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Aneuploidy

Aneuploidy is a chromosomal abnormality characterized by the presence of an abnormal number of chromosomes in a cell.
This condition can occur during cell division, resulting in some cells having more or less than the normal 46 chromosomes.
Aneuploid cells are often unviable or may lead to genetic disorders such as Down syndrome, Patau syndrome, and Edwards syndrome.
Understanding and studying aneuploidy is crucial for advancing research in areas like reproductive health, developmental biology, and cancer biology.
PubCompare.ai can help optimize this research by providing AI-driven comparisons of protocols from literature, pre-prints, and patents, ensuring reproducibility and efficiency in your aneuploidy studies.

Most cited protocols related to «Aneuploidy»

Comprehensive Aneuploidy Analysis Algorithm

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Publication 2018

Aneuploidy Arm, Upper Chromosomes Chromosomes, Human, Pair 13 Gene Deletion Genome Inclusion Bodies Neoplasms Python

Single-Cell Tumor and Normal Cell Classification

Classification of tumor and normal cells was performed in two steps. We assumed that the major genetic distance among the cell populations is the difference between diploid and aneuploid genomes and therefore forced the single cells into two major clusters using hierarchical clustering with Ward linkage and Euclidean distance. To determine the identities of each clusters, we integrated the clustering results with the predefinition of the ‘confident normal cells’ that are defined by a very stringent criteria (seeOnline Methods section on Estimating Copy Number Baseline Values in Diploid Cells).The cluster that has significantly higher enrichment of predefined normal cells is defined as the normal diploid cell cluster. In cases where there is no significant difference in the enrichment test, we switch to the ‘GMM definition’ approach to determine if the consensus profiles of each cluster pass the ‘normal cell criteria’, where at least 95% of the regions fall into the neutral distribution. In some challenging samples that have aneuploidy too close to 2N, we use an alternative slower approach by predicting the cells one-by-one using the ‘GMM definition’ approach and ‘normal cell criteria’.
To evaluate the accuracy of this copy number-based classification of tumor and normal cells, we applied an empirical approach to decide tumor and normal cells based on clustering and expression of cancer-specific marker genes. We first clustered all single cells within a tumor using ‘SNN’ method in R package ‘Seurat’⁴². Next we obtained the expression levels of a panel of four epithelial markers (EPCAM, KRT19, KRT18, and KRT8). We calculated the average expression values of this epithelial markers panel as a consolidated epithelial score in each cell. Single cell gene expression clusters with high epithelial scores (kernel density center is above 0) were labeled as putative tumor cell clusters. In tumors that have both normal epithelial and tumor epithelial cell clusters, we further applied evaluated cancer type specific markers, including KRT19 for PDAC tumor epithelial cells, KRT8 for ATC, EPCAM for TNBC and IBC, and EGFR for GBM cancer cells. Furthermore, expression clusters that expressed immune cells markers (CD45, CD3, CD4, CD8) or fibroblast markers (ACTA2, FN1) were classified as normal cells. Single cells that had consistent aneuploid prediction results in both CopyKAT and by gene expression clusters with high epithelial score were considered to be tumor cells. The prediction accuracy of CopyKAT using aneuploid copy number profiles alone was then calculated as the number of cells with the correct prediction divided by the total number of single cells in the analysis.

Gao R., Bai S., Henderson Y.C., Lin Y., Schalck A., Yan Y., Kumar T., Hu M., Sei E., Davis A., Wang F., Shaitelman S.F., Wang J.R., Chen K., Moulder S., Lai S.Y, & Navin N.E. (2021). Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes. Nature biotechnology, 39(5), 599-608.

Publication 2021

ACTA2 protein, human Aneuploidy Anophthalmia with pulmonary hypoplasia Cells Diploid Cell Diploidy EGFR protein, human Epithelial Cells Fibroblasts Gene Expression Genome KRT8 protein, human KRT18 protein, human KRT19 protein, human Malignant Neoplasms Neoplasms Neoplasms, Epithelial Self Confidence TACSTD1 protein, human

Comprehensive Genetic Variant Analysis of UK Biobank

After phasing the UK Biobank genetic data (carried out on 81
chromosomal chunks using Eagle v.2.4), the phased data were converted from
GRCh37 to GRCh38 using LiftOver^{112 (link)}. Imputation was performed using Minimac4¹¹¹.
We compared the correlation of genotypes between the
exome-sequencing data released by the UK Biobank (following their SPB
pipeline^{113 (link)}) and
the TOPMed-imputed genotypes. The comparison assessed 49,819 individuals and
3,052,260 autosomal variants that were found in both the exome-sequencing
and TOPMed-imputed datasets (matched by chromosome, position and alleles,
and with an imputation quality of at least 0.3 in the TOPMed-imputed data).
We split the variants into MAF bins for which the MAF from the exome data
was used to define the bins, and computed Pearson correlations averaged
within each bin.
We tested single pLOF, nonsense, frameshift and essential
splice-site variants^{85 ,86 (link)} for association with 1,419
PheCodes constructed from composites of ICD-10 (International Classification
of Diseases 10th revision) codes to define cases and controls. Construction
of the PheCodes has been previously described^{114 (link)}. We performed the association
analysis in the ‘white British’ individuals, which resulted in
408,008 individuals after the following quality control metrics were
applied: (1) samples did not withdraw consent from the UK Biobank study as
of the end of 2019; (2) ‘submitted gender’ matches
‘inferred sex’; (3) phased autosomal data available; (4)
outliers for the number of missing genotypes or heterozygosity removed; (5)
no putative sex chromosome aneuploidy; (6) no excess of relatives; (7) not
excluded from kinship inference; and (8) in the UK Biobank defined the
‘white British’ ancestry subset. To perform the association
analyses, we used a logistic mixed model test implemented in SAIGE^{114 (link)} with birth year and the
top four principal components (computed from the white British subset) as
covariates. For the pLOF burden tests, for each autosomal gene with at least
two rare pLOF variants (n = 12,052 genes), a burden
variable was created in which dosages of rare pLOF variants were summed for
each individual. This sum of dosages was tested for association with the
1,419 traits using SAIGE. The same covariates used in the single-variant
tests were included. For both the single-variant and the burden tests, we
used 5 × 10⁻⁸ as the genome-wide significance
threshold.

Taliun D., Harris D.N., Kessler M.D., Carlson J., Szpiech Z.A., Torres R., Taliun S.A., Corvelo A., Gogarten S.M., Kang H.M., Pitsillides A.N., LeFaive J., Lee S.B., Tian X., Browning B.L., Das S., Emde A.K., Clarke W.E., Loesch D.P., Shetty A.C., Blackwell T.W., Smith A.V., Wong Q., Liu X., Conomos M.P., Bobo D.M., Aguet F., Albert C., Alonso A., Ardlie K.G., Arking D.E., Aslibekyan S., Auer P.L., Barnard J., Barr R.G., Barwick L., Becker L.C., Beer R.L., Benjamin E.J., Bielak L.F., Blangero J., Boehnke M., Bowden D.W., Brody J.A., Burchard E.G., Cade B.E., Casella J.F., Chalazan B., Chasman D.I., Chen Y.D., Cho M.H., Choi S.H., Chung M.K., Clish C.B., Correa A., Curran J.E., Custer B., Darbar D., Daya M., de Andrade M., DeMeo D.L., Dutcher S.K., Ellinor P.T., Emery L.S., Eng C., Fatkin D., Fingerlin T., Forer L., Fornage M., Franceschini N., Fuchsberger C., Fullerton S.M., Germer S., Gladwin M.T., Gottlieb D.J., Guo X., Hall M.E., He J., Heard-Costa N.L., Heckbert S.R., Irvin M.R., Johnsen J.M., Johnson A.D., Kaplan R., Kardia S.L., Kelly T., Kelly S., Kenny E.E., Kiel D.P., Klemmer R., Konkle B.A., Kooperberg C., Köttgen A., Lange L.A., Lasky-Su J., Levy D., Lin X., Lin K.H., Liu C., Loos R.J., Garman L., Gerszten R., Lubitz S.A., Lunetta K.L., Mak A.C., Manichaikul A., Manning A.K., Mathias R.A., McManus D.D., McGarvey S.T., Meigs J.B., Meyers D.A., Mikulla J.L., Minear M.A., Mitchell B.D., Mohanty S., Montasser M.E., Montgomery C., Morrison A.C., Murabito J.M., Natale A., Natarajan P., Nelson S.C., North K.E., O’Connell J.R., Palmer N.D., Pankratz N., Peloso G.M., Peyser P.A., Pleiness J., Post W.S., Psaty B.M., Rao D.C., Redline S., Reiner A.P., Roden D., Rotter J.I., Ruczinski I., Sarnowski C., Schoenherr S., Schwartz D.A., Seo J.S., Seshadri S., Sheehan V.A., Sheu W.H., Shoemaker M.B., Smith N.L., Smith J.A., Sotoodehnia N., Stilp A.M., Tang W., Taylor K.D., Telen M., Thornton T.A., Tracy R.P., Van Den Berg D.J., Vasan R.S., Viaud-Martinez K.A., Vrieze S., Weeks D.E., Weir B.S., Weiss S.T., Weng L.C., Willer C.J., Zhang Y., Zhao X., Arnett D.K., Ashley-Koch A.E., Barnes K.C., Boerwinkle E., Gabriel S., Gibbs R., Rice K.M., Rich S.S., Silverman E.K., Qasba P., Gan W., Papanicolaou G.J., Nickerson D.A., Browning S.R., Zody M.C., Zöllner S., Wilson J.G., Cupples L.A., Laurie C.C., Jaquish C.E., Hernandez R.D., O’Connor T.D, & Abecasis G.R. (2021). Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature, 590(7845), 290-299.

Publication 2021

Alleles Aneuploidy Childbirth Chromosomes Eagle Exome Frameshift Mutation Genes Genome Genotype Heterozygote Mutation, Nonsense Reproduction Sex Chromosomes White Person

Tumor Genomic Alteration Profiling

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Publication 2018

Allelic Imbalance Aneuploidy Arm, Upper Chromosomes Clone Cells Copy Number Polymorphism Deletion Mutation DNA Damage Genetic Heterogeneity Genome Genome, Human Homologous Recombination INDEL Mutation Mutation Neoplasms Nitrogen

Comprehensive Genetic Profiling Protocol

Genetic profiling included cytogenetic analyses and sequencing of 111 genes (Table S2 in the Supplementary Appendix). Sequencing data have been deposited in the European Genome-Phenome Archive (www.ebi.ac.uk/ega) under accession number EGAS00001000275. We based our analysis on variants that we classified as driver mutations, using widely accepted genetic criteria.12 (link) Recurrent fusion genes, aneuploidies, and leukemia gene mutations, including base substitutions and small (<200-bp) insertions or deletions (indels), were all included as drivers.

Papaemmanuil E., Gerstung M., Bullinger L., Gaidzik V.I., Paschka P., Roberts N.D., Potter N.E., Heuser M., Thol F., Bolli N., Gundem G., Van Loo P., Martincorena I., Ganly P., Mudie L., McLaren S., O’Meara S., Raine K., Jones D.R., Teague J.W., Butler A.P., Greaves M.F., Ganser A., Döhner K., Schlenk R.F., Döhner H, & Campbell P.J. (2016). Genomic Classification and Prognosis in Acute Myeloid Leukemia. The New England journal of medicine, 374(23), 2209-2221.

Publication 2016

Aneuploidy Cytogenetic Analysis Europeans Gene Deletion Genes Genome INDEL Mutation Insertion Mutation Leukemia Mutation Reproduction

Most recents protocols related to «Aneuploidy»

Genome-wide Analysis of Age-Related Hearing Loss

The UK Biobank samples were genotyped on two arrays: Affymetrix UK BiLEVE Axiom array (50,000 individuals) and the Affymetrix UK Biobank Axiom array (450,000 individuals) (Bycroft et al., 2018 (link)). The two arrays share 733,322 autosomal and 20,214 X chromosome variants. Genotypes were phased using SHAPEIT3 (O’Connell et al., 2016 (link)) and imputation was performed with IMPUTE4 (Bycroft et al., 2018 (link)) using reference data from the Haplotype Reference Consortium and UK10K (Huang et al., 2015 (link); McCarthy et al., 2016 (link)). The pseudoautosomal regions (PARs) and non-pseudoautosomal region (non-PAR) were phased and imputed independently. Genotyping, haplotype phasing, and imputation have been previously described (Bycroft et al., 2018 (link)).
Individuals with putative sex chromosome aneuploidy, inconsistent sex (reported sex did not match genetic sex) or were missing >3% of their genotype array data were removed. Analysis was restricted to individuals of white-European ancestry (N = 459,267) based upon principal components analysis. The genotype variants with call rate >95%, Hardy Weinberg Equilibrium p-value < 5.0 × 10⁻⁸ (estimated in non-PAR region in females and the variants out of HWE were removed in both females and males), and minor allele frequency (MAF) > 0.001 were used for whole-genome ridge regression (N = 442,313, Supplementary Table S1) implemented in REGENIE (Mbatchou et al., 2021 (link)). Principal components (PCs) of genetic data to include in association analysis were generated using a subset of genotyped markers (N = 144,905) that were pruned for linkage disequilibrium (LD) (r² > 0.1) (Supplementary Table S1).
SNVs on the X chromosome obtain from the genotype arrays that did not deviate from HWE and had a MAF>0.001 (1,207 PAR and 11,653 non-PAR) were analyzed. Imputed X chromosome SNVs and insertion/deletions (InDels) with a MAF>0.001 and INFO Score ≥0.3 (45,519 PAR and 2,050,039 non-PAR) were also analyzed. To estimate the contribution of common variants on X chromosome to heritability of ARHL we analyzed variants obtained from the genotype array data with MAF>0.01 in the non-PAR region (N = 18,773).

Naderi E., Cornejo-Sanchez D.M., Li G., Schrauwen I., Wang G.T., Dewan A.T, & Leal S.M. (2023). The genetic contribution of the X chromosome in age-related hearing loss. Frontiers in Genetics, 14, 1106328.

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Publication 2023

Aneuploidy Chromosomes Chromosomes, Human, Pair 20 Europeans Females Gene Components Genome Genotype Haplotypes INDEL Mutation Males Pseudoautosomal Regions Reproduction White Person X Chromosome

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

Genotyping and Whole-Exome Sequencing in CRC

CRC cases were defined based on self-reported code of 1022 or 1023 (in data field 20,001), or ICD-10 code of C18.X or C20.X, D01.[0,1,2], D37.[4, 5], or ICD-9 of 153.X or 154.[0,1] (in hospitalization records). Control samples were those that had no previous diagnosis of any cancer. The study includes people of all ethnicities. Outliers for heterozygosity or genotype missing rates, putative sex chromosome aneuploidy, and discordant reported sex versus genotypic sex were excluded. Only individuals (n = 200,643) who had both genotyping and whole-exome sequencing (WES) data were considered. If the genetic relationship between individuals was closer than the second degree, defined as kinship coefficient > 0.0884 as computed by the UK Biobank, we removed one from each pair of related individuals (cases were retained if exist).

Hassanin E., Spier I., Bobbili D.R., Aldisi R., Klinkhammer H., David F., Dueñas N., Hüneburg R., Perne C., Brunet J., Capella G., Nöthen M.M., Forstner A.J., Mayr A., Krawitz P., May P., Aretz S, & Maj C. (2023). Clinically relevant combined effect of polygenic background, rare pathogenic germline variants, and family history on colorectal cancer incidence. BMC Medical Genomics, 16, 42.

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Publication 2023

Aneuploidy Demecolcine Diagnosis Ethnicity Genotype Genotypic Sex Heterozygote Hospitalization Malignant Neoplasms Sex Chromosomes

UK Biobank Accelerometer Study

The UK Biobank is a population-based prospective study with over 500,000 participants aged 40–73 years recruited in 2006–2010^{34 (link)}. Participants underwent detailed baseline assessments including various sociodemographic, lifestyle, health, and physical assessments through touch-screen questionnaires and physical measurements. Further details of the study are available online (www.ukbiobank.ac.uk). Between February 2013 and December 2015 (on average, approximately 5.5 years after their baseline recruitment), 236,519 UK Biobank participants were invited to participate in an accelerometer study. A total of 106,053 (44.8%) participants agreed to take part and were provided with a wrist-worn Axivity AX3 accelerometer (Axivity, Newcastle upon Tyne, UK). Finally, 103,712 raw accelerometer datasets were received for data analysis^{35 (link)}. Participants who accepted to undergo accelerometer measurement showed similar baseline demographic and health-related characteristics as those who declined the measurement^{17 (link)}.
Using the raw accelerometer data, the UK Biobank accelerometer expert working group conducted data processing and generated the physical activity intensity data in 5-s epochs for 103,682 participants. The flowchart of participant selection of the current study is shown in Supplementary Fig. 1. Based on the data quality metrics provided by the UK Biobank accelerometer working group, the exclusion criteria were as follows: 1) those data flagged by the UK Biobank as being unreliable due to unexpectedly small or large size; 2) those with accelerometer data for less than 72 h or did not provide data for all 1-h periods within a 24-h cycle during the 7-day data collection; 3) those data identified by the UK Biobank as not being well-calibrated; 4) those data were recalibrated using the previous accelerometer record from the same device worn by a different participant; 5) those data with a non-zero count of interrupted recording periods; and 6) those data with more than 768 (Q3 + 1.5 × IQR) data recording errors. Furthermore, during the quality control process of genetic data, participants with missingness (>10%), outliers for heterozygosity, biologically related, and those whose reported sex was inconsistent with sex inferred from the genetic data as well as those with sex chromosome aneuploidy, those who were genetically defined as not white British, and those with prevalent AF based on self-report or medical records were also excluded. Finally, 62,927 participants were included in our study.
The UK Biobank received ethical approval from the NHS (National Health Service) National Research Ethics Service (Ref11/NW/0382). All participants gave written informed consent before enrolment in the study, which was conducted in accordance with the principles of the Declaration of Helsinki. This study was performed under UK Biobank application number 58082.

Yang L., Feng H., Ai S., Liu Y., Lei B., Chen J., Tan X., Benedict C., Wang N., Wing Y.K., Qi L, & Zhang J. (2023). Association of accelerometer-derived circadian abnormalities and genetic risk with incidence of atrial fibrillation. NPJ Digital Medicine, 6, 31.

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Publication 2023

Aneuploidy EPOCH protocol Genetic Processes Health Services, National Heterozygote Medical Devices Physical Examination Sex Chromosomes Touch Wrist Joint

Determining Genomic Instability in LUSC

An unsupervised clustering algorithm was applied to classify the MSI status of TCGA–LUSC samples based on the expression of seven genes encoding MMR proteins (MSH2, MSH3, MSH6, MLH1, MLH3, PMS2, and PMS1). The median absolute deviation (MAD) of the data matrix was used for further cluster analysis. 1,000 time repetitions were applied for guaranteeing the stability of classification. The agglomerative hierarchical clustering algorithm was based upon Pearson’s correlation distance. The highest cluster group was set as 6 (k = 6). The heatmap of consensus matrices, cluster-consensus plot, and item-consensus plot were used for defining the ultimate MSI clusters by taking the stability and purity of clusters into consideration (Hänzelmann et al., 2013 (link)). The aforementioned steps were carried out using the ConsensusClusterPlus package.
Gene set variation analysis (GSVA) was performed to derive the MSI score based on the MMR system gene set that contained the seven genes to identify the MSI status of each sample (Chalmers et al., 2017 (link)). Genomic instability of different groups based on MSI status was characterized and compared by measuring TMB, mutant-allele tumor heterogeneity (MATH), DNA ploidy status, and aneuploidy score. TMB was calculated as the rate of somatic non-synonymous mutations per megabase of sequenced DNA. The exome size was estimated as 38 Mb (Mayakonda et al., 2018 (link)). To evaluate tumor genomic heterogeneity, MATH was calculated as the MAD and the median of variant allele frequencies of non-synonymous variants using the “inferHeterogeneity” implemented in maftools (Carter et al., 2012 (link)). DNA content is the main biologic index of tumor multiplication potentiality. Ploidy reflects the actual DNA content of cancer cells (Taylor et al., 2018 (link)). Aneuploidy reflects the imbalance and complication of DNA replication. DNA ploidy calculated using the Absolute algorithm and the aneuploidy score of TCGA–LUSC samples was directly downloaded from https://gdc.cancer.gov/about-data/publications/panimmune (Langfelder and Horvath, 2008 (link)).

Hu Z., Liu Z., Zheng J., Peng Y., Lu X., Li J., Tan K, & Cui H. (2023). Microsatellite instability-related prognostic risk score (MSI-pRS) defines a subset of lung squamous cell carcinoma (LUSC) patients with genomic instability and poor clinical outcome. Frontiers in Genetics, 14, 1061002.

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Publication 2023

Alleles Aneuploidy Biopharmaceuticals Diploid Cell DNA Replication Exome Gene Expression Genes Genetic Diversity Genetic Heterogeneity Genitalia Genome Genomic Instability Malignant Neoplasms mismatch repair protein 1, human Missense Mutation MLH1 protein, human MLH3 protein, human MSH6 protein, human Neoplasms PMS2 protein, human Proteins Silent Mutation

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What is Aneuploidy?

Aneuploidy is a chromosomal abnormality characterized by the presence of an abnormal number of chromosomes in a cell. This condition can occur during cell division, resulting in some cells having more or less than the normal 46 chromosomes. Aneuploid cells are often unviable or may lead to genetic disorders such as Down syndrome, Patau syndrome, and Edwards syndrome. Understanding and studying aneuploidy is crucial for advancing research in areas like reproductive health, developmental biology, and cancer biology.

What are the different types of Aneuploidy?

There are several types of Aneuploidy, including: - Trisomy: The presence of three copies of a particular chromosome instead of the usual two. - Monosomy: The presence of only one copy of a particular chromosome instead of the usual two. - Partial Aneuploidy: The presence of an abnormal number of chromosomes or chromosome segments, but not involving the whole chromosome.

How can PubCompare.ai assist with Aneuploidy research?

PubCompare.ai allows you to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. The platform can help researchers identify the most effective protocols related to Aneuploidy for their specific research goals. The AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuarcy in your Aneuploidy studies.

What are some common applications of Aneuploidy research?

Aneuploidy research has important applications in several fields, including: - Reproductive Health: Studying Aneuploidy can provide insights into causes of infertility, miscarriages, and genetic disorders like Down syndrome. - Developmental Biology: Understanding Aneuploidy can contribute to our knowledge of how chromosome abnormalities affect embryonic and fetal development. - Cancer Biology: Aneuploidy is a hallmark of many cancer cells, and research in this area can lead to better diagnostic and therapeutic approaches.

More about "Aneuploidy"

Aneuploidy is a chromosomal abnormality characterized by an abnormal number of chromosomes in a cell.
This condition can occur during cell division, resulting in some cells having more or less than the normal 46 chromosomes.
Aneuploid cells are often unviable or may lead to genetic disorders such as Down syndrome, Patau syndrome, and Edwards syndrome.
Understanding and studying aneuploidy is crucial for advancing research in areas like reproductive health, developmental biology, and cancer biology.
Researchers can leverage various tools and techniques to investigate aneuploidy, including the Ion Reporter software, which helps analyze DNA sequencing data, and the DNeasy Blood & Tissue Kit, which is used for DNA extraction.
Colcemid, a mitotic inhibitor, can be used to arrest cells in metaphase, allowing for the visualization of chromosomes.
The BlueFuse Multi software can then be used to analyze the chromosomal structure and detect aneuploidies.
The Axio Imager Z2, a high-resolution microscope, can be employed to capture detailed images of the chromosomes, which can be stained with Giemsa for better visualization.
Next-generation sequencing technologies, such as the HiSeq 2000 and Ion S5 system, enable the identification and quantification of aneuploid cells with high accuracy.
The QIAamp Circulating Nucleic Acid Kit can be used to extract and purify cell-free DNA from blood samples, which can be analyzed on the NextSeq CN500 platform to detect aneuploidy-related genetic variations.
By utilizing these tools and techniques, researchers can optimize their aneuploidy studies, ensuring reproducibility and efficiency in their research efforts.
PubCompare.ai can further enhance this process by providing AI-driven comparisons of protocols from literature, pre-prints, and patents, helping researchers identify the most effective and reliable methods for their investigations.