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Chromatin

Most cited protocols related to «Chromatin»

Extensive Genomic Variant Annotation Pipeline

Cited 637 times

We used the Ensembl Variant Effect Predictor (VEP, Ensembl Gene annotation v68)^{16 (link)} to obtain gene model annotation for single nucleotide and indel variants. For single nucleotide variants within coding sequence, we also obtained SIFT^{7 (link)} and PolyPhen-2^{6 (link)} scores from VEP. We combined output lines describing MotifFeatures with the other annotation lines, reformatted it to a pure tabular format and reduced the different Consequence output values to 17 levels and implemented a four-level hierarchy in case of overlapping annotations (see Supplementary Note). To the 6 VEP input derived columns (chromosome, start, reference allele, alternative allele, variant type: SNV/INS/DEL, length) and 26 actual VEP output derived columns, we added 56 columns providing diverse annotations (e.g. mapability scores and segmental duplication annotation as distributed by UCSC^{51 (link),52 (link)}; PhastCons and phyloP conservation scores^{53 (link)} for three multi-species alignments^{9 (link)} excluding the human reference sequence in score calculation; GERP++ single-nucleotides scores, element scores and p-values^{54 (link)}, also defined from alignments with the human reference excluded; background selection score^{40 (link),55 (link)}; expression value, H3K27 acetylation, H3K4 methylation, H3K4 trimethylation, nucleosome occupancy and open chromatin tracks provided for ENCODE cell lines in the UCSC super tracks^{52 (link)}; genomic segment type assignment from Segway^{56 (link)}; predicted transcription factor binding sites and motifs^{11 (link)}; overlapping ENCODE ChIP-seq transcription factors^{11 (link)}, 1000 Genome variant^{14 (link)} and Exome Sequencing Project^{57 (link)} variant status and frequencies, Grantham scores^{20 (link)} associated with a reported amino acid substitution). The Supplementary Note provides a full description and Supplementary Table 1 lists all columns of the obtained annotation matrix.

Kircher M., Witten D.M., Jain P., O’Roak B.J., Cooper G.M, & Shendure J. (2014). A general framework for estimating the relative pathogenicity of human genetic variants. Nature genetics, 46(3), 310-315.

Publication 2014

Acetylation Alleles Amino Acid Substitution Binding Sites Cell Lines Chromatin Chromatin Immunoprecipitation Sequencing Chromosomes Gene Annotation Genome Homo sapiens INDEL Mutation Methylation Nucleosomes Nucleotides Open Reading Frames Segmental Duplications, Genomic Transcription, Genetic Transcription Factor

Chromatin State Sequencing Fragment Size Analysis

Cited 423 times

First, the distribution of paired-end sequencing fragment sizes overlapping each chromatin state (http://www.ensembl.org/info/docs/funcgen/regulatory_segmentation.html) were computed. The distributions were then normalized to the percent maximal within each state and enrichment was computed relative to the genome-wide set of fragment sizes.

Buenrostro J.D., Giresi P.G., Zaba L.C., Chang H.Y, & Greenleaf W.J. (2013). Transposition of native chromatin for multimodal regulatory analysis and personal epigenomics. Nature methods, 10(12), 1213-1218.

Publication 2013

Chromatin

Integrative Analysis of Chromatin States

Cited 367 times

ChIP-seq analysis was performed in biological replicate as described4 using antibodies validated by Western blots and peptide competitions. ChIP DNA and input controls were sequenced using the Illumina Genome Analyzer. Expression profiles were acquired using Affymetrix GeneChip arrays. Chromatin states were learned jointly by applying an HMM8 to 10 data tracks for each of the 9 cell types. We focused on a 15 state model that provides sufficient resolution to resolve biologically-meaningful patterns yet is reproducible across cell types when independently processed. We used this model to produce 9 genome-wide chromatin state annotations, which were validated by additional ChIP experiments and reporter assays. Multi-cell type clustering was conducted on locations assigned to strong promoter state 1 (or strong enhancer state 4) in at least one cell type using the k-means algorithm. Enhancer-target gene linkages were predicted by correlating normalized signal intensities of H3K27ac, H3K4me1 and H3K4me2 with gene expression across cell types as a function of distance to the TSS. Upstream regulators were predicted using a set of known TF motifs assembled from multiple sources. Motif instances were identified by sequence match and evolutionary conservation. P-values for GWAS studies were based on randomizing the location of SNPs, and the FDR based on randomizing the assignment of SNPs across studies. Datasets are available from the ENCODE website (http://genome.ucsc.edu/ENCODE), the supporting website for this paper (http://compbio.mit.edu/ENCODE_chromatin_states), and the Gene Expression Omnibus (GSE26386).

Ernst J., Kheradpour P., Mikkelsen T.S., Shoresh N., Ward L.D., Epstein C.B., Zhang X., Wang L., Issner R., Coyne M., Ku M., Durham T., Kellis M, & Bernstein B.E. (2011). Systematic analysis of chromatin state dynamics in nine human cell types. Nature, 473(7345), 43-49.

Publication 2011

Antibodies Biological Assay Biological Evolution Biopharmaceuticals Cells Chromatin Chromatin Immunoprecipitation Sequencing DNA Chips DNA Replication Gene Chips Gene Expression Genome Genome-Wide Association Study Linkage, Genetic Peptides Physiology, Cell Single Nucleotide Polymorphism Western Blot

Genetic Architecture of Major Depression

Cited 325 times

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 analysis^{19 (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

h_{SNP}^{2}

from GWA summary statistics. Estimates of

h_{SNP}^{2}

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 (r_g) 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

h_{SNP}^{2}

by genomic features^{24 (link),46 (link)}. We tested for enrichment of

h_{SNP}^{2}

based on genomic annotations partitioning

h_{SNP}^{2}

proportional to bp length represented by each annotation. We used the “baseline model” which consists of 53 functional categories. The categories are fully described elsewhere^{46 (link)}, and included conserved regions^{47 (link)}, USCC gene models (exons, introns, promoters, UTRs), and functional genomic annotations constructed using data from ENCODE ^{87 (link)} and the Roadmap Epigenomics Consortium^{88 (link)}. We complemented these annotations by adding introgressed regions from the Neanderthal genome in European populations^{89 (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 MACS^{91 (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 package^{92 (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 r_g between major depression and a range of other disorders, diseases, and human traits^{22 (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 r_g. These r_g 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, r_g 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

Multimodal Single-Cell Data Integration

Cited 206 times

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

Most recents protocols related to «Chromatin»

Optimizing Receptor Expression

Not available on PMC !

Example 4

With a view to optimising expression of the receptor, the following were tested: (a) inclusion of a scaffold attachment region (SAR) into the cassette; (b) inclusion of chicken beta hemoglobin chromatin insulator (CHS4) into the 3′LTR and (c) codon optimization of the open reading frame (FIG. 6a). It was shown that inclusion of a SAR improved the nature of expression as did codon-optimization while the CHS4 had little effect (FIG. 6b). Combining SAR and codon-optimization improved expression additively (FIG. 6c)

US11879016B2. Chimeric antigen receptor (2024-01-23). AUTOLUS LIMITED [GB]. Inventors: Martin Pulé [GB], John Anderson [GB], Simon Thomas [GB].

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Patent 2024

Chickens Chromatin Codon hemoglobin B Matrix Attachment Regions

Investigating Genome Size and TE Silencing Pathways

To test for an association between overall piRNA or KRAB-ZFP pathway activity and genome size, we first compiled male and female gonad RNA-Seq datasets for vertebrates of diverse genome sizes, including P. ornatum (ornate burrowing frog), Gallus gallus (chicken), D. rerio (zebrafish), Xenopus tropicalis (Western clawed frog), A. carolinensis (green anole), Mus musculus (mouse), Geotrypetes seraphini (Gaboon caecilian), Rhinatrema bivittatum (two-lined caecilian), and Caecilia tentaculata (bearded caecilian) spanning genomes sizes from 1.0—5.5 Gb, and P. waltl (the Iberian ribbed newt), A. mexicanum (the Mexican axolotl), C. orientalis (the fire-bellied newt), P. annectens, and P. aethiopicus (African and marbled lungfishes) spanning genome sizes from 20—∼130 Gb (Supplementary Files S8,S9). We performed de novo assemblies using the same pipeline as for R. sibiricus on all obtained datasets.
We identified transcripts of 21 genes receiving a direct annotation of piRNA processing in vertebrates in the Gene Ontology knowledgebase that were present in the majority of our target species: ASZ1, BTBD18 (BTBDI), DDX4, EXD1, FKBP6, GPAT2, HENMT1 (HENMT), MAEL, MOV10l1 (M10L1), PIWIL1, PIWIL2, PIWIL4, PLD6, TDRD1, TDRD5, TDRD6, TDRD7, TDRD9, TDRD12 (TDR12), TDRD15 (TDR15), and TDRKH. In addition, we identified transcripts of 14 genes encoding proteins that create a transcriptionally repressive chromatin environment in response to recruitment by PIWI proteins or KRAB-ZFP proteins, 12 of which received a direct annotation of NuRD complex in the Gene Ontology knowledgebase and 2 of which were taken from the literature: CBX5, CHD3, CHD4, CSNK2A1 (CSK21), DNMT1, GATAD2A (P66A), MBD3, MTA1, MTA2, RBBP4, RBBP7, SALL1, SETDB1 (SETB1), and ZBTB7A (ZBT7A) (Ecco et al., 2017 (link); Wang et al., 2023 (link)). Finally, we identified TRIM28, which bridges this repressive complex to TE-bound KRAB-ZFP proteins in tetrapods, lungfishes, and coelacanths (Ecco et al., 2017 (link)). For comparison, we identified transcripts of 14 protein-coding genes receiving a direct annotation of miRNA processing in vertebrates in the Gene Ontology knowledgebase, which we did not predict to differ in expression based on genome size: ADAR (DSRAD), AGO1, AGO2, AGO3, AGO4, DICER1, NUP155 (NU155), PUM1, PUM2, SNIP1, SPOUT1 (CI114), TARBP2 (TRBP2), TRIM71 (LIN41), and ZC3H7B. Expression levels for each transcript in each individual were measured with Salmon (Patro et al., 2017 (link)) (Supplementary File S10).
As a proxy for overall piRNA silencing activity, for each individual, we calculated the ratio of total piRNA pathway expression (summed TPM of 21 genes) to total miRNA pathway expression (summed TPM of 14 genes). As a proxy for transcriptional repression driven by both the piRNA pathway and KRAB-ZFP binding activity, we calculated the ratio of total transcriptional repression machinery expression (summed TPM of 14 genes) to total miRNA pathway expression. Finally, we calculated the ratio of TRIM28 expression to total miRNA pathway expression for each individual. We also calculated these ratios with a more conservative dataset allowing for no missing genes; this yielded 15 piRNA pathway genes, 9 KRAB-ZFP genes, and 13 miRNA genes. We plotted these ratios to reveal any relationship between TE silencing pathway expression and genome size.

Wang J., Yuan L., Tang J., Liu J., Sun C., Itgen M.W., Chen G., Sessions S.K., Zhang G, & Mueller R.L. (2023). Transposable element and host silencing activity in gigantic genomes. Frontiers in Cell and Developmental Biology, 11, 1124374.

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Publication 2023

Ambystoma mexicanum BRAF protein, human CHD4 protein, human Chickens Chromatin CSNK2A1 protein, human DICER1 protein, human DNMT1 protein, human EIF2C2 protein, human Gene Products, Protein Genes Genome Males methyl-CpG binding domain protein 3, human Mi-2 Nucleosome Remodeling and Deacetylase Complex Mice, House MicroRNAs Mta1 protein, human Mus Negroid Races Newts Ovary Piwi-Interacting RNA Proteins PUM2 protein, human Rana RBBP7 protein, human Repression, Psychology RNA-Seq Salmon SETDB1 protein, human Transcription, Genetic TRIM28 protein, human Vertebrates Xenopus laevis ZBTB7A protein, human ZC3H7B protein, human Zebrafish

ChIP-seq Protocol for Chromatin Fragmentation

4×10⁷ cells were washed in PBS and cross-linked with 1% formaldehyde for 10 min at room temperature and then quenched by addition of glycine (125 mM final concentration) for 5 min. For Nuclei isolation, cells were resuspended in cell lysis buffer (50 mM Tris pH8.0, 140 mM NaCl, 1 mM EDTA, 10% glycerol, 0.5% NP-40, 0.25% Triton X-100), incubated the tube on ice for 20 min to swell. Harvested the nuclei by centrifugation at 2000g for 5 min at 4 °C resuspended in 1 ml ChIP lysis buffer (1% SDS, 10 mM EDTA, 50 mM Tris-HCl, pH8.0) and incubated on ice for 10 min. Chromatin was fragmented to 200–500 bp using 12 cycles using the Vibra-Cell Ultrasonic Liquid Processors (SONICS, Newtown, CT, USA). For each IP, chromatin was immunoprecipitated with 2 mg of antibody in IP dilution buffer (1% Triton X-100, 2 mM EDTA, 150 mM NaCl, 20 mM Tris-HCl, pH 8.0) at 4 °C overnight. Chromatin was precleared for 2 h each with protein G agarose beads (Cell Signaling Technology, Danvers, MA, USA) before immunoprecipitation. The immunoprecipitated material was washed, once in TSE I buffer (20 mM TrisHCl pH 8.0, 2 mM EDTA pH8.0, 150 mM NaCl, 1% Triton X-100, 0.1% SDS), once in TSE II buffer (20 mM TrisHCl pH 8.0, 2 mM EDTA pH8.0, 500 mM NaCl, 1% Triton X-100, 0.1% SDS), once in LiCl buffer (10 mM TrisHCl pH 8.0, 250 mM LiCl, 1% deoxycholic acid, 1% NP40) and once in TE buffer (10 mM Tris pH 8.0, 1 mM EDTA pH8.0) before elution in elution buffer (100 mM NaHCO3, 1% SDS). Antibodies used in this study were listed in the Supplementary Information. The samples were removed from beads, reversed cross-linked overnight at 65 °C and DNA was isolated using QIAquick PCR Purification Kit (Germantown, MD, USA). Precipitated DNA was analyzed by high-throughput sequencing (Beijing Genomics Institute, Beijing, China).

Li X., Wang S., Xie Y., Jiang H., Guo J., Wang Y., Peng Z., Hu M., Wang M., Wang J., Li Q., Wang Y, & Liu Z. (2023). Deacetylation induced nuclear condensation of HP1γ promotes multiple myeloma drug resistance. Nature Communications, 14, 1290.

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Publication 2023

Antibodies Bicarbonate, Sodium Buffers Cell Nucleus Cells Centrifugation Chromatin Deoxycholic Acid DNA Chips Edetic Acid Formaldehyde G-substrate Glycerin Glycine Immunoglobulins Immunoprecipitation isolation Nonidet P-40 Sepharose Sodium Chloride Technique, Dilution Triton X-100 Tromethamine Ultrasonics

Multicolor-GISH for Wheat Chromosomes

Seeds were germinated on moist filter paper in a Petri dish at room temperature for 2–3 days. Growing roots were cut from seedlings and treated in nitrous oxide with 15 bars of pressure for approximately 2 h. The roots were subsequently fixed in 90% acetic acid for 5 min and then stored in 70% v/v ethanol at -20°C. Chromosome spread preparation was performed as previously described (Kato et al., 2004 (link)).
Oligo-pSc119.2-1 combined with Oligo-pTa535-1 was used to distinguish the whole set of 42 wheat chromosomes (Tang et al., 2014 (link)). Oligo-pSc119.2-1 (10 ng/µl) and Oligo-pTa535-1 (10 ng/µl) were 5’ end-labeled with 6-carboxyfluorescein (6-FAM) and 6-carboxytetramethylrhodamine (6-Tamra) (Invitrogen^TM, Shanghai, China), respectively. Genomic DNA was isolated from the leaves of Th. intermedium accession PI 440001, T. urartu accession TMU38, Ae. speltoides accession AE739, Ae. tauschii accession TQ27, and CS using the cetyltrimethylammonium bromide (CTAB) method (Murray and Thompson, 1980 (link)). The green or red probes with a concentration of 100 ng/µl were prepared according to the nick translation method (Kato et al., 2011 (link)). The genomic DNA of Th. intermedium, T. urartu and the plasmid of St₂-80 reported by Wang et al. (2017) (link) were labeled with Alexa Fluor-488-5-2'-deoxyuridine 5'-triphosphate (dUTP) (Invitrogen^TM, Shanghai, China). The genomic DNA of A. tauschii and the centromeric retrotransposon of wheat (CRW) clone 6C6 was labeled with Texas-red-5-dCTP (Invitrogen^TM, Shanghai, China). The genomic DNA of CS and A. speltoides in a concentration of 3,000 ng/µl was used for blocking in multicolor-GISH (mc-GISH). For each slide, FISH was performed in 10 µl reaction volumes, in which 0.2 µl Oligo-pSc119.2-1, 0.2 µl Oligo-pTa535-1, and 0.3 µl 6C6, 0.5 µl St₂-80 were used and the 2x SSC, 1x TE buffer was used to adjust the volume. For Th. Intermedium chromatin detection, 10 µl reaction volumes for each slide contain 0.5 µl labeled genomic DNA of PI 440001 and 2.5 µl genomic DNA of CS. For the mc-GISH on wheat, the 10 µl reaction volumes for each slide contain the 2 µl labeled genomic DNA of TMU38, 2 µl genomic DNA of AE739, and 1 µl labeled genomic DNA of TQ27. All chromosomes were counterstained with 4, 6-diamidino-2-phenylindole (DAPI) (Vectashield, Vector Laboratories, Burlingame, CA, USA). Chromosomes on microscope slides were examined using a BX61 fluorescence microscope (Olympus, Tokyo, Japan) equipped with a U-CMAD3 camera (Olympus, Tokyo, Japan) and appropriate filter sets. The signal capture and picture processing were performed using MetaMorph software (Molecular Devices, LLC., San Jose, CA, USA). The final image adjustment was done in Adobe Photoshop CS5 (Adobe Systems Incorporated, San Jose, CA, USA).

Guo X., Huang Y., Wang J., Fu S., Wang C., Wang M., Zhou C., Hu X., Wang T., Yang W, & Han F. (2023). Development and cytological characterization of wheat–Thinopyrum intermedium translocation lines with novel stripe rust resistance gene. Frontiers in Plant Science, 14, 1135321.

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Publication 2023

6-carboxytetramethylrhodamine Acetic Acid alexa fluor 488 Buffers carboxyfluorescein Cardiac Arrest Centromere Cetrimonium Bromide Chromatin Chromosomes Clone Cells Cloning Vectors deoxyuridine triphosphate Ethanol Fishes Genome Hyperostosis, Diffuse Idiopathic Skeletal Medical Devices Microscopy Microscopy, Fluorescence Oligonucleotides Oxide, Nitrous Plant Embryos Plant Roots Plasmids Pressure Retrotransposons Seedlings Texas Red-5-dCTP Triticum aestivum

Cell-type specific chromatin accessibility

To assess the co-accessibility effect of cell-type-specific trans-open chromatin regions on Z-scores we used the output of Signac’s CallPeaks() function to retrieve from which cell-type a peak was called by MACS2^{21 (link)} (implemented in Signac). The cell-types were categorised in 4 broader classes representative of the UMAP and dendrogram:

Lymphoid; CD8 Naive, CD4 Naive, CD4 TCM, CD8 TEM, CD8 TCM, CD4 TEM, MAIT, Treg

NK cells; gdT, NK, CD8 TEM, MAIT

Monocytes; CD14 Mono, CD16 Mono, cDC2, pDC

B cells; B intermediate, B memory, B naive

For ATACseq peaks that were called in all 4 broad cell-type classes, no filtering was done. For ATACseq peaks with some specificity (i.e., not called in all 4 broad cell-type), we removed all trans-peaks from the trans-peak pool to match the cis-peak that were also called in the same broad cell-type class. Therefore, a tested ATACseq peaks called only in B cells and Monocytes by MACS2 would have a null distribution composed of trans-peaks called in lymphoid and/or NK cells.

Leblanc F.J, & Lettre G. (2023). Major cell-types in multiomic single-nucleus datasets impact statistical modeling of links between regulatory sequences and target genes. Scientific Reports, 13, 3924.

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Publication 2023

B-Lymphocytes CDK1 protein, human Cells Chromatin Lymph Memory Monocytes Natural Killer Cells

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Bioruptor by Diagenode

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The Bioruptor is a laboratory instrument designed for the efficient disruption and homogenization of biological samples. It uses high-intensity ultrasound waves to shear and fragment a wide range of sample types, including cells, tissues, and macromolecules, without the use of bead beating or other mechanical disruption methods. The Bioruptor is a versatile tool for a variety of applications, including DNA/RNA extraction, protein extraction, and chromatin shearing.

Qiaquick pcr purification kit by Qiagen

Sourced in Germany, United States, United Kingdom, Netherlands, Spain, France, Japan, China, Canada, Italy, Australia, Switzerland, Singapore, Sweden, India, Malaysia

The QIAquick PCR Purification Kit is a lab equipment product designed for the rapid purification of PCR (Polymerase Chain Reaction) amplicons. It utilizes a silica-membrane technology to efficiently capture and purify DNA fragments from PCR reactions, removing unwanted primers, nucleotides, and enzymes.

Ez chip kit by Merck Group

Sourced in United States, Germany, United Kingdom, Australia

The EZ-ChIP kit is a laboratory equipment product designed for chromatin immunoprecipitation (ChIP) experiments. It provides a streamlined protocol and necessary reagents to facilitate the process of isolating and analyzing DNA-protein interactions within a cellular sample.

Chip assay kit by Merck Group

Sourced in United States, Germany, United Kingdom, France

The ChIP assay kit is a laboratory tool used to study protein-DNA interactions within cells. It enables researchers to identify the specific DNA sequences that are bound by a particular protein of interest. The kit provides the necessary reagents and protocols to perform chromatin immunoprecipitation (ChIP) experiments, a widely used technique in molecular biology and epigenetics research.

Formaldehyde by Merck Group

Sourced in United States, Germany, United Kingdom, India, Italy, France, Japan, China, Switzerland, Belgium, Sao Tome and Principe, Spain, Sweden, Canada, Brazil, Netherlands, Macao, Australia, Poland, Austria, Portugal, Thailand

Formaldehyde is a chemical compound with the formula CH2O. It is a colorless, flammable gas with a pungent odor. Formaldehyde is used as a chemical building block in the production of various products, including resins, adhesives, and disinfectants.

Pcr purification kit by Qiagen

Sourced in Germany, United States, Netherlands, United Kingdom, Spain, France, China, Italy, Canada, Switzerland, Cameroon, Australia, India

The PCR purification kit is a laboratory product designed to purify and concentrate DNA samples obtained from Polymerase Chain Reaction (PCR) amplification. The kit provides a streamlined process for removing unwanted components, such as primers, nucleotides, and salts, from the PCR reaction mixture, allowing for the recovery of high-quality, purified DNA samples.

Hiseq 2500 by Illumina

Sourced in United States, China, Germany, United Kingdom, Canada, Switzerland, Sweden, Japan, Australia, France, India, Hong Kong, Spain, Cameroon, Austria, Denmark, Italy, Singapore, Brazil, Finland, Norway, Netherlands, Belgium, Israel

The HiSeq 2500 is a high-throughput DNA sequencing system designed for a wide range of applications, including whole-genome sequencing, targeted sequencing, and transcriptome analysis. The system utilizes Illumina's proprietary sequencing-by-synthesis technology to generate high-quality sequencing data with speed and accuracy.

Simplechip plus enzymatic chromatin ip kit by Cell Signaling Technology

Sourced in United States

The SimpleChIP Plus Enzymatic Chromatin IP Kit is a laboratory product designed for chromatin immunoprecipitation (ChIP) experiments. It provides reagents and protocols for the enzymatic digestion of chromatin and the subsequent immunoprecipitation of protein-DNA complexes.

Dynabeads protein g by Thermo Fisher Scientific

Sourced in United States, Norway, Germany, United Kingdom, China, Canada, Japan, Denmark, Australia, France

Dynabeads Protein G is a magnetic bead-based product used for the purification and isolation of immunoglobulins (Ig) and other proteins that bind to Protein G. It provides a reliable and efficient method for the capture and separation of target proteins from complex samples.

What is Chromatin and why is it important?

What are the different types or variations of Chromatin?

There are several types and variations of chromatin, including euchromatin (open and accessible chromatin) and heterochromatin (condensed and less accessible chromatin). These different chromatin states are dynamically regulated and play important roles in controlling gene expression and other cellular processes. Understanading these chromatin variations is key to deciphering how the genome is organized and utilized within the cell.

What are some common challenges in working with Chromatin?

Some common challenges in working with chromatin include ensuring effective and reproducible isolation, characterization, and analysis techniques. Chromatin structure and composition can be highly variable, making it difficult to standardize experimental protocols. Additionally, the complex nature of chromatin can complicte downstream applications like gene expression studies and epigenetic profiling.

How can PubCompare.ai help with Chromatin research?

PubCompare.ai can revolutionize chromatin research by helping researchers quickly and efficiently screen the literature, pre-prints, and patents to identify the most effective protocols. The platfrom's AI-driven analysis can highlight key differences in protocol effectiveness, enabling researchers to choose the best option for their specific needs and ensure high reproducibility and accuracy in their chromatin studies. This can unlock new discoveries and insights by giving researchers confidence in their experimental approaches.

More about "Chromatin"

Chromatin is the complex of DNA and proteins that makes up the contents of the cell nucleus.
It plays a crucial role in gene regulation, chromosomal organization, and epigenetic modifications.
Chromatin research is essential for understanding cellular processes and disease mechanisms.
PubCompare.ai, an AI-driven platform, revolutionizes this field by helping researchers locate the most effective chromatin protocols from the literature, pre-prints, and patents.
This enhances reproducibility and accuracy, ensuring researchers find the most appropriate methods for their chromatin studies.
Chromatin-related techniques and tools include the SimpleChIP Enzymatic Chromatin IP Kit, Bioruptor for chromatin shearing, QIAquick PCR Purification Kit for DNA purification, EZ-ChIP kit for chromatin immunoprecipitation (ChIP), and ChIP assay kits.
Formaldehyde is commonly used for chromatin crosslinking, and PCR purification kits are utilized in chromatin-related workflows.
High-throughput sequencing platforms like the HiSeq 2500 are employed for chromatin profiling and analysis.
The SimpleChIP Plus Enzymatic Chromatin IP Kit and Dynabeads Protein G are also valuable tools for chromatin research.
By leveraging these resources and the insights provided by PubCompare.ai, researchers can unlock new discoveries and gain deeper understanding of cellular processes and disease mechanisms related to chromatin.