Log In Sign up
The largest database of trusted experimental protocols
> Physiology > Organism Attribute > Mosaicism

Mosaicism

Mosaicism refers to the presence of two or more genetically distinct cell populations within an individual, orginating from a single zygote.
This condition can arise from chromosomal abnormalities, such as aneuploidy or structural rearrangements, occuring during cell division.
Mosaicism may be present in somatic cells, affecting various tissues, or in germ cells, potentially leading to heritable genetic disorders.
Recognizing and understanding mosaicism is crucial for accurate diagnosis, prognosis, and management of associated clinical manifestations.
This complex genetic phenomenon requires specialized analysis techniques to detect and characterize the diverse cell populations present within an individual.

Most cited protocols related to «Mosaicism»

1
Inherited Bone Marrow Failure Syndromes Cohort Study
The NCI IBMFS cohort is an open retrospective/prospective cohort, established in January 2002, with approval from the NCI Institutional Review Board. Data reported here include individuals enrolled prior to December, 2007, with follow-up through to December, 2008. The protocol, NCI 02-C-0052 [NCT00056121] (http://www.marrowfailure.cancer.gov), was advertised by mailing to paediatric haematologists/oncologists, medical geneticists, and IBMFS family support groups. Voluntary enrollment by the family contact (usually a parent or proband; a proxy was used for deceased patients) began with a telephone interview. Discussion at a team meeting determined whether the proband met the criteria for the suspected syndrome or needed further testing. A Family History Questionnaire provided medical information about relatives. Written informed consent and medical record release forms were signed. Individual Information Questionnaires (medical history, cancer risk factors, etc.) were sent to the proband (or proxy) and first-degree relatives. Biannual follow-up was obtained on all participants. Cancer diagnoses were confirmed by medical record review. All participants were enrolled in the ‘Field Cohort.’ Those who visited the National Institutes of Health (NIH) Warren G. Magnuson Clinical Center were reassigned to the ‘Clinic Cohort.’ Families in the Clinic Cohort visited the NIH for 5 d, for thorough review of medical histories and physical examinations by haematologists and multiple subspecialists, as well as aetiologically-focused laboratory tests.
Participants were assigned to a specific syndrome according to standard criteria and confirmed by syndrome-specific tests where available (Alter, 2003 ). FA was diagnosed by abnormal chromosome breakage in peripheral blood lymphocytes, using both diepoxybutane and mitomycin C (Cervenka et al, 1981 (link); Auerbach et al, 1989 ). Skin fibroblasts were analysed when lymphocytes were normal but FA remained highly suspect (seeking evidence for haematopoietic mosaicism) (Alter et al, 2005 (link)). FA complementation group analyses were performed using retroviral correction (Chandra et al, 2005 (link)).
The clinical diagnosis of DC was made in individuals with components of the diagnostic triad (nail dystrophy, reticular pigmentation, and oral leucoplakia), or those with at least one other typical physical finding (Vulliamy et al, 2006 (link)), in association with marrow failure. We expanded the inclusion criteria to patients with marrow failure, any of the above physical parameters, and blood leucocyte subset telomere lengths below the first percentile of normal-for-age (Alter et al, 2007a (link)). We also classified as ‘DC’ probands and healthy family members who had pathogenic mutations in known DC genes, such as DKC1, TERC, TERT, and TINF2, including those with none of the typical physical findings (Savage & Alter, 2009 (link)).
The diagnosis of DBA was made in those with macrocytic pure red cell aplasia, and supported by finding increased red cell adenosine deaminase (Glader & Backer, 1988 (link)). Patients with SDS had neutropenia and exocrine pancreatic insufficiency, confirmed by detection of sub-normal levels of serum pancreatic trypsinogen and isoamylase (Ip et al, 2002 (link)).
All living affected individuals were specifically screened for all of the major IBMFS; genotyping was performed when possible (Ameziane et al, 2008 (link); Moghrabi et al, 2009 (link)). Affected individuals who had not received a transplant had bone marrow aspirations, biopsies and cytogenetic studies. Individuals who could not be classified as having a specific IBMFS were designated as ‘Others.’ Categories of ‘DC-like,’ ‘FA-like,’ and ‘SDS-like’ were used for individuals whose features initially suggested DC, FA, or SDS but who failed to meet diagnostic criteria. Severe bone marrow failure was defined as impaired haematopoiesis sufficiently severe to lead to bone marrow transplant (BMT) or death (Rosenberg et al, 2003 (link)); MDS required severe pancytopenia and dyspoietic morphology, with or without a cytogenetic clone (Alter et al, 2000 (link)).
Analyses used Microsoft Excel 11.0 (Microsoft, Redmond, WA, USA), Stata 10.1 (StataCorp, College Station, TX, USA), and MATLAB 2008b software (The MathWorks, Natick, MA, USA). The Kaplan-Meier product limit estimator was used to calculate actuarial survival probabilities by age and cumulative incidences in the absence of competing risks; subjects were censored at death (Kaplan & Meier, 1958 ). Subgroup survivals were compared using the log-rank test for equality of survivor functions. Cause-specific hazards and cumulative incidence curves accounting for competing risks were calculated as described previously (Rosenberg et al, 2003 (link)). The observed number of cancers was compared with the expected number (O/E ratio), based on general population incidence data from the Surveillance, Epidemiology, and End Results (SEER) Program, adjusting for age, sex, race, and birth cohort (Ries et al, 2008 ). Sex ratios were examined using the binomial test of comparison with a male:female ratio of 1:1. Statistical tests were 2-sided, and P-values ≤0·05 were considered significant.
Alter B.P., Giri N., Savage S.A., Peters J.A., Loud J.T., Leathwood L., Carr A.G., Greene M.H, & Rosenberg P.S. (2010). Malignancies and survival patterns in the National Cancer Institute inherited bone marrow failure syndromes cohort study. British journal of haematology, 150(2), 179-188.
Publication 2010
Aspiration, Psychology Biopsy Birth Cohort BLOOD Bone Marrow Bone Marrow Transplantation Chromosome Aberrations Chromosome Breakage Clone Cells Congenital Bone Marrow Failure Syndromes Deaminase, Adenosine Diagnosis erythritol anhydride Erythrocytes Ethics Committees, Research Family Member Fibroblasts Genes Grafts Hematopoiesis Hematopoietic System Isoamylase Leukocytes Leukopenia Leukoplakia, Oral Lymphocyte Males Malignant Neoplasms Marrow Mitomycin Mosaicism Mutation Nails Oncologists Pancreas Pancreatic Insufficiency, Exocrine Pancytopenia Parent pathogenesis Patients Physical Examination Pigmentation Pure Red-Cell Aplasia Retroviridae Serum Skin Survivors Syndrome telomerase RNA component Telomere TERT protein, human TINF2 protein, human Triad resin Trypsinogen Woman
2
Fosmid Recombineering and Neuronal Expression Reporters

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2015
Animals, Transgenic Bacteria Codon, Terminator Ganglia Gene Expression Gene Fusion Helminths Mosaicism Neurons Open Reading Frames Strains Systems, Nervous Transcription, Genetic Transgenes
3
Germline Variant Analysis Using NGS
The sequencing data were analyzed for the presence of single-nucleotide variants and small insertions and deletions10 (link) and for evidence of germline mosaicism (Supplementary Appendix 1). Germline copy-number variations and structural variations were identified with the use of the Copy Number Segmentation by Regression Tree in Next Generation Sequencing (CONSERTING)11 (link) and Clipping Reveals Structure (CREST)12 (link) algo-rithms. Common germline structural variations and structural variations that did not affect coding exons were excluded from the analysis.
Nonsilent coding variants that passed quality-control and minor-allele population frequency checks were classified as pathogenic, probably pathogenic, of uncertain significance, probably benign, or benign. Classification criteria included information from curated databases, computational predictions of mutational effect on protein function, and recent ACMG guidelines for interpretation.13 (link) Full details of the data analysis and interpretation are provided in Figure S1 in Supplementary Appendix 1.
Zhang J., Walsh M.F., Wu G., Edmonson M.N., Gruber T.A., Easton J., Hedges D., Ma X., Zhou X., Yergeau D.A., Wilkinson M.R., Vadodaria B., Chen X., McGee R.B., Hines-Dowell S., Nuccio R., Quinn E., Shurtleff S.A., Rusch M., Patel A., Becksfort J.B., Wang S., Weaver M.S., Ding L., Mardis E.R., Wilson R.K., Gajjar A., Ellison D.W., Pappo A.S., Pui C.H., Nichols K.E, & Downing J.R. (2015). Germline Mutations in Predisposition Genes in Pediatric Cancer. The New England journal of medicine, 373(24), 2336-2346.
Publication 2015
Alleles Copy Number Polymorphism Exons Germ-Line Mutation Germ Line Insertion Mutation Mosaicism Mutation Nucleotides Pathogenicity Proteins Trees
4
Stringent Filtering for De Novo Mutations
Screening for new mutations represents a challenging task and has to be treated with the utmost care (see, e.g., the useful discussion by Keightley et al. 2014 (link)). Roach et al. (2010) (link) noted “… most apparent aberrations in allele inheritance will be due to errors in the data and not to mutation.” We applied extremely stringent filtering in attempts to minimize the false-discovery rate. For each individual in the F1 and F2 generations, heterozygous positions were extracted from the background and had to meet the following criteria to be considered as potential de novo mutations:

No alternative reads in any of the parents (making parental mosaicism unlikely),

No other individuals in the same or the previous generation(s) are heterozygous or homozygous for the alternative allele,

At least 25% of the reads support the alternative allele,

Does not overlap with known SNPs from genomic resequencing of more than 100 birds from the same population (Burri et al. 2015 (link); Kardos et al. 2016 (link)), and

Both parents are homozygous for the reference allele.

To manually curate potentially mutated positions, we used the SAMtools mpileup of BAM files and, similar to Keightley et al. (2014) (link), the Integrated Genomics Viewer (IGV) (Thorvaldsdóttir et al. 2013 (link)). The latter was particularly valuable for detection of mapping errors and insertions or deletions associated with candidate mutations. The type of false positives detected in this way are well described by the examples shown in the supplemental figures S1 through S4 by Keightley et al. (2014) (link). In Table 3 the number of candidate mutations remaining after each filtering step is provided, and we suggest that reporting this should be standard in this type of study.
Smeds L., Qvarnström A, & Ellegren H. (2016). Direct estimate of the rate of germline mutation in a bird. Genome Research, 26(9), 1211-1218.
Publication 2016
Alleles Aves Gene Deletion Genome Heterozygote Homozygote Insertion Mutation Mosaicism Mutation Parent Pattern, Inheritance Single Nucleotide Polymorphism Strains
5
Robust Indexing of Diffraction Patterns
For subsequent symmetry determination, it is necessary to calculate the transformation to a reduced basis as discussed in §9.3 of the International Tables for Crystallography, Vol. A (Burzlaff et al., 1996 ▶ ). The essential requirement for the subsequent steps is that the reduced basis has vectors of minimum length. The reduced basis defined in the International Tables for Crystallography fulfills this requirement, but conventional iterative cell reduction algorithms leading to Buerger-reduced cells (Buerger, 1957 ▶ ; Gruber, 1973 ▶ ) or Niggli-reduced cells (Křivý & Gruber, 1976 ▶ ) are numerically unstable. A comprehensive treatment of this problem is given by Grosse-Kunstleve et al. (2004 ▶
a). Except where otherwise specified below, we adopted the minimum reduction presented in that work because it is fast and combines numerical stability with maximum portability.
After reduction, 12 model parameters are refined using conjugate-gradient minimization, with the minimization target being the root-mean-square difference (r.m.s.d.) between the observed and predicted Bragg spot positions introduced in §2.6. It should be emphasized that the target function includes only abstracted information about the positions of the ∼600 spots chosen for autoindexing; no information is present about pixel intensities on the original image. The first round of minimization adjusts the x and y coordinates of the direct beam, the next adds the crystal-to-detector distance, and the final round adds the nine components of the [A] matrix. First derivatives of the target function with respect to each parameter are calculated for the LBFGS minimization algorithm (Liu & Nocedal, 1989 ▶ (no links found)) implemented in our package CCTBX (Grosse-Kunstleve et al., 2002 ▶ ). After minimization, the minimum reduction is applied again.
An estimate of the effective mosaic spread of the crystal is obtained separately. Diffraction patterns are calculated (Rossmann, 1979 ▶ ) using many trial values of mosaic spread ranging from 0 to 1.5°. The effective mosaic spread is taken to be the minimum value, which correctly predicts the observed positions of 80% of the ∼600 Bragg spot candidates used for indexing. The 80% requirement is chosen to allow a small fraction of outliers due to non-Bragg scattering or other pathologies. If the image is so poor that no value of mosaicity less than 1.5° will cover 80% of the spots, no further estimate is made.
Sauter N.K., Grosse-Kunstleve R.W, & Adams P.D. (2004). Robust indexing for automatic data collection. Journal of Applied Crystallography, 37(Pt 3), 399-409.
Publication 2004
Cells Cloning Vectors Crystallography derivatives Exanthema Mosaicism Plant Roots

Most recents protocols related to «Mosaicism»

1
Robust Metagenomic Taxonomic Classification
Training sets were necessary to properly employ regression in a way that converted the raw model scores to relative, bound values. Contaminations in GenBank genome assemblies are a documented problem. Contaminant sources, such as extraneous DNA or adapter sequences, must be identified and eliminated. On the other hand, horizontally acquired genomic regions are commonly present across prokaryotes and are integral parts of their genomes [26 (link), 27 (link), 35 (link)]. Not adequately accounting for these mobile elements in genomes could result in the misclassification of a significant fraction of metagenomic reads.
In addition to horizontal gene transfer, genomic mosaicism may arise due to other evolutionary or biological factors [27 (link)]. These compositionally disparate regions need to be accounted for in order to render a genome model that adequately represents the variability within a genome. For example, there must be distinct models representing horizontally acquired regions from distinct lineages and a model representing the vertically transmitted regions in a genome. Accounting for mosaic compositional structure of prokaryotic genomes is paramount to establishing a high-quality training dataset for regression. To address this, we used the Markovian Jensen–Shannon divergence (MJSD) based segmentation and clustering method that has previously been applied to predict genomic islands in prokaryotic genomes [25 (link), 27 (link)]. This enabled isolation of compositionally distinct regions within each genome in our custom genome database. An optimized algorithm, based on the same methodology for segmentation and clustering as in IslandCafe [27 (link)] but designed to be computationally more efficient, allowed analysis of genomes at a rate capable of handling the entire RefSeq database on a single desktop computer within a reasonable time. For our test system based on a Ryzen 1600 CPU, our segmentation and clustering algorithm processes approximately 2 prokaryotic genomes per minute using all 6 physical cores. The new algorithm uses an optimized technique for computing entropies to estimate the divergence between DNA sequences through MJSD. The new algorithm uses a reverse-calculation step that allows rapid nucleotide-wise iteration across the entire genome (see below). This resulted in a 16-fold reduction of the average time for segmentation and clustering of a prokaryotic genome (average size ~ 5 Mbp), from over 41 min to approximately 2.5 min. For segmentation, we recursively iterated divergence computation at each position of the genome and segmented at the position with the highest MJSD between two resulting subsegments provided the associated p-value was less than 0.05. The significance threshold for clustering was set to 10−5 (readers should refer to Azad and Li [25 (link)] or Jani and Azad [27 (link)] for details).
Clusters less than 0.001% the size of the genome were discarded. The remaining clusters were queried for human, viral, and adapter sequence contamination using BLAST and those with significant similarity to these were also discarded. As segments within a cluster are compositionally similar, we expect these segments to generate more similar Markov model scores than the segments from different clusters. By using a random number generator, we generated fragments of random lengths between 30 and 500 bp from each cluster to generate labeled fragment sampling pools. Randomly sampling fragments from each cluster ensured representation of each compositionally distinct region in our training data. Multiple datasets of 250,000 reads were randomly sampled from these pools to generate 10 unique metagenomic training datasets for each taxon (phylum, class, order, family, genus, and species). By cycling through these datasets with a Bayesian optimization scheme (see below), we generated regression models that were used for taxonomic classification of reads as further discussed below.
Burks D.J., Pusadkar V, & Azad R.K. (2023). POSMM: an efficient alignment-free metagenomic profiler that complements alignment-based profiling. Environmental Microbiome, 18, 16.
Publication 2023
Biological Evolution Biological Factors Entropy Gene Transfer, Horizontal Genome Genomic Islands Homo sapiens isolation Metagenome Mosaicism Nucleotides Physical Examination Prokaryotic Cells
2
Synchrotron-based Differential Phase-Contrast Microscopy
Our DFXM experiments were conducted at Beamline ID06-HXM at the European Synchrotron Radiation Facility (ESRF)47 . We used 17 keV photons, selected by a Si (111) Bragg–Bragg double crystal monochromator, with a bandwidth of ΔE/E=10-4 . The beam was focused in the vertical direction using a Compound Refractive Lens (CRL) comprised of 58 1D Be lenslets with an R=100 μm radius of curvature, yielding an effective focal length of 72 cm. The beam profile on the sample was approximately 200×0.6μm2 (FWHM) in the horizontal and vertical directions, respectively. The horizontal line beam illuminated a single plane that sliced through the depth of the crystal, defining the microscope’s observation plane, as shown in Fig. 1. A near-field alignment camera was placed 40 mm behind the sample, and used to orient the crystal into the Bragg condition. Following alignment, the near-field camera was removed and the image was magnified by an X-ray objective lens comprised of 88 Be parabolic lenslets (2D focusing optics), each with a R=50 μ m radii of curvature. The entry plane of the imaging CRL was positioned 281 mm from the sample along the diffracted beam, and aligned to the beam using a far-field detector. The objective projected a magnified image of the diffracting sample onto the far-field detector, with an X-ray magnification of Mx=17.9
× .
Our far-field imaging detector used an indirect X-ray detection scheme, using an zoom to impart additional magnification. This detector was comprised of a scintillator crystal, a visible microscope and a 2160×2560 pixel PCO.edge sCMOS camera. It was positioned 5010 mm from the sample. The visible optics inside the far-field detector could switch between 10× and 2× objectives to achieve an effective pixel size of 0.75 μm or 3.75 μ m, respectively. This paper focuses on analysis from the highest-magnification 10× magnification images (total magnification of Mt=179× ).
Three types of scans were performed in this work: rocking scans, mosaicity scans, and axial strain scans. The rocking scans acquired images while scanning the tilt angle ϕ , see Fig. 1, over a range of Δϕ=0.12∘ in 30 steps (i.e. δϕ=0.004∘ per step). These 1D “scans of the rocking-curve” map components of the displacement gradient tensor field (i.e. strain and misorientation) to indicate the local variation in structure that is relevant to visualize dislocations29 (link). We collected this type of data for a total of 301 layers, with 1  μ m steps between the layers. The resulting information in four dimensions (x,y,z,ϕ) was imported into Matlab for subsequent processing and feature identification.
Additional supporting scans were collected to more thoroughly sample the mosaicity for selected layers, by measuring distortions along the two orthogonal tilts χ and ϕ , cf. Fig. 1. The χ -range and step size was Δχ=0.24∘ and δχ=0.024∘ , respectively, while the ϕ -range and step size was the same as for the rocking scans. With this data, each voxel can be associated with a subset of a (002) pole figure, allowing us to generate Center of Mass (COM) maps to describe the average direction of the (002) orientation for each voxel in the layer28 . We note that the angular resolution in the COM maps is substantially better than the step size. Finally, axial strain scans were collected by keeping all orientations fixed, while scanning the 2θ axis to resolve the axial strain component ε33 , then reconstructed into the same COM map to quantify the residual strain in each voxel.
Yildirim C., Poulsen H.F., Winther G., Detlefs C., Huang P.H, & Dresselhaus-Marais L.E. (2023). Extensive 3D mapping of dislocation structures in bulk aluminum. Scientific Reports, 13, 3834.
Publication 2023
Epistropheus Europeans Eye Lens, Crystalline Mental Orientation Microscopy Microtubule-Associated Proteins Mosaicism Ocular Refraction Radiography Radiotherapy Radius Strains
3
Fragile X Syndrome Participant Characterization
A total of 138 participants aged 1–67 years were enrolled in this study (Table 1). The Cincinnati Fragile X Research and Treatment Center recruited participants with Fragile X Syndrome (FXS) (55 male, 22 female), premutation carriers (PMCs) (2 male, 27 female), and typically developing controls (TDCs) (26 male, 6 female). Rush University completed clinical southern blot (SB) and/or polymerase chain reaction (PCR) testing on 105 participants (76 with FXS, 26 PMCs, and 3 TDCs) to confirm diagnosis and to evaluate repeat and/or methylation mosaicism status. Only one out of 77 FXS participants did not have clinical testing at the time of this study. In these tests, 26 of the 76 participants with FXS were classified as repeat and/or methylation mosaics. TDCs were recruited through online advertisement and did not have a history of developmental or neuropsychiatric disorders. All participants or legal guardians gave written or verbal assent. The CCHMC Institutional Review Board approved this project. Human subject work followed all relevant regulations and was in accordance with the Declaration of Helsinki.
Straub D., Schmitt L.M., Boggs A.E., Horn P.S., Dominick K.C., Gross C, & Erickson C.A. (2023). A sensitive and reproducible qRT-PCR assay detects physiological relevant trace levels of FMR1 mRNA in individuals with Fragile X syndrome. Scientific Reports, 13, 3808.
Publication 2023
Blot, Southern Diagnosis Ethics Committees, Research Females Fragile X Syndrome Legal Guardians Males Methylation Mosaicism Polymerase Chain Reaction
4
Mosaicism Mutation Ratio Profiling
For families with possible mosaicism, at least 100 T vectors from suspected mosaicism sources were sequenced to calculate the mutation ratio.
Kessi M., Chen B., Shan L.D., Wang Y., Yang L., Yin F., He F., Peng J, & Wang G. (2023). Genotype-phenotype correlations of STXBP1 pathogenic variants and the treatment choices for STXBP1-related disorders in China. BMC Medical Genomics, 16, 46.
Publication 2023
Cloning Vectors Mosaicism Mutation
5
Chromosomal Abnormalities in Miscarriage Samples
All fresh miscarriage specimens were rinsed with saline solution for three times. Chorionic villi were separated from maternal decidua according to the standardized technology [9 (link)]. Samples where chorionic villi could not be clearly identified were excluded from this study. Genomic DNA was extracted from chorionic villi with the protocol of QIAamp DNA Mini Kit (Qiagen, Germany). Chromosomal abnormalities of POCs were detected by two CMA platforms in the current study, including CytoScan 750K array (Affymetrix, USA) and HumanCyto12-SNP array (Illumina, USA). SNP array experiments and molecular karyotype analysis for both platforms were performed as previously described [10 (link)]. Quantitative fluorescent polymerase chain reaction (QF-PCR) was subsequently performed to identify the percentage of maternal and foetal DNA if MCC was detected by CMA. Significant MCC referred to the proportion of MCC exceeding 30%. Samples with significant MCC were excluded from our study.
The two platforms could detect CNVs at an effective minimal resolution of 100 kb and regions of allelic homozygosity (ROHs) at a threshold of 5 Mb. Mosaicism for aneuploidies or CNVs ≥ 5 Mb was reported when the detection threshold of 30% was exceeded. CNVs were further classified as partial aneuploidy (CNVs ≥ 10 Mb, large CNVs) and microdeletions/microduplications (CNVs < 10 Mb, submicroscopic CNVs) based on their sizes. Pathogenicity of detected CNVs were evaluated according to the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen) [11 (link)].
Xia Z., Zhou R., Li Y., Meng L., Huang M., Tan J., Qiao F., Zhu H., Hu P., Zhu Q., Xu Z, & Wang Y. (2023). Reproductive outcomes in couples with sporadic miscarriage after embryonic chromosomal microarray analysis. Annals of Medicine, 55(1), 837-848.
Publication 2023
Alleles Aneuploidy Care, Prenatal Chromosome Aberrations Decidua Genome Homozygote Karyotype Mosaicism Pathogenicity Polymerase Chain Reaction Saline Solution Spontaneous Abortion Villi, Chorionic

Top products related to «Mosaicism»

1
Peg custom by Hampton Research
PEG-custom is a versatile laboratory product used to prepare customized polyethylene glycol (PEG) solutions. It provides researchers with the ability to create PEG solutions with specific molecular weights and concentrations to meet their experimental requirements.
2
Peg ion by Hampton Research
Sourced in United States, Germany, United Kingdom
The PEG/Ion is a laboratory instrument designed to facilitate the study of protein crystallization. It automates the process of screening a wide range of solution conditions, including varying concentrations of precipitants, salts, and other additives. The device can precisely control temperature and other environmental factors to create the optimal conditions for protein crystal growth.
3
Sureprint g3 human cgh 8 60 k by Agilent Technologies
Sourced in United States
The SurePrint G3 Human CGH 8 × 60 K is a high-density microarray platform designed for comparative genomic hybridization (CGH) analysis. It features 60,000 probes that provide comprehensive coverage of the human genome.
4
Nexus copy number 10 by BioDiscovery
Sourced in United States
The Nexus Copy Number 10.0 is a laboratory equipment designed to accurately measure and analyze copy number variations in DNA samples. It utilizes advanced algorithms and high-resolution data processing to provide precise copy number assessments.
5
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.
6
Qiaamp dna blood mini kit by Qiagen
Sourced in Germany, United States, Spain, United Kingdom, Japan, Netherlands, France, China, Canada, Italy, Australia, Switzerland, India, Brazil, Norway
The QIAamp DNA Blood Mini Kit is a laboratory equipment designed for the extraction and purification of genomic DNA from small volumes of whole blood, buffy coat, plasma, or serum samples. It utilizes a silica-based membrane technology to efficiently capture and wash DNA, while removing contaminants and inhibitors.
7
Trizol reagent by Thermo Fisher Scientific
Sourced in United States, China, Japan, Germany, United Kingdom, Canada, France, Italy, Australia, Spain, Switzerland, Netherlands, Belgium, Lithuania, Denmark, Singapore, New Zealand, India, Brazil, Argentina, Sweden, Norway, Austria, Poland, Finland, Israel, Hong Kong, Cameroon, Sao Tome and Principe, Macao, Taiwan, Province of China, Thailand
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.
8
Cytoscan 750k array by Thermo Fisher Scientific
Sourced in United States
The CytoScan 750K array is a high-resolution, genome-wide microarray platform designed for cytogenetic analysis. It provides comprehensive coverage of the human genome, with approximately 750,000 markers, including both SNP and non-polymorphic probes. The CytoScan 750K array is a tool for researchers to detect and analyze chromosomal variations, copy number changes, and other genetic abnormalities.
9
Saltrx by Hampton Research
Sourced in United States, United Kingdom
SaltRx is a laboratory equipment designed for the purification and crystallization of salt samples. It utilizes a controlled evaporation process to facilitate the formation of salt crystals from aqueous solutions.
10
Ion reporter software by Thermo Fisher Scientific
Sourced in United States, Singapore, Germany
Ion Reporter software is a bioinformatics analysis tool designed for processing and analyzing data from next-generation sequencing (NGS) platforms. It provides a comprehensive workflow for the identification and annotation of genetic variants from sequencing data.

More about "Mosaicism"

Mosaicism is a complex genetic phenomenon characterized by the presence of two or more genetically distinct cell populations within an individual, originating from a single zygote.
This condition can arise from chromosomal abnormalities, such as aneuploidy (an abnormal number of chromosomes) or structural rearrangements, occurring during cell division.
Mosaicism can be present in somatic cells, affecting various tissues, or in germ cells, potentially leading to heritable genetic disorders.
Recognizing and understanding mosaicism is crucial for accurate diagnosis, prognosis, and management of associated clinical manifestations.
Specialized analysis techniques, such as CytoScan 750K array, SurePrint G3 Human CGH 8 × 60 K, and Nexus Copy Number 10.0, are used to detect and characterize the diverse cell populations present within an individual.
PEG-custom, PEG/Ion, and Ion Reporter software can be utilized to facilitate the analysis and interpretation of mosaicism data.
Additionally, techniques like QIAquick PCR Purification Kit and QIAamp DNA Blood Mini Kit can be employed for DNA extraction and purification, while TRIzol reagent can be used for RNA isolation.
Platforms like PubCompare.ai, an AI-powered research optimization tool, can help researchers identify the most effective and reproducible methods for studying mosaicism by allowing them to easily locate and compare protocols from published literature, pre-prints, and patents.
By leveraging cutting-edge machine learning, PubCompare.ai empowers researchers to optimize their research protocols and improve reproducibility.
Understanding the complexities of mosaicism and utilizing the right tools and technologies is essential for advancing our knowledge and providing effective clinical management of associated genetic disorders.