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> Chemicals & Drugs > Amino Acid > Deoxyribonuclease I

Deoxyribonuclease I

Deoxyribonuclease I is an enzyme that catalyzes the hydrolytic cleavage of DNA, producing 5'-phosphorylated di-, oligo- or mononucleotides.
It is found in a variety of tissues and is involved in the degradation of extracelluar DNA.
Deoxyribonuclease I plays a role in immune system function, cell signaling, and other biological processes.
Researchers can optimiize their Deoxyribonuclease I studies using PubCompare.ai's AI-driven platform to quickly locate the best experimental methods from literature, preprints, and patents.
This streamlines workflows and helps get the most out of Deoxyribonuclease I experiments.

Most cited protocols related to «Deoxyribonuclease I»

1
Curating Genome Browser's Data Ecosystem
Cited 171 times
A large number of datasets originate outside UCSC and contribute to the Genome Browser’s core idea of hosting as many high-quality resources as possible. In most cases, UCSC does not perform significant postprocessing or computation on the data, limiting intervention to converting file formats or other parsing tasks and quality-assurance review. Examples of this type of track include probe sets for commercially available microarrays, human copy-number variation (CNV) data from the Database of Genomic Variants (DGV) [25 (link)]; human dismorphology data from DECIPHER [26 (link)]; expression data for mouse and human from the GNF Expression Atlas [27 (link)]; and segmental duplication data for human, mouse, rat, dog and chicken [28 (link)].
The ENCODE project, for which the UCSC Genome Browser is the Data Coordination Center [29 (link)], presents a large number of functional annotations: including DNAse hypersensitivity sites, indicating open chromatin; histone marks, implicated in gene regulation; and gene expression levels from whole-genome RNA-seq experiments. These data, which are available on the human and mouse assemblies hg19 and mm9, are mapped across multiple cell lines. The resulting tracks represent tissue specificity and developmental mileposts (e.g. embryonic stem cells) for these elements. They can be displayed along with any other tracks on the same assembly, such as GenBank mRNAs or multispecies conservation.
A complete list of tracks available for any assembly can be found by visiting the Gateway page for any genome assembly (http://genome.ucsc.edu/cgi-bin/hgGateway) and clicking the button, ‘configure tracks and display’ or by simply inspecting the track controls beneath the main Browser graphic. The Track Search feature provides keyword lookup.
Examples of data tracks that do undergo further processing or filtering at UCSC include dbSNP [30 (link)] and OMIM (Online Mendelian Inheritance in Man) [31 (link)]. In these tracks, data from the providers are subdivided into categories to make them more useful to our users. For example, dbSNP data are presented in their entirety in one track, but three other tracks offer subsets: Common Single Nucleotide Polymorphisms (SNPs) (those with minor allele frequency >1%), Flagged SNPs (those identified in dbSNP as ‘clinical’—may be associated with disease, but use with caution!) and Multiple SNPs (those mapping to more than one genomic location).
Similarly, the OMIM data set has been filtered by UCSC to create three separate tracks, including one track of Allelic Variant SNPs that have phenotypic associations annotated by OMIM. These filtered sets are transmitted to OMIM for redistribution to their licensees. As always, details of how the filtering was done are available by clicking into an item or via the track configuration page.
Users may read about the filtering options available when using tracks by clicking on the small button to the left of the track in the Genome Browser image, or on the label in the track control area below the image. This configuration page gives users an opportunity to set colors and filters to suit themselves.
For users who do not know exactly which data set contains the information they seek, each data track is accompanied by a description outlining the rationale for the production of the data, implementation details, interpretation guidelines and references to the literature. All of this information is indexed and may be searched by keyword via the Track Search button beneath the Browser graphic. The result is a list of all tracks that have the search term in the documentation and a link to the track description.
Kuhn R.M., Haussler D, & Kent W.J. (2012). The UCSC genome browser and associated tools. Briefings in Bioinformatics, 14(2), 144-161.
Publication 2012
Alleles Cell Lines Chickens Chromatin Deoxyribonuclease I Embryonic Stem Cells Gene Expression Gene Expression Regulation Genome Genome, Human Histone Code Homo sapiens Hypersensitivity Microarray Analysis Mus Phenotype RNA, Messenger RNA-Seq Segmental Duplications, Genomic Single Nucleotide Polymorphism Tissue Specificity
2
Uniformly Processed Cistrome DB Data
Cited 121 times
The data in these public databases were produced by numerous laboratories, and the processed results were derived using a variety of algorithms. To improve the consistency of Cistrome DB data, raw DNA sequence data for each sample was downloaded and uniformly processed by the ChiLin pipeline (22 (link)), which uses BWA (23 (link)) to map reads to the hg38 or mm10 genomes and MACS2 (24 (link)) to identify statistically significant peaks. The raw data of SRA file was downloaded from NCBI at ftp://ftp-trace.ncbi.nih.gov/sra/sra-instant/reads/ByRun/sra/SRR/. We obtained FASTQ files from SRA files using the fastq-dump software (https://ncbi.github.io/sra-tools/fastq-dump.html). Motif scanning was also performed on transcription factor or chromatin regulator ChIP-seq samples based on enrichment of the motif sequence relative to the center of the peaks (25 (link)). Target genes were predicted from ChIP-seq peaks using the regulatory potential model which weighs the impact of each peak by exponential decay of distance to gene transcription start site (TSS) (26 (link)). Additional information about these data can be found on the Cistrome DB document page at http://cistrome.org/db/#/documents.
Cistrome DB data quality controls include six metrics, representing DNA sequencing quality, ChIP quality, and genomic distribution characteristics. Read quality is based on the median FASTQ read quality, mapping quality is measured by the percentage of reads that each map to a unique genomic locus, and the PCR bottleneck coefficient (PBC) is used to estimate the rate of read duplication through PCR amplification (27 (link),28 (link)). The fraction of non-mitochondrial reads in peak regions (FRiP) and the number of peaks with 10-fold enrichment are used to reflect the quality of the ChIP experiment (27 (link),28 (link)). A union of DNase hypersensitive sites (Union DHS) was summarized using a large collection of DNase-seq samples from the Cistrome DB (19 (link),29 (link)). The percentage of peaks that overlap with the union of DHS sites is used to characterize the data quality based on the genomic distribution of the peaks. Although most TFs and chromatin associated factors tend to bind at DHS sites, some histone marks and factors do not follow this trend. Cutoffs were determined based on the distribution of these quality control metrics in the Cistrome DB (22 (link)), and a red dot indicates data with lower quality on a metric while a green dot indicates higher quality of a sample (Figure 1). These QC measures are meant to guide users in their appraisal of data, instead of being used strictly to categorize samples as pass or fail. Although the Cistrome DB includes some samples which appear to be of poor quality by several metrics, these samples may nevertheless hold valuable clues to some aspect of regulatory biology not represented by other samples in the database.
Zheng R., Wan C., Mei S., Qin Q., Wu Q., Sun H., Chen C.H., Brown M., Zhang X., Meyer C.A, & Liu X.S. (2018). Cistrome Data Browser: expanded datasets and new tools for gene regulatory analysis. Nucleic Acids Research, 47(Database issue), D729-D735.
Publication 2018
2'-deoxyuridylic acid Chromatin Chromatin Immunoprecipitation Sequencing Deoxyribonuclease I DNA Chips Genes Genome Histone Code Hypersensitivity Mitochondrial Inheritance Specimen Collection Transcription Factor Transcription Initiation Site
3
Benchmarking Epigenomic Profiling Techniques
Cited 118 times
All sequencing tracks were made using the Washington University
Epigenome Browser. Sequencing-coverage tracks that were used to compare DNase,
standard ATAC-seq, and Omni-ATAC-seq data were generated by subsampling 60
million reads from an aligned and de-duplicated BAM file that had not been
additionally filtered. These equal-depth BAM files were then converted to bigwig
for visualization. For comparisons involving DNase-seq, all ATAC-seq reads were
trimmed to 36 bp to match the single-end 36-bp sequencing reads used in DNase
before alignment. The y-axis scale for all sequencing tracks
was set to range from 0 to the maximum height among the three data sets. In this
way, the heights of the tracks were comparable across techniques, as they were
derived from the same number of equal-length input reads. Sequencing tracks that
were used to compare 500-cell Omni-ATAC to 500-cell standard ATAC-seq data in
GM12878 cells were not normalized. In these visualizations, all pass-filter
reads were used to generate sequencing tracks under the assumption that these
libraries were sequenced to near-full depth. This assumption is necessary due to
differences in the library sizes of the Omni-ATAC and standard ATAC-seq 500-cell
libraries. Sequencing tracks related to frozen human brain tissue were all
normalized by the total number of reads in the peaks.
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
ATAC-Seq Brain Cells Deoxyribonuclease I DNA Library Epistropheus Freezing Homo sapiens Tissues XCL1 protein, human
4
Integrative Epigenomic Analysis of 127 Consolidated Genomes
Cited 114 times
All genome-wide maps of histone modifications, DNA accessibility, DNA methylation and RNA expression are freely available online. Raw sequencing data deposited at the Short Read Archive or dbGAP is linked from http://www.ncbi.nlm.nih.gov/geo/roadmap/epigenomics/. All primary processed data (including mapped reads) for profiling experiments are contained within Release 9 of the Human Epigenome Atlas (http://genboree.org/EdaccData/Release-9/). Complete metadata associated with each dataset in this collection is archived at GEO and describes samples, assays, data processing details and quality metrics collected for each profiling experiment.
Release 9 of the compendium contains uniformly pre-processed and mapped data from multiple profiling experiments (technical and biological replicates from multiple individuals and/or datasets from multiple centers). In order to reduce redundancy, improve data quality and achieve uniformity required for our integrative analyses, experiments were subjected to additional processing to obtain comprehensive data for 111 consolidated epigenomes (See methods sections below for additional details). Numeric epigenome identifiers EIDs (e.g. E001) and mnemonics for epigenome names were assigned for each of the consolidated epigenomes. Table S1 (QCSummary sheet) summarizes the mapping of the individual Release 9 samples to the consolidated epigenome IDs. Key metadata such as age, sex, anatomy, epigenome class (see below), ethnicity and solid/liquid status were summarized for the consolidated epigenomes. Datasets corresponding to 16 cell-lines from the ENCODE project (with epigenome IDs ranging from E114-E129) were also used in the integrative analyses23 (link). All datasets from the 127 consolidated epigenomes were subjected to processing filters to ensure uniformity in terms of read length based mappability and sequencing depth as described below.
Each of the 127 epigenomes included consolidated ChIP-seq datasets for a core set of histone modifications - H3K4me1, H3K4me3, H3K27me3, H3K36me3, H3K9me3 as well as a corresponding whole-cell extract sequenced control. 98 epigenomes and 62 epigenomes had consolidated H3K27ac and H3K9ac histone ChIP-seq datasets respectively. A smaller subset of epigenomes had ChIP-seq datasets for additional histone marks, giving a total of 1319 consolidated datasets (Table S1, QCSummary sheet). 53 epigenomes had DNA accessibility (DNase-seq) datasets. 56 epigenomes had mRNA-seq gene expression data. For the 127 consolidated epigenomes, a total of 104 DNA methylation datasets across 95 epigenomes involved either bisulfite treatment (WGBS or RRBS assays) or a combination of MeDIP-seq and MRE-seq assays. In addition to the 1936 datasets analyzed here across 111 reference epigenomes, the NIH Roadmap Epigenomics Project has generated an additional 869 genome-wide datasets, linked from GEO, the Human Epigenome Atlas, and NCBI, and also publicly and freely available.
Kundaje A., Meuleman W., Ernst J., Bilenky M., Yen A., Kheradpour P., Zhang Z., Heravi-Moussavi A., Liu Y., Amin V., Ziller M.J., Whitaker J.W., Schultz M.D., Sandstrom R.S., Eaton M.L., Wu Y.C., Wang J., Ward L.D., Sarkar A., Quon G., Pfenning A., Wang X., Claussnitzer M., Coarfa C., Harris R.A., Shoresh N., Epstein C.B., Gjoneska E., Leung D., Xie W., Hawkins R.D., Lister R., Hong C., Gascard P., Mungall A.J., Moore R., Chuah E., Tam A., Canfield T.K., Hansen R.S., Kaul R., Sabo P.J., Bansal M.S., Carles A., Dixon J.R., Farh K.H., Feizi S., Karlic R., Kim A.R., Kulkarni A., Li D., Lowdon R., Mercer T.R., Neph S.J., Onuchic V., Polak P., Rajagopal N., Ray P., Sallari R.C., Siebenthall K.T., Sinnott-Armstrong N., Stevens M., Thurman R.E., Wu J., Zhang B., Zhou X., Beaudet A.E., Boyer L.A., De Jager P., Farnham P.J., Fisher S.J., Haussler D., Jones S., Li W., Marra M., McManus M.T., Sunyaev S., Thomson J.A., Tlsty T.D., Tsai L.H., Wang W., Waterland R.A., Zhang M., Chadwick L.H., Bernstein B.E., Costello J.F., Ecker J.R., Hirst M., Meissner A., Milosavljevic A., Ren B., Stamatoyannopoulos J.A., Wang T, & Kellis M. (2015). Integrative analysis of 111 reference human epigenomes. Nature, 518(7539), 317-330.
Publication 2015
Biological Assay Biopharmaceuticals Cell Extracts Cell Lines Chromatin Immunoprecipitation Sequencing Deoxyribonuclease I DNA Methylation Epigenome Ethnicity Gene Expression Genome Histone Code histone H3 trimethyl Lys4 Histones Homo sapiens hydrogen sulfite RNA, Messenger Transcription, Genetic
5
Integrative Regulatory Region Mapping
Cited 84 times
For each of the 39 Roadmap reference epigenomes with DNase data, peak positions are combined across reference epigenomes by defining peak island areas, defined by stacking all DNase peak positions across epigenomes, and considering the Full Width at Half Maximum (FWHM). Note that for this we are only considering peak locations, not intensities. The goal of this is to obtain an estimate of the area of open chromatin, not to quantify the level of ‘openness’, as these data are not available for all reference epigenomes. In cases when peak islands overlap, they are merged because it means that the original DNase peak area populations overlap at least for half of the epigenomes with DNase peaks in that area (given the FWHM approach). Peak island summits are defined as the median peak summit of all peak island member DNase peaks. This results in a total of 3,516,964 DNase enriched regions across epigenomes.
We then annotate each of the ~3.5M DNase peaks with the chromatin states they overlap with in each of the 111 Roadmap reference epigenomes, using the core 15-state chromatin state model, and focusing on states TssA, TssAFlnk, and TssBiv for promoters, and EnhG, Enh, and EnhBiv for enhancers, and state BivFlnk (flanking bivalent Enh/Tss) for ambiguous regions. Out of these, ~2.5M regions are called as either enhancer or promoter across any of the 111 Roadmap reference epigenomes. Note that because DNase data is not available for all Roadmap epigenomes, the set of regulatory regions defined may exclude DNase regions active in cell types for which DNase was not profiled (Fig. 2g). Although most regions are undisputedly called exclusively promoter or enhancer, there are ~530k regions that needed further study to decide whether they should be called promoters, enhancers, or both (‘dyadic’). We arbitrate on these regions by first clustering them (using the methods in the following section) with an expected cluster size of 10,000 regions, and then for each cluster calculating (a) the mean posterior probabilities for promoter and enhancer calls separately, and (b) the mean number of reference epigenomes in which regions were called promoter or enhancer. Clusters of regions for which the differences in mean posterior probabilities (a) is smaller than 0.05, or for which the absolute log2-ratio of the number of epigenomes called as promoter or enhancer (b) is smaller than 0.05 are called true ‘dyadic’ regions, along with a small number of ‘ambiguous’ regions in state BivFlnk. Note that this particular clustering is only to arbitrate on these regions using group statistics instead of one-by-one; the final clusterings are described next. Overall, we define ~2.3M putative enhancer regions (12.63% of genome), ~80k promoter regions (1.44% of genome) and ~130k dyadic regions (0.99% of genome), showing either promoter or enhancer signatures across epigenomes.
Kundaje A., Meuleman W., Ernst J., Bilenky M., Yen A., Kheradpour P., Zhang Z., Heravi-Moussavi A., Liu Y., Amin V., Ziller M.J., Whitaker J.W., Schultz M.D., Sandstrom R.S., Eaton M.L., Wu Y.C., Wang J., Ward L.D., Sarkar A., Quon G., Pfenning A., Wang X., Claussnitzer M., Coarfa C., Harris R.A., Shoresh N., Epstein C.B., Gjoneska E., Leung D., Xie W., Hawkins R.D., Lister R., Hong C., Gascard P., Mungall A.J., Moore R., Chuah E., Tam A., Canfield T.K., Hansen R.S., Kaul R., Sabo P.J., Bansal M.S., Carles A., Dixon J.R., Farh K.H., Feizi S., Karlic R., Kim A.R., Kulkarni A., Li D., Lowdon R., Mercer T.R., Neph S.J., Onuchic V., Polak P., Rajagopal N., Ray P., Sallari R.C., Siebenthall K.T., Sinnott-Armstrong N., Stevens M., Thurman R.E., Wu J., Zhang B., Zhou X., Beaudet A.E., Boyer L.A., De Jager P., Farnham P.J., Fisher S.J., Haussler D., Jones S., Li W., Marra M., McManus M.T., Sunyaev S., Thomson J.A., Tlsty T.D., Tsai L.H., Wang W., Waterland R.A., Zhang M., Chadwick L.H., Bernstein B.E., Costello J.F., Ecker J.R., Hirst M., Meissner A., Milosavljevic A., Ren B., Stamatoyannopoulos J.A., Wang T, & Kellis M. (2015). Integrative analysis of 111 reference human epigenomes. Nature, 518(7539), 317-330.
Publication 2015
Cells Chromatin Deoxyribonuclease I Epigenome Genome Indium-111 Regulatory Sequences, Nucleic Acid

Most recents protocols related to «Deoxyribonuclease I»

1
In Vitro Transcription for Customized mRNA Synthesis
Not available on PMC !

Example 3

The in vitro transcription reactions can generate polynucleotides containing uniformly modified polynucleotides. Such uniformly modified polynucleotides can comprise a region or part of the polynucleotides of the invention. The input nucleotide triphosphate (NTP) mix can be made using natural and un-natural NTPs.

A typical in vitro transcription reaction can include the following:

    • 1 Template cDNA—1.0
    • 2 10× transcription buffer (400 mM Tris-HCl pH 8.0, 190 mM MgCl2, 50 mM DTT, mM Spermidine)—2.0
    • 3 Custom NTPs (25 mM each)—7.2 μl
    • 4 RNase Inhibitor—20 U
    • 5 T7 RNA polymerase—3000 U
    • 6 dH2O—Up to 20.0 μl. and
    • 7 Incubation at 37° C. for 3 hr-5 hrs.

The crude IVT mix can be stored at 4° C. overnight for cleanup the next day. 1 U of RNase-free DNase can then be used to digest the original template. After 15 minutes of incubation at 37° C., the mRNA can be purified using Ambion's MEGACLEAR™ Kit (Austin, TX) following the manufacturer's instructions. This kit can purify up to 500 μg of RNA. Following the cleanup, the RNA can be quantified using the NanoDrop and analyzed by agarose gel electrophoresis to confirm the RNA is the proper size and that no degradation of the RNA has occurred.

US11859215B2. Polynucleotides encoding ornithine transcarbamylase for the treatment of urea cycle disorders (2024-01-02). ModernaTX, Inc. [US]. Inventors: Zhijian Zhuo [US], Andrea Lea Frassetto [US], Paolo G. V. Martini [US], Vladimir Presnyak [US], Patrick Finn [US].
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Patent 2024
austin Buffers Deoxyribonuclease I DNA, Complementary DNA-Directed RNA Polymerase Electrophoresis, Agar Gel Endoribonucleases Magnesium Chloride Nucleotides Polynucleotides ribonuclease U RNA, Messenger RNA Degradation Spermidine Transcription, Genetic triphosphate Tromethamine
2
Transcriptome Analysis of Nitrate-Deprived Diatom
The experiment was carried out using a newly established clonal strain of C. socialis, APC12, genotyped by sequencing the LSU rDNA region ([21 ]). A non-axenic stock has been maintained in a culture chamber at 18 ± 2 °C, under sinusoidal illumination (12L:12D h photoperiod, ~ 90 μmol photons·m−2· s−1 daily average) in control medium made with artificial seawater at a salinity of 36 (Sea salts, Sigma-Aldrich; [21 ]) and with the following concentration of inorganic nutrients: 580 μM of NaNO3, 300 μM of Na2SiO3 and 29 μM of NaH2PO4. An exponentially growing culture was used to inoculate, at an initial cell density of ~ 3 × 103 cells·mL−1, three 5 L glass flasks filled with 3 L of control medium and three flasks filled with low nitrate medium (23 μM of NaNO3, 300 μM Na2SiO3, 29 μM NaH2PO4). Temperature and light conditions were monitored during the experiment with a HOBO Pendant® Temperature/Light Data Logger. To estimate cell concentration, 4 mL of sample were collected every day, fixed with 1.6% formaldehyde solution, and vegetative cells and spores were enumerated using a Sedgwick-Rafter chamber on a Zeiss Axiophot (ZEISS, Oberkochen, Germany) microscope at 400 × magnification.
Total RNA was extracted from each replicate of the control in mid-exponential growth phase at day 2 (C2) and from the replicates growing in N deplete conditions on three consecutive days: before the formation of spores (T2), when spore formation started (T3), and when they reached > 75% of the whole population (T4) (Fig. 6). A total of ~ 1.2 × 107 cells were harvested from each replicate by filtration onto 1.2 μm pore size filters (RAWP04700 Millipore) and extracted with Trizol™ (Invitrogen) following manufacturer’s instructions. A DNase I (Qiagen) treatment was applied to remove gDNA contamination, and RNA was further purified using RNeasy Plant Mini Kit (Qiagen). All samples were quantified with Qubit® 2.0 Fluorometer (Invitrogen) and quality checked with an Agilent 2100 bioanalyzer (Agilent Technologies, California, USA) and a NanoDrop ND-1000 Spectrophotometer (Nanodrop Tecnologies Inc., Wilmington, USA). Samples were then pooled in equal concentrations of 100 ng·μl−1 for sequencing at the Molecular Service of Stazione Zoologica with an Ion Proton™ sequencer (Life Technologies, Carlsbad, USA) using an Ion P1 sequencing Kit v2, generating single-read sequences. Highly abundant ribosomal RNAs (rRNA) were removed from total RNA by positive polyA + selection. Raw reads coming from each replicate were collected in fastqc format files. One of the T3 replicates was removed from downstream analyses due to a sequencing error during library construction. The resulting raw reads were deposited in the Sequence Read Archive (SRA) partition at NCBI with the accession number PRJNA826817.

Schematic representation of the bioinformatic pipeline used in this study

Pelusi A., Ambrosino L., Miralto M., Chiusano M.L., Rogato A., Ferrante M.I, & Montresor M. (2023). Gene expression during the formation of resting spores induced by nitrogen starvation in the marine diatom Chaetoceros socialis. BMC Genomics, 24, 106.
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Publication 2023
Cells Clone Cells Deoxyribonuclease I DNA, Ribosomal DNA Library DNA Replication Filtration Formalin Light Lighting Microscopy Nitrates Nutrients Plants Poly A Protons Ribosomal RNA Salinity Salts Sinusoidal Beds Spores Strains trizol
3
Leaf Transcriptome Profiling by RNA-seq
Mature leaf tissue was collected from four plants per treatment in one replicate and ground in liquid nitrogen. The PureLink RNA Mini on-column kit with TRIzol (ThermoFisher Scientific, Inc., USA) was used to extract total RNA. An on-column DNAse treatment with additional off-column DNAse I treatments were used to remove DNA contamination. The mRNA library preparation and sequencing were performed by BGI Genomics Co., Ltd. (Shenzhen, China) with polyA selection by an oligo dT library. All 32 samples were multiplexed, pooled and loaded together. Sequencing was conducted on a DNBSEQ™ Technology Platform.
Charles M., Edwards B., Ravishankar E., Calero J., Henry R., Rech J., Saravitz C., You W., Ade H., O’Connor B, & Sederoff H. (2023). Emergent molecular traits of lettuce and tomato grown under wavelength-selective solar cells. Frontiers in Plant Science, 14, 1087707.
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Publication 2023
Deoxyribonuclease I Deoxyribonucleases DNA Contamination DNA Library DNA Replication Nitrogen oligo (dT) Plant Leaves Plants Poly A RNA, Messenger Tissues trizol
4
Astrocyte Culture from Postnatal Mouse Cortex

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2023
Astrocytes Blood Vessel Brain Cells Centrifugation Cortex, Cerebral Culture Media Deoxyribonuclease I Diagnosis Edetic Acid Mice, Inbred C57BL Tissue, Membrane Tissues Trypsin
5
Detailed Solution Composition for Cell Contractions
The composition of all solutions in this study is summarized as follows. Normal Krebs solution contained the following (in mM): 120 NaCl, 5.9 KCl, 25 NaHCO3, 1.2 NaH2PO4, 11.5 dextrose, 2.5 CaCl2, 1.2 MgCl2 (Biosharp, China). High KCl solution (96 mM) was prepared as normal Krebs but with equimolar substitution of NaCl with KCl. Oxytocin (MedChemExpress, China) was dissolved in deionized water to prepare 10-11 to 10-6 M concentration for isometric contractions. Digestion solution was prepared: 2 mg/ml type II collagenase, 1 mg/ml BSA, and 0.5 mg/ml deoxyribonuclease I was dissolved in Dulbecco’s Modified Eagle Medium (DMEM) (Sigma-Aldrich, American). The normal glucose medium consisted of DMEM with 5.5 mmol/L glucose, 10% fetal bovine serum (FBS) (Sigma-Aldrich, American), and 1% penicillin/streptomycin (Gibco, American) solution. The high glucose medium consisted of DMEM with 25 mmol/L glucose, 10% FBS, and 1% penicillin/streptomycin solution. CoCl2 medium was prepared in normal glucose containing 200 µmol/L CoCl2 (Sigma-Aldrich, American). High glucose with echinomycin medium was prepared in high glucose containing 10 nmol/L echinomycin (MedChemExpress, China). CoCl2 with echinomycin medium was prepared in CoCl2 medium containing 10 nmol/L echinomycin. CoCl2 with L-methionine medium was 10 µmol/L L-methionine (Sigma-Aldrich, American) dissolved in CoCl2 medium. High glucose with L-methionine medium was 10 µmol/L of L-methionine dissolved in high glucose medium. High KCl (96 mM) and oxytocin (10-7 M) were dissolved in the respective medium for cell contractions.
Li T., Fei J., Yu H., Wang X., Bai J., Chen F., Li D, & Yin Z. (2023). High glucose induced HIF-1α/TREK1 expression and myometrium relaxation during pregnancy. Frontiers in Endocrinology, 14, 1115619.
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Publication 2023
Bicarbonate, Sodium Cells Collagenase Deoxyribonuclease I Digestion Eagle Echinomycin Fetal Bovine Serum Glucose Isometric Contraction Krebs-Ringer solution Magnesium Chloride Methionine mutalipocin II Oxytocin Penicillins Sodium Chloride Streptomycin

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TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
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The RNeasy Mini Kit is a laboratory equipment designed for the purification of total RNA from a variety of sample types, including animal cells, tissues, and other biological materials. The kit utilizes a silica-based membrane technology to selectively bind and isolate RNA molecules, allowing for efficient extraction and recovery of high-quality RNA.
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TRIzol is a monophasic solution of phenol and guanidine isothiocyanate that is used for the isolation of total RNA from various biological samples. It is a reagent designed to facilitate the disruption of cells and the subsequent isolation of RNA.
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DNase I is a laboratory enzyme that functions to degrade DNA molecules. It catalyzes the hydrolytic cleavage of phosphodiester linkages in the DNA backbone, effectively breaking down DNA strands.
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DNase I is a lab equipment product that serves as an enzyme used for cleaving DNA molecules. It functions by catalyzing the hydrolytic cleavage of phosphodiester bonds in the DNA backbone, effectively breaking down DNA strands.
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High capacity cdna reverse transcription kit by Thermo Fisher Scientific
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The High-Capacity cDNA Reverse Transcription Kit is a laboratory tool used to convert RNA into complementary DNA (cDNA) molecules. It provides a reliable and efficient method for performing reverse transcription, a fundamental step in various molecular biology applications.
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Dnase 1 by Takara Bio
Sourced in Japan, China, United States, Germany, United Kingdom
DNase I is a type of enzyme that catalyzes the hydrolytic cleavage of DNA. It is commonly used in various molecular biology and biotechnology applications to degrade DNA.
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Dnase 1 by Qiagen
Sourced in Germany, United States, United Kingdom, Canada, France, Japan, Italy, Netherlands, Spain, China, Sweden, Australia, Switzerland
DNase I is a laboratory enzyme used to degrade DNA. It catalyzes the hydrolytic cleavage of phosphodiester linkages in the DNA backbone, effectively fragmenting DNA molecules.
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Rneasy kit by Qiagen
Sourced in Germany, United States, United Kingdom, Spain, Netherlands, Canada, France, Japan, China, Italy, Switzerland, Australia, Sweden, India, Singapore, Denmark, Belgium
The RNeasy kit is a laboratory equipment product that is designed for the extraction and purification of ribonucleic acid (RNA) from various biological samples. It utilizes a silica-membrane-based technology to efficiently capture and isolate RNA molecules.

More about "Deoxyribonuclease I"

Deoxyribonuclease I (DNase I) is a crucial enzyme that plays a vital role in numerous biological processes.
It catalyzes the hydrolytic cleavage of DNA, breaking it down into smaller di-, oligo-, or mononucleotides.
This enzyme is found in a variety of tissues and is involved in the degradation of extracellular DNA, which is essential for immune system function, cell signaling, and other important biological activities.
Researchers studying Deoxyribonuclease I can optimize their workflows and maximize the impact of their experiments by using PubCompare.ai's AI-driven platform.
This innovative tool allows researchers to quickly locate the best experimental methods from the vast pool of literature, preprints, and patents, streamlining their research process and helping them get the most out of their Deoxyribonuclease I studies.
In addition to Deoxyribonuclease I, researchers may also utilize other related tools and reagents, such as TRIzol reagent, DNase I, RNeasy Mini Kit, and the High-Capacity cDNA Reverse Transcription Kit, to further enhance their experimental capabilities and obtain high-quality, reliable results.
By leveraging the power of PubCompare.ai's AI-driven platform, researchers can effortlessly navigate the wealth of information available and identify the most effective methods for their Deoxyribonuclease I experiments, ultimately leading to more efficient and impactful research outcomes.