Rna-based Health Expression Models
Rna-based Health Expression Models - 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.
03:47
Short Hairpin RNA-Mediated Gene Knockdown in iHSPCs In Vitro: A Lentivirus-Based shRNA Expression System Delivery into iHSPCs for Knockdown of Specific Gene Expression
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2025
In this video, we demonstrate a method to perform transduction of shRNA lentiviral vectors to obtain stable knockdown cell lines in immortalized hematopoietic stem and progenitor cells (iHSPCs).
10:52
Visualization and Quantification of Intermolecular RNA Base Pairing in in vitro RNA Clusters Using Split Broccoli RNA Reporters
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2026
This protocol describes a visual assay using split Broccoli RNA reporters to detect and quantify intermolecular base pairing in RNA clusters in vitro.
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.
11:12
Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
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Cited by 8 •
2018
Nucleic acid degradation in archival tissue, tumor heterogeneity, and a lack of fresh frozen tissue specimens can negatively impact cancer diagnostic services in pathology laboratories worldwide. This manuscript describes the optimization of a panel of biomarkers using a multiplex magnetic bead assay to classify breast cancers.
FAQs
Differential expression analysis identifies RNA molecules whose measured levels differ between biological samples or conditions. In these models, those changes are interpreted as signatures of altered health, disease, or infection states rather than as isolated measurements. Comparing expression patterns helps researchers characterize host responses to pathogens and distinguish inflammatory states.
Transcriptional signatures summarize coordinated expression patterns associated with a biological state. Pathway activity analysis then connects those patterns to broader cellular or biological processes, making the results easier to interpret than a list of changed transcripts alone. In infection studies, this combination can clarify how immune responses shift during microbial challenge.
Biomarkers add an outcome-oriented layer to expression analysis. A measured RNA pattern can be examined for links with disease progression or treatment response, while transcriptional signatures describe broader coordinated expression states. Together, these interpretations can connect molecular measurements with biologically relevant changes in health and infection research.
An analysis typically begins with RNA measurements from biological samples, generated by RNA sequencing or quantitative PCR. Researchers then compare expression patterns across samples or conditions, identify differential expression, and interpret transcriptional signatures or pathway activity. This sequence converts expression measurements into evidence about health, disease, infection, or immune response.
For infection profiling, the model can organize RNA patterns around host responses to pathogens and distinguish different inflammatory states. This supports comparison of immune-related expression across biological samples and can reveal changes associated with microbial challenge. The resulting profiles help researchers interpret infection biology through host-response measurements.
Vaccine evaluation can use these models to examine expression changes associated with immune responses after an intervention. Therapeutic evaluation similarly considers whether RNA patterns relate to treatment response. Because the models connect gene-expression measurements with response states, they can provide molecular evidence for comparing interventions and identifying expression patterns linked to their effects.