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> Disorders > Cell or Molecular Dysfunction > Polyploidy

Polyploidy

Polyploidy refers to the condition where an organism has more than the normal diploid number of chromosomes.
This genetic state can arise through various mechanisms, such as failure of cell division or fusion of gametes.
Polyploid organisms often exhibit unique characteristics, such as increased size, altered growth patterns, and enhanced stress tolerance.
The study of polyploidy is crucial for understanding genome evolution, crop improvement, and the development of novel therapies.
Researchers can leverage AI-driven comparisons using tools like PubCompare.ai to identify the best protocols and products from literature, pre-prints, and patents, enhanceing reproducibility and accuracy in polyploidy research.
Experinece the future of scientific discovery today with PubCompare.ai's intuitive platform.

Most cited protocols related to «Polyploidy»

1
Hifiasm: Robust Genome Assembly for Diploid and Polyploid Genomes
After haplotype-aware error correction, most sequencing errors have been removed while the marker positions are still kept. With nearly error-free reads, hifiasm is able to perform phasing accurately to determine if one overlap is among the reads coming from different haplotypes (i.e. inconsistent overlap). The next step is to build the assembly string graph3 (link), 19 (link). In this graph, nodes represent oriented reads and each edge between two nodes represents the overlap between the corresponding two reads. Note that only consistent overlaps are used to build the graph. Since hifiasm builds the graph on top of nearly error-free reads and highly accurate haplotype phasing, the produced assembly graph of hifiasm is simpler and cleaner than those of current assemblers for haploid genomes. However, for diploid genomes or polyploid genomes, its graph becomes more complicated as reads from different haplotypes are clearly separated out by phasing. Fig. 1 gives an example. Since there is a heterozygous allele on reads in orange and blue, hifiasm separates them into two groups in which all reads in the same color belong to one group. Only the reads from same group are overlapped with each other. For reads in green, they are overlapped with the reads in both groups because the overlaps among them are not long enough to cover at least one heterozygous allele. As a result, hifiasm generates a bubble in the assembly graph. A bubble is a subgraph consisting of a single source node v and a single sink node w with more than one path between v and w, and all nodes in this bubble except v and w do not connect to the rest of the whole graph. Most existing assemblers aim to produce one contiguous contig from the graph (i.e. single path in the graph) as much as possible. They tend to collapse bubbles when building the assembly graph. As a result, they will lose all but one allele in each bubble. In contrast, hifiasm is designed to retain all bubbles on the assembly graph. Owing to the fact that there are still a few errors at the corrected reads, hifiasm adopts a topological-aware graph cleaning strategy. It first identifies substructures embedding local phasing information like bubbles, and then only cuts too short overlaps outside these substructures. Hifiasm additionally records the inconsistent overlaps, which are helpful in the following assembly construction steps.
Cheng H., Concepcion G.T., Feng X., Zhang H, & Li H. (2021). Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nature methods, 18(2), 170-175.
Publication 2021
Alleles Diploidy Genome Haplotypes Heterozygote Polyploidy Shock
2
Measuring Genome Instability Indices
Structural Complexity Score (SCS): defined as the sum of all structurally aberrant regions. Regions of intra-chromosomal gain and loss were defined relative to the modal copy number of the chromosome, and each region counted as one structural aberration. To avoid over-estimation, aberrant regions <1 MB were excluded. Numerical complexity score (NCS): the sum of all whole chromosome gains and losses (chromosomes with >75% of SNP copy number values higher or lower than the ploidy of the sample were counted as whole chromosome gains or losses respectively). Multiple copy number events affecting the same chromosome were scored separately (e.g. −2 copies = 2 chromosome losses). NCS and SCS scores were divided by 1.5 for triploid cell lines, and by 2 for tetraploid cell lines, to account for the increased likelihood of karyotypic abnormalities in polyploid genomes. Weighted genome instability index: As FACS-based DNA index measures were not available for the TCGA tumours, and information about MSI status was unavailable for a sufficient number of tumours, an alternative means of classification was required. The genome instability index (GII)33 is the percentage of SNPs across the genome present at an aberrant copy number, relative to the baseline ploidy of the sample. We adapted the GII in order to account for variation in chromosome size, so that large chromosomes do not have a greater effect on the score than small chromosomes: % aberrant SNPs for each chromosome was calculated separately and mean % aberration then calculated across all 22 chromosomes. To define a threshold for CIN− versus CIN+, the weighted GII (wGII) was calculated for the cell lines. A threshold of 0.2 accurately distinguished CIN+ from CIN−, as previously defined28 . The same threshold was then applied to the TCGA cohort of tumours.
Burrell R.A., McClelland S.E., Endesfelder D., Groth P., Weller M.C., Shaikh N., Domingo E., Kanu N., Dewhurst S.M., Gronroos E., Chew S.K., Rowan A.J., Schenk A., Sheffer M., Howell M., Kschischo M., Behrens A., Helleday T., Bartek J., Tomlinson I.P, & Swanton C. (2013). Replication stress links structural and numerical cancer chromosomal instability. Nature, 494(7438), 492-496.
Publication 2013
Cell Lines Chromosome Aberrations Chromosomes Chromosomes, Human, Pair 2 Chromosomes, Human, Pair 22 Genome Genomic Instability Neoplasms Polyploidy Single Nucleotide Polymorphism Tetraploidy Triploidy
3
Measuring Genome Instability Indices
Structural Complexity Score (SCS): defined as the sum of all structurally aberrant regions. Regions of intra-chromosomal gain and loss were defined relative to the modal copy number of the chromosome, and each region counted as one structural aberration. To avoid over-estimation, aberrant regions <1 MB were excluded. Numerical complexity score (NCS): the sum of all whole chromosome gains and losses (chromosomes with >75% of SNP copy number values higher or lower than the ploidy of the sample were counted as whole chromosome gains or losses respectively). Multiple copy number events affecting the same chromosome were scored separately (e.g. −2 copies = 2 chromosome losses). NCS and SCS scores were divided by 1.5 for triploid cell lines, and by 2 for tetraploid cell lines, to account for the increased likelihood of karyotypic abnormalities in polyploid genomes. Weighted genome instability index: As FACS-based DNA index measures were not available for the TCGA tumours, and information about MSI status was unavailable for a sufficient number of tumours, an alternative means of classification was required. The genome instability index (GII)33 is the percentage of SNPs across the genome present at an aberrant copy number, relative to the baseline ploidy of the sample. We adapted the GII in order to account for variation in chromosome size, so that large chromosomes do not have a greater effect on the score than small chromosomes: % aberrant SNPs for each chromosome was calculated separately and mean % aberration then calculated across all 22 chromosomes. To define a threshold for CIN− versus CIN+, the weighted GII (wGII) was calculated for the cell lines. A threshold of 0.2 accurately distinguished CIN+ from CIN−, as previously defined28 . The same threshold was then applied to the TCGA cohort of tumours.
Burrell R.A., McClelland S.E., Endesfelder D., Groth P., Weller M.C., Shaikh N., Domingo E., Kanu N., Dewhurst S.M., Gronroos E., Chew S.K., Rowan A.J., Schenk A., Sheffer M., Howell M., Kschischo M., Behrens A., Helleday T., Bartek J., Tomlinson I.P, & Swanton C. (2013). Replication stress links structural and numerical cancer chromosomal instability. Nature, 494(7438), 492-496.
Publication 2013
Cell Lines Chromosome Aberrations Chromosomes Chromosomes, Human, Pair 2 Chromosomes, Human, Pair 22 Genome Genomic Instability Neoplasms Polyploidy Single Nucleotide Polymorphism Tetraploidy Triploidy
4
Wheat SNP Discovery and Genotyping
A set of 135 SNPs discovered in a panel of 32 lines of tetraploid and hexaploid wheat by sequencing of 92 gene fragments was downloaded from the Wheat SNP Database (http://wheat.pw.usda.gov/SNP/new/index.shtml). The total length of sequenced regions was 51,493 bp. The discovery panel included 10 accessions of wild emmer, 13 accessions of hexaploid wheat represented by landraces and 9 accessions of synthetic wheats (http://wheat.pw.usda.gov/SNP/new/index.shtml and Supplementary Table S1). The list of SNPs is provided in the Supplementary materials. Repetitive elements in the sequences detected by comparing them with the TREP (http://wheat.pw.usda.gov/ITMI/Repeats/) and GIRI (www.girinst.org) databases were masked. The SNP-harboring sequences were then submitted to Illumina for processing by Illumina® Assay Design Tool (ADT). ADT generates scores for each SNP that could vary from 0 to 1; SNPs with the scores above 0.6 have a high probability to be converted into a successful genotyping assay. In a set of 135 submitted SNPs, the ADT score varied from 0.18 to 0.99 with mean 0.85 (Table S2). A total of 96 SNP sites that were present at the frequency above 2 in the discovery panel and having ADT scores above 0.6 were selected for OPA design (Tables S2, S3). Out of 96 SNPs, 26 were in the wheat D-genome and 70 SNPs were in the A-genome.
A total of 150 ng of genomic DNA per plant was used for Illumina SNP genotyping at the UC Davis Genome center (www.genomecenter.ucdavis.edu/dna_technologies) using the Illumina BeadArray platform and GoldenGate Assay following the manufacturer’s protocol. The fluorescence images of an array matrix carrying Cy3- and Cy5-labeled beads were generated with the two-channel scanner. Raw hybridization intensity data processing, clustering and genotype calling were performed using the genotyping module in the BeadStudio package (Illumina, San Diego, CA, USA). Illumina developed a self-normalization algorithm that relies on information contained in each array. This algorithm adjusts for channel-dependent intensity variations, differences in the background between the channels, and possible crosstalk between the dyes. The normalization procedure implemented in the BeadStudio genotyping module includes outlier removal and background correction and scaling (details of this proprietary normalization algorithm could be obtained from Illumina, San Diego, CA). Before genotype calling, the trimmed mean intensities were calculated from the normalized intensity values obtained for each bead type on the array by rejecting outliers to ensure high quality of genotype data. Genotype calls were generated using the GenCall software incorporated into the BeadStudio package. This algorithm uses a Bayesian model to assign normalized intensity values to one of the three possible homozygous and heterozygous genotype clusters. In the presence of only two homozygous clusters, GenCall computes the location of a missing heterozygous cluster by simulating data using the artificial neural network (Shen et al. 2005 (link)). Since only two clusters were expected for homozygous polyploid wheat lines (see “Discussion” for details), the genotype clusters generated for each SNP locus by GenCall were edited manually after visual inspection of Cy3 and Cy5 fluorescence intensity clustering on two-dimensional Cartesian plots. SNPs that failed to show two-group clustering were excluded from the analysis.
Genotyping error rate was assessed by comparing SNP genotypes determined with the GoldenGate assay with those determined by Sanger sequencing. Trace files for 56 SNP-harboring gene loci were downloaded from the Wheat SNP project database (http://wheat.pw.usda.gov/SNP/new/index.shtml). Base calling and sequence assembly were performed using the phred/phrap and consed programs (Ewing and Green 1998 (link); Ewing et al. 1998 (link); Gordon et al. 1998 (link)). SNP discovery was performed with the polyphred program using the default settings (Stephens et al. 2006 (link)) followed by visual inspection of sequence trace files and manual verification of each discovered SNP. Genetic diversity, defined as the probability that two randomly chosen alleles from the population are different (Weir 1996 , p. 150, 151), was calculated using the PowerMarker program (Liu and Muse 2005 (link)).
Akhunov E., Nicolet C, & Dvorak J. (2009). Single nucleotide polymorphism genotyping in polyploid wheat with the Illumina GoldenGate assay. TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik, 119(3), 507-517.
Publication 2009
Alleles Biological Assay Crossbreeding Cross Reactions DNA, A-Form Dyes Fluorescence Genes Genetic Diversity Genetic Loci Genome Genotype Heterozygote Homozygote Muse Plants Polyploidy Repetitive Region Tetraploidy Triticum aestivum
5
Genomic Analysis of Strawberry Diversity
A Japanese variety, ‘Reikou’, and its progeny (S1) were subjected to genome sequencing as representatives of F. x ananassa. ‘Reikou’ was bred in the Chiba Prefectural Agriculture and Forestry Research Center. Like other strawberry varieties, ‘Reikou’ maintains heterozygosity in the genome. The S1 progenies were sequenced for SNP discovery within ‘Reikou’ for further analysis (data were not shown in this study). A total of 20 wild Fragaria species with a diverse range of polyploidy were used in phylogenetic analyses with SSR markers (Supplementary Table S1). Along with the 20 wild species, the following four Japanese F. x ananassa varieties were subjected to the phylogenic analysis: ‘Reikou’, ‘Hokowase’, ‘Sachinoka’, and ‘Akihime’. Whole-genome sequencing was subsequently performed for four wild species, F. iinumae, F. nipponica, F. nubicola, and F. orientalis. The genomic DNA was extracted from young leaves using a DNeasy Plant Mini Kit (Qiagen, Inc., CA, USA). DNA quantification and quality checks were performed using a Nanodrop ND1000 spectrophotometer (Nanodrop Technologies, DE, USA) and 0.8% agarose gel electrophoresis, respectively.
Hirakawa H., Shirasawa K., Kosugi S., Tashiro K., Nakayama S., Yamada M., Kohara M., Watanabe A., Kishida Y., Fujishiro T., Tsuruoka H., Minami C., Sasamoto S., Kato M., Nanri K., Komaki A., Yanagi T., Guoxin Q., Maeda F., Ishikawa M., Kuhara S., Sato S., Tabata S, & Isobe S.N. (2013). Dissection of the Octoploid Strawberry Genome by Deep Sequencing of the Genomes of Fragaria Species. DNA Research: An International Journal for Rapid Publication of Reports on Genes and Genomes, 21(2), 169-181.
Publication 2013
Electrophoresis, Agar Gel Fragaria Genome Heterozygote Japanese Plants Polyploidy Strawberries WILD 20

Most recents protocols related to «Polyploidy»

1
Comprehensive Cotton Germplasm Evaluation
This research was approved by the biosafety committee of USDA-ARS-SRRC. Field research was performed according to the policy and practices of USDA-ARS. The cottonseeds with Plant Introduction (PI) or Plant Variety Protection (PVP) numbers including diploid G. arboreum A2-100 (PI 529728), and G. raimondii D5-6 (PI530903) and D5-31 (PI 530928) as well as polyploid G. hirsutum Texas Marker 1 (TM-1, PI 607172), Sure-Grow 747 (SG-747, PVP 9800118) and Delta Pine 5690 (DP-5690, PVP 9100116) were obtained from the U.S. National Cotton Germplasm Collection (NCGC). The cottonseeds of short fiber mutants, Ligon-lintless 1 (Li1) mutant, Ligon-lintless 2 (Li2), and diploid G. arboreum Shixiya1 (SXY1) were provided by Dr. Rickie Turley of USDA-ARS-SEA and Dr. Xianliang Song of Shandong Agricultural University, China.
Each variety of G. arboreum (A2-100 and SXY1) and G. hirsutum (TM-1, SG-747, DP-5690, Li1, and Li2) were planted on two-row plots located at the Southern Regional Research Center (New Orleans, LA; 2017) with naturally neutral-day conditions. The soil type of the cotton plot was aquents dredged over alluvium in an elevated location to provide adequate drainage. Single row plots were 12 m long with approximately 40 plants per plot. The distance between two rows was 0.5 m, and the distance between two plants within a row was 0.3 m. To minimize environmental effects, boll samples were not collected from plants on the perimeter of the field and the end of each row. At harvest, approximately 60 naturally opened bolls were randomly collected from two plots for each cotton variety, and separated into two biological replicates with 30 bolls per biological replicate for further analyzing physical and chemical properties of each cotton variety.
To collect developing fibers at various developmental stages, wild diploid G. raimondii D5-6 and D5-31 along with G. arboreum and G. hirsutum were grown in a growth chamber (Percival Intellus Environmental Controller, Perry, IA) in 8 L pots at 28°C (day) / 24°C (night) with a short photoperiod condition (9h day light, 300 μmolm-2 s-1) during the vegetative stage, and reduced to 26°C (day)/ 18°C (night) during flowering and boll development stages. The pots were filled with Metro-Mix 350 soil. For fiber length measurement, two plants of wild diploid G. raimondii D5-6 were grown in 167 L containers at an NCGC greenhouse located at College Station, Texas during a winter season for a short photoperiod condition. The two G. raimondii D5-6 plants produced four bolls, and separated into two biological replicates with two bolls per biological replicate. To obtain sufficient G. raimondii fibers for fiber length and chemical analyses, G. raimondii D5-31 was grown perennially at the cotton winter nursery at Tecoman, Colima, Mexico in association with the location of the Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias [26 ]. Three G. raimondii D5-31 plants (240 days after planting) were transplanted on the ground of the cotton winter nursery. In the second year, they produced 400 bolls that were separated into two biological replicates with 200 bolls per biological replicate for further analyzing fiber length and chemical analyses. All G. raimondii grown in the growth chamber, greenhouse, and cotton winter nursery produced a common phenotype demonstrating short and green colored fibers.
Kim H.J., Liu Y., Thyssen G.N., Naoumkina M, & Frelichowski J. (2023). Phenomics and transcriptomics analyses reveal deposition of suberin and lignin in the short fiber cell walls produced from a wild cotton species and two mutants. PLOS ONE, 18(3), e0282799.
Publication 2023
Biopharmaceuticals chemical properties Cottonseed Diploidy DNA Replication Drainage Fibrosis Gossypium Light Marijuana Abuse Perimetry Phenotype Physical Examination Pinus Plants Polyploidy
2
BCP-ALL Cytogenetic Profiling Protocol
Bone marrow and/or peripheral blood samples were collected from children with BCP-ALL at time of diagnosis. In accordance with the declaration of Helsinki, and as approved by the Medical Ethics Committee of the Erasmus Medical Center, Rotterdam, The Netherlands, written informed consent to use excess diagnostic material for research purposes was obtained from parents or guardians. Children with newly diagnosed ALL in three consecutive Dutch Childhood Oncology Group trials (DCOG ALL-8, ALL-9, and ALL-10) (18 (link)), and two German Cooperative ALL trials (COALL 06-97 and 07-03) (19 (link)) were included in this study. The major known cytogenetic subtypes of BCP-ALL, BCR-ABL1, ETV6-RUNX1, KMT2A/MLL-rearranged, TCF3-PBX1, as well as ploidy status (high hyperdiploidy; 51-65 chromosomes, near tetraploidy; >65 chromosomes, and low hypodiploidy; <39 chromosomes) were determined using karyotyping, fluorescence in situ hybridization (FISH) and RT-PCR by reference laboratories. Mononuclear cells were obtained from bone marrow and/or peripheral blood samples by Lymphoprep density gradient centrifugation and blast percentage was determined based on morphology using May-Grunwald-Giemsa staining (20 (link)). If necessary, samples were enriched to >90% leukemic blasts using negative beads enrichment. RNA and DNA were routinely isolated from samples using TRIzol reagents.
Hormann F.M., Hoogkamer A.Q., Boeree A., Sonneveld E., Escherich G., den Boer M.L, & Boer J.M. (2023). Integrating copy number data of 64 iAMP21 BCP-ALL patients narrows the common region of amplification to 1.57 Mb. Frontiers in Oncology, 13, 1128560.
Publication 2023
BLOOD Bone Marrow Cells Centrifugation, Density Gradient Child Chromosomes Ethics Committees ETV6 protein, human Fluorescent in Situ Hybridization Legal Guardians lymphoprep May-Grunwald Giemsa stain MLL protein, human Neoplasms Parent pbx1 protein, human Polyploidy Reverse Transcriptase Polymerase Chain Reaction RUNX1 protein, human TCF3 protein, human Tetraploidy trizol
3
Differential Gene Expression Analysis of iAMP21 Pediatric ALL
Gene expression microarrays (Affymetrix U133 Plus 2) from a previously described population-based paediatric ALL cohort were used (25 (link), 26 (link)). In short, expression data was normalized using vsnrma (27 (link)), and batch effects were removed using the empirical Bayes method (28 (link)). Differential gene expression between 12 iAMP21 and 143 B-other cases was determined using Limma with false discovery rate (FDR) multiple testing correction (29 ). Gene expression data are available at GEO under accession number GSE87070 (see Supplementary Table S1 for used samples). As a validation cohort, RNA sequencing gene expression data from the Pediatric Cancer (PeCan) database (https://pecan.stjude.cloud/) were used. Gene expression in fragments per kilobase per million mapped reads (FPKM) of selected genes was extracted. BCP-ALL samples annotated as BCR-ABL1, ETV6-RUNX1, TCF3-PBX1, high hyperdiploidy, low hypodiploidy, and infant ALL were excluded from analysis, resulting in a validation cohort of 17 iAMP21 samples and 174 B-other samples. Differential gene expression of target genes was determined using the Mann Whitney U test with Bonferroni multiple testing correction.
Hormann F.M., Hoogkamer A.Q., Boeree A., Sonneveld E., Escherich G., den Boer M.L, & Boer J.M. (2023). Integrating copy number data of 64 iAMP21 BCP-ALL patients narrows the common region of amplification to 1.57 Mb. Frontiers in Oncology, 13, 1128560.
Publication 2023
ETV6 protein, human Gene Expression Genes Infant Malignant Neoplasms Microarray Analysis pbx1 protein, human Pecans Polyploidy RUNX1 protein, human TCF3 protein, human
4
Colorectal Polyps and Associated Factors
Based on the criteria above, data were collected from 475 patients, in which including 298 patients with colorectal polyps (CP group) and 177 patients with normal endoscopic and inflammatory lesions (control group). Polyploidy lesions were found during endoscopic surgery, and the pathology was non-neoplastic lesions, while in the control group, there were normal or inflammatory lesions. Data included family history, relative diseases, personal diet habits, and pathological results were collected.
Huang Y., Liu Y., Yin X., Zhang T., Hao Y., Zhang P., Yang Y., Gao Z., Liu S., Yu S., Li H, & Wang G. (2023). Establishment of clinical predictive model based on the study of influence factors in patients with colorectal polyps. Frontiers in Surgery, 10, 1077175.
Publication 2023
Endoscopy Inflammation Neoplasms Patients Polyploidy Polyps Surgical Endoscopy
5
Wheat Genotyping and GWAS Analysis
All of the 198 accessions were profiled using the wheat 660 K genotyping array by Capital Bio Technology Co. Ltd. (Beijing, China). The polyploid version of the Affymetrix Genotyping Console software (Affymetrix, Santa Clara, CA) was used to conduct SNP allele clustering and genotype calling on the raw SNP data. The physical locations of SNPs were identified based on the IWGSC wheat genome sequence (IWGSC RefSeq v1.0). After filtering SNPs with a missing rate of more than 10% or with a minor allele frequency (MAF) of <5% using the PLINK software74 (link), the filtered SNPs were 419,606 retained for GWAS with the whole population10 (link). Population structure was investigated using the ADMIXTURE software75 (link) and evaluating each K from 2 to 5. The stack graphs visualizing the population structure data were generated using the R script. The stack graphs visualizing the population structure data were generated using the R script. Principal components and kinship analysis (PCA) of the population were performed using the software GCTA76 (link). Heat maps of kinship were generated based on the K-matrix using the pheatmap R package. The SNP genotypes, Q matrix, and trait scores for 198 accessions were incorporated into a compressed mixed linear model77 (link) implemented in the GAPIT R package78 (link). The Phenotypic Variance Explanation (PVE) of each SNP and the kinship matrix used in this analysis were also automatically generated by GAPIT. Manhattan plots and quantile–quantile plot (Q–Q plot) were plotted by the qqman package in R script to present the results of association with the individual trait and important P value distributions, respectively. After a Bonferroni-adjusted correction, the P value for the suggestive threshold was set to 2.3832 × 10−6 (1/419,604). LD and haplotype blocks were constructed using the LDBlockShow software79 (link).
Tian G., Wang S., Wu J., Wang Y., Wang X., Liu S., Han D., Xia G, & Wang M. (2023). Allelic variation of TaWD40-4B.1 contributes to drought tolerance by modulating catalase activity in wheat. Nature Communications, 14, 1200.
Publication 2023
Alleles Base Sequence Genome Genome-Wide Association Study Genotype Haplotypes Microtubule-Associated Proteins Phenotype Physical Examination Polyploidy Triticum aestivum

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More about "Polyploidy"

Polyploidy is a fascinating genetic condition where an organism possesses more than the standard diploid number of chromosomes.
This genetic state can arise through various mechanisms, such as failed cell division or fusion of gametes.
Polyploid organisms often exhibit unique characteristics, including increased size, altered growth patterns, and enhanced stress tolerance.
Understanding polyploidy is crucial for deciphering genome evolution, improving crops, and developing novel therapies.
Researchers can leverage AI-powered tools like PubCompare.ai to identify the best protocols and products from literature, preprints, and patents, enhancing reproducibility and accuracy in polyploidy research.
PubCompare.ai's intuitive platform allows scientists to experinece the future of scientific discovery today, simplifying the research process and uncovering the most reliable information.
Polyploid studies may also involve techniques like flow cytometry using instruments like FACSCalibur, anesthesia with MS-222, and cell surface marker analysis with antibodies such as Anti-CD41-FITC (GpIIb/IIIa), Anti-CD71-FITC, Anti-CD33-FITC, Anti-CD41-PE, and Anti-CD45-PerCp.
Additionally, DNA extraction using the DNeasy Blood and Tissue Kit can be a valuable tool in polyploidy research.
Explore the power of polyploidy optimization and experience the future of scientific discovery with PubCompare.ai.