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

Li, Siran; Kendall, Jude; Park, Sarah; Wang, Zihua; Alexander, Joan; Moffitt, Andrea; Ranade, Nissim; Danyko, Cassidy; Gegenhuber, Bruno; Fischer, Stephan; Robinson, Brian D.; Lepor, Herbert; Tollkühn, Jessica; Gillis, Jesse; Brouzés, Eric; Krasnitz, A.; Levy, Dan; Wigler, Michael

doi:10.1101/gr.253047.119

Cited by 9 publications

(24 citation statements)

References 41 publications

(51 reference statements)

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“…The copy-number analysis was performed as described previously (66). Briefly, the genome was divided into either 5,000 or 20,000 bins.…”

Section: Copy-number Analysis Of Bulk Dna From Organoids and Ffpe Secmentioning

confidence: 99%

“…Briefly, the genome was divided into either 5,000 or 20,000 bins. After the normalization, the normalized bincount vectors were segmented using Bioconductor DNAcopy package version 1.50.5 as described previously (66). DNAcopy parameters used were alpha = 0.02, nperm = 1000, undo.SD = 0.5, and min.width = 3.…”

Section: Copy-number Analysis Of Bulk Dna From Organoids and Ffpe Secmentioning

confidence: 99%

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

et al. 2020

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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.

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“…The copy-number analysis was performed as described previously (66). Briefly, the genome was divided into either 5,000 or 20,000 bins.…”

Section: Copy-number Analysis Of Bulk Dna From Organoids and Ffpe Secmentioning

confidence: 99%

Section: Copy-number Analysis Of Bulk Dna From Organoids and Ffpe Secmentioning

confidence: 99%

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

et al. 2020

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“…We therefore provided a complete, extensively documented, and easily deployable solution for all steps in the analysis of sparse single-cell genomic data. Our tools were conceived initially to meet the needs of our in-house single-cell technology development 3 , 4 , 20 and its applications to cancer. 1 , 2 , 21 In particular, this work gave rise to novel methods for collective analysis of multiple single-cell CN profiles, described in Results, including representation of CN profiles in terms of CP-based features, the definition of dissimilarity between CN profiles, and the statistically supported identification of clones.…”

Section: Discussionmentioning

confidence: 99%

Integrated Computational Pipeline for Single-Cell Genomic Profiling

Chorbadjiev

Kendall

Alexander

et al. 2020

JCO Clinical Cancer Informatics

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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.

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Section: High-throughput Scrna-seq Methodsmentioning

confidence: 99%

“…All of the above droplet-based methods basically use the strategy of co-encapsulation of cells and barcoded beads to deliver cell-specific barcodes during RT. Recently, Li et al [85] reported a completely different strategy utilizing cell barcodes (Figure 4d). They used the droplet generation microfluidic chip in DroNc [47] (a modified version of Drop-Seq for single-cell nuclear sequencing) to package single cells.…”

Section: Bag-seqmentioning

confidence: 99%

Microfluidics Facilitates the Development of Single-Cell RNA Sequencing

Pan

Cao

et al. 2022

Biosensors

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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.

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

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Cited by 9 publications

References 41 publications

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

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

Integrated Computational Pipeline for Single-Cell Genomic Profiling

Microfluidics Facilitates the Development of Single-Cell RNA Sequencing

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