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這個(gè)專題叫Schedule for Single-cell RNA-seq workshop，那就把這個(gè)專題叫做【scRW】吧

第二課 Introduction to Single Cell RNA Sequencing

## <Introduction to Single Cell RNA Sequencing>
## 目錄
## 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

Time Course or Development Experiment

Stochastic Gene Expression

More Cells or More Sequencing Reads?

2.Overview of single cell RNA sequencing platforms

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

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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

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

2.2.3 Doublets / Cell Density

Doublets / Cell Density

10xgenomics

2.2.4 Scrublet: Computational Identification of Doublets

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

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

4.3.2 Sample Preparation: single cell suspension

4.4 Sample preparation protocol varies by cell-type

Sample preparation protocol varies by cell-type

Sample Preparation Varies by Cell-Type

4.5 Enrichment Methods: pros & cons

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4.6 Enrichment Methods: cell staining

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4.7 Sample Preparation: cell numbers

液滴法的最小細(xì)胞數(shù)為10,000-25,000
-需要約50-100個(gè)具有獨(dú)特轉(zhuǎn)錄組的細(xì)胞來(lái)鑒定種群群
-每ul 100-1000個(gè)細(xì)胞=每毫升100,000-1,000,000個(gè)細(xì)胞
通過(guò)血細(xì)胞計(jì)數(shù)器計(jì)數(shù)細(xì)胞–不要相信分類計(jì)數(shù)
-來(lái)自分選器的計(jì)數(shù)通常是實(shí)際細(xì)胞計(jì)數(shù)的?
嘗試負(fù)選擇以去除不需要的細(xì)胞
在更broader的標(biāo)記上進(jìn)行分類以增加細(xì)胞數(shù)
對(duì)于不可避免的低密度樣品
-將具有明顯表達(dá)特征的細(xì)胞摻入樣品中（沒懂）

sample preparation

4.8 Sample Preparation: buffers

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確保緩沖液不含鈣，鎂，EDTA或肝素（抑制RT-PCR）

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4.9 Sample Preparation: viability checks 樣品制備：可行性檢查

檢查樣品隨時(shí)間的生存能力
-如果生存能力在短時(shí)間內(nèi)降低，這將反映在轉(zhuǎn)錄數(shù)據(jù)中；
-線粒體讀取計(jì)數(shù)很高。
檢查單細(xì)胞懸液上清液中是否存在游離的浮動(dòng)RNA（Ribogreen）
-在所有樣品中產(chǎn)生背景噪音并使分析復(fù)雜化；
臺(tái)盼藍(lán)trypan陽(yáng)性的死細(xì)胞數(shù)量是和廢掉的reads數(shù)量是呈正比的
-如果在封裝時(shí)有30％的細(xì)胞死亡，那么最多將可以使用70％的測(cè)序數(shù)據(jù)。

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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.

值得深思的問(wèn)題

您要去除多少死細(xì)胞？
這對(duì)您正在研究的生物學(xué)意味著什么？
記錄您的樣品制備元數(shù)據(jù)?。?！

4.11 Sample Preparation: cryopreservation

各種冷凍保存技術(shù)對(duì)樣本（PBMC或細(xì)胞系）有幾篇論文的相關(guān)報(bào)道。
冷凍保存成功與否取決于樣品類型。
血液細(xì)胞和免疫細(xì)胞冷凍效果很好。
關(guān)鍵是補(bǔ)液后細(xì)胞的活力。
將Nuc-seq作為冷凍保存細(xì)胞的選項(xiàng)。

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冷凍時(shí)組織的質(zhì)量是下游數(shù)據(jù)質(zhì)量的主要因素。
單細(xì)胞核心已將細(xì)胞冷凍在補(bǔ)充了5％DMSO的標(biāo)準(zhǔn)生長(zhǎng)培養(yǎng)基中，效果最佳。
觀察到解凍后原代細(xì)胞具有20％的細(xì)胞死亡。
如果要冷凍組織以備后用，您可能需要考慮在BAM Banker冷凍保存劑中冷凍保存50 mg組織塊。

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4.12 Sample Preparation: single nuclei RNA-seq

從目標(biāo)樣品中提取核。
去除死細(xì)胞/垂死細(xì)胞的轉(zhuǎn)錄噪音。
最常用于神經(jīng)元樣本。
適用于速凍臨床樣品。
多項(xiàng)研究表明核轉(zhuǎn)錄本占整個(gè)細(xì)胞轉(zhuǎn)錄本的很大一部分。
由于內(nèi)含子和非編碼RNA的存在，分析更加困難。

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