformity and low allelic dropout of targeted genomic loci.To apply our single-cell sequencing technology to human tumor samples, we developed a targeted panel to partially sequence 23 genes frequently mutated in acute myeloid leukemia (AML) including TP53 , DNMT3A , FLT3 , NPM1 , NRAS , IDH1 and IDH2 . Using this panel, we were able to sensitively assay SNP and indel defined clones within AML samples collected longitudinally at the time of diagnosis, remission and relapse. Our single-cell data indicates that clonal populations inferred from VAFs obtained from bulk sequencing data may not fully resolve the heterogeneity within tumors; moreover, the single-cell nature of our approach enabled the unambiguous colocalization of multiple mutations within subclones not possible with bulk measurements. Collectively, our results show a greater degree of heterogeneity in AML tumor samples than is commonly appreciated with traditional sequencing paradigms and they demonstrate the value of single-cell analysis for AML. [Display omitted] DisclosuresEastburn:Mission Bio: Employment, Equity Ownership. Pellegrino:Mission Bio: Employment, Equity Ownership. Sciambi:Mission Bio: Employment, Equity Ownership. Treusch:Mission Bio: Employment, Equity Ownership. Gokhale:Mission Bio: Employment, Equity Ownership. Jacob:Mission Bio: Employment, Equity Ownership. Chen:Mission Bio: Employment, Equity Ownership. Jones:Mission Bio: Employment, Equity Ownership. Takahashi:Symbio Pharmaceuticals: Consultancy.

Abstract 2109: Amplicon design algorithm for single cell targeted DNA sequencing using machine learning

Abstract Background: To realize the promise of precision medicine for cancer, assessing genetic variation present in rare cells and understanding the role that these cells play in the evolution of tumor progression is essential. High throughput single cell DNA targeted sequencing enables detection of rare mutations in cells and identification of subclones defined by co-occurrence of mutations. The big challenge with multiplex sequencing at single cell level is the non-uniform amplification of the targeted regions during PCR. This results in inadequate coverage of mutations of interest in the panel and hence makes genotyping challenging. To address this challenge, we developed a machine learning engine to optimize amplicon design for uniform amplification by making reliable performance prediction. Methods: Multiple panels with various sizes were designed with amplicons spanning a wide range of design properties such as amplicon GC, length, secondary structure prediction, primer specificity. These panels were synthesized and processed through Tapestri single cell DNA platform. The tested amplicons are classified into low-performer, OK-performer and high flyer based on their normalized reads-per-cell value. Design properties and property distribution of the amplicons and the panel are the features. We used random forest classifier to calculate feature importance and analyzed the range of the top features for each class and their significance of variance between classes. These ranges were then used as parameters in the assay design pipeline. Next, we train machine learning models with performance data to develop a performance prediction engine. Results: To test the performance of the design pipeline with new parameters, we designed a small (31), medium (128) and large (287) amplicon panel. Multiple runs were conducted for each panel with different cell types. We were able to achieve high panel performance of 97%, 92% and 88% across the three panels. The new parameters resulted in ~10-20% improvement in panel uniformity. We are working on further optimizing the performance prediction engine by using different ML classification models with K-fold cross validation, training using larger group of amplicons and optimizing features using combinations of properties. Citation Format: Shu Wang, Saurabh Gulati, Dong Kim, Sombeet Sahu, Saurabh Parikh, Nianzhen Li, Manimozhi Manivannan, Nigel Beard. Amplicon design algorithm for single cell targeted DNA sequencing using machine learning [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2109.

Abstract 2501: Delineating mutational lineages with single cell DNA sequencing using multi-patient specific panels

Abstract Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer with high rates of metastasis. Although copy number aberrations (CNAs) and genome doubling have been extensively studied in relation to TNBC initiation, little is known regarding the timing and impact of point mutations during tumor initiation, outside of TP53. A major challenge in sequencing mutations in single cells is the significant cost of unbiased whole-exome analysis of many cells. To address this challenge, we developed an approach called multi-patient-panel (MPP) sequencing which involves first performing bulk deep-exome sequencing of many patient's tumors, after which hundreds of mutations are identified and pooled across patients to design a targeted panel for high-throughput single cell DNA sequencing. We applied MPP sequencing to profile 330 mutations across 23,000 single cells from 5 TNBC patients using a microdroplet system (Mission Bio). From this data we identified 1-4 major clonal subpopulations in each tumor which shared a common evolutionary lineage. Copy number data was inferred from the targeted panel coverage depth, which showed that CNAs were correlated with different subpopulations in the tumor. By integrating both the mutation and copy number data, we inferred phylogenetic lineages and identified the timing of mutations during primary tumor growth, which detected early truncal mutations (in addition to TP53) that were involved in the initiation of TNBC tumors. The MPP approach provides a powerful method to scale-up single cell DNA sequencing to tens of thousands of cells to detect mutations and infer cancer lineages and clonal substructure. Citation Format: Jake Leighton, Min Hu, Alexander Davis, Emi Sei, Yong Wang, Nicholas Navin. Delineating mutational lineages with single cell DNA sequencing using multi-patient specific panels [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2501.

Abstract 1663: Analytical methods to identify tumor heterogeneity and rare subclones in single cell DNA sequencing data from targeted panels

Abstract Current bulk sequencing paradigms are inadequate to characterize complicated biological systems where somatic variation is buried in the landscape of populations. Single-cell DNA sequencing can simultaneously identify zygosity, rare alleles and determine whether mutations co-occur within the same cell. While high-throughput single-cell DNA analysis has been a recent innovation, it is essential to develop new capabilities for assessing genetic variation present in rare cells and to better understand the role that these cells play in the evolution of tumor progression. To address these challenges and enable the characterization of genetic diversity in cancer cell populations, we developed analytical methods to identify mutation signatures which define subclones present in a tumor population. Here we present a workflow for subclone identification using data generated on the Tapestri single-cell DNA platform. The analytical pipeline steps include obtaining raw reads from the sequencer, removing adapters, aligning and mapping the reads, calling individual cells, error correction of reads assigned to cells and identifying genetic variants within each cell. After filtering for high quality variants, we then filter for data completeness to ensure high quality data is used in downstream processing. The variant-cell matrix is then subjected to clustering to identify subclones. Top variants that define the signature of each subclone are also identified. To validate our methodology, we used data from two different targeted panels on different model systems with known truth mutations. Our pipeline shows the distinct clusters correlating with titration and cell line ratios. Cluster associated signature mutations were also identified. The pipeline can be used for multi sample analysis with time-series data from diagnosis to relapse or from primary site to metastasis to understand clonal diversity. These data demonstrate the utility of the Tapestri platform, the analytical pipeline, and associated data visualization capability. Our approach addresses key issues of identifying rare subpopulations of cells down to 0.1%, and transforms the ability to accurately characterize clonal heterogeneity in tumor samples. This high throughput method advances research efforts to improve patient stratification and therapy selection for various cancer indications. Citation Format: Manimozhi Manivannan, Sombeet Sahu, Shu Wang, Dong Kim, Niranjan Vissa, Jordan Wilheim, Adam Sciambi, Nianzhen Li, Robert Durruthy-Durruthy, Anup Parikh, Keith Jones. Analytical methods to identify tumor heterogeneity and rare subclones in single cell DNA sequencing data from targeted panels [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1663.

Abstract 4696: High-throughput single-cell targeted DNA sequencing using an updated TapestriTM Platform reveals rare clones and clonal evolution for multiple blood cancers

Abstract Introduction: The challenge in precision medicine has been improving the understanding of cancer heterogeneity and clonal evolution, which has major implications in targeted therapy selection and disease monitoring. However, current bulk sequencing methods are unable to unambiguously identify rare pathogenic or drug-resistant cell populations and determine whether mutations co-occur within the same cell. Single-cell sequencing has the potential to provide unique insights on the cellular and genetic composition, drivers, and signatures of cancer at unparalleled sensitivity. Methods: Previously we have developed a high-throughput single-cell DNA analysis platform (TapestriTM) that leverages droplet microfluidics and a multiplex-PCR based targeted DNA sequencing approach, and demonstrated the generation of high-resolution maps of clonal architecture from acute myeloid leukemia (AML) tumors. Here we present an update to the Tapestri Platform which employs new biochemistry and features improved firmware, software, workflow, and data analysis solution resulting in higher throughput, better sensitivity, specificity and unprecedented flexibility. Results: From cell prep to sequencing-ready libraries, the workflow can be completed within 2 days, and new modifications have doubled the throughput to up to 20,000 genotyped cells per run from 10,000 shown previously. We have validated the performance of an AML (19 genes, 50 amplicons) and a CLL (chronic lymphocytic leukemia) (34 genes, 286 amplicons) panel. We also developed a robust web-based design portal for custom targets. The updated biochemistry enables easy addition of new gene and loci targets into existing panels for improved coverage and updated studies. Using longitudinal AML and CLL samples, we were able to detect rare subclones of <0.1% prevalence, identify mutation co-occurrence, and characterize clonal evolution due to disease progression and drug treatment. Conclusion: We demonstrate that single-cell DNA sequencing can reveal the heterogeneity of blood cancers and map the clonal architecture and clonal evolution with higher sensitivity than bulk NGS methods. This is critical in patient stratification and drug selection over the entire course of treatment. Besides the catalog AML and CLL panels, the flexibility of system allows for analyzing SNV and indel mutations of any custom cancer DNA targets. Additionally, the system provides capabilities for quality control of gene edited cells, further advancing research into cancer therapies. Citation Format: Nianzhen Li, Daniel Mendoza, Adam Sciambi, Mani Manivannan, Jacob Ho, Kaustubh Gokhale, Jacqueline Marin, Kathryn Thompson, Jamie Yates, Vasu Sharma, Steven Chow, Sombeet Sahu, Shu Wang, Dennis Eastburn, Keith Jones. High-throughput single-cell targeted DNA sequencing using an updated TapestriTM Platform reveals rare clones and clonal evolution for multiple blood cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4696.

Single-Cell Atlas of Driver Mutations in Acute Myeloid Leukemia (AML)

Single-Cell Atlas of Driver Mutations in Acute Myeloid Leukemia (AML)

Longitudinal Monitoring of AML Tumors with High-Throughput Single-Cell DNA Sequencing Reveals Rare Clones Prognostic for Disease Progression and Therapy Response

AML (acute myeloid leukemia) is increasingly being treated with precision medicine. To better inform treatment, the mutational content of patient samples must be determined. However, current tumor sequencing paradigms are inadequate to fully characterize many instances of the disease. A major challenge has been the unambiguous identification of potentially rare and genetically heterogeneous neoplastic cell populations, capable of critically impacting tumor evolution and the acquisition of therapeutic resistance. Standard bulk population sequencing is unable to identify rare alleles and definitively determine whether mutations co-occur within the same cell. Single-cell sequencing has the potential to address these key issues and transform our ability to accurately characterize clonal heterogeneity in AML. Previous single-cell studies examining genetic variation in AML have relied upon laborious, expensive and low-throughput technologies that are not readily scalable for routine analysis of the disease.We applied a newly developed platform technology to perform targeted single-cell DNA sequencing on over 140,000 cells and generated high-resolution maps of clonal architecture from AML tumor samples. Marrow and/or peripheral blood samples were collected prior to, during treatment, and at clinical progression to the FLT3 inhibitor gilteritinib given on a clinical trial for relapsed/refractory AML with FLT3 mutation. Single-cell sequencing of multiple patient samples demonstrated that relapse clones acquired oncogenic RAS mutations. We utilized the high-throughput and sensitivity of our single-cell approach to more definitively assess where in the course of treatment these RAS mutated clones were acquired. Oncogenic RAS harboring clones, comprising between 0.4%, and 0.1% of tumor populations, were identified in patient samples either prior to or shortly after onset of treatment. Significantly, these RAS variant alleles were not detectable with targeted bulk sequencing. Throughout the course of treatment with the FLT3 inhibitor gilteritinib, the RAS mutant clones selectively expanded and were responsible for resistance to therapy and relapse. These findings point to the presence of underlying genetic heterogeneity in AML and demonstrate the utility of sensitively assaying clonal architecture to better inform patient stratification and therapy selection. DisclosuresEastburn:Mission Bio, Inc.: Employment, Equity Ownership. Durruthy-Durruthy:Mission Bio, Inc.: Employment, Equity Ownership. Smith:Astellas Pharma: Research Funding. Perl:Actinium Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; NewLink Genetics: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Daiichi Sankyo: Consultancy; Arog: Consultancy; Novartis: Membership on an entity's Board of Directors or advisory committees; Astellas: Consultancy; AbbVie: Membership on an entity's Board of Directors or advisory committees.

High-throughput single-cell DNA sequencing of acute myeloid leukemia tumors with droplet microfluidics.

To enable the characterization of genetic heterogeneity in tumor cell populations, we developed a novel microfluidic approach that barcodes amplified genomic DNA from thousands of individual cancer cells confined to droplets. The barcodes are then used to reassemble the genetic profiles of cells from next-generation sequencing data. By using this approach, we sequenced longitudinally collected acute myeloid leukemia (AML) tumor populations from two patients and genotyped up to 62 disease relevant loci across more than 16,000 individual cells. Targeted single-cell sequencing was able to sensitively identify cells harboring pathogenic mutations during complete remission and uncovered complex clonal evolution within AML tumors that was not observable with bulk sequencing. We anticipate that this approach will make feasible the routine analysis of AML heterogeneity, leading to improved stratification and therapy selection for the disease.

Abstract 5398: High-throughput clonal analysis of AML tumors with droplet microfluidics

Abstract Single cell analysis tools are crucial to better understand the role that rare or heterogeneous cancer cells play in the evolution of tumor progression. Although, it is now feasible to perform single-cell RNA-Seq on thousands of, several challenges remain for high-throughput single-cell DNA sequencing. To address these challenges and enable the characterization of genetic diversity in cancer cell populations, we developed a novel approach that barcodes amplified genomic DNA of individual cells confined to microfluidic droplets. The barcodes are used to reassemble the genetic profiles of individual cells from next generation sequencing data. A key feature of our approach is the “two-step” microfluidic workflow that releases genomic DNA from cellular proteins prior to amplification. The microfluidic workflow first encapsulates individual cells in droplets, lyses the cells and prepares the lysate for genomic DNA amplification using proteases. Following this lysate preparation step, the proteases are inactivated and droplets containing the genomes of individual cells are then paired with molecular barcodes and PCR reagents. We demonstrate that the two-step microfluidic approach is vastly superior to workflows without the two-step process for efficient DNA amplification on tens of thousands of individual cells per run with high coverage uniformity and low allelic dropout of targeted genomic regions. To apply our single-cell sequencing technology to the study of acute myeloid leukemia (AML), we developed a targeted panel to sequence genes frequently mutated in AML including TP53, DNMT3A, FLT3, NPM1, NRAS, KRAS, JAK2, IDH1 and IDH2. Using this panel, we were able to identify clonal populations from AML research samples; moreover, the single-cell nature of our approach enabled the correlation of multiple mutations within subclones and determination of whether the mutations existed as as a homozygote or heterozygote. Collectively, our results show a greater degree of heterogeneity in AML tumor samples than is commonly appreciated with bulk sequencing methods. Citation Format: Dennis Eastburn, Maurizio Pellegrino, Sebastian Treusch, Adam Sciambi, Bill Hyun, Jamie Yates. High-throughput clonal analysis of AML tumors with droplet microfluidics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5398. doi:10.1158/1538-7445.AM2017-5398

Baring cellular souls.

Coupling chemical approaches for disrupting DNA-protein complexes in intact nuclei with combinatorial barcoding enables high-throughput single-cell DNA sequencing.

Cell trapping, DNA extraction and Molecular Combing in a microfluidic device for high throughput genetic analysis of human DNA

We report on an integrated microfluidic device for cell trapping, DNA extraction and Molecular Combing which can be used for genetic analysis of human DNA. Our results show that it is possible to isolate and linearize the genetic material of a few cells without introducing any manipulation step of DNA molecules, thus increasing dramatically the size of the events that can be studied on the genome. Such extraction strategy is simple and fast, providing a new possibility of high throughput single-cell DNA analysis which should be applicable to cancer research, genome mapping, and genotoxicity studies.