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【scRW】[2]Introduction to Single

【scRW】[2]Introduction to Single

作者: RachaelRiggs | 来源:发表于2020-05-20 23:04 被阅读0次

    这个专题叫Schedule for Single-cell RNA-seq workshop,那就把这个专题叫做【scRW】吧

    第二课 Introduction to Single Cell RNA Sequencing

    ## <Introduction to Single Cell RNA Sequencing>
    ## 目录
    ## 1 Common applications of single cell RNA sequencing.
    ## 2 Overview of single cell RNA sequencing platforms.
    ## 3 Modified scRNA-seq workflows
    ## 4 Sample preparation and experimental design.
    ## 5 Effects of sample prep and sample type on analysis
    

    Bulk vs Single Cell RNA Sequencing (scRNA-seq)


    1.1
    • Transcriptome Coverage (mRNA)


      Transcriptome Coverage (mRNA)
    • The World Between Bulk & scRNA-seq


      The World Between Bulk & scRNA-seq

    ps. throughput = the amount of material or items passing through a system or process.

    1.Common Applications of scRNA-seq


    Common Applications of scRNA-seq Tumor, Tissue, Organoid Heterogeneity Development Lineage Tracing Development Lineage Tracing Time Course or Development Experiment Stochastic Gene Expression Stochastic Gene Expression

    More Cells or More Sequencing Reads?

    More Cells or More Sequencing Reads? http://satijalab.org/howmanycells

    2.Overview of single cell RNA sequencing platforms


    Comparison of Single Cell Methods Comparison of Single Cell Methods

    C. Ziegenhain et al., Comparative Analysis of Single-Cell RNA Sequencing Methods, Molecular Cell 2017 (doi: 10.1016/j.molcel.2017.01.023)

    2.1.1 Full Length Transcripts: SMART-seq (v3)

    SMART-seq (v3)

    H Lim et al, Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR. J. Vis. Exp. (87), e51408, 2014 (doi:10.3791/51408)
    M Hagemann-Jensen et al, Single-cell RNA counting at allele- and isoform-resolution using Smart-seq3 bioRxiv 2019 (doi: https://doi.org/10.1101/817924)

    2.1.2 Seq-Well: Honeycomb Biotechnologies

    image.png Seq-Well: Honeycomb Biotechnologies

    TM Gierahn et al, Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods. 2017 Apr;14(4):395-398. doi: 10.1038/nmeth.4179

    2.1.3 Droplet scRNA-seq

    Droplet scRNA-seq

    2.1.4 inDrops Method Overview

    1 2

    A. M. Klein et al., Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells, Cell 2015 (doi: 10.1016/j.cell.2015.04.044)
    R. Zilionis et al., Single-cell barcoding and sequencing using droplet microfluidics, Nature Protocols 2016 (doi: 10.1038/nprot.2016.154 )

    2.2.1 scRNA-seq Library Structure (inDrops)

    scRNA-seq Library Structure (inDrops)

    2.2.2 10x Genomics Method Overview

    1 2

    2.2.3 Doublets / Cell Density

    Doublets / Cell Density

    10xgenomics

    2.2.4 Scrublet: Computational Identification of Doublets

    1

    S. Wolock et al. Scrublet: computational identification of cell doublets in single-cell transcriptomic data, bioRxiv 2018 (DOI: 10.1101/357368)

    inDrops vs. 10x Genomics

    2.2.5 On the Horizon: Spatial Transcriptomics

    On the Horizon: Spatial Transcriptomics

    Rodriques et al, Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution.
    Science. 2019 Mar 29;363(6434):1463-1467.

    3.Modified scRNA-seq workflows

    3.1 Transcript Specific Library Prep

    1 2 3 4 5

    3.2 CITE-seq / Cell Hashing

    CITE-seq / Cell Hashing

    3.3 Cell Hashing / CITE-seq

    Cell Hashing / CITE-seq

    3.4 Label-Free Multiplexing of Patient Samples

    Label-Free Multiplexing of Patient Samples

    3.5 10x Capture Sequence / Feature Barcode

    10x Capture Sequence / Feature Barcode

    3.5.1 10x V(D)J Immune Profiling & 5’ gene expression

    10x V(D)J Immune Profiling & 5’ gene expression

    3.5.2 10x V(D)J Immune Profiling

    10x V(D)J Immune Profiling

    3.6 TotalSeq

    TotalSeq

    4.Sample preparation and experimental design

    4.1 Single Cell Core Sample Repertoire

    Single Cell Core Sample Repertoire

    4.2 Key to Success: Sample Preparation

    Key to Success: Sample Preparation

    4.3 Sample Preparation

    Key to Success: Sample Preparation
    4.3.1 Sample Preparation: increasing cell viability
    1
    4.3.2 Sample Preparation: single cell suspension
    2

    4.4 Sample preparation protocol varies by cell-type

    Sample preparation protocol varies by cell-type Sample Preparation Varies by Cell-Type 1 2 3

    4.5 Enrichment Methods: pros & cons

    image.png

    4.6 Enrichment Methods: cell staining

    image.png

    4.7 Sample Preparation: cell numbers

    • 液滴法的最小细胞数为10,000-25,000
      -需要约50-100个具有独特转录组的细胞来鉴定种群群
      -每ul 100-1000个细胞=每毫升100,000-1,000,000个细胞
    • 通过血细胞计数器计数细胞–不要相信分类计数
      -来自分选器的计数通常是实际细胞计数的½
    • 尝试负选择以去除不需要的细胞
    • 在更broader的标记上进行分类以增加细胞数
    • 对于不可避免的低密度样品
      -将具有明显表达特征的细胞掺入样品中(没懂)


      sample preparation

    4.8 Sample Preparation: buffers

    image.png

    确保缓冲液不含钙,镁,EDTA或肝素(抑制RT-PCR)


    image.png

    4.9 Sample Preparation: viability checks 样品制备:可行性检查

    • 检查样品随时间的生存能力
      -如果生存能力在短时间内降低,这将反映在转录数据中;
      -线粒体读取计数很高。
    • 检查单细胞悬液上清液中是否存在游离的浮动RNA(Ribogreen)
      -在所有样品中产生背景噪音并使分析复杂化;
    • 台盼蓝trypan阳性的死细胞数量是和废掉的reads数量是呈正比的
      -如果在封装时有30%的细胞死亡,那么最多将可以使用70%的测序数据。
      image.png

    4.10 Sample Preparation: dead cell removal

    • FACS out dead cells
      -Will have all associated complications of FACS.
    • Miltenyi dead cell removal kit
      -Magnetic beads used to remove dead cells & debris.

    值得深思的问题

    • 您要去除多少死细胞?
    • 这对您正在研究的生物学意味着什么?
    • 记录您的样品制备元数据!!!

    4.11 Sample Preparation: cryopreservation

    • 各种冷冻保存技术对样本(PBMC或细胞系)有几篇论文的相关报道。
    • 冷冻保存成功与否取决于样品类型。
    • 血液细胞和免疫细胞冷冻效果很好。
    • 关键是补液后细胞的活力。
    • 将Nuc-seq作为冷冻保存细胞的选项。
    image.png
    • 冷冻时组织的质量是下游数据质量的主要因素。
    • 单细胞核心已将细胞冷冻在补充了5%DMSO的标准生长培养基中,效果最佳。
    • 观察到解冻后原代细胞具有20%的细胞死亡。
    • 如果要冷冻组织以备后用,您可能需要考虑在BAM Banker冷冻保存剂中冷冻保存50 mg组织块。


      image.png
    image.png

    4.12 Sample Preparation: single nuclei RNA-seq

    • 从目标样品中提取核。
    • 去除死细胞/垂死细胞的转录噪音。
    • 最常用于神经元样本。
    • 适用于速冻临床样品。
    • 多项研究表明核转录本占整个细胞转录本的很大一部分。
    • 由于内含子和非编码RNA的存在,分析更加困难。


      image.png
      image.png
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      image.png
      image.png
    image.png

    4.13 Best practices to obtain high quality sample

    image.png
    sample prep地址
    -https://www.protocols.io/
    -https://support.10xgenomics.com/single-cell-geneexpression/sample-prep
    -https://community.10xgenomics.com/ image.png image.png

    5.Effects of sample prep and sample type on analysis

    5.1 How Sample Prep Effects Data

    image.png image.png

    5.2 Data Analysis: Quality Control (QC) metrics

    image.png

    5.2 关于scRNA-seq的最终想法

    image.png

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