Excitement over the Single-Cell Genomics Market

Single-Cell Genomics Market Expected To Reach A Highest Growth During Forecast Period

Single Cell Genomics is a rapidly growing market due to the new emerging methodologies in which the genomic technologies are applied at the single cell level, rather to all the cells collectively. The single cell genomic technologies are opening new boundaries by separating the contributions of single cells to the diversity of ecosystem and organisms. The single cell genomics is also creating new insight into multifaceted biological systems that range from the microbial ecosystem diversity to the human cancer genomics. To mention an example, the single cell genomics can probably be used to identify as well as assemble the genomes of the microorganisms which cannot be cultured, single cell genomics also evaluates the part genetic mosaic plays in the normal physiology and also determines the role of intra tumor genetic variation responsible for cancer development or treatment. However, the single cell genomics has the ability to evaluate a single DNA molecule from single isolated cells, but the process is technically challenging.

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High-resolution, single-cell DNA domain analysis in vivo, identify domain structures that change with cell identity

Visualizing DNA folding and RNA in embryos at single-cell resolution

Leslie J. Mateo, Sedona E. Murphy, Antonina Hafner, Isaac S. Cinquini, Carly A. Walker & Alistair N. Boettiger Nature (Research Article)

Abstract

The establishment of cell types during development requires precise interactions between genes and distal regulatory sequences. We have a limited understanding of how these interactions look in three dimensions, vary across cell types in complex tissue, and relate to transcription. Here we describe optical reconstruction of chromatin architecture (ORCA), a method that can trace the DNA path in single cells with nanoscale accuracy and genomic resolution reaching two kilobases. We used ORCA to study a Hox gene cluster in cryosectioned Drosophila embryos and labelled around 30 RNA species in parallel. We identified cell-type-specific physical borders between active and Polycomb-repressed DNA, and unexpected Polycomb-independent borders. Deletion of Polycomb-independent borders led to ectopic enhancer–promoter contacts, aberrant gene expression, and developmental defects. Together, these results illustrate an approach for high-resolution, single-cell DNA domain analysis in vivo, identify domain structures that change with cell identity, and show that border elements contribute to the formation of physical domains in Drosophila.

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Targeting Brain Tumors with Single-Cell RNA-seq

Brain Tumors Through the Single-Cell RNA Sequencing Lens: Researcher Interview with Mario Suvà

Read Peggy Wang’s interview with Mario Suvà for the National Cancer Institute. Dr. Suvà is an assistant professor of pathology at Massachusetts General Hospital and Harvard Medical School, an Institute Member at the Broad Institute, and uses single-cell RNA sequencing as a discovery tool for understanding brain cancer. Lean more about his work and this powerful new approach to understanding this important disease…

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Single sperm sequencing to understand meiotic recombination

Factors influencing meiotic recombination revealed by whole-genome sequencing of single sperm

Anjali Gupta Hinch, Gang Zhang, Philipp W. Becker, Daniela Moralli, Robert Hinch, Benjamin Davies, Rory Bowden, & Peter Donnelly

Science (Research Article)

Sequencing and the single sperm

During meiosis, homologous chromosomes undergo doublestrand breaks in DNA that can cross over, shuffling genetic material. However, not every double-strand break resolves in a crossover event. Hinch et al. wanted to determine the rules governing DNA recombination. They developed a method to sequence individual mouse sperm and applied it to mice carrying two different alleles of a protein involved in mammalian crossovers. A high-resolution genetic map revealed the relationships between the distribution of crossovers, proteins involved in recombination, and specific factors determining whether a double-strand break becomes a crossover.

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Cell population mapping from bulk single-cell RNA data

Cell composition analysis of bulk genomics using single-cell data

Amit Frishberg, Naama Peshes-Yaloz, Ofir Cohn, Diana Rosentul, Yael Steuerman, Liran Valadarsky, Gal Yankovitz, Michal Mandelboim, Fuad A. Iraqi, Ido Amit, Lior Mayo, Eran Bacharach, & Irit Gat-Viks

Nature Methods (Research Article)

Abstract—Single-cell RNA sequencing (scRNA-seq) is a rich resource of cellular heterogeneity, opening new avenues in the study of complex tissues. We introduce Cell Population Mapping (CPM), a deconvolution algorithm in which reference scRNA-seq profiles are leveraged to infer the composition of cell types and states from bulk transcriptome data (‘scBio’ CRAN R-package). Analysis of individual variations in lungs of influenza-virus-infected mice reveals that the relationship between cell abundance and clinical symptoms is a cell-state-specific property that varies gradually along the continuum of cell-activation states. The gradual change is confirmed in subsequent experiments and is further explained by a mathematical model in which clinical outcomes relate to cell-state dynamics along the activation process. Our results demonstrate the power of CPM in reconstructing the continuous spectrum of cell states within heterogeneous tissues.

Distinguishing amplification artifacts from biologically derived somatic mutations in single-cell sequencing data

Linked-read analysis identifies mutations in single-cell DNA-sequencing data

Craig L. Bohrson, Alison R. Barton, Michael A. Lodato, Rachel E. Rodin, Lovelace J. Luquette, Vinay V. Viswanadham, Doga C. Gulhan, Isidro Cortés-Ciriano, Maxwell A. Sherman, Minseok Kwon,  Michael E. Coulter, Alon Galor, Christopher A. Walsh & Peter J. Park

Nature Genetics (Research Article)

Whole-genome sequencing of DNA from single cells has the potential to reshape our understanding of mutational heterogeneity in normal and diseased tissues. However, a major difficulty is distinguishing amplification artifacts from biologically derived somatic mutations. Here, we describe linked-read analysis (LiRA), a method that accurately identifies somatic single-nucleotide variants (sSNVs) by using read-level phasing with nearby germline heterozygous polymorphisms, thereby enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells.

Single-cell sequencing reveals important cancer mutation signatures (original article)

Characterizing Mutational Signatures in Human Cancer Cell Lines Reveals Episodic APOBEC Mutagenesis

Multiple signatures of somatic mutations have been identified in cancer genomes. Exome sequences of 1,001 human cancer cell lines and 577 xenografts revealed most common mutational signatures, indicating past activity of the underlying processes, usually in appropriate cancer types. To investigate ongoing patterns of mutational-signature generation, cell lines were cultured for extended periods and subsequently DNA sequenced. Signatures of discontinued exposures, including tobacco smoke and ultraviolet light, were not generated in vitro. Signatures of normal and defective DNA repair and replication continued to be generated at roughly stable mutation rates. Signatures of APOBEC cytidine deaminase DNA-editing exhibited substantial fluctuations in mutation rate over time with episodic bursts of mutations. The initiating factors for the bursts are unclear, although retrotransposon mobilization may contribute. The examined cell lines constitute a resource of live experimental models of mutational processes, which potentially retain patterns of activity and regulation operative in primary human cancers.

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