Introduction
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam in Introduction to Computational Health Informatics, 2019
The computational techniques to identify biomarkers are based upon data mining the proteins or molecules in blood, urine, stool, spinal fluid, sweat or other (mostly fluid) samples that are excreted from the human body and are easily extracted from the body. Microarray analysis and other biochemical and biophysical tests are done on these samples to check if certain protein-content is changing consistently and significantly in the test fluid. Microarray analysis is a bioinformatics technique to find out the variations of genes and proteins associated with different diseases (bacterial or genetic). Two or more microarray results are compared to identify the differences between the quantities of protein expressed in the test sample. This comparison result is then analyzed using computational techniques to identify one or more molecule that can act as biomarkers.
Diagnosis: Nanosensors in Diagnosis and Medical Monitoring
Harry F. Tibbals in Medical Nanotechnology and Nanomedicine, 2017
The emergence of proteomics comes from the growing base of DNA sequence information and new analysis technologies. The proteomics field has been fed by a range of new methods for determining protein localization, protein-protein interactions, posttranslational modifications, and the alteration of protein composition (e.g., differential expression) in tissues and body fluids. Protein analysis is used to characterize gene function, to understand functional relationships between protein molecules, and to provide insight into the mechanisms of complex biological process networks. High throughput techniques such as yeast two-hybrid analysis and affinity tag purification are used to build protein-protein interaction maps. Large-scale protein tagging with subcellular-specific localization provides information about protein function in the cell, and for intercell signaling. MS has emerged as a powerful tool for the analysis of protein complexes. Recent developments in protein microarray technology provide versatile tools for analysis of protein-protein, protein-nucleic acid, protein-lipid, enzyme-substrate, and protein-drug interactions [392-395].
Biochemical Markers in Ophthalmology
Ching-Yu Cheng, Tien Yin Wong in Ophthalmic Epidemiology, 2022
High-throughput methods include protein microarrays [145]. This process involves the application of small amounts of sample to a “chip” for analysis. Antibodies are subsequently fixated to the chip surface and used to capture target proteins in a complex model. This process is often referred to as analytical protein microarray [145]. Functional microarrays allow for the characterization of protein functions, including enzyme substrate turnover and protein–RNA interactions [146]. Reverse-phase protein microarray involves the process of using both healthy and diseased tissue bound to a chip, which is subsequently probed with antibodies against target proteins. MS-based proteomics is also a form of complex gel-free methods of separating proteins. This includes isotope-coded affinity tag, stable isotope labeling with amino acids in culture, and isobaric tags [147].
The peripheral blood transcriptome identifies dysregulation of inflammatory response genes in polycystic ovary syndrome
Published in Gynecological Endocrinology, 2018
Nian-Jun Su, Jian Ma, De-Feng Feng, Shuai Zhou, Zi-Tao Li, Wei-Ping Zhou, Hua Deng, Jia-Ying Liang, Xu-Hui Yang, Yue-Mei Zhang, Feng-Hua Liu, Liang Zhang
Microarray is a high-throughput technology for simultaneously examining expression of thousands of genes in a single experiment. Recently, some researchers have explored the molecular profiles of PCOS with this powerful tool. Samples used in these experiments were from disease direct-related or local specimens such as ovarian tissue, theca cells, cumulus cells, oocytes and endometrium. They identified some genes/pathways responsible for the development of PCOS [5,6]. However, these assays relied on invasive organ biopsies or cultivated cells, and they are generally not readily accessible or available particularly in clinical sites. Also, other ‘-omics’ technologies such as epigenomics and proteomics have been used to identify novel candidate molecules linked to PCOS [7,8]. Despite these efforts, much has still to be uncovered.
Screening of biomarkers for liver adenoma in low-dose-rate γ-ray-irradiated mice
Published in International Journal of Radiation Biology, 2018
Takashi Sugihara, Satoshi Tanaka, Ignacia Braga-Tanaka, Hayato Murano, Masako Nakamura-Murano, Jun-ichiro Komura
Liver and HCA samples were collected as shown in Figure 1. Whole-gene expression was analysed by microarray. The 41,267 genes in the microarray chip ‘whole mouse genome 4 × 44 K′ (Agilent Technologies) were filtered by the one-way ANOVA statistical method in Genespring GX13. Compared to the non-irradiated control livers, C(a), we found 891 (up: 387, down: 504), 722 (up: 182, down: 540) and 9714 (up: 3,186, down: 6,528) differentially expressed genes (>1.5-fold change and p < .05) in LDR (b), HCA(c1) and HCA(c2), respectively. Moreover, 4181 genes (up: 1474, down: 2707) were differentially expressed in C(a) vs HCA(c2) when general cut value (>2.0-fold) of microarray was used. Hierarchical clustering analysis (Figure 2) shows that C(a), LDR(b) and HCA(c1) have rather similar gene expression patterns in contrast to that of HCA(c2).
The first reported case of a deletion of the entire RPGR gene in a family with X-linked retinitis pigmentosa
Published in Ophthalmic Genetics, 2022
Nataša Mihailovic, Simone Schimpf-Linzenbold, Inga Sattler, Nicole Eter, Peter Heiduschka
Array comparative genomic hybridization was performed using the Agilent Sureprint G3 Unrestricted CGH ISCA 180k microarray providing complete coverage of the human genome and a practical resolution of 100kb for genomic losses and gains (copy number variants (CNVs)). Briefly, genomic DNA was isolated and amplified. The DNA was labelled with Cy3-dUTP, the reference DNA, sex matched human genomic DNA was labelled with Cy5-dUTP. The labelled test and reference DNA were combined and purified, and then loaded onto the chips and hybridized according to manufacturer’s instructions. Images of the array were acquired with Agilent surescan scanner and analyzed with Feature Extraction Software v5.0 (Agilent Technology, Santa Clara, CA, USA). The data was aligned to the reference genome described in NCBI Human Genome Build 19 and analyzed with CytoGenomics software v5.0 (Agilent Technology, Santa Clara, CA, USA). Databases used for the evaluation of detected variants include DECIPHER, DGV, ClinVar and gnomAD SV. Genes affected by the annotated CNVs were screened for clinical and functional relevance and listed in detail only if they might be clinically related to the patient’s phenotype at the time of analysis. The evaluation of variants is dependent on available clinical information at the time of analysis. Variants are named according to ISCN guidelines (2020).
Related Knowledge Centers
- Antibody Microarray
- DNA Microarray
- Microrna
- Multiplex
- Substrate
- Microscope Slide
- Assay
- Biotic Material
- High-Throughput Screening
- Protein Microarray