Copy Number Polymorphism
This phenomenon can have significant implications for gene expression, disease susceptibility, and evolutionary adaptation.
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Example 2
A tumor single cell and a population of 50 stromal cells, belonging to a disaggregated FFPE section, were digitally sorted by DEPArray™ (Menarini Silicon Biosystems) and whole-genome amplified using the Ampli1™ WGA kit.
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 (
Both h-pES10 and h-pES12 exhibited classical human pluripotent stem cell features, including typical colony morphology and alkaline phosphatase activity (
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 (
In all melanoma cell lines, for each modality (promoter hypomethylation, promoter hypermethylation, gene amplification, and gene deletion) and for each gene, we built an ANOVA model using TF targeting as the response variable across all melanoma cell lines while the status of that gene (either promoter methylation or copy number status) was the explanatory variable. For example, in modeling promoter hypermethylation, we chose positive instances to represent hypermethylated promoters and negative instances for nonmethylated promoters along with an additional factor correcting for the cell lineage. Similarly, for copy number variation analysis, we chose positive instance to represent amplified genes and negative instances for nonamplified genes while correcting for cell lineage. We only computed the associations if they had at least three positive instances of the explanatory variable (for example, promoter hypomethylation in at least three cell lines).
To predict drug response using TF targeting, we conducted a linear regression with elastic net [45 (link)] regularization as implemented in the Python package sklearn v1.1.3 using an equal weight of 0.5 for L1 and L2 penalties using regorafenib cell viability assays in melanoma cell lines as a response variable and the targeting scores of 1,132 TFs (Table S
Finally, to model EMT in melanoma, we used MONSTER on two LIONESS networks of melanoma cancer cell lines, one representing a primary tumor (Depmap ID: ACH-000580) as the initial state and the other a metastasis cell line (Depmap ID: ACH-001569) as the end state. We modified the original implementation of MONSTER that implements its own network reconstruction procedure to take any input network, such as LIONESS networks. MONSTER identifies differentially involved TFs in the transition by shuffling the columns of the initial and final state adjacency matrices 1000 times to build a null distribution, which is then used to compute a standardized differential TF involvement score by scaling the obtained scores by those of the null distribution.
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More about "Copy Number Polymorphism"
This phenomenon, also known as Copy Number Polymorphism (CNP), can have significant implications for gene expression, disease susceptibility, and evolutionary adaptation.
Understanding and analyzing CNV is crucial in various fields, including genetics, genomics, and biomedical research.
CNV can be studied using a range of advanced technologies, such as the HiSeq 2500, NextSeq 500, HiSeq 2000, and NovaSeq 6000 sequencing platforms, as well as the CytoScan HD array.
These tools enable researchers to detect and quantify the copy number of specific genetic regions with high accuracy and resolution.
Sample preparation and DNA extraction methods, such as the QIAamp DNA Mini Kit and the DNeasy Blood & Tissue Kit, are commonly used to obtain high-quality DNA samples for CNV analysis.
Additionally, the Genome-Wide Human SNP Array 6.0 and TaqMan Copy Number Assays provide valuable tools for targeted CNV assessment.
Analyzing CNV can provide insights into the genetic basis of complex traits, disease susceptibility, and evolutionary adaptations.
Researchers can leverage PubCompare.ai's AI-driven insights to optimize their CNV research by easily identifying relevant protocols from literature, preprints, and patents, while utilizing AI-powered comparisons to select the most suitable protocols and products for their specific needs.
By harnessing the power of these resources, researchers can take their Copy Number Polymorphism research to new heights and make valuable contributions to the field.