Gene Profiling
Gene Profiling - Related Videos
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.
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...
10:50
Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
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Cited by 7 •
2017
We describe a method to sort single mammalian cells and to quantify the expression of up to 96 target genes of interest in each cell. This method includes the use of internal qPCR standards to enable the estimation of absolute transcript counts.
10:06
Cell Type-specific Gene Expression Profiling in the Mouse Liver
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Cited by 1 •
2019
Translating ribosome affinity purification (TRAP) enables rapid and efficient isolation of cell type-specific translating mRNA. Here, we demonstrate a method that combines hydrodynamic tail-vein injection in a mouse model of liver repopulation and TRAP to examine the expression profile of repopulating hepatocytes.
FAQs
Expression profiling focuses on RNA transcripts because they provide evidence of gene activity in sampled cells or tissue. Researchers isolate RNA, convert it to complementary DNA, and measure the resulting transcript patterns. Comparing those patterns shows which genetic programs are active or altered, rather than simply listing genes associated with the sample.
Microarrays and high-throughput sequencing serve as measurement approaches within expression profiling. After complementary DNA preparation, either approach can generate data describing transcript patterns for a sample. Those data become informative when profiles are compared across cells, tissues, conditions, or time points, allowing researchers to identify changes associated with a biological context.
Comparisons are essential because a single profile describes one biological context, whereas differences between profiles indicate what changes between contexts. Examining cells, tissues, environmental conditions, treatments, or time points can connect altered transcript patterns to development, disease, or responses to changing conditions, revealing biological changes that one sample alone cannot show.
Statistical analysis helps evaluate expression patterns within the collected measurements, while genomic analysis places those patterns in a broader genetic context. Combining these approaches can clarify biological pathways rather than treating each altered gene as an isolated observation. The result is a stronger basis for interpreting findings and refining subsequent research questions.
A typical workflow begins by isolating RNA from the selected biological sample. Researchers then convert the RNA to complementary DNA and measure transcripts using microarrays or high-throughput sequencing. The resulting profiles are compared across the chosen cells, tissues, conditions, or time points, after which genomic and statistical analyses help interpret the observed patterns.
Gene profiling can help characterize cell types, examine developmental changes, investigate disease-related differences, and assess responses to environmental changes or treatments. It can also support the identification of molecular biomarkers. These applications allow researchers to connect transcript patterns with biological states and focus further experiments on relevant processes or conditions.
Distinctive molecular patterns in a profile can highlight candidate biomarkers associated with a biological condition or response. When researchers integrate those patterns with genomic and statistical analyses, they can also identify relationships among genes and clarify biological pathways. Together, these outcomes support more precise questions about disease, development, environmental change, or treatment response.