Rna Expression Profiling
Rna 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...
11:00
Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture
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Cited by 105 •
2013
Total cellular RNA provides a poor template for studying short-term changes in RNA synthesis and decay as well as the kinetics of RNA processing. Here, we describe metabolic labeling of newly transcribed RNA with 4-thiouridine followed by thiol-specific biotinylation and purification of newly transcribed RNA allowing to overcome these limitations.
10:27
Determining Genetic Expression Profiles in C. elegans Using Microarray and Real-time PCR
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Cited by 12 •
2011
Microarray analysis was conducted to determine genetic expression profiles in C. elegans, and real-time PCR was used to validate and quantify microarray data.
10:06
Non-Laser Capture Microscopy Approach for the Microdissection of Discrete Mouse Brain Regions for Total RNA Isolation and Downstream Next-Generation Sequencing and Gene Expression Profiling
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Cited by 11 •
2011
RNA expression profiling of discrete mouse brain regions requires a precise and repeatable tissue collection strategy. A protocol that uses both coronal brain sectioning and tissue corer-assisted microdissection is described here. The yield and quality of total RNA obtained from the resulting samples confirms the utility of the outlined method.
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.
FAQs
Microarrays quantify signals from transcripts represented on a predefined set of probes, whereas RNA sequencing reads complementary DNA-derived sequences and can reveal transcripts outside that set. This makes sequencing useful when researchers want to detect previously unrecognized transcripts or splice variants, while microarrays support expression comparisons using an established transcript collection.
Complementary DNA provides a molecular form of the original RNA that can be measured by the selected profiling platform. After RNA isolation, conversion to complementary DNA precedes quantification with approaches such as microarrays or RNA sequencing. The resulting measurements reflect transcript abundance, allowing expression levels to be compared among biological samples.
RNA sequencing can identify previously unrecognized transcripts and splice variants in addition to measuring expression patterns. This broader sequence-based view helps researchers examine which RNA forms are present, not only whether a familiar transcript changes in abundance. Consequently, sequencing can expand the set of transcripts considered in studies of gene regulation and cellular state.
Differences in profiles indicate that transcript levels vary between the samples, but their biological meaning depends on the comparison being made. Profiles can distinguish cell types, developmental states, disease-associated changes, or responses to environmental conditions and treatments. Researchers can then use these patterns to investigate gene regulation and identify altered biological pathways.
A basic workflow begins by isolating RNA from the biological samples being compared. Depending on the chosen approach, the RNA is converted to complementary DNA, followed by transcript quantification with a microarray or RNA sequencing method. Researchers then compare the resulting expression profiles across samples to identify differences relevant to the biological question.
Researchers may choose this approach when they need to compare gene activity across distinct biological conditions. Suitable comparisons include different cell types, developmental states, disease-associated samples, or treated and untreated material. The resulting profiles can reveal coordinated expression changes and provide evidence for how cells respond to environmental conditions or experimental treatments.
Comparative profiles support several downstream biological investigations, including biomarker discovery, pathway analysis, and studies of gene regulation. They can also help characterize cellular identity and developmental state or describe changes associated with disease and treatment response. These applications use expression patterns as evidence for distinguishing biological conditions and identifying transcripts or processes associated with them.