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Biochemistry and Molecular Biology... 2014This review provides a perspective on the initial development of microarray technologies by two independent groups in the late 1980s. (Review)
Review
This review provides a perspective on the initial development of microarray technologies by two independent groups in the late 1980s.
Topics: History, 20th Century; History, 21st Century; Humans; Microarray Analysis
PubMed: 24344052
DOI: 10.1002/bmb.20756 -
BMC Pregnancy and Childbirth Nov 2020To explore the application value of chromosomal microarray analysis (CMA) in prenatal diagnosis. (Comparative Study)
Comparative Study
BACKGROUND
To explore the application value of chromosomal microarray analysis (CMA) in prenatal diagnosis.
METHODS
The results of chromosome karyotype analysis and CMA of 477 cases undergoing amniocentesis were analyzed. The results of the no ultrasound abnormality group and the ultrasound abnormality group were compared separately. Within the ultrasound abnormality group, the results of the ultrasound structural malformation group, the ultrasound soft index abnormality group, and other ultrasound abnormality (including abnormal amniotic fluid volume and fetal growth restriction) groups were compared.
RESULTS
Abnormal chromosome and CMA results were found in a total of 71 cases (15.88%, 71/447), which can be broken down into a total of 23 karyotype abnormalities (5.15%, 23/447), consisting of 18 cases of aneuploidy (4.03%, 18/447), 2 cases of unbalanced chromosome rearrangements (0.44%, 2/447), and 3 cases of chimerism (0.67%, 3/447); 17 cases with detection of pathogenic copy number variations (pCNVs) (3.80%, 17/447); and 31 cases of detection of clinical variants of unknown significance (VOUS) (6.93%, 31/447). CMA detected 3.8% more genetic abnormalities than karyotype analysis (in addition to the abnormalities detected simultaneously by karyotype analysis). Between the no ultrasound abnormality group and the ultrasound abnormality group, there was an extremely significant difference in the detection rate of an abnormal chromosomal karyotype (P < 0.01) and of VOUS (P < 0.01), but there was no significant difference in the detection rate of pCNV (P > 0.05). Comparing the ultrasound structural malformation group, the ultrasound soft index abnormality group, and the other ultrasound abnormality group, there were no significant differences in the detection rate of abnormal chromosomal karyotypes (P > 0.05), pCNV (P > 0.05) or VOUS (P > 0.05).
CONCLUSIONS
The detection rate of chromosomal karyotype abnormalities in prenatal diagnosis in cases with no ultrasound abnormalities was higher. For cases with fetal ultrasound structural abnormalities, when compared with traditional karyotype analysis, CMA can improve the detection rate of fetal genetic abnormalities. However, the no ultrasound abnormality group also had a high VOUS abnormality detection rate, so it is necessary to strictly define the CMA indications.
Topics: Adult; Amniocentesis; Chromosome Disorders; DNA Copy Number Variations; Female; Fetus; Genetic Testing; Humans; Karyotyping; Microarray Analysis; Pregnancy; Prenatal Diagnosis; Ultrasonography, Prenatal; Young Adult
PubMed: 33198662
DOI: 10.1186/s12884-020-03368-y -
Human Reproduction Update Nov 2020The placenta is the active interface between mother and foetus, bearing the molecular marks of rapid development and exposures in utero. The placenta is routinely... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
The placenta is the active interface between mother and foetus, bearing the molecular marks of rapid development and exposures in utero. The placenta is routinely discarded at delivery, providing a valuable resource to explore maternal-offspring health and disease in pregnancy. Genome-wide profiling of the human placental transcriptome provides an unbiased approach to study normal maternal-placental-foetal physiology and pathologies.
OBJECTIVE AND RATIONALE
To date, many studies have examined the human placental transcriptome, but often within a narrow focus. This review aims to provide a comprehensive overview of human placental transcriptome studies, encompassing those from the cellular to tissue levels and contextualize current findings from a broader perspective. We have consolidated studies into overarching themes, summarized key research findings and addressed important considerations in study design, as a means to promote wider data sharing and support larger meta-analysis of already available data and greater collaboration between researchers in order to fully capitalize on the potential of transcript profiling in future studies.
SEARCH METHODS
The PubMed database, National Center for Biotechnology Information and European Bioinformatics Institute dataset repositories were searched, to identify all relevant human studies using 'placenta', 'decidua', 'trophoblast', 'transcriptome', 'microarray' and 'RNA sequencing' as search terms until May 2019. Additional studies were found from bibliographies of identified studies.
OUTCOMES
The 179 identified studies were classifiable into four broad themes: healthy placental development, pregnancy complications, exposures during pregnancy and in vitro placental cultures. The median sample size was 13 (interquartile range 8-29). Transcriptome studies prior to 2015 were predominantly performed using microarrays, while RNA sequencing became the preferred choice in more recent studies. Development of fluidics technology, combined with RNA sequencing, has enabled transcript profiles to be generated of single cells throughout pregnancy, in contrast to previous studies relying on isolated cells. There are several key study aspects, such as sample selection criteria, sample processing and data analysis methods that may represent pitfalls and limitations, which need to be carefully considered as they influence interpretation of findings and conclusions. Furthermore, several areas of growing importance, such as maternal mental health and maternal obesity are understudied and the profiling of placentas from these conditions should be prioritized.
WIDER IMPLICATIONS
Integrative analysis of placental transcriptomics with other 'omics' (methylome, proteome and metabolome) and linkage with future outcomes from longitudinal studies is crucial in enhancing knowledge of healthy placental development and function, and in enabling the underlying causal mechanisms of pregnancy complications to be identified. Such understanding could help in predicting risk of future adversity and in designing interventions that can improve the health outcomes of both mothers and their offspring. Wider collaboration and sharing of placental transcriptome data, overcoming the challenges in obtaining sufficient numbers of quality samples with well-defined clinical characteristics, and dedication of resources to understudied areas of pregnancy will undoubtedly help drive the field forward.
Topics: Female; Gene Expression Profiling; Humans; Microarray Analysis; Placenta; Placentation; Pregnancy; Pregnancy Complications; Pregnancy Trimester, First; Trophoblasts
PubMed: 33043357
DOI: 10.1093/humupd/dmaa028 -
Microbial Biotechnology Mar 2008The coming of age of whole-cell biosensors, combined with the continuing advances in array technologies, has prepared the ground for the next step in the evolution of... (Review)
Review
The coming of age of whole-cell biosensors, combined with the continuing advances in array technologies, has prepared the ground for the next step in the evolution of both disciplines - the whole-cell array. In the present review, we highlight the state-of-the-art in the different disciplines essential for a functional bacterial array. These include the genetic engineering of the biological components, their immobilization in different polymers, technologies for live cell deposition and patterning on different types of solid surfaces, and cellular viability maintenance. Also reviewed are the types of signals emitted by the reporter cell arrays, some of the transduction methodologies for reading these signals and the mathematical approaches proposed for their analysis. Finally, we review some of the potential applications for bacterial cell arrays, and list the future needs for their maturation: a richer arsenal of high-performance reporter strains, better methodologies for their incorporation into hardware platforms, design of appropriate detection circuits, the continuing development of dedicated algorithms for multiplex signal analysis and - most importantly - enhanced long-term maintenance of viability and activity on the fabricated biochips.
Topics: Bacteria; Biosensing Techniques; Genetic Engineering; Microarray Analysis
PubMed: 21261831
DOI: 10.1111/j.1751-7915.2007.00021.x -
Bioinformatics (Oxford, England) Feb 2013Microarrays are commonly used to detect changes in gene expression between different biological samples. For this purpose, many analysis tools have been developed that...
SUMMARY
Microarrays are commonly used to detect changes in gene expression between different biological samples. For this purpose, many analysis tools have been developed that offer visualization, statistical analysis and more sophisticated analysis methods. Most of these tools are designed specifically for messenger RNA microarrays. However, today, more and more different microarray platforms are available. Changes in DNA methylation, microRNA expression or even protein phosphorylation states can be detected with specialized arrays. For these microarray technologies, the number of available tools is small compared with mRNA analysis tools. Especially, a joint analysis of different microarray platforms that have been used on the same set of biological samples is hardly supported by most microarray analysis tools. Here, we present InCroMAP, a tool for the analysis and visualization of high-level microarray data from individual or multiple different platforms. Currently, InCroMAP supports mRNA, microRNA, DNA methylation and protein modification datasets. Several methods are offered that allow for an integrated analysis of data from those platforms. The available features of InCroMAP range from visualization of DNA methylation data over annotation of microRNA targets and integrated gene set enrichment analysis to a joint visualization of data from all platforms in the context of metabolic or signalling pathways.
AVAILABILITY
InCroMAP is freely available as Java™ application at www.cogsys.cs.uni-tuebingen.de/software/InCroMAP, including a comprehensive user's guide and example files.
Topics: DNA Methylation; Gene Expression; Gene Expression Profiling; MicroRNAs; Oligonucleotide Array Sequence Analysis; Protein Array Analysis; Proteins; RNA, Messenger; Software
PubMed: 23257199
DOI: 10.1093/bioinformatics/bts709 -
FEBS Letters Dec 2018The neoglycolipid (NGL) technology is the basis of a state-of-the-art oligosaccharide microarray system, which we offer for screening analyses to the broad scientific... (Review)
Review
The neoglycolipid (NGL) technology is the basis of a state-of-the-art oligosaccharide microarray system, which we offer for screening analyses to the broad scientific community. We review here the sequential development of the technology and its power in pinpointing and isolating naturally occurring ligands for glycan-binding proteins (GBPs) within glycan populations. We highlight our Designer Array approach and Beam Search Array approach for generating natural glycome arrays to identify novel ligands of biological relevance. These two microarray approaches have been applied for assignments of ligands or antigens on glucan polysaccharides for effector proteins of the immune system (Dectin-1, DC-SIGN and DC-SIGNR) and carbohydrate-binding modules (CBMs) on bacterial hydrolases. We also discuss here the more recent applications to elucidate the structure of a prostate cancer- associated antigen F77 and identify ligands for adhesins of two rotaviruses, P[10] and P[19], expressed on an epithelial mucin glycoprotein.
Topics: Animals; Glucans; Glycolipids; Glycomics; Humans; Lectins, C-Type; Ligands; Microarray Analysis; Oligosaccharides
PubMed: 30074246
DOI: 10.1002/1873-3468.13217 -
Analytical and Bioanalytical Chemistry Oct 2009
Topics: Biosensing Techniques; Microarray Analysis; Microfluidics
PubMed: 19685237
DOI: 10.1007/s00216-009-3002-7 -
BMC Bioinformatics Jul 2010Calibration of a microarray scanner is critical for accurate interpretation of microarray results. Shi et al. (BMC Bioinformatics, 2005, 6, Art. No. S11 Suppl. 2.)...
BACKGROUND
Calibration of a microarray scanner is critical for accurate interpretation of microarray results. Shi et al. (BMC Bioinformatics, 2005, 6, Art. No. S11 Suppl. 2.) reported usage of a Full Moon BioSystems slide for calibration. Inspired by the Shi et al. work, we have calibrated microarray scanners in our previous research. We were puzzled however, that most of the signal intensities from a biological sample fell below the sensitivity threshold level determined by the calibration slide. This conundrum led us to re-investigate the quality of calibration provided by the Full Moon BioSystems slide as well as the accuracy of the analysis performed by Shi et al.
METHODS
Signal intensities were recorded on three different microarray scanners at various photomultiplier gain levels using the same calibration slide from Full Moon BioSystems. Data analysis was conducted on raw signal intensities without normalization or transformation of any kind. Weighted least-squares method was used to fit the data.
RESULTS
We found that initial analysis performed by Shi et al. did not take into account autofluorescence of the Full Moon BioSystems slide, which led to a grossly distorted microarray scanner response. Our analysis revealed that a power-law function, which is explicitly accounting for the slide autofluorescence, perfectly described a relationship between signal intensities and fluorophore quantities.
CONCLUSIONS
Microarray scanners respond in a much less distorted fashion than was reported by Shi et al. Full Moon BioSystems calibration slides are inadequate for performing calibration. We recommend against using these slides.
Topics: Calibration; Fluorescence; Microarray Analysis; Sensitivity and Specificity
PubMed: 20594322
DOI: 10.1186/1471-2105-11-361 -
Molecular Oncology Apr 2011A typical array experiment yields at least tens of thousands of measurements on often not more than a hundred patients, a situation often denoted as the curse of... (Review)
Review
A typical array experiment yields at least tens of thousands of measurements on often not more than a hundred patients, a situation often denoted as the curse of dimensionality. With a focus on prognostic multi-biomarker scores derived from microarrays, we highlight the multidimensionality of the problem and the issues in the multidimensionality of the data. We go over several statistical challenges raised by this curse occurring in each step of microarray analysis on patient data, from the hypothesis and the experimental design to the analysis methods, interpretation of results and clinical utility. Different analytical tools and solutions to answer these challenges are provided and discussed.
Topics: Animals; Biomarkers; Genomics; Humans; Microarray Analysis; Models, Statistical
PubMed: 21349780
DOI: 10.1016/j.molonc.2011.01.002 -
Expert Review of Molecular Diagnostics 2015Methods to detect immunolabeled molecules at increasingly higher resolutions, even when present at low levels, are revolutionizing immunohistochemistry (IHC). These... (Review)
Review
Methods to detect immunolabeled molecules at increasingly higher resolutions, even when present at low levels, are revolutionizing immunohistochemistry (IHC). These technologies can be valuable for the management and examination of rare patient tissue specimens, and for improved accuracy of early disease detection. The purpose of this article is to highlight recent multiplexing methods that are candidates for more prevalent use in clinical research and potential translation to the clinic. Multiplex IHC methods, which permit identification of at least 3 and up to 30 discrete antigens, have been divided into whole-section staining and spatially-patterned staining categories. Associated signal enhancement technologies that can enhance performance and throughput of multiplex IHC assays are also discussed. Each multiplex IHC technique, detailed herein, is associated with several advantages as well as tradeoffs that must be taken into consideration for proper evaluation and use of the methods.
Topics: Humans; Immunohistochemistry; Microarray Analysis; Microfluidics
PubMed: 26289603
DOI: 10.1586/14737159.2015.1069182