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ATAC-Seq

ATAC-Seq (Assay for Transposase-Accessible Chromatin using sequencing) is a powerful epigenomic profiling technique that enables the identification of open chromatin regions within the genome.
This method utilizes a hyperactive Tn5 transposase to selectively tag and amplify accessible DNA regions, providing insights into cellular regulatory mechanisms and chromatin architecture.
ATAC-Seq has become an essential tool for researchers studying gene expression, transcriptional regulation, and chromatin dynamics acoss a variety of biological systems and disease states.
With its high sensitivity, low input requirements, and streamlined workflow, ATAC-Seq has emerged as a preffered approach for interrogating the epigenomic landscape.

Most cited protocols related to «ATAC-Seq»

1
Multiomics Analysis of Human PBMCs
10x Genomics multiomic (RNA + ATAC) data for human PBMCs was obtained from 10X website (https://support.10xgenomics.com/single-cell-multiome-atac-gex/datasets) and was processed using Signac (Stuart et al., 2020 (link)) and Seurat. ATAC-seq peaks were then identified for each cell type separately using MACS2, using the function CallPeaks in Signac 1.1.0 with arguments group.by = ‘celltype’ and additional.args = ‘–max-gap 50’. Fragment counts for each peak were quantified per cell using the FeatureMatrix function in Signac. Per-cell quality control metrics were computed using the TSSEnrichment and NucleosomeSignal functions, and cells retained with a nucleosome signal score < 2, TSS enrichment score > 1, and total RNA counts < 100,000 and > 25,000. We apply SCTransform to normalize RNA counts and TFIDF to normalize ATAC peaks. We use LSI to reduce the dimensionality of ATAC data, and PCA to reduce the dimensionality of RNA. Then, we used 2-40 LSI dimensions and 1-40 RNA PCA dimensions to construct the WNN graph.
Hao Y., Hao S., Andersen-Nissen E., Mauck WM I.I.I., Zheng S., Butler A., Lee M.J., Wilk A.J., Darby C., Zager M., Hoffman P., Stoeckius M., Papalexi E., Mimitou E.P., Jain J., Srivastava A., Stuart T., Fleming L.M., Yeung B., Rogers A.J., McElrath J.M., Blish C.A., Gottardo R., Smibert P, & Satija R. (2021). Integrated analysis of multimodal single-cell data. Cell, 184(13), 3573-3587.e29.
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Publication 2021
ATAC-Seq Cells Homo sapiens Nucleosomes XCL1 protein, human
2
Genetic Architecture of Major Depression
Genotyping procedures can be found in the primary reports for each cohort (summarized in Supplementary Table 3). Individual genotype data for all PGC29 samples, GERA, and iPSYCH were processed using the PGC “ricopili” pipeline (URLs) for standardized quality control, imputation, and analysis19 (link). The cohorts from deCODE, Generation Scotland, UK Biobank, and 23andMeD were processed by the collaborating research teams using comparable procedures. SNPs and insertion-deletion polymorphisms were imputed using the 1000 Genomes Project multi-ancestry reference panel (URLs)86 (link). More detailed information on sample QC is provided in the Supplementary Note.
Linkage disequilibrium (LD) score regression (LDSC)22 (link),24 (link) was used to estimate
hSNP2 from GWA summary statistics. Estimates of
hSNP2 on the liability scale depend on the assumed lifetime prevalence of MDD in the population (K), and we assumed K=0.15 but also evaluated a range of estimates of K to explore sensitivity including 95% confidence intervals (Supplementary Fig. 1). LDSC bivariate genetic correlations attributable to genome-wide SNPs (rg) were estimated across all MDD and major depression cohorts and between the full meta-analyzed cohort and other traits and disorders.
LDSC was also used to partition
hSNP2 by genomic features24 (link),46 (link). We tested for enrichment of
hSNP2 based on genomic annotations partitioning
hSNP2 proportional to bp length represented by each annotation. We used the “baseline model” which consists of 53 functional categories. The categories are fully described elsewhere46 (link), and included conserved regions47 (link), USCC gene models (exons, introns, promoters, UTRs), and functional genomic annotations constructed using data from ENCODE 87 (link) and the Roadmap Epigenomics Consortium88 (link). We complemented these annotations by adding introgressed regions from the Neanderthal genome in European populations89 (link) and open chromatin regions from the brain dorsolateral prefrontal cortex. The open chromatin regions were obtained from an ATAC-seq experiment performed in 288 samples (N=135 controls, N=137 schizophrenia, N=10 bipolar, and N=6 affective disorder)90 . Peaks called with MACS91 (link) (1% FDR) were retained if their coordinates overlapped in at least two samples. The peaks were re-centered and set to a fixed width of 300bp using the diffbind R package92 (link). To prevent upward bias in heritability enrichment estimation, we added two categories created by expanding both the Neanderthal introgressed regions and open chromatin regions by 250bp on each side.
We used LDSC to estimate rg between major depression and a range of other disorders, diseases, and human traits22 (link). The intent of these comparisons was to evaluate the extent of shared common variant genetic architectures in order to suggest hypotheses about the fundamental genetic basis of major depression (given its extensive comorbidity with psychiatric and medical conditions and its association with anthropometric and other risk factors). Subject overlap of itself does not bias rg. These rg are mostly based on studies of independent subjects and the estimates should be unbiased by confounding of genetic and non-genetic effects (except if there is genotype by environment correlation). When GWA studies include overlapping samples, rg remains unbiased but the intercept of the LDSC regression is an estimate of the correlation between association statistics attributable to sample overlap. These calculations were done using the internal PGC GWA library and with LD-Hub (URLs)60 (link).
Wray N.R., Ripke S., Mattheisen M., Trzaskowski M., Byrne E.M., Abdellaoui A., Adams M.J., Agerbo E., Air T.M., Andlauer T.F., Bacanu S.A., Bækvad-Hansen M., Beekman A.T., Bigdeli T.B., Binder E.B., Blackwood D.H., Bryois J., Buttenschøn H.N., Bybjerg-Grauholm J., Cai N., Castelao E., Christensen J.H., Clarke T.K., Coleman J.R., Colodro-Conde L., Couvy-Duchesne B., Craddock N., Crawford G.E., Crowley C.A., Dashti H.S., Davies G., Deary I.J., Degenhardt F., Derks E.M., Direk N., Dolan C.V., Dunn E.C., Eley T.C., Eriksson N., Escott-Price V., Kiadeh F.F., Finucane H.K., Forstner A.J., Frank J., Gaspar H.A., Gill M., Giusti-Rodríguez P., Goes F.S., Gordon S.D., Grove J., Hall L.S., Hannon E., Hansen C.S., Hansen T.F., Herms S., Hickie I.B., Hoffmann P., Homuth G., Horn C., Hottenga J.J., Hougaard D.M., Hu M., Hyde C.L., Ising M., Jansen R., Jin F., Jorgenson E., Knowles J.A., Kohane I.S., Kraft J., Kretzschmar W.W., Krogh J., Kutalik Z., Lane J.M., Li Y., Li Y., Lind P.A., Liu X., Lu L., MacIntyre D.J., MacKinnon D.F., Maier R.M., Maier W., Marchini J., Mbarek H., McGrath P., McGuffin P., Medland S.E., Mehta D., Middeldorp C.M., Mihailov E., Milaneschi Y., Milani L., Mill J., Mondimore F.M., Montgomery G.W., Mostafavi S., Mullins N., Nauck M., Ng B., Nivard M.G., Nyholt D.R., O’Reilly P.F., Oskarsson H., Owen M.J., Painter J.N., Pedersen C.B., Pedersen M.G., Peterson R.E., Pettersson E., Peyrot W.J., Pistis G., Posthuma D., Purcell S.M., Quiroz J.A., Qvist P., Rice J.P., Riley B.P., Rivera M., Mirza S.S., Saxena R., Schoevers R., Schulte E.C., Shen L., Shi J., Shyn S.I., Sigurdsson E., Sinnamon G.C., Smit J.H., Smith D.J., Stefansson H., Steinberg S., Stockmeier C.A., Streit F., Strohmaier J., Tansey K.E., Teismann H., Teumer A., Thompson W., Thomson P.A., Thorgeirsson T.E., Tian C., Traylor M., Treutlein J., Trubetskoy V., Uitterlinden A.G., Umbricht D., Van der Auwera S., van Hemert A.M., Viktorin A., Visscher P.M., Wang Y., Webb B.T., Weinsheimer S.M., Wellmann J., Willemsen G., Witt S.H., Wu Y., Xi H.S., Yang J., Zhang F., Arolt V., Baune B.T., Berger K., Boomsma D.I., Cichon S., Dannlowski U., de Geus E., DePaulo J.R., Domenici E., Domschke K., Esko T., Grabe H.J., Hamilton S.P., Hayward C., Heath A.C., Hinds D.A., Kendler K.S., Kloiber S., Lewis G., Li Q.S., Lucae S., Madden P.A., Magnusson P.K., Martin N.G., McIntosh A.M., Metspalu A., Mors O., Mortensen P.B., Müller-Myhsok B., Nordentoft M., Nöthen M.M., O’Donovan M.C., Paciga S.A., Pedersen N.L., Penninx B.W., Perlis R.H., Porteous D.J., Potash J.B., Preisig M., Rietschel M., Schaefer C., Schulze T.G., Smoller J.W., Stefansson K., Tiemeier H., Uher R., Völzke H., Weissman M.M., Werge T., Winslow A.R., Lewis C.M., Levinson D.F., Breen G., Børglum A.D, & Sullivan P.F. (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature genetics, 50(5), 668-681.
Publication 2018
ATAC-Seq Brain Chromatin DNA Library Dorsolateral Prefrontal Cortex Europeans Exons Genetic Diversity Genetic Polymorphism Genome Genome-Wide Association Study Genotype Genotyping Techniques Homo sapiens Hypersensitivity INDEL Mutation Introns Mood Disorders Neanderthals Reproduction Schizophrenia Single Nucleotide Polymorphism Unipolar Depression Untranslated Regions
3
Demultiplexing Cells in Single-Cell ATAC-Seq
Raw sequencing data was converted to FastQ format using the ‘cellranger-atac mkfastq’ pipeline (10x Genomics, version 1.0.0). scATAC-seq reads were aligned to the hg19 reference genome (https://support.10xgenomics.com/single-cell-atac/software/downloads/latest) and quantified using the ‘cellranger-count’ pipeline (10x Genomics, version 1.0.0). Genotypes used to perform demuxlet were determined as follows for each cell line: bulk ATAC-seq FastQ files were processed and aligned using PEPATAC (http://code.databio.org/PEPATAC/) as described previously34 (link). Peaks were identified using MACS2, and a union set of variable-width accessible regions was identified using bedtools merge (version 2.26.0). These accessible regions were genotyped across all samples using SAMtools mpileup (version 1.5) and VarScan mpileup2snp (version 2.4.3) with the following parameters: ‘--min-coverage 5 --min-reads2 2 --min-var-freq 0.1 --strand-filter 1 --output-vcf 1’. All positions containing a single-nucleotide variant were compiled into a master set, and then each cell line was genotyped at those specific single-base locations using SAMtools mpileup. The allelic depth at each position was converted into a quaternary genotype (homozygous A, heterozygous AB, homozygous B or insufficient data to generate a confident call). Next, for each cell line, inferred genotype probabilities were created based on those quaternary genotypes, and a VCF file was created for input to demuxlet using recommended parameters. Demuxlet was used to identify the cell line of origin for individual cells and to identify doublets based on mixed genotypes.
Granja J.M., Corces M.R., Pierce S.E., Bagdatli S.T., Choudhry H., Chang H.Y, & Greenleaf W.J. (2021). ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nature Genetics, 53(3), 403-411.
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Publication 2021
Alleles ATAC-Seq Cell Lines Cells Dietary Fiber Genome Genotype Heterozygote Homozygote Nucleotides Self Confidence XCL1 protein, human
4
Multimodal Single-Cell Data Integration
The WNN procedure begins by first applying standard analytical workflows to each modality independently and constructing KNN graphs for each one. In this manuscript we analyze data falling into three categories: measurements of single-cell gene expression, single-cell surface protein expression, and single-cell chromatin accessibility (ATAC-seq). For most analyses in this manuscript, we use a default value of k = 20, which is also the default value of k in the standard Seurat clustering workflow. For the analysis of the multimodal PBMC atlas, due to the substantial size of the dataset, we used a value of k = 30. In Figure S2, we show that we obtain very similar results from the WNN procedure when varying k across a series of values ranging from 10 to 50.
For clarity, we overview the analytical workflows for each data type below:

Single-cell gene expression: We analyze scRNA-seq data using standard pipelines in Seurat which include normalization, feature selection, and dimensional reduction with PCA. We then construct a KNN graph after dimensional reduction.

We emphasize that WNN analysis can leverage any scRNA-seq preprocessing workflow that generates a KNN graph. For example, users can preprocess their scRNA-seq data with a variety of normalization tools including log-normalization, scran (Lun et al., 2016 (link)) or SCTransform (Hafemeister and Satija, 2019 (link)), and can utilize alternative dimensional reduction procedures such as factor analysis or variational autoencoders. In this manuscript, we use workflows that are available in the Seurat package, and detail exact settings for each analysis later in this document.

Single-cell cell surface protein level expression: We analyze single-cell protein data (representing the quantification of antibody-derived tags (ADTs) in CITE-seq or ASAP-seq data) using a similar workflow to scRNA-seq. We normalize protein expression levels within a cell using the centered-log ratio (CLR) transform, followed by dimensional reduction with PCA, and subsequently construct a KNN graph. Unless otherwise specified, we do not perform feature selection on protein data, and use all measured proteins during dimensional reduction.

Single-cell chromatin accessibility: We analyze single-cell ATAC-seq data using our previously described workflow (Stuart et al., 2019 (link)), as implemented in the Signac package. We reduced the dimensionality of the scATAC-seq data by performing latent semantic indexing (LSI) on the scATAC-seq peak matrix, as suggested by Cusanovich et al. (2018) (link). We first computed the term frequency-inverse document frequency (TF-IDF) of the peak matrix by dividing the accessibility of each peak in each cell by the total accessibility in the cell (the “term frequency”), and multiplied this by the inverse accessibility of the peak in the cell population. This step ‘upweights’ the contribution of highly variable peaks and down-weights peaks that are accessible in all cells. We then multiplied these values by 10,000 and log-transformed this TF-IDF matrix, adding a pseudocount of 1 to avoid computing the log of 0. We decomposed the TF-IDF matrix via SVD to return LSI components, and scaled LSI loadings for each LSI component to mean 0 and standard deviation 1.

As described for scRNA-seq analysis, while we use Seurat and Signac functions in this manuscript, any analytical workflow that computes a KNN graph for surface protein or chromatin accessibility data can also be used in the first step of WNN analysis.
Hao Y., Hao S., Andersen-Nissen E., Mauck WM I.I.I., Zheng S., Butler A., Lee M.J., Wilk A.J., Darby C., Zager M., Hoffman P., Stoeckius M., Papalexi E., Mimitou E.P., Jain J., Srivastava A., Stuart T., Fleming L.M., Yeung B., Rogers A.J., McElrath J.M., Blish C.A., Gottardo R., Smibert P, & Satija R. (2021). Integrated analysis of multimodal single-cell data. Cell, 184(13), 3573-3587.e29.
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Publication 2021
ATAC-Seq Cell Membrane Proteins Cells Chromatin Gene Expression Immunoglobulins Membrane Proteins Multimodal Imaging Protein Domain Proteins Single-Cell RNA-Seq Staphylococcal Protein A
5
Nuclei Isolation for ATAC-Seq Analysis
See Supplementary
Protocol 2 for a detailed protocol. This protocol is highly similar
to the INTACT method19 (link) and
either protocol can be used for the isolation of nuclei with equivalent results.
All of the steps were carried out at 4 °C. A frozen tissue fragment ~20
mg was placed into a pre-chilled 2-ml Dounce homogenizer containing 2 ml of cold
1× homogenization buffer (320 mM sucrose, 0.1 mM EDTA, 0.1%
NP40, 5 mM CaCl2, 3 mM Mg(Ac)2, 10 mM Tris pH 7.8,
1× protease inhibitors (Roche, cOmplete), and 167 μM
β-mercaptoethanol, in water). Tissue was homogenized with approximately
ten strokes with the loose ‘A’ pestle, followed by 20 strokes
with the tight ‘B’ pestle. Connective tissue and residual debris
were precleared by filtration through an 80-μm nylon mesh filter
followed by centrifugation for 1 min at 100 r.c.f. While avoiding the pelleted
debris, 400 μl was transferred to a pre-chilled 2-ml round bottom
Lo-Bind Eppendorf tube. An equal volume (400 μl) of a 50%
iodixanol solution (50% iodixanol in 1× homogenization buffer)
was added and mixed by pipetting to make a final concentration of 25%
iodixanol. 600 μl of a 29% iodixanol solution (29%
iodixanol in 1× homogenization buffer containing 480 mM sucrose) was
layered underneath the 25% iodixanol mixture. A clearly defined
interface should be visible. In a similar fashion, 600 μl of a
35% iodixanol solution (35% iodixanol in 1×
homogenization containing 480 mM sucrose) was layered underneath the 29%
iodixanol solution. Again, a clearly defined interface should be visible between
all three layers. In a swinging-bucket centrifuge, nuclei were centrifuged for
20 min at 3,000 r.c.f. After centrifugation, the nuclei were present at the
interface of the 29% and 35% iodixanol solutions. This band with
the nuclei was collected in a 300 μl volume and transferred to a
pre-chilled tube. Nuclei were counted after addition of trypan blue, which
stains all nuclei due to membrane permeabilization from freezing. 50,000 counted
nuclei were then transferred to a tube containing 1 ml of ATAC-seq RSB with
0.1% Tween-20. Nuclei were pelleted by centrifugation at 500 r.c.f. for
10 min in a pre-chilled (4 °C) fixed-angle centrifuge. Supernatant was
removed using the two pipetting steps described above. Because the nuclei were
already permeabilized, no lysis step was performed, and the transposition mix
(25 μl 2× TD buffer, 2.5 μl transposase (100 nM final),
16.5 μl PBS, 0.5 μl 1% digitonin, 0.5 μl
10% Tween-20, 5 μl water) was added directly to the nuclear
pellet and mixed by pipetting up and down six times. Transposition reactions
were incubated at 37 °C for 30 min in a thermomixer with shaking at
1,000 r.p.m. Reactions were cleaned up with Zymo DNA Clean and Concentrator 5
columns. The remainder of the ATAC-seq library preparation was performed as
described previously18 .
Corces M.R., Trevino A.E., Hamilton E.G., Greenside P.G., Sinnott-Armstrong N.A., Vesuna S., Satpathy A.T., Rubin A.J., Montine K.S., Wu B., Kathiria A., Cho S.W., Mumbach M.R., Carter A.C., Kasowski M., Orloff L.A., Risca V.I., Kundaje A., Khavari P.A., Montine T.J., Greenleaf W.J, & Chang H.Y. (2017). An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nature methods, 14(10), 959-962.
Publication 2017
2-Mercaptoethanol ATAC-Seq Buffers Cell Nucleus Centrifugation Cerebrovascular Accident Connective Tissue Digitonin DNA Library Edetic Acid Filtration iodixanol isolation Nylons Protease Inhibitors Sucrose Tissue, Membrane Tissues Transposase Tromethamine Trypan Blue Tween 20

Most recents protocols related to «ATAC-Seq»

1
Integrative Multi-omics Analysis of Esophageal Cancer
All data were obtained from ArrayExpress, unless stated otherwise. Human tissue RNA-seq data were obtained from: OCCAMS consortium (European Genome-Phenome Archive, EGAD00001007496). Human tissue ATAC-seq data were obtained from: E-MTAB-5169 (Britton et al., 2017 (link)), E-MTAB-6751 (Rogerson et al., 2019 (link)), and E-MTAB-8447 (Rogerson et al., 2020 (link)). The Cancer Genome Atlas OAC ATAC-seq data were obtained from the GDC data portal (https://portal.gdc.cancer.gov/; Corces et al., 2018 (link)). OE19 H3K27ac ChIP-seq was obtained from: E-MTAB-10319 (Ogden et al., 2022 (link)). GAC H3K4me1 and H3K4me3 ChIP-seq were obtained from: Gene Expression Omnibus, GSE75898 (Ooi et al., 2016 (link)). OE19 siKLF5 RNA-seq and KLF5 ChIP-seq were obtained from: E-MTAB-8446 and E-MTAB-8568, respectively (Rogerson et al., 2020 (link)). OE19 dnFOS RNA-seq was obtained from E-MTAB-10334 (Ogden et al., 2023 (link)).
Ahmed I., Yang S.H., Ogden S., Zhang W., Li Y, & Sharrocks A.D. (2023). eRNA profiling uncovers the enhancer landscape of oesophageal adenocarcinoma and reveals new deregulated pathways. eLife, 12, e80840.
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Publication 2023
ATAC-Seq Chromatin Immunoprecipitation Sequencing Europeans Gene Expression Genome histone H3 trimethyl Lys4 Homo sapiens Malignant Neoplasms RNA-Seq Tissues
2
Integrative Analysis of Patient ATAC-seq and RNA-seq Data
Patient tissue ATAC-seq data processing was performed as described previously (Britton et al., 2017 (link)). Reads were mapped to GRCh38 (hg38) using Bowtie2 v2.3.0 (Langmead and Salzberg, 2012 (link)) with the following options: -X 2000 -dovetail. Mapped reads ( ≥ q30) were retained using SAMtools (Li et al., 2009 (link)). Reads mapping to blacklisted regions were removed using BEDtools (Quinlan and Hall, 2010 (link)). Peaks were called using MACS2 v2.1.1 (Zhang et al., 2008 (link)) with the following parameters: -q 0.01, -nomodel-shift –75 -extsize 150 -B –SPMR. A custom union peakset was formed from all BO and OAC patient samples, using HOMER v4.9 mergePeaks.pl -d 250 (Heinz et al., 2010 (link)) as described previously (Rogerson et al., 2019 (link)) and filtered to retain only intergenic regions ≥2 kb upstream from a TSS or ≥500 bp downstream from a TTS.
RNA-seq reads were mapped to the human genome GRCh38 (hg38) using STAR v2.3.0 (Dobin et al., 2013 (link)). The expression threshold for eRNAs was determined using an adapted method from Zhang et al., 2019 (link). Briefly, total RNA-seq reads were integrated into genomic regions from the intergenic patient ATAC-seq peakset. Putative eRNA and mRNA read counts were determined using featureCounts (Liao et al., 2014 (link)) and FPM values determined using DESeq2 (Love et al., 2014 (link)). Putative eRNA regions with average counts and FPM values of ≥3 and 1.5, respectively, were taken forward for further analysis. Differentially expressed eRNAs and mRNAs were determined using DESeq2 (Love et al., 2014 (link)). For eRNAs, a log2-fold change of ±0.5 and p-valueadj <0.05 defined differential expression. For BO and OAC mRNAs, a log2-fold change of ±0.9 and ±1.5, respectively, and p-valueadj <0.05 defined differential expression. ERBB2-positive OAC samples (ERBB2AMP) were determined based on these samples having expression of ERBB2 greater than the median ERBB2 expression +2 SD. Morpheus (https://software.broadinstitute.org/morpheus/) was used to generate heatmaps and perform hierarchical clustering.
HOMER v4.9 was used for de novo transcription factor motif enrichment analysis. To analyse footprinting signatures at putative eRNA regions, TOBIAS v0.5.1 was used (Bentsen et al., 2020 (link)). eRNAs were annotated to genes by the nearest gene model and assessed for CpG content using HOMER v4.9. Super enhancers were identified using HOMER v4.9 findPeaks.pl -style super. Net enhancer activity was calculated as in Bi et al., 2020 (link). Briefly, neighbouring genes of eRNA regions in both BO and OAC were identified and stratified into nine groups based on the net eRNA change within 200 kb of the TSS of each gene: + (or −1) stands for 1 net gained (or lost) eRNA from BO to OAC. Bidirectionality score was calculated using HOMER v4.9 analyzeRepeats.pl with the −strand option applied for each strand and score defined as log10((+strand expression score + 1)/(−strand expression score + 1)) + 1.
Ahmed I., Yang S.H., Ogden S., Zhang W., Li Y, & Sharrocks A.D. (2023). eRNA profiling uncovers the enhancer landscape of oesophageal adenocarcinoma and reveals new deregulated pathways. eLife, 12, e80840.
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Publication 2023
ATAC-Seq Genes Genome Genome, Human herstatin protein, human Homo sapiens Intergenic Region Love Patients RNA, Messenger RNA-Seq Tissues Transcription, Genetic Whole Transcriptome Sequencing
3
ATAC-seq for Chromatin Accessibility Profiling
ATAC-seq was performed as previously described [40 (link)]. Briefly, a total of 50,000 cells were washed once with 50 μl of cold PBS and resuspended in 50 μl lysis buffer (10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.2% (v/v) IGEPAL CA-630). The suspension of nuclei was then centrifuged for 10 min at 500 g, 4 °C, and then 50 μl transposition reaction mix (10 μl 5 × TTBL, 5 μl TTE Mix V50 and 35 μl nuclease-free H2O) of TruePrep™ DNA Library Prep Kit V2 for Illumina® (TD501, Vazyme) was added. Samples were incubated at 37 °C for 30 min for PCR amplified. DNA was isolated using a MinElute® PCR Purification Kit (QIAGEN). ATAC-seq libraries were constructed using the TruePrep™ DNA Library Prep Kit V2 for Illumina® (TD501, Vazyme) and the library was then PCR amplified for the appropriate number of cycles. Libraries were purified with a MinElute ® PCR Purification Kit (QIAGEN). Library concentration was measured using VAHTS Library Quantification Kit for Illumina (NQ102, Vazyme). Finally, the ATAC library was sequenced on a NextSeq 500 using a NextSeq 500 High Output Kit v2 (150 cycles) (FC-404–2002, Illumina) according to the manufacturer’s instructions. All the sequencing data were aligned to the mouse genome assembly (mm10) using the Bowtie2 (version 2.2.5) with the options: -p 20 --very-sensitive --end-to-end --no-unal. Reads mapping to mitochondrial DNA or unassigned sequences were discarded. For pair-end sequence data, only concordantly aligned pairs were kept. Alignment bam files were transformed into read coverage files (bigwig format) using deep Tools with the RPKM (Reads Per Kilobase per Million mapped reads) normalization method.
Jin L., He J., Feng H., Li S., Liu H., Dong H., Hu M., Huang J., Wu H., Chen J., Qi L, & Wu K. (2023). Transposable elements activation triggers necroptosis in mouse embryonic stem cells. Cell Death & Disease, 14(3), 184.
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Publication 2023
ATAC-Seq Buffers Cell Nucleus Cells Cold Temperature DNA, Mitochondrial DNA Library Genome Igepal CA-630 Magnesium Chloride Mice, House Sodium Chloride Tromethamine XCL1 protein, human
4
Integrated Genomic Data Analysis Pipeline
Prior to mapping to the human reference genome (GRCh37/hg19) with bowtie2 (v.2.3.1), quality of the raw sequencing data of CUT&RUN, ChIP-seq and ATAC-seq was evaluated using FastQC and adapter trimming was done using TrimGalore (v0.6.5). Quality of aligned reads were filtered using min MAPQ 30 and reads with known low sequencing confidence were removed using Encode Blacklist regions. For CUT&RUN, because of oversequencing, reads were subsampled, and mapping was done with 10 M reads (recommended read depth), for ChIP-seq and ATAC-seq all sequenced reads were mapped. Peak calling was performed using MACS2 (v2.1.0) taking a q value of 0.05 as threshold and peaks were filtered for chr2p amplified regions in the case of IMR-32 cells. Homer37 (link) (v4.10.3) was used to perform motif enrichment analysis, with 200 bp around the peak summit as input. Overlap of peaks, annotation, heatmaps and pathway enrichment was analysed using DeepTools (v3.5.1), the R package ChIPpeakAnno (v3.28.1), and the web tool enrichR. Sushi (v1.32.0) was used for visualization of the data upon RPKM normalization or log likelihood ratio calculation with MACS2 (v2.1.0).
Decaesteker B., Louwagie A., Loontiens S., De Vloed F., Bekaert S.L., Roels J., Vanhauwaert S., De Brouwer S., Sanders E., Berezovskaya A., Denecker G., D’haene E., Van Haver S., Van Loocke W., Van Dorpe J., Creytens D., Van Roy N., Pieters T., Van Neste C., Fischer M., Van Vlierberghe P., Roberts S.S., Schulte J., Ek S., Versteeg R., Koster J., van Nes J., Zimmerman M., De Preter K, & Speleman F. (2023). SOX11 regulates SWI/SNF complex components as member of the adrenergic neuroblastoma core regulatory circuitry. Nature Communications, 14, 1267.
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Publication 2023
ATAC-Seq Cells Chromatin Immunoprecipitation Sequencing Homo sapiens
5
Genome-wide Chromatin Profiling by ChIP-seq and ATAC-seq
Chromatin immunoprecipitation (ChIP) and Assay for Transposase-Accessible Chromatin (ATAC) sequencing was performed as previously described36 (link). For ChIP-seq a total of 10 × 107 cells were crosslinked with 1% formaldehyde while shaking for 7 min at room temperature, quenched with 125 mM glycine, lysed and sonicated with the S2 Covaris for 30 min to obtain 200–300 bp long fragments. Chromatin fragments were immunoprecipitated overnight using 1 µg antibody of SOX11-PAb antibody (custom made by Absea biotechnology, China) and 20 µl Protein A UltraLink® Resin (Thermo Scientific, #53139) beads per 10 × 107 cells. Reverse crosslinking was done at 65 °C for 15 h and chromatin was resuspended in TE-buffer, incubated for 2 h at 37 °C with 0.2 mg/ml RNase and followed by an incubation of 2 h at 55 °C with 0.2 mg/ml proteinase K. DNA was isolated using 400 µl phenol:chloroform:isoamylalcohol (P:C:IA) in phase lock gel tubes (5Prime). Upon centrifugation, the aqueous layer was transferred to a new tube with 200 mM NaCl, 30 µg glycogen and 800 µl 100% ethanol, and incubated for 30 min at −20 °C. Upon centrifugation, the pellet was washed with 80% Ethanol and resuspended in RNase/DNase free water. DNA concentration was measured using the Qubit® dsDNA HS Assay Kit. Library prep was done using the NEBNExt Ultra DNA library Prep Kit for Illumina (E7370S) with 500 ng starting material and using 8 PCR cycles according to the manufacturer’s instructions. For ATAC-seq, 50,000 cells were lysed and fragmented using digitonin and Tn5 transposase. The transposed DNA fragments were amplified and purified using Agencourt AMPure XP beads (Beckman Coulter). ChIP and ATAC library concentrations were measured with the Illumina Kapa Library quantification kit (Roche #07960140001) and libraries were sequenced on the NextSeq 500 (Illumina) using the Nextseq 500 High Output kit V2 75 cycles single-end (Illumina).
Decaesteker B., Louwagie A., Loontiens S., De Vloed F., Bekaert S.L., Roels J., Vanhauwaert S., De Brouwer S., Sanders E., Berezovskaya A., Denecker G., D’haene E., Van Haver S., Van Loocke W., Van Dorpe J., Creytens D., Van Roy N., Pieters T., Van Neste C., Fischer M., Van Vlierberghe P., Roberts S.S., Schulte J., Ek S., Versteeg R., Koster J., van Nes J., Zimmerman M., De Preter K, & Speleman F. (2023). SOX11 regulates SWI/SNF complex components as member of the adrenergic neuroblastoma core regulatory circuitry. Nature Communications, 14, 1267.
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Publication 2023
AT 125 ATAC-Seq Biological Assay Buffers Cells Centrifugation Chloroform Chromatin Deoxyribonucleases Digitonin DNA, Double-Stranded DNA Library Ethanol Formaldehyde Glycine Glycogen Immunoglobulins Immunoprecipitation, Chromatin Phenol proteinase C Resins, Plant ribonuclease C Ribonucleases Sodium Chloride SOX11 protein, human Staphylococcal Protein A Tn5 transposase Transposase XCL1 protein, human

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More about "ATAC-Seq"

ATAC-Seq (Assay for Transposase-Accessible Chromatin using sequencing) is a powerful epigenomic profiling technique that enables the identification of open chromatin regions within the genome.
This method utilizes a hyperactive Tn5 transposase to selectively tag and amplify accessible DNA regions, providing insights into cellular regulatory mechanisms and chromatin architecture.
ATAC-Seq has become an essential tool for researchers studying gene expression, transcriptional regulation, and chromatin dynamics across a variety of biological systems and disease states.
With its high sensitivity, low input requirements, and streamlined workflow, ATAC-Seq has emerged as a preferred approach for interrogating the epigenomic landscape.
The technique involves the use of the MinElute PCR Purification Kit, NextSeq 500, HiSeq 2500, MinElute kit, AMPure XP beads, and the Nextera DNA Library Prep Kit to prepare and sequence the ATAC-Seq libraries.
The Tn5 transposase is a key component, responsible for selectively tagging and amplifying the accessible DNA regions.
ATAC-Seq can be performed on a variety of sequencing platforms, including the HiSeq 4000, NovaSeq 6000, and HiSeq 2000, allowing researchers to generate high-quality, genome-wide data on chromatin accessibility.
By leveraging the insights from ATAC-Seq, researchers can uncover the underlying regulatory mechanisms governing gene expression, cellular differentiation, and disease pathogenesis, ultimately advancing our understanding of the epigenomic landscape.