Jeffrey R. Marks, PhD

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I have worked with scientists from BioSkryb over the past year and a half. Their technology is cutting edge and produces data with amazing resolution. Breast cancer evolution described by both point mutations and copy number changes is apparent at the single cell level. Their staff have tremendous expertise and have been exceptional collaborators in generating data that will form the basis for manuscripts and grant proposals.”

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Vice Chief, Division of Surgical Sciences, Professor in Surgery, Professor of Pathology, Duke University School of Medicine

Our proprietary approach to ultra low-input DNA and single-cell WGA accurately calls variants in genomes and reproducibly captures more than 95% of the genomic material in single cells. 

Resolve cancer heterogeneity with single-cell resolution

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Primary Template-directed Amplification (PTA) is a novel single-cell whole-genome amplification (WGA) method which yields unprecedented genomic coverage and uniformity for accurate calling of single nucleotide variation (SNV) and copy number variation (CNV). PTA is employed here to genomically profile Ductal Carcinoma In Situ (DCIS) at the single cell level, revealing distinct DNA lesions and suggesting remarkably diverse mechanisms of oncogenesis between single cells.

Single-cell oncogenic mechanistic heterogeneity defined by PTA in primary Ductal Carcinoma In Situ

ResolveDNA in primary breast cancer

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ResolveDNA® in primary breast cancer

Advances in genomic technologies have significantly improved our understanding of cancer heterogeneity. Single cell whole genome sequencing enables the genetic characterization of distinct tumor cells, providing actionable information for treatment decisions and disease management. In this Application Note, we discuss a novel and accurate method for single cell whole genome amplification Primary Template directed Amplification (PTA) and provide examples of how it supports the detection of low frequency variants in populations of cells.

BioSkryb's ResolveDNA and BaseJumper bioinformatics platform provide an order of magnitude of improvement in sensitivity over conventional bulk sequencing methods.

Accurate Single-cell Genomic Analysis Holds the Key to Understanding Cancer Heterogeneity

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Primary Template-directed Amplification (PTA) is a novel and accurate whole-genome amplification (WGA) method for the genomic analysis of single cells. Here we demonstrate the importance of understanding the heterogeneity of genomic modifications that occur between individual cells within a cancer specimen.

Here we demonstrate the importance of understanding the heterogeneity of genomic modifications that occur between individual cells within a cancer specimen.

Assessing the Heterogeneity of Single Cancer Cells using Primary Template directed Amplification (PTA)

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BioSkryb Genomics and ALS Jena have established a workflow to simplify the isolation of single cells from a variety of samples to enable low input and single-cell genomics. The workflow presented highlights how next generation single cell selection and sequencing technologies are able to obtain high quality single-cell genomic data from individual cells at a spatially specific location.

BioSkryb Genomics and ALS Jena have established a workflow to simplify the isolation of single cells from a variety of samples to enable low input and single-cell genomics.

Integrated Workflow for Spatial Single Cell Genome Analysis

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The remarkable resilience of cancer cells to drug treatment can occur via genomic, transcriptomic, or epigenomic modulation. While one of these modes can be the primary driver, there is increasing evidence that the modes are not mutually exclusive and instead can synergize to change the cell state, leading to resistance. Thus, a nucleic acid mutation that partially contributes to drug resistance may be also accompanied by transcriptional adaptation and chromatin remodeling to more fully overcome the action of the drug. It will therefore become paramount to assay these multiple omic tiers in single cells, because bulk sequencing is not capable of revealing the inherent heterogeneity in each of these tiers, either in isolation or in concert. We developed a method, ResolveOME™, to ascertain the genome and transcriptome simultaneously from the same individual cell. The workflow unifies template-switching single-cell RNA sequencing (scRNA-seq) chemistry and whole-genome amplification (WGA) with Primary Template-directed Amplification (PTA), followed by affinity purification of first-strand complementary DNA (cDNA) and subsequent separation of the RNA/DNA fractions for sequencing library preparation. To demonstrate the efficacy of the technique in the context of cancer drug resistance, we generated a model of acute myeloid leukemia (AML) resistant to the FLT3 inhibitor quizartinib. The PTA arm of the ResolveOME™ protocol validated the presence of the internal tandem duplication (ITD) in FLT3 in both parental and quizartinib-resistant MOLM-13 AML cells, and, intriguingly, identified a secondary FLT3 mutation, N841K, that is exclusive to the quizartinib-resistant population. The RNA arm of the protocol identified a clade of differentially expressed transcripts between parental and resistant cells, which included the upregulation of GAS6, a ligand for the receptor tyrosine kinase AXL and a known bypass pathway for FLT3 inhibition. The degree of GAS6 expression upregulation varied between single cells. These data suggest a model that the N841K FLT3 mutation drives resistance to quizartinib in conjugation with transcriptional adaptation. Combined genomic and transcriptomic insights were also extended to single triple-negative breast cancer (TNBC) cells in a model of resistance to the MEK inhibitor trametinib. Not limited to drug resistance studies, the utility of ResolveOME™ extends to any application requiring defining DNA-level changes paired with simultaneous information about cell state revealed by transcriptomics in the same cell.

BioSkryb Genomics developed ResolveOME to ascertain the genome and transcriptome simultaneously from the same individual cell. 

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Unifying genomics and transcriptomics in single cells to illuminate cancer...

Dr. Gawad discusses the transformation of our understanding of cancer biology through single-cell genomics with a focus on tumor heterogeneity and specific phenotype correlation.

Toward Personalized High-Resolution Leukemia Treatments Strategies using Single-Cell Genomics

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Harnessing biotechnological innovation to improve the outcomes of pediatric cancer patients. 

Harnessing Biotechnological Innovation for Pediatric Cancer

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Discovering genomic variation in the absence of information about transcriptional consequence of that variation or, conversely, a transcriptional signature without understanding underlying genomic contributions, hinders understanding of molecular mechanisms of disease. To assess this genomic and transcriptomic coordination, we developed a new chemistry and method, ResolveOME, to extract this information out of the individual cell. The workflow unifies template-switching full-transcript RNA-Seq chemistry and whole genome amplification (WGA), followed by affinity purification of first-strand cDNA and subsequent separation of the RNA/DNA fractions for sequencing library preparation. In the ResolveOME methodology we leverage the attributes of primary template-directed amplification (PTA)1 to enable accurate assessment of single-nucleotide variation as a DNA feature—not achieved with existing workflows to assess DNA + RNA information in the same cell.

The study highlights that both the genome and transcriptome are dynamic, leading to a set of combinatorial alterations that affect cellular evolution and that fate can be identified through ResolveOME application to individual cells.

Unifying genomics and transcriptomics in single cells with ResolveOME amplification chemistry to illuminate oncogenic and drug resistance mechanisms

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Unifying genomics and transcriptomics in single cells with ResolveOME™ amplification chemistry to illuminate oncogenic and drug resistance mechanisms

Primary Template-directed Amplification or PTA employs controlled reaction parameters to reproducibly recover greater than 95% of the genome of single-cells and severely limited input samples with best-in-class fidelity and uniformity. 

Discover the difference BioSkryb Genomics can make for you in WGA

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Improvements in whole genome amplification (WGA) would enable new types of basic and applied biomedical research, including studies of intratissue genetic diversity that require more accurate single-cell genotyping. Here, we present primary template-directed amplification (PTA), an isothermal WGA method that reproducibly captures >95% of the genomes of single cells in a more uniform and accurate manner than existing approaches, resulting in significantly improved variant calling sensitivity and precision. To illustrate the types of studies that are enabled by PTA, we developed direct measurement of environmental mutagenicity (DMEM), a tool for mapping genome-wide interactions of mutagens with single living human cells at base-pair resolution. In addition, we utilized PTA for genome-wide off-target indel and structural variant detection in cells that had undergone CRISPR-mediated genome editing, establishing the feasibility for performing single-cell evaluations of biopsies from edited tissues. The improved precision and accuracy of variant detection with PTA overcomes the current limitations of accurate WGA, which is the major obstacle to studying genetic diversity and evolution at cellular resolution.

The improved precision and accuracy of variant detection with PTA overcomes the current limitations of accurate WGA, which is the major obstacle to studying genetic diversity and evolution at cellular resolution.

Accurate Genomic Variant Detection in Single Cells with Primary Template-Directed Amplification

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The broad application of single-cell RNA sequencing has revealed transcriptional cell state heterogeneity across diverse healthy and malignant somatic tissues. Recent advances in lineage tracing technologies have further enabled the simultaneous capture of cell transcriptional state along with cellular ancestry thus enabling the study of somatic evolution at an unprecedented resolution; however, new analytical approaches are needed to fully harness these data. Here we introduce PATH (Phylogenetic Analysis of Transcriptional Heritability), an analytical framework, which draws upon classic approaches in species evolution, to quantify heritability and plasticity of somatic phenotypes, including transcriptional states. The PATH framework further allows for the inference of cell state transition dynamics by linking a model of cellular evolutionary dynamics with our measure of heritability versus plasticity. We evaluate the robustness of this approach by testing a range of biological and technical features in simulations of somatic evolution. We then apply PATH to characterize previously published and newly generated single-cell phylogenies, reconstructed from either native or artificial lineage markers, with matching cellular state profiling. PATH recovered developmental relationships in mouse embryogenesis, and revealed how anatomic proximity influences neural relatedness in the developing zebrafish brain. In cancer, PATH dissected the heritability of the epithelial-to-mesenchymal transition in a mouse model of pancreatic cancer, and the heritability versus plasticity of transcriptionally-defined cell states in human glioblastoma. Finally, PATH revealed phenotypic heritability patterns in a phylogeny reconstructed from single-cell whole genome sequencing of a B-cell acute lymphoblastic leukemia patient sample. Altogether, by bringing together perspectives from evolutionary biology and emerging single-cell technologies, PATH formally connects the analysis of cell state diversity and somatic evolution, providing quantification of critical aspects of these processes and replacing qualitative conceptions of “plasticity” with quantitative measures of cell state transitions and heritability.

Here we introduce PATH, an analytical framework, which draws upon classic approaches in species evolution, to quantify heritability and plasticity of somatic phenotypes, including transcriptional states.

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Defining ancestry, heritability and plasticity of 2 cellular phenotypes in somatic evolution

Featured Resources

## Why look through the blurry lens of bulk sequencing if you can pinpoint and track the rare variants that determine cancer outcomes?

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Cancer is genetically heterogeneous, with a single tumor harboring a constellation of constantly evolving genomes. The key to early detection, personalized therapy, and effective disease management lies in our ability to identify, characterize, and track conserved, rare, and de novo variant alleles and copy number variation (CNV) in pathogenic cells with extremely high sensitivity.

Next-generation omics approaches have vastly improved our understanding of cancer immune escape, metastasis, and drug resistance. Nevertheless, conventional bulk sequencing approaches reveal only the most abundant variants in a sample, and are unable to unravel the chronology of clonal evolution, which is fundamental to oncogenesis and drug resistance.

With BioSkryb’s highly accurate and scalable single-cell whole genome amplification ([WGA](/products/)) technology ([PTA](/technology/)) and powerful bioinformatics platform ([BaseJumper™](/basejumper/)) it is possible to interrogate cohorts of individual tumor genomes to discern ultra-low-frequency single nucleotide variants (SNVs), observe copy number variation between single cells, analyze single-cell tumor mutation burden (TMB), and gain insights into minimal residual disease (MRD). This provides the actionable information needed to improve the understanding of the tumor.