Transcriptional Profiling
Transcriptional Profiling - Related Videos
08:23
Single Cell Transcriptional Profiling of Adult Mouse Cardiomyocytes
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Cited by 13 •
2011
Single cell expression profiling allows the detailed gene expression analysis of individual cells. We describe methods for the isolation of cardiomyocytes, and preparing the resulting lysates for either whole transcriptome microarray or qPCR of specific targets.
09:31
Laser-assisted Microdissection (LAM) as a Tool for Transcriptional Profiling of Individual Cell Types
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Cited by 19 •
2016
Here we present a protocol for laser-assisted microdissection of specific plant cell types for transcriptional profiling. While the protocol is suitable for different species and cell types, the focus is on highly inaccessible cells of the female germline important for sexual and apomictic reproduction in the crucifer genus Boechera.
10:50
Transcript and Metabolite Profiling for the Evaluation of Tobacco Tree and Poplar as Feedstock for the Bio-based Industry
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Cited by 5 •
2014
Plant biomass offers a renewable resource for multiple products, including fuel, feed, food, and a variety of materials. In this paper we investigate the properties of tobacco tree (Nicotiana glauca) and poplar as suitable sources for a biorefinery pipeline.
02:16
Transcription Factors
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2019
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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...
FAQs
Comparison focuses on differences in transcript abundance between defined biological conditions. Genes whose measured RNA levels change can be classified as differentially expressed, creating a bridge between altered gene activity and cellular functions. This approach helps researchers move from a broad expression pattern toward candidate regulatory processes, pathways, or mechanisms associated with a particular state or treatment.
RNA is converted to complementary DNA before transcript abundance is quantified in the workflow described. This conversion creates the material used by downstream approaches such as RNA sequencing or microarray hybridization. Including this step connects RNA isolation with measurement, allowing transcript-level information to be processed through the selected quantification method and compared across biological samples.
The biological source and the conditions under which it is examined determine the scope of the resulting profile. Measurements from a cell, tissue, or organism can capture different levels of gene activity, while environmental, developmental, disease-related, or treatment conditions provide the comparison context. Careful interpretation therefore links transcript changes to the specific system and condition measured.
RNA sequencing and microarray hybridization are two methods identified for quantifying transcript abundance after RNA processing and complementary DNA conversion. They support the same broader goal of characterizing gene activity, but each represents a distinct measurement approach. Selecting or comparing these approaches should be considered alongside the biological question and the type of transcript profile required.
A typical workflow begins by isolating RNA from the selected cell, tissue, or organism. The RNA is then converted to complementary DNA, followed by transcript quantification through RNA sequencing or microarray hybridization. Researchers compare the resulting profiles under defined conditions to identify differentially expressed genes and relate those changes to cellular functions or biological mechanisms.
It is useful when researchers need to characterize how gene activity changes after an environmental signal or during a developmental state. Comparing profiles across those conditions can reveal coordinated transcript changes and help connect them with cellular functions. The resulting patterns provide evidence for how regulatory activity may accompany transitions in biological state.
Disease-associated expression patterns can highlight pathways whose activity differs from another biological condition, while treatment comparisons can show transcript changes associated with an experimental intervention. These results help evaluate how disease states or treatments relate to cellular functions. The profiles can therefore guide investigation of biological mechanisms and provide molecular context for observed phenotypic changes.