Gene Expression Profiling
Gene Expression Profiling - Related Videos
08:29
Expression Profiling with Microarrays
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2023
Microarrays are important tools for profiling gene expression, and are based on complementary binding between probes that are attached to glass chips and nucleic acids derived from samples. Using these arrays, scientists can simultaneously evaluate the expression of thousands of genes. In addition, the expression profiles of different cells or tissue types can be compared, allowing researchers to deduce how the expression of different genes change during biological processes, and thus gain...
09:13
Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform
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Cited by 1 •
2016
We developed a protocol that is fast, sensitive and reproducible for pathogen gene expression profiling during an infection.
15:59
Laser Microdissection Applied to Gene Expression Profiling of Subset of Cells from the Drosophila Wing Disc
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Cited by 11 •
2010
Laser microdissection was applied to analyse gene expression profiling in specific compartments of Drosophila wing disc subjected to localised RNAi in vivo. RNA extracted from equivalent areas of silenced and unsilenced compartments was analysed by quantitative RT-PCR to determine comparative gene expression profiling within the context of native tissue microecology.
10:34
Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
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Cited by 2 •
2010
This procedure describes a quick and easy workflow to introduce siRNA into difficult to transfect cell lines and follow gene expression by real-time PCR. Use of an automated cell counter, multi-well electroporation plate, and automated electrophoresis station provide quick and reliable results without the need for expensive robotic handling.
11:03
An Overview of Gene Expression
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2023
Gene expression is the complex process where a cell uses its genetic information to make functional products. This process is regulated at multiple stages, and any misregulation could lead to diseases such as cancer. This video highlights important historical discoveries relating to gene expression, including the understanding of how distinct combinations of DNA bases encode the amino acids that make up proteins. Key questions in the field of gene expression research are explored, followed by a...
FAQs
Both approaches quantify transcript abundance after messenger RNA is converted to complementary DNA, but they use different measurement strategies. Microarrays assess signals from transcripts represented on a defined array, whereas RNA sequencing measures sequence-derived transcript information. This distinction affects how researchers compare samples and interpret expression patterns across biological conditions.
Experimental design helps separate biological differences between samples from variation introduced during measurement. Normalization adjusts expression data so that technical differences do not dominate comparisons. Without these safeguards, apparent changes may reflect processing or measurement noise rather than altered cellular activity, weakening conclusions about development, disease, or experimental responses.
Groups of genes that change together across samples can indicate coordinated biological responses and suggest relationships among cellular processes. These patterns may reveal candidate regulators or pathways involved in a condition, but they primarily generate hypotheses about gene regulation and function. Additional biological investigation is needed to determine whether an observed association reflects a causal mechanism.
Comparing profiles from different cells, tissues, developmental stages, disease-associated states, or experimental conditions can identify transcripts that vary with biological context. The resulting patterns provide molecular evidence for differences in cellular state and may help characterize cell types. Interpretation depends on well-designed comparisons and careful attention to variation within and between samples.
A typical workflow begins by collecting an appropriate cell or tissue sample and isolating messenger RNA. The RNA is converted to complementary DNA, after which transcript abundance is measured with a microarray or RNA sequencing. Researchers then compare profiles across samples or conditions, applying normalization before interpreting expression differences and biological patterns.
The source material supports using both microarrays and RNA sequencing to quantify transcript abundance, but it does not specify selection criteria, performance differences, or instrument requirements. In either case, the method provides data for comparing expression patterns across samples. Researchers should therefore base platform choice on the study design and the biological question being investigated.
In biology, these profiles can help identify cell types, examine developmental changes, and characterize disease-associated states. They can also show how cells or tissues respond to environmental or experimental stimuli. Because the measurements capture patterns across many genes, the results are useful for describing molecular states and generating testable hypotheses about cellular function.