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標(biāo)題：Chromatin accessibility profiling by ATAC-seq 發(fā)表：Nat Protoc. 2022 Apr 27;17(6):1518–1552. DOI: 10.1038/s41596-022-00692-9 鏈接：https://pmc.ncbi.nlm./articles/PMC9189070/ Omni-ATAC協(xié)議概述ATAC-seq需要相對(duì)較少的輸入細(xì)胞,，并且不需要預(yù)先了解調(diào)控系統(tǒng)動(dòng)態(tài)的表觀遺傳標(biāo)記或轉(zhuǎn)錄因子,。在此，作者描述了一種更新和優(yōu)化的ATAC-seq協(xié)議,，稱為Omni-ATAC，適用于廣泛的細(xì)胞和組織類型,。本協(xié)議詳細(xì)介紹了生成和測(cè)序ATAC-seq文庫的步驟,，并對(duì)樣本制備和下游生物信息學(xué)分析提出了建議。ATAC-seq工作流程主要包括五個(gè)步驟： - data analysis：數(shù)據(jù)分析
如下圖所示,，圖中還包括了每個(gè)步驟大約需要的時(shí)間：  Figure 2: Schematic overview of ATAC-seq protocol與其他技術(shù)比較現(xiàn)有的用于繪制DNA調(diào)控元件的技術(shù)種類繁多,，在特定應(yīng)用中選擇最適當(dāng)且信息量最大的技術(shù)變得具有挑戰(zhàn)性。在下表中,，作者比較了用于繪制DNA調(diào)控元件的最常用技術(shù)的一些技術(shù)和實(shí)驗(yàn)方面：ATAC-seq,、DNase-seq、MNase-seq,、ChIP-seq和靶向CUT&TAG,，以幫助新用戶決定哪種檢測(cè)方法最適合他們的特定應(yīng)用。 選取的原則： 一般來說,，表觀基因組分析適用于回答細(xì)胞類型或組織可能表現(xiàn)出基因調(diào)控變化的“如何”或“為什么”這類問題。對(duì)于主要涉及“發(fā)生了什么變化”的問題,，我們建議從RNA測(cè)序開始,。
| ATAC-seq | DNase-seq | MNase-seq | CUT&TAG or related ChIC techniques |
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| 酶的種類 | Tn5 | endonuclease | endonuclease and exonuclease | Tn5 conjugated to an antibody via Protein A. | | 是否存在測(cè)序偏倚? | Yes; complex, Tn5 insertion bias, with preference for A/Ts in insertion site and C/Gs flanking133-135 | Yes; complex, partially dependent on enzyme concentration and on methylation status of CpGs85,136 | Yes; preferential cutting upstream of A/T compared to G/C137,138 | Yes; dictated by antibody used to guide Tn5 and by Tn5 bias. | | 標(biāo)準(zhǔn)分析中輸入的細(xì)胞/細(xì)胞核數(shù) | 500-50,000 | 1-10 million | 10,000-100,000 | 100,000-500,000 | | 是否有低起始量/單細(xì)胞方法可用？ | Yes86,87; commercial solutions available. | Yes67 | Yes66 | Yes62,64,139-141 | | 樣本類型 | Fresh or cryopreserved cells or nuclei. Fresh or frozen tissues. | Fresh or cryopreserved cells or nuclei. Fresh or frozen tissues. Formaldehyde cross-linked or formalin-fixed paraffin-embedded samples. | Fresh or cryopreserved cells or nuclei. Fresh or frozen tissues. Formaldehyde cross-linked samples. | Fresh or cryopreserved cells or nuclei. Fresh or frozen tissues. | | 文庫準(zhǔn)備時(shí)間 | ~10 hours for 12 samples (this protocol) | 1-3 days | ~ 2-days | 1-2 days | | 技術(shù)考量 | Library quality is highly dependent on cell viability. Protocol alterations are required for use on fixed cells and data quality is often reduced for those samples. | Enzyme concentration and digestion duration may need to be optimized to sample type. Size of fragments selected affects downstream analysis.28 | Enzyme concentration and digestion duration may need to be optimized to sample type. Apparent nucleosome occupancy is a function of MNase concentration. | The amount of antibody used must be titrated for the cell type or sample. This will be a function of the strength of the antibody and the abundance of the target protein. The assay is as specific as the primary antibody used. Additionally, this is a targeted technique, so additional libraries must be made of each modification or protein tested. | | 測(cè)序類型 | Paired-end | Single-end | Single-end | Single-end or paired-end | | 測(cè)序深度 | Low; 10 million read-pairs per sample with Omni-ATAC. | Medium/high: 20-50 million uniquely mapping reads per sample; 200 million for TF footprinting. | High; 150-200 million reads per sample (human)142 | Very low; 3 million read-pairs per sample. | | 數(shù)據(jù)產(chǎn)量 | Tn5-accessible chromatin; | DNase-accessible chromatin; TF footprinting. | Nucleosome positioning, inaccessible chromatin. | Location of target on DNA. | | 主要優(yōu)勢(shì) | Links labeling of accessible regions and NGS library preparation, making preparation of library straightforward. | Footprinting analysis. | Method of choice for nucleosome positioning and quantitative nucleosome dynamics. | Enables mapping of specific TF or histone modification in low cell numbers. Some histone modifications, like H3K27ac, can be used to look for active enhancers. |
與以前的 ATAC-seq 方法比較早期的 ATAC-Seq 方法中仍存在多個(gè)不足之處,。例如,， - 由于線粒體DNA未被染色質(zhì)化，如果ATAC-seq反應(yīng)中有裂解的線粒體存在,，會(huì)導(dǎo)致大量ATAC-seq測(cè)序讀段映射到線粒體DNA上,。
- 在許多細(xì)胞類型和情境中，低信噪比使得將ATAC-seq應(yīng)用于某些實(shí)驗(yàn)系統(tǒng)變得困難甚至不可能
作者針對(duì)上述一些情況,，之前開發(fā)了一種通用且優(yōu)化的ATAC-seq方法,，稱為Omni-ATAC，它解決了許多限制ATAC-seq廣泛應(yīng)用的細(xì)胞或情境特異性問題,。 Omni-ATAC協(xié)議開發(fā)Omni-ATAC 協(xié)議通過減少比對(duì)到線粒體 DNA 的 reads ,，并提高各種細(xì)胞系、組織和冷凍樣本中的信噪比，改進(jìn)了原始的ATAC-seq方法,。這一改進(jìn)是通過優(yōu)化細(xì)胞裂解,、細(xì)胞核分離和轉(zhuǎn)座反應(yīng)實(shí)現(xiàn)的。Omni-ATAC協(xié)議中的優(yōu)化措施通過添加Tween-20和皂角苷（digitonin）,，以及傳統(tǒng)的Nonidet P40（NP40）,，使得多種細(xì)胞類型的裂解成為可能。  Figure 1: Schematic of the ATAC-seq transposition reaction and library preparationExperimental Design1,、輸入材料的準(zhǔn)備- 適用于多種哺乳動(dòng)物細(xì)胞和組織類型：
- 以低至500個(gè)細(xì)胞（或細(xì)胞核）,，用50,000個(gè)細(xì)胞能夠獲得最佳結(jié)果
- 樣本最好為：新鮮或冷凍保存的完整細(xì)胞或細(xì)胞核
- 生物學(xué)重復(fù)與技術(shù)重復(fù)：當(dāng)資源有限時(shí)，建議使用生物復(fù)制,，而不是技術(shù)復(fù)制,；如果獲取生物重復(fù)有限，可以 最好進(jìn)行2-3次技術(shù)復(fù)制
2,、ATAC-seq文庫的質(zhì)量控制作者強(qiáng)烈建議通過低深度測(cè)序（每樣本5萬到10萬條讀對(duì)）來確定最終ATAC-seq文庫的質(zhì)量,。ATAC-seq文庫生成的成功與否取決于四個(gè)關(guān)鍵因素： - （i）轉(zhuǎn)座酶插入在已知染色質(zhì)可及區(qū)域的富集程度（信噪比）
- （ii）唯一片段的總數(shù)（文庫復(fù)雜度）
- （iii）比對(duì)到細(xì)胞核基因組的比對(duì)率與線粒體基因組比對(duì)率
下圖，如 - （e）一個(gè)成功的ATAC-seq文庫：具有較高的轉(zhuǎn)錄起始位點(diǎn)（TSS）富集評(píng)分,，但在Bioanalyzer電泳圖中觀察到的核小體周期性不明顯
- （f）一個(gè)不成功的ATAC-seq文庫：具有較低的TSS富集評(píng)分,，且在Bioanalyzer電泳圖中沒有明顯的核小體周期性
 Figure 3: Assessing ATAC-seq library quality3、測(cè)序參數(shù)指導(dǎo)| 測(cè)序應(yīng)用 | Insight gained | 最短read長度? | Index 長度* | 雙端還是單端 | 測(cè)序數(shù)據(jù)量（reads數(shù)/樣本） |
|---|
| Gene regulatory landscape profiling | Peaks, differential peaks between samples, motif analysis of peaks | 36 bp | 8 | Paired | 10M | | Genotyping | Gene regulatory landscape + genotype of sample; useful for patient samples and to determine if sequence variants affect a peak. | 100 bp | 8 | Paired | 10M | | Footprinting Analysis | Footprinting of different TFs to determine binding sequence at base-pair resolution | 36 bp | 8 | Paired | 200M | | Nucleosome occupancy | Location of nucleosomes along DNA | 36 bp | 8 | Paired | 60M |
更加詳細(xì)的要求可以參考原文,。 數(shù)據(jù)分析測(cè)序完成后,，作者建議使用公開可用的分析流程來執(zhí)行比對(duì)和下游分析，比如： - PEPATAC 流程：https://pepatac./en/latest/
- ENCODE：https://www./atac-seq/
 Figure 4: Overview of the steps of ATAC-seq data analysis上述三種分析管道的對(duì)比：| Step/Process | ENCODE ATAC-seq | PEPATAC | nf-core atacseq |
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| 用于比較的版本 | v1.10.0 | v0.10.0 | v1.2.1 | | 運(yùn)行環(huán)境 | Cromwell/caper | Pypiper | Nextflow | | 去接頭, 比對(duì)以及去重 | Cutadapt,、bowtie2,、Picard | TRIMMOMATIC、skewer,、bowtie2,、BWA、samblaster,、Picard | TrimGalore,、BWA、Picard | | Tn5偏移校正 | Yes | Yes | No | | 線粒體基因過濾 | Yes | Yes | Yes | | Peak calling 方法 | MACS2 | MACS2 (default), F-seq,、Genrich | MACS2 | | 方法 | Based on the irreproducible discovery rate (IDR) for replicates – does not merge for a whole set of samples | Fixed-width, iterative overlap | Raw peak overlap using bedtools109 merge | | 輸出結(jié)果 | BAM files, bigwig files (one representing fold enrichment over expected background and the other representing statistical significance), BED file of peaks for each file and for the merged peak set | QC plots including alignment scoring, TSS scores and library complexity, BED peaks and counts, bam files, bigwig files (nucleotide resolution and smoothed) | QC html report, bam files, normalized bigwig files, BED peaks, annotation of peaks (HOMER), merged peak set, differential accessibility (DESeq2), IGV output. | | 代碼地址 | https://github.com/ENCODE-DCC/atac-seq-pipeline | https://github.com/databio/pepatac | https://github.com/nf-core/atacseq |
下游分析中peaks合并策略： Figure 5: Schematic of peak merging strategies and the resulting merged peak setsSingle-cell ATAC-seqOmini-ATAC 是專門為 bulk ATAC-seq 設(shè)計(jì)的，單細(xì)胞的 ATAC-seq 可以參考成熟的商業(yè)化應(yīng)用如 10X Genomics,。
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