The Experts below are selected from a list of 103614 Experts worldwide ranked by ideXlab platform

David A Ferenbach - One of the best experts on this subject based on the ideXlab platform.

Lan T. Nguyen - One of the best experts on this subject based on the ideXlab platform.

  • Systematic comparison of single-cell and single-nucleus RNA-Sequencing methods
    Nature Biotechnology, 2020
    Co-Authors: Jiarui Ding, Xian Adiconis, Sean K. Simmons, Monika S. Kowalczyk, Cynthia C. Hession, Nemanja D. Marjanovic, Travis K. Hughes, Marc H. Wadsworth, Tyler Burks, Lan T. Nguyen
    Abstract:

    Seven methods for single-cell RNA Sequencing are benchmarked on cell lines, primary cells and mouse cortex. The scale and capabilities of single-cell RNA-Sequencing methods have expanded rapidly in recent years, enabling major discoveries and large-scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, we directly compare seven methods for single-cell and/or single-nucleus profiling—selecting representative methods based on their usage and our expertise and resources to prepare libraries—including two low-throughput and five high-throughput methods. We tested the methods on three types of samples: cell lines, peripheral blood mononuclear cells and brain tissue, generating 36 libraries in six separate experiments in a single center. To directly compare the methods and avoid processing differences introduced by the existing pipelines, we developed scumi, a flexible computational pipeline that can be used with any single-cell RNA-Sequencing method. We evaluated the methods for both basic performance, such as the structure and alignment of reads, sensitivity and extent of multiplets, and for their ability to recover known biological information in the samples.

Eoin D Osullivan - One of the best experts on this subject based on the ideXlab platform.

Holger Heyn - One of the best experts on this subject based on the ideXlab platform.

  • Tutorial: guidelines for the experimental design of single-cell RNA Sequencing studies
    Nature Protocols, 2018
    Co-Authors: Atefeh Lafzi, Catia Moutinho, Simone Picelli, Holger Heyn
    Abstract:

    Single-cell RNA Sequencing is at the forefront of high-resolution phenotyping experiments for complex samples. Although this methodology requires specialized equipment and expertise, it is now widely applied in research. However, it is challenging to create broadly applicable experimental designs because each experiment requires the user to make informed decisions about sample preparation, RNA Sequencing and data analysis. To facilitate this decision-making process, in this tutorial we summarize current methodological and analytical options, and discuss their suitability for a range of research scenarios. Specifically, we provide information about best practices for the separation of individual cells and provide an overview of current single-cell capture methods at different cellular resolutions and scales. Methods for the preparation of RNA Sequencing libraries vary profoundly across applications, and we discuss features important for an informed selection process. An erroneous or biased analysis can lead to misinterpretations or obscure biologically important information. We provide a guide to the major data processing steps and options for meaningful data interpretation. These guidelines will serve as a reference to support users in building a single-cell experimental framework—from sample preparation to data interpretation—that is tailored to the underlying research context. In this tutorial, the authors provide a comprehensive description of the considerations for designing single-cell transcriptomics studies, from sample preparation and single-cell RNA Sequencing methodologies through data processing and analysis.

Jiarui Ding - One of the best experts on this subject based on the ideXlab platform.

  • Systematic comparison of single-cell and single-nucleus RNA-Sequencing methods
    Nature Biotechnology, 2020
    Co-Authors: Jiarui Ding, Xian Adiconis, Sean K. Simmons, Monika S. Kowalczyk, Cynthia C. Hession, Nemanja D. Marjanovic, Travis K. Hughes, Marc H. Wadsworth, Tyler Burks, Lan T. Nguyen
    Abstract:

    Seven methods for single-cell RNA Sequencing are benchmarked on cell lines, primary cells and mouse cortex. The scale and capabilities of single-cell RNA-Sequencing methods have expanded rapidly in recent years, enabling major discoveries and large-scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, we directly compare seven methods for single-cell and/or single-nucleus profiling—selecting representative methods based on their usage and our expertise and resources to prepare libraries—including two low-throughput and five high-throughput methods. We tested the methods on three types of samples: cell lines, peripheral blood mononuclear cells and brain tissue, generating 36 libraries in six separate experiments in a single center. To directly compare the methods and avoid processing differences introduced by the existing pipelines, we developed scumi, a flexible computational pipeline that can be used with any single-cell RNA-Sequencing method. We evaluated the methods for both basic performance, such as the structure and alignment of reads, sensitivity and extent of multiplets, and for their ability to recover known biological information in the samples.