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Centromere

Centromotrs are essential chromosomal structures that play a pivotal role in cell division and genome stability.
These specialized regions, located at the primary constriction of each chromosome, serve as the attachment point for spindle fibers during mitosis and meiosis.
Centromotrs are highly conserved across eukaryotic organisms and are critical for ensuring proper chromosomal segregation and the maintenance of genomic integrity.
Researchers studying centromotr biology and function can leverage the power of PubCompare.ai to effortlessly locate the most reliable and up-to-date protocols, products, and research from a vast array of literature, preprints, and patents.
By optimizing centromere research through AI-driven comparisons, PubCompare.ai helps boost reproducibility and accuracy, empowering scientists to advance our understanding of this fundamental cellular component.

Most cited protocols related to «Centromere»

All samples were stored at − 80 °C until use. Serum levels of C-reactive protein (CRP) were determined by an immuno-turbidimetric technique using an Olympus AU 400 biochemical analyzer (Olympus Optical, Tokyo, Japan), and erythrocyte sedimentation rate (ESR) was measured according to the Fahreus and Westergren method. ANAs were detected using indirect immunofluorescence on HEP2 cells, and the autoantibodies of the ENA complex (anti-U1RNP, anti-Ro, anti-La, anti-DNA-topoisomerase I, anti-Jo-1, anti-P protein, anti-Sm, and anti-centromere) were assayed by immunoblot. Plasma levels of Hsp90 were assessed by a high-sensitivity ELISA kit (eBioscience, Vienna, Austria) according to the manufacturer's protocol. The assay recognizes human Hsp90 alpha. The calculated sensitivity is 0.03 ng/mL. The absorbance value was established at 450 nm by an ELISA reader (SUNRISE; Tecan, Grödig, Austria).
Publication 2021
Autoantibodies Biological Assay Cells Centromere DNA Topoisomerases, Type I Ducks Enzyme-Linked Immunosorbent Assay Homo sapiens HSP90 Heat-Shock Proteins Hypersensitivity Indirect Immunofluorescence OCA2 protein, human Plasma Sedimentation Rates, Erythrocyte Serum Proteins Turbidimetry Vision
The majority of the sets of summary statistics that we analyzed did not contain information about sample minor allele frequency or imputation quality. In order to restrict to a set of common, well-imputed variants, we retained only those SNPs in the HapMap 3 reference panel41 (link) for the LD Score regression. To guard against underestimation of LD Score from summing only LD with variants within a 1cM window, we removed variants in regions with exceptionally long-range LD42 (link) from the LD Score regression (NB LD with these variants were included in the estimation of LD Score). Lastly, we excluded pericentromeric regions (defined as ± 3 cM from a centromere) from the LD Score regression, because these regions are enriched for sequence gaps, which may lead to underestimation of LD Score, and depleted for genes, which may reduce the probability of association to phenotype43 (link),44 (link). The final set of variants retained for LD Score regression on real data consisted of approximately 1.1 million variants.
Publication 2015
Centromere Genes HapMap Single Nucleotide Polymorphism
Before applying ChromHMM, we converted the normalized signal tracks into binarized data at a 200-bp resolution. We used the maximum signal for a mark in each 200-bp interval to represent the mark in that interval. The threshold for each mark was the maximum of 4.0 and the value corresponding to a Poisson tail distribution probability of 0.0001. Requiring a fold threshold, in addition to the tail distribution threshold, enabled more meaningful binarization of some of the most deeply sequenced datasets. We excluded regions that associated with repetitive elements such as α- and β-satellite repeats, ribosomal and mitochondrial DNA (http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeMapability/wgEncodeDukeMapabilityRegionsExcludable.bed.gz).
For Segway, we excluded the ENCODE Data Analysis Consortium Blacklisted Regions (http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeMapability/wgEncodeDacMapabilityConsensusExcludable.bed.gz), comprising a comprehensive set of regions in the human genome that exhibit anomalous or unstructured read-counts in next gen sequencing experiments, independent of cell line and type of experiment. To identify these regions, we used 80 open chromatin tracks (DNase and FAIRE datasets) and 20 ChIP-seq input/control tracks spanning ∼60 human tissue types/cell lines in total. The regions tend to have a very high ratio of multi-mapping to unique mapping reads and high variance in mappability. Some of these regions overlap pathological repeat elements such as satellite, centromeric and telomeric repeats. However, simple filters based on mappability do not account for most of these regions.
Publication 2012
Cell Lines Centromere Chromatin Chromatin Immunoprecipitation Sequencing Deoxyribonuclease I DNA, Mitochondrial Genome, Human Histocompatibility Testing Homo sapiens Repetitive Region Ribosomes Tail Telomere
The 4046 tumors assembled into tissue microarrays, in BCCA series, were examined with immunohistochemistry and fluorescent in situ hybridization. Immunohistochemistry for ER, PR, HER2, and Ki67 was performed concurrently on serial sections with the standard streptavidin–biotin complex method with 3,3′-diaminobenzidine as the chromogen. Staining for ER, PR, and HER2 interpretation was as described previously (20 (link)). Briefly, the Ki67 antibody (clone SP6; ThermoScientific, Fremont, CA) was applied at a 1:200 dilution for 32 minutes, by following the Ventana Benchmark automated immunostainer (Ventana, Tucson AZ) standard Cell Conditioner 1 (CC1, a proprietary buffer) protocol at 98°C for 30 minutes. ER antibody (clone SP1; ThermoFisher Scientific, Fremont CA) was used at 1:250 dilution with 10-minute incubation, after an 8-minute microwave antigen retrieval in 10 mM sodium citrate (pH 6.0). Ready-to-use PR antibody (clone 1E2; Ventana) was used by following the CC1 protocol as above. HER2 staining was done with the SP3 antibody (ThermoFisher Scientific) at a 1:100 dilution after antigen retrieval in 0.05 M Tris buffer (pH 10.0) with heating to 95°C in a steamer for 30 minutes. For HER2 fluorescent in situ hybridization assay, slides were hybridized with probes to LSI (locus-specific identifier) HER2/neu and to centromere 17 by use of the PathVysion HER-2 DNA Probe kit (Abbott Molecular, Abbott Park, IL) according to manufacturer's instructions, with modifications to pretreatment and hybridization as previously described (29 (link)). Slides were counterstained with 4′,6-diamidino-2-phenylindole, stained material was visualized on a Zeiss Axioplan epifluorescent microscope, and signals were analyzed with a Metafer image acquisition system (Metasystems, Altlussheim, Germany). Biomarker expression from immunohistochemistry assays was scored by two surgical pathologists (T. O. Nielsen and D. Gao), who were blinded to the clinicopathological characteristics and outcome and who used previously established and published criteria for biomarker expression levels that had been developed on other breast cancer cohorts (12 (link),30 (link)). Tumors were considered positive for ER (27 (link)) or PR (31 ) if immunostaining was observed in more than 1% of tumor nuclei, as described previously. Tumors were considered positive for HER2 if immunostaining was scored as 3+ according to HercepTest criteria, with an amplification ratio for fluorescent in situ hybridization of 2.0 or more being the cut point that was used to segregate immunohistochemistry equivocal tumors (scored as 2+) (32 (link)). Ki67 was visually scored for percentage of tumor cell nuclei with positive immunostaining above the background level by two pathologists (T. O. Nielsen and D. Gao). Tissue microarray core samples with fewer than 50 tumor cells were considered uninterpretable (27 (link),28 (link)). All the stained tissue microarrays were digitally scanned, and primary image data are available for public access (http://www.gpecimage.ubc.ca; username, luminalB; password, luminalb).
Publication 2009
Acid Hybridizations, Nucleic Antigens azo rubin S Biological Assay Biological Markers biotin-streptavidin complex Breast Carcinoma Buffers Cell Nucleus Cells Centromere Clone Cells DNA Probes ERBB2 protein, human Fluorescent in Situ Hybridization Immunoglobulins Immunohistochemistry Microarray Analysis Microscopy Microwaves Neoplasms Operative Surgical Procedures Pathologists Receptor, ErbB-2 Sodium Citrate Technique, Dilution Tissues Tissue Stains Tromethamine
CLARK accepts inputs in fasta/fastq format; alternatively the input can be given as a text file containing the k-mer distribution (i.e., each line contains a k-mer and its number of occurrences). CLARK first builds an index from the target sequences, unless one already exists for the specified input files. If a user wants to classify objects at the genus level (or another taxonomic rank), he/she is expected to generate targets by grouping genomes of the same genus (or with the same taxonomic label). This strategy represents a major difference with other tools (such as LMAT, or KRAKEN). The index is a hash-table storing, for each distinct k-mer w (1) the ID for the target containing w, (2) the number of distinct targets containing w, and (3) the number of occurrences of w in all the targets. This hash-table uses separate chaining to resolve collisions (at each bucket). CLARK then removes any k-mer that appears in more than one target, except in the case of chromosome arm assignment. In the latter case, k-mers shared by the two arms of the same chromosome are used to define centromeric regions of overlap. Also, k-mers in the index may be removed based on their number of occurrences if the user has specified a minimum number of occurrences. These rare k-mers tend to be spurious from sequencing errors. Other metagenomic classifiers like KRAKEN and LMAT do not offer this protection against noise, which is very useful when target sequences are reads (or low-quality assemblies). Then, the resulting sets of target-specific k-mers are stored in disk for the next phase. The time and memory needed to create the index (for k=31) are given in Additional file 1: Table S1. This table also contains the time and memory required by NBC and KRAKEN. Observe that CLARK is faster than NBC and KRAKEN to create the index, and it uses less RAM and disk space than KRAKEN for classifying objects.
The concept of “target-specific k-mers” is similar to the notion of “clade-specific marker genes” proposed in [7 (link)] or “genome-specific markers” recently proposed in [30 (link)]. While CLARK uses exact matching to identify the target-specific k-mers derived from any region in the genome, the authors in [7 (link)] disregard intergenic regions. The authors of [30 (link)] focus on strain-specific markers identified by approximate string matching, while CLARK uses exact matching. Another important difference is that the method presented in [30 (link)] relies on MEGABLAST [31 (link)] to perform the classification, which is several orders of magnitude slower than KRAKEN [11 (link)].
For users that want to run CLARK on workstations with limited amounts of RAM, we have designed CLARK-l (“light”). CLARK-l is a variant of CLARK that has a much smaller RAM footprint but can classify objects with similar speed and accuracy. The reduction in RAM can be achieved by constructing a hash-table of smaller size and by constructing smaller sets of discriminative k-mers. Instead of considering all k-mers in a target, CLARK-l samples a fraction of them. CLARK-l uses 27-mers (27-mers appeared to be a good tradeoff between speed, low memory usage and precision) and skips four consecutive/non-overlapping 27-mers. As a result, CLARK-l’s peak RAM usage is about 3.8 GB during the index creation, and 2.8 GB when computing the classification (see Additional file 1: Table S1). CLARK-l has also the advantage to be very fast in building the hash table. Table 1 includes the performance of CLARK-l. While the precision and sensitivity are lower compared to CLARK, CLARK-l still achieves high precision and high speed.
Publication 2015
Arm, Upper Centromere Chromosomes Discrimination, Psychology Genes Genome GPER protein, human Hypersensitivity Intergenic Region Light Marijuana Abuse Memory Metagenome Strains

Most recents protocols related to «Centromere»

Example 3

We generated and analyzed a collection of 14 early-passage (passage ≤9) human pES cell lines for the persistence of haploid cells. All cell lines originated from activated oocytes displaying second polar body extrusion and a single pronucleus. We initially utilized chromosome counting by metaphase spreading and G-banding as a method for unambiguous and quantitative discovery of rare haploid nuclei. Among ten individual pES cell lines, a low proportion of haploid metaphases was found exclusively in a single cell line, pES10 (1.3%, Table 1B). We also used viable FACS with Hoechst 33342 staining, aiming to isolate cells with a DNA content corresponding to less than two chromosomal copies (2c) from four additional lines, leading to the successful enrichment of haploid cells from a second cell line, pES12 (Table 2).

Two individual haploid-enriched ES cell lines were established from both pES10 and pES12 (hereafter referred to as h-pES10 and h-pES12) within five to six rounds of 1c-cell FACS enrichment and expansion (FIG. 1C (pES10), FIG. 5A (pES12)). These cell lines were grown in standard culture conditions for over 30 passages while including cells with a normal haploid karyotype (FIG. 1D, FIG. 5B). However, since diploidization occurred at a rate of 3-9% of the cells per day (FIG. 1E), cell sorting at every three to four passages was required for maintenance and analysis of haploid cells. Further, visualization of ploidy in adherent conditions was enabled by DNA fluorescence in situ hybridization (FISH) (FIG. 1F, FIG. 5c) and quantification of centromere protein foci (FIG. 1G, FIG. 5D; FIG. 6). In addition to their intact karyotype, haploid ES cells did not harbor significant copy number variations (CNVs) relative to their unsorted diploid counterparts (FIG. 5E). Importantly, we did not observe common duplications of specific regions in the two cell lines that would result in pseudo-diploidy. Therefore, genome integrity was preserved throughout haploid-cell isolation and maintenance. As expected, single nucleotide polymorphism (SNP) array analysis demonstrated complete homozygosity of diploid pES10 and pES12 cells across all chromosomes.

Both h-pES10 and h-pES12 exhibited classical human pluripotent stem cell features, including typical colony morphology and alkaline phosphatase activity (FIG. 2A, FIG. 2B). Single haploid ES cells expressed various hallmark pluripotency markers (NANOG, OCT4, SOX2, SSEA4 and TRA1-60), as confirmed in essentially pure haploid cultures by centromere foci quantification (>95% haploids) (FIG. 2C, FIG. 7). Notably, selective flow cytometry enabled to validate the expression of two human ES-cell-specific cell surface markers (TRA-1-60 and CLDN618) in single haploid cells (FIG. 2D). Moreover, sorted haploid and diploid ES cells showed highly similar transcriptional and epigenetic signatures of pluripotency genes (FIG. 2E, FIG. 2F). Since the haploid ES cells were derived as parthenotes, they featured distinct transcriptional and epigenetic profiles of maternal imprinting, owing to the absence of paternally-inherited alleles (FIG. 8).

Haploid cells are valuable for loss-of-function genetic screening because phenotypically-selectable mutants can be identified upon disruption of a single allele. To demonstrate the applicability of this principle in haploid human ES cells, we generated a genome-wide mutant library using a piggyBac transposon gene trap system that targets transcriptionally active loci (FIG. 2G, FIG. 8E), and screened for resistance to the purine analog 6-thioguanine (6-TG). Out of six isolated and analyzed 6-TG-resistant colonies, three harbored a gene trap insertion localizing to the nucleoside diphosphate linked moiety X-type motif 5 (NUDT5) autosomal gene (FIG. 2H). NUDT5 disruption was recently confirmed to confer 6-TG resistance in human cells,51 by acting upstream to the production of 5-phospho-D-ribose-1-pyrophosphate (PRPP), which serves as a phosphoribosyl donor in the hypoxanthine phosphoribosyltransferase 1 (HPRT1)-mediated conversion of 6-TG to thioguanosine monophosphate (TGMP) (FIG. 2I). Detection of a loss-of-function phenotype due to an autosomal mutation validates that genetic screening is feasible in haploid human ES cells.

Patent 2024
Alkaline Phosphatase Alleles Cell Lines Cell Nucleus Cells Cell Separation Centromere Chromosomes Copy Number Polymorphism Diphosphates Diploid Cell Diploidy Embryonic Stem Cells Flow Cytometry Fluorescent in Situ Hybridization Genes Genes, vif Genitalia Genome Genomic Library Haploid Cell HOE 33342 Homo sapiens Homozygote Human Embryonic Stem Cells Hypoxanthine Phosphoribosyltransferase isolation Jumping Genes Karyotype Metaphase Mothers Mutation Nucleosides Oocytes Phenotype Pluripotent Stem Cells Polar Bodies POU5F1 protein, human Proteins purine Ribose Single Nucleotide Polymorphism SOX2 protein, human stage-specific embryonic antigen-4 Tissue Donors Transcription, Genetic
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) (InvitrogenTM, 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) (InvitrogenTM, Shanghai, China). The genomic DNA of A. tauschii and the centromeric retrotransposon of wheat (CRW) clone 6C6 was labeled with Texas-red-5-dCTP (InvitrogenTM, 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).
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
To detect SVs from the two samples, we applied LoMA to the WGS data. We first searched for target regions (unclear regions) by scanning all chromosomes from telomere to telomere. We split each autosome and sex chromosome binned per 500 bp, step size 250 bp, and defined an “unclear” region as follows: (1) average coverage between 10 and 200, (2) total number of reads containing indels (≥ 100 bp) or hard- or soft-clipped sequences (≥ 500 bp) > 10, and (3) the proportion of reads containing indels (≥ 100 bp) or hard- or soft-clipped sequences (≥ 500 bp) > 0.2 (Fig. 2A). Then, multiple bins within 10 kbp were merged into one bin. We defined each merged bin as an unclear region in this study. After defining the unclear regions for both NA18943 and NA19240, we collected reads mapped within 10 kbp from both ends of each unclear region using SAMtools [24 (link)].

Whole-genome LoMA analysis. A. The workflow of the whole-genome analysis. Unclear regions were first defined based on the alignment status (indels and clips) of ONT reads. Reads mapped to the regions were separately collected. For each region, LoMA attempted a localized assembly to obtain CSs. CAMPHOR collectively detected structural variants from the CSs for NA18943 and NA19240. B. The relative density of unclear regions of NA18943 and NA19240 are shown in red- and blue-colored heatmaps, respectively. The light-colored regions are dense with unclear regions. The white arrows indicate the autosomal centromeres except chromosome 6. The arrows on chromosome 6 represent the HLA region. C. The precision of indels to the standard SV set was assessed for NA19240. The left vertical axis (bar graphs) shows the number of indels found in each bin (the number of constitutive reads). The right (line graphs) shows the precision of indels in each bin. Both graphs were binned per 2 reads

Publication 2023
Camphor Centromere Chromosomes Chromosomes, Human, Pair 6 Clip Epistropheus Genome INDEL Mutation Light Loma Sex Chromosomes Telomere
To estimate the accuracy of LoMA, we compared CSs assembled using the ONT data of NA18943 with GRCh38. We randomly selected 108 positions from the human genome while excluding centromeres and gaps (Additional file 1: Table S1). We collected all reads mapped within 20 kbp of each position from the data of NA18943 and constructed CSs using LoMA. We aligned the generated CSs to GRCh38 using minimap2 [16 (link)] and calculated the error rates from the edit distance. We also aligned all raw reads to GRCh38 and calculated error rates for the raw reads again using the edit distance. For a comparison, we assembled matched regions using lamassemble [15 ]:
-P 8 -a -v -p 2e-3 -m 2*(number of reads) -z 1000 promethion.mat
The error rate of lamassemble was calculated as above.
We also evaluated LoMA using simulated data. We randomly selected one hundred regions from GRCh38 (Additional file 1: Table S2). Simulated reads were generated using NanoSim with the error profile of NA12878 (total error rate, 10.8%) provided by the developers [23 (link)]. Various data sets were generated for each region: coverage 10, 20, 30, 40, and 50 (with a fixed size of 20 kbp), targeted size 20 kbp, 40 kbp, 60 kbp, 80 kbp, and 100 kbp (with a fixed mean coverage of 30×). The error rate, CPU time, and peak memory (RSS) were measured. A computer with M1 chip (Apple) was used to measure the performance. The error rate (edit distance) was calculated as described above.
Publication 2023
Centromere DNA Chips Genome, Human Loma Memory
Fragile sites are the cause of genome instability in the model. We let fragile sites cause large‐scale chromosomal mutations with a per‐fragile site probability μf. We take into account that large‐scale mutations in Streptomyces preferentially disrupt telomeric regions (Chen et al, 2002 (link); Hopwood, 2006 (link); Hoff et al, 2018 (link); Tidjani et al, 2020 (link)) by letting fragile site‐induced mutations delete the entire chromosomal region downstream (i.e., to the right) of the genomic location of the fragile site (see Fig 1C). Effectively, this means that we model one arm of the chromosome, and that the model centromere and telomere result from the asymmetric effect of fragile site deletions. No other type of mutation has any left/right preference in the model.
Publication 2023
Centromere Chromosome Fragile Sites Chromosomes Gene Deletion Genome Genomic Instability Mutation Streptomyces Telomere

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More about "Centromere"

Centromotrs, also known as kinetochores, are essential chromosomal structures that play a pivotal role in cell division and genome stability.
These specialized regions, located at the primary constriction of each chromosome, serve as the attachment point for spindle fibers during mitosis and meiosis.
Centromotrs are highly conserved across eukaryotic organisms and are critical for ensuring proper chromosomal segregation and the maintenance of genomic integrity.
Researchers studying centromere biology and function can leverage the power of PubCompare.ai to effortlessly locate the most reliable and up-to-date protocols, products, and research from a vast array of literature, preprints, and patents.
By optimizing centromere research through AI-driven comparisons, PubCompare.ai helps boost reproducibility and accuracy, empowering scientists to advance their understanding of this fundamental cellular component.
The DAPI (4',6-diamidino-2-phenylindole) stain is commonly used to visualize centromeres and other chromatin structures under a fluorescence microscope.
The PathVysion HER2 DNA Probe Kit, used in conjunction with the ISIS software, can help analyze the amplification of the HER2 gene, which is often associated with certain types of cancer.
The Nick translation kit can be utilized to label DNA fragments, including those found in centromeric regions, with fluorescent probes like Spectrum Green and Spectrum Orange.
Vectashield is a common mounting medium that helps preserve the fluorescence signal during microscopy.
By leveraging these tools and techniques, researchers can gain deeper insights into the structure, function, and dynamics of centromotrs, ultimately contributing to our understanding of cell division, genome stability, and related disease processes.