Copolymerization of single-cell nucleic acids into balls of acrylamide gel

Recent Innovations and Technical Advances in High-Throughput Parallel Single-Cell Whole-Genome Sequencing Methods

Single-cell whole-genome sequencing (scWGS) detects cell heterogeneity at the aspect of genomic variations, which are inheritable and play an important role in life processes such as aging and cancer progression. The recent explosive development of high-throughput single-cell sequencing methods has enabled high-performance heterogeneity detection through a vast number of novel strategies. Despite the limitation on total cost, technical advances in high-throughput single-cell whole-genome sequencing methods are made for higher genome coverage, parallel throughput, and level of integration. This review highlights the technical advancements in high-throughput scWGS in the aspects of strategies design, data efficiency, parallel handling platforms, and their applications on human genome. The experimental innovations, remaining challenges, and perspectives are summarized and discussed.

Magnetically functionalized hydrogels for high-throughput genomic applications

Single-cell genomics has revolutionized tissue analysis by revealing the genetic program of individual cells. The key aspect of the technology is the use of barcoded beads to unambiguously tag sequences originating from a single cell. The generation of unique barcodes on beads is mainly achieved by split-pooling methods, which are labor-intensive due to repeated washing steps. Towards the automation of the split-pooling method, we developed a simple method to magnetize hydrogel beads. We show that these hydrogel beads provide increased yields and washing efficiencies for purification procedures. They are also fully compatible with single-cell sequencing using the BAG-Seq workflow. Our work opens the automation of the split-pooling technique, which will improve single-cell genomic workflows.

Practical Considerations for Single-Cell Genomics

The single-cell revolution in the field of genomics is in full bloom, with clever new molecular biology tricks appearing regularly that allow researchers to explore new modalities or scale up their projects to millions of cells and beyond. Techniques abound to measure RNA expression, DNA alterations, protein abundance, chromatin accessibility, and more, all with single-cell resolution and often in combination. Despite such a rapidly changing technology landscape, there are several fundamental principles that are applicable to the majority of experimental workflows to help users avoid pitfalls and exploit the advantages of the chosen platform. In this overview article, we describe a variety of popular single-cell genomics technologies and address some common questions pertaining to study design, sample preparation, quality control, and sequencing strategy. As the majority of relevant publications currently revolve around single-cell RNA-seq, we will prioritize this genomics modality in our discussion. © 2022 Wiley Periodicals LLC.

Microfluidics Facilitates the Development of Single-Cell RNA Sequencing

Single-cell RNA sequencing (scRNA-seq) technology provides a powerful tool for understanding complex biosystems at the single-cell and single-molecule level. The past decade has been a golden period for the development of single-cell sequencing, with scRNA-seq undergoing a tremendous leap in sensitivity and throughput. The application of droplet- and microwell-based microfluidics in scRNA-seq has contributed greatly to improving sequencing throughput. This review introduces the history of development and important technical factors of scRNA-seq. We mainly focus on the role of microfluidics in facilitating the development of scRNA-seq technology. To end, we discuss the future directions for scRNA-seq.

Intraductal Transplantation Models of Human Pancreatic Ductal Adenocarcinoma Reveal Progressive Transition of Molecular Subtypes

Pancreatic ductal adenocarcinoma (PDAC) is the most lethal common malignancy, with little improvement in patient outcomes over the past decades. Recently, subtypes of pancreatic cancer with different prognoses have been elaborated; however, the inability to model these subtypes has precluded mechanistic investigation of their origins. Here, we present a xenotransplantation model of PDAC in which neoplasms originate from patient-derived organoids injected directly into murine pancreatic ducts. Our model enables distinction of the two main PDAC subtypes: intraepithelial neoplasms from this model progress in an indolent or invasive manner representing the classical or basal-like subtypes of PDAC, respectively. Parameters that influence PDAC subtype specification in this intraductal model include cell plasticity and hyperactivation of the RAS pathway. Finally, through intratumoral dissection and the direct manipulation of RAS gene dosage, we identify a suite of RAS-regulated secreted and membrane-bound proteins that may represent potential candidates for therapeutic intervention in patients with PDAC. SIGNIFICANCE: Accurate modeling of the molecular subtypes of pancreatic cancer is crucial to facilitate the generation of effective therapies. We report the development of an intraductal organoid transplantation model of pancreatic cancer that models the progressive switching of subtypes, and identify stochastic and RAS-driven mechanisms that determine subtype specification.See related commentary by Pickering and Morton, p. 1448.This article is highlighted in the In This Issue feature, p. 1426.

Integrated Computational Pipeline for Single-Cell Genomic Profiling

PURPOSE

Copy-number profiling of multiple individual cells from sparse sequencing may be used to reveal a detailed picture of genomic heterogeneity and clonal organization in a tissue biopsy specimen. We sought to provide a comprehensive computational pipeline for single-cell genomics, to facilitate adoption of this molecular technology for basic and translational research.

MATERIALS AND METHODS

The pipeline comprises software tools programmed in Python and in R and depends on Bowtie, HISAT2, Matplotlib, and Qt. It is installed and used with Anaconda.

RESULTS

Here we describe a complete pipeline for sparse single-cell genomic data, encompassing all steps of single-nucleus DNA copy-number profiling, from raw sequence processing to clonal structure analysis and visualization. For the latter, a specialized graphical user interface termed the single-cell genome viewer (SCGV) is provided. With applications to cancer diagnostics in mind, the SCGV allows for zooming and linkage to the University of California at Santa Cruz Genome Browser from each of the multiple integrated views of single-cell copy-number profiles. The latter can be organized by clonal substructure or by any of the associated metadata such as anatomic location and histologic characterization.

CONCLUSION

The pipeline is available as open-source software for Linux and OS X. Its modular structure, extensive documentation, and ease of deployment using Anaconda facilitate its adoption by researchers and practitioners of single-cell genomics. With open-source availability and Massachusetts Institute of Technology licensing, it provides a basis for additional development by the cancer bioinformatics community.

Novel insights into breast cancer copy number genetic heterogeneity revealed by single-cell genome sequencing

Copy number alterations (CNAs) play an important role in molding the genomes of breast cancers and have been shown to be clinically useful for prognostic and therapeutic purposes. However, our knowledge of intra-tumoral genetic heterogeneity of this important class of somatic alterations is limited. Here, using single-cell sequencing, we comprehensively map out the facets of copy number alteration heterogeneity in a cohort of breast cancer tumors. Ou/var/www/html/elife/12-05-2020/backup/r analyses reveal: genetic heterogeneity of non-tumor cells (i.e. stroma) within the tumor mass; the extent to which copy number heterogeneity impacts breast cancer genomes and the importance of both the genomic location and dosage of sub-clonal events; the pervasive nature of genetic heterogeneity of chromosomal amplifications; and the association of copy number heterogeneity with clinical and biological parameters such as polyploidy and estrogen receptor negative status. Our data highlight the power of single-cell genomics in dissecting, in its many forms, intra-tumoral genetic heterogeneity of CNAs, the magnitude with which CNA heterogeneity affects the genomes of breast cancers, and the potential importance of CNA heterogeneity in phenomena such as therapeutic resistance and disease relapse.