【scRW】[2]Introduction to Single

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
  image.png
  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