Rna Expression Analysis
Rna Expression Analysis - Related Videos
15:21
RNA Isolation from Embryonic Zebrafish and cDNA Synthesis for Gene Expression Analysis
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Cited by 111 •
2009
The isolation of high quality, intact RNA is an essential step in many laboratory protocols. Here, we demonstrate RNA extraction from whole zebrafish embryos and cDNA synthesis for subsequent application in various experimental procedures including gene expression microarray analysis.
14:53
Analysis of Global RNA Synthesis at the Single Cell Level following Hypoxia
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Cited by 2 •
2014
We describe a technique for analysis of global RNA synthesis in hypoxia using imaging. Click-chemistry labeling of RNA has not previously been performed under hypoxia and allows visualization of global RNA changes at the single cell level. This approach complements the existing averaged RNA techniques, allowing direct visualization of cell-to-cell changes in global RNA synthesis.
09:23
Optimized Hot Phenol–Based RNA Extraction from Mycobacteria: A Robust Approach for Reliable Gene Expression Analysis
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2026
This study optimizes a Hot Phenol–based RNA extraction method for Mycobacterium, yielding high-quality RNA suitable for transcriptomic analyses. When benchmarked against TRIzol and RNeasy under identical conditions, the method provided higher yield, comparable integrity, and improved cost-efficiency, offering a practical alternative for large-scale gene expression studies.
10:10
Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Cited by 314 •
2021
A detailed protocol of differential expression analysis methods for RNA sequencing was provided: limma, EdgeR, DESeq2.
12:04
RNA Analysis of Environmental Samples Using RT-PCR
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2023
Source: Laboratories of Dr. Ian Pepper and Dr. Charles Gerba - The University of Arizona Demonstrating Author: Bradley Schmitz Reverse transcription-polymerase chain reaction (RT-PCR) involves the same process as conventional PCR — cycling temperature to amplify nucleic acids. However, while conventional PCR only amplifies deoxyribonucleic acids (DNA), RT-PCR enables the amplification of ribonucleic acids (RNA) through the formation of complementary DNA (cDNA). This enables RNA-based organisms...
FAQs
The key signal is a difference in transcript abundance between samples, not simply the presence of a gene. Measuring which RNA molecules increase or decrease helps indicate which genes are more or less active under the conditions examined. Comparing these patterns can therefore connect gene regulation with differences in cell identity, development, environmental response, or disease.
Reverse transcription converts messenger RNA into complementary DNA before measurement. In this workflow, the resulting complementary DNA provides the material used for downstream quantification by reverse transcription quantitative PCR or for broader transcript profiling through RNA sequencing. The conversion bridges the RNA molecules collected from cells or tissues and the analytical methods used to compare expression.
Reverse transcription quantitative PCR focuses on selected transcripts, making it suited to targeted comparisons. RNA sequencing supports profiling of many transcripts, allowing a broader view of expression patterns. The choice therefore depends on whether the investigation centers on particular RNA molecules or seeks to examine transcript changes across a wider set of genes in the biological sample.
Changes in RNA levels can be examined in relation to cell identity, developmental stage, environmental response, or disease. These comparisons help researchers ask whether gene regulation differs between biological states and can point toward biomarkers or affected pathways. In functional studies, expression patterns also provide evidence about how cellular activity changes under a defined condition.
The workflow begins with isolating RNA from cells or tissues. Messenger RNA is then converted into complementary DNA, after which selected transcripts can be quantified by reverse transcription quantitative PCR or many transcripts can be profiled with RNA sequencing. Keeping these stages conceptually separate helps distinguish sample preparation, transcript conversion, and the final measurement strategy.
Expression comparisons can support biomarker identification by highlighting transcript changes associated with a biological state, while pathway analysis organizes those changes into broader biological processes. These uses extend beyond listing altered RNAs: they help connect measured transcript patterns with disease, development, environmental responses, or experimental conditions and guide functional studies.
Researchers compare RNA expression patterns between relevant samples and examine which transcripts change in abundance. The resulting differences can show how cellular activity responds to the condition and provide molecular evidence for altered gene regulation. This approach is useful when the goal is to relate an intervention or environment to a biological response.