Neuronal Rna Profiling
Neuronal Rna Profiling - Related Videos
09:12
Calcified Artery Preparation and Processing with Preserved Morphology and RNA for Digital Spatial Profiling
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2026
This protocol includes a step-by-step workflow for calcified vascular specimens: tissue handling, decalcification, RNA validation, calcification level detection, and region-of-interest selection strategies on the Nanostring GeoMx Digital Spatial Profiler (DSP). The goal is to present a comprehensive method for preserving vascular tissue morphology and RNA for reliable spatial transcriptomic analysis.
10:20
Single-cell Profiling of Developing and Mature Retinal Neurons
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Cited by 13 •
2012
A method for the isolation of single retinal cells and subsequent amplification of their cDNAs is described. Single-cell transcriptomics reveals the degree of cellular heterogeneity present in a tissue and uncovers new marker genes for rare cell populations. The accompanying protocol can be adjusted to suit many different cell types.
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.
09:54
RNA-Seq
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2023
Among different methods to evaluate gene expression, the high-throughput sequencing of RNA, or RNA-seq. is particularly attractive, as it can be performed and analyzed without relying on prior available genomic information. During RNA-seq, RNA isolated from samples of interest is used to generate a DNA library, which is then amplified and sequenced. Ultimately, RNA-seq can determine which genes are expressed, the levels of their expression, and the presence of any previously unknown transcripts.
FAQs
Changes in transcript abundance provide a comparative readout of which gene-expression programs are more or less prominent in neurons. Examining these patterns across experimental conditions can associate particular RNA signatures with altered cellular states or responses to stimuli. In practice, the comparison is more informative than an isolated measurement because it highlights condition-linked pathway changes.
Analyzing individual neurons can separate transcript patterns belonging to distinct cells, whereas a population measurement combines RNA from many neurons into an aggregate signal. This distinction helps identify cell-type-specific programs and molecular differences within a neuronal sample. The choice between individual-cell and population-level profiling therefore depends on whether cellular heterogeneity or an overall response is the primary question.
Converting messenger RNA into complementary DNA creates the form used for subsequent transcript measurement in the described workflow. The resulting cDNA can be examined through sequencing or targeted quantification, linking the original RNA molecules to measurable transcript data. This step is especially important when the goal is to compare expression patterns across neuronal samples or experimental conditions.
A typical workflow begins with RNA isolated from neurons, followed by conversion of messenger RNA into complementary DNA and measurement through sequencing or targeted quantification. The resulting transcript data are then compared across neuronal populations, individual cells, or experimental conditions. These comparisons can reveal cell-type-specific expression programs and changes associated with stimulation, disease, development, or drug exposure.
It is useful when researchers need molecular signatures that distinguish developmental states, activity-related responses, or disease-associated changes in neurons. Profiling can show which transcript patterns accompany these contexts and can support comparisons between conditions. The resulting expression information helps connect neuronal phenomena with altered gene-expression programs rather than relying only on observable cellular or physiological changes.
After drug exposure, comparing neuronal RNA patterns with an untreated or alternative condition can identify transcripts and pathways associated with the response. Such profiles may support biomarker discovery by highlighting molecular signatures linked to the exposure. They can also guide studies of therapeutic targets by showing which gene-expression programs change during the neuronal response to a compound.