Human sample authentication in biomedical research: comparison of two platforms.
Ontology highlight
ABSTRACT:
SUBMITTER: Manjunath HS
PROVIDER: S-EPMC8263568 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND: In this study we assessed the respective ability of Affymetrix and Illumina microarray methodologies to answer a relevant biological question, namely the change in gene expression between resting monocytes and macrophages derived from these monocytes. Five RNA samples for each type of cell were hybridized to the two platforms in parallel. In addition, a reference list of differentially expressed genes (DEG) was generated from a larger number of hybridizations (mRNA from 86 individuals) using the RNG/MRC two-color platform. RESULTS: Our results show an important overlap of the Illumina and Affymetrix DEG lists. In addition, more than 70% of the genes in these lists were also present in the reference list. Overall the two platforms had very similar performance in terms of biological significance, evaluated by the presence in the DEG lists of an excess of genes belonging to Gene Ontology (GO) categories relevant for the biology of monocytes and macrophages. Our results support the conclusion of the MicroArray Quality Control (MAQC) project that the criteria used to constitute the DEG lists strongly influence the degree of concordance among platforms. However the importance of prioritizing genes by magnitude of effect (fold change) rather than statistical significance (p-value) to enhance cross-platform reproducibility recommended by the MAQC authors was not supported by our data. CONCLUSION: Functional analysis based on GO enrichment demonstrates that the 2 compared technologies delivered very similar results and identified most of the relevant GO categories enriched in the reference list.
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Project description:The nanomaterials research spectrum has seen the continuous emergence of two-dimensional (2D) materials over the years. These highly anisotropic and ultrathin materials have found special attention in developing biomedical platforms for therapeutic applications, biosensing, drug delivery, and regenerative medicine. Three-dimensional (3D) printing and bioprinting technologies have emerged as promising tools in medical applications. The convergence of 2D nanomaterials with 3D printing has extended the application dynamics of available biomaterials to 3D printable inks and bioinks. Furthermore, the unique properties of 2D nanomaterials have imparted multifunctionalities to 3D printed constructs applicable to several biomedical applications. 2D nanomaterials such as graphene and its derivatives have long been the interest of researchers working in this area. Beyond graphene, a range of emerging 2D nanomaterials, such as layered silicates, black phosphorus, transition metal dichalcogenides, transition metal oxides, hexagonal boron nitride, and MXenes, are being explored for the multitude of biomedical applications. Better understandings on both the local and systemic toxicity of these materials have also emerged over the years. This review focuses on state-of-art 3D fabrication and biofabrication of biomedical platforms facilitated by 2D nanomaterials, with the comprehensive summary of studies focusing on the toxicity of these materials. We highlight the dynamism added by 2D nanomaterials in the printing process and the functionality of printed constructs.
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Project description:High-resolution melt analysis (HRMA) is a cheap and reliable post-polymerase chain reaction (PCR) cancer mutation screening technique, which is fast gaining clinical relevance. The HRMA capabilities of the LightScanner (Idaho Technology) have been severally studied. However, the ABI 7500 HRM has not been tested against the purpose-built HRM instrument such as the LightScanner. DNA from formalin-fixed, paraffin-embedded gastric cancer, colorectal cancer, and normal tissue as well as from colorectal cancer cell lines were amplified at exons 2, 3, and 4 of KRAS, and at exons 11 and 15 of BRAF in the ABI 7500 fast real-time PCR machine and subjected to melting both on the ABI and on the LightScanner. HRMA data were analysed with the ABI HRM software v2.0.1 and the LightScanner Call-IT 2.5. We tested the ABI 7500 HRM for internal precision, accuracy, sensitivity, and specificity at mutation screening relative to the LightScanner, using crude percentage concordance, kappa statistics, and the area under the receiver operator characteristics (AUROC) curve on SPSS version 19. The results show that the ABI 7500 HRMA has a high internal precision, and excellent concordance, sensitivity, and specificity at mutation screening compared with the LightScanner. However, in contrast to the LightScanner HRM software analysis, the ABI HRM software v.2.0.1, cannot distinguish real from certain pseudovariations in PCR amplicons that are sometimes brought about by the artefacts of the melting process. In conclusion, the ABI HRM has a comparable performance level with the LightScanner, although in certain respects mentioned previously, the LightScanner has an edge over the ABI.
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Project description:BackgroundExome sequencing, which allows the global analysis of protein coding sequences in the human genome, has become an effective and affordable approach to detecting causative genetic mutations in diseases. Currently, there are several commercial human exome capture platforms; however, the relative performances of these have not been characterized sufficiently to know which is best for a particular study.ResultsWe comprehensively compared three platforms: NimbleGen's Sequence Capture Array and SeqCap EZ, and Agilent's SureSelect. We assessed their performance in a variety of ways, including number of genes covered and capture efficacy. Differences that may impact on the choice of platform were that Agilent SureSelect covered approximately 1,100 more genes, while NimbleGen provided better flanking sequence capture. Although all three platforms achieved similar capture specificity of targeted regions, the NimbleGen platforms showed better uniformity of coverage and greater genotype sensitivity at 30- to 100-fold sequencing depth. All three platforms showed similar power in exome SNP calling, including medically relevant SNPs. Compared with genotyping and whole-genome sequencing data, the three platforms achieved a similar accuracy of genotype assignment and SNP detection. Importantly, all three platforms showed similar levels of reproducibility, GC bias and reference allele bias.ConclusionsWe demonstrate key differences between the three platforms, particularly advantages of solutions over array capture and the importance of a large gene target set.
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