Gene Expression Microarray Analysis is a complex term used in genetics research. The phonetic transcription of its spelling in IPA is dʒin ɪkˈsprɛʃən ˈmaɪkrəʊˌɛrɛɪ əˈnæləsɪs. The first syllable, "gene," is pronounced with a soft g sound, followed by an "ee" sound, and ends with a short "n". The next word, "expression," includes the sound "ksh" and a long "sh" sound. "Microarray" is pronounced with emphasis on the "cro" syllable while "analysis" ends with a long "iss" sound. Overall, mastering IPA can help with the pronunciation of complex words such as this.
Gene expression microarray analysis is a technique used in molecular biology and genetics to measure the expression levels of genes within a biological sample. It involves the use of gene chips or microarrays, which are tiny glass slides or silicon chips that contain thousands of DNA probes. Each probe on the microarray corresponds to a specific gene of interest.
The process begins by extracting RNA from the sample, which represents the active genes in the cells. The RNA is then converted into complementary DNA (cDNA), which is labeled with a fluorescent dye. Next, the labeled cDNA is applied to the microarray, allowing the cDNA to hybridize or bind to the specific DNA probes on the chip.
After hybridization, the microarray is scanned using specialized equipment to measure the fluorescent signals emitted by each probe. The intensity of the signal reflects the expression level of the corresponding gene in the sample. By comparing the signals from different samples, researchers can identify genes that are upregulated or downregulated in response to various conditions or stimuli.
Gene expression microarray analysis allows scientists to simultaneously study the expression patterns of thousands of genes, providing valuable information about cellular processes, disease mechanisms, and drug responses. By identifying genes that are differentially expressed, researchers can gain insights into the underlying molecular mechanisms driving biological processes and disease states. This technique has wide-ranging applications in genomics, personalized medicine, biomarker discovery, and drug development.