Rna Cish Scoring
Rna Cish Scoring - Related Videos
02:42
RNA CISH Signal Detection: A Technique to Detect Chromogenic Signals During RNA In Situ Hybridization in Intact Tissue Specimens
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2023
This video presents the protocol for signal detection during RNA chromogenic in situ hybridization. This technique can be used to diagnose an active oncogenic infection visually in an intact tissue specimen.
03:53
FFPE Tissue Pretreatment for RNA CISH: A Procedure to Process Formalin-Fixed Paraffin-Embedded Tissue Sections for RNA Chromogenic In Situ Hybridization
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2023
This video demonstrates the steps involved in the pretreatment of FFPE tissue specimens for RNA chromogenic in situ hybridization. The pretreatment process helps maintain nucleic acid integrity within the cells and overall tissue morphology.
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.
01:06
Introduction to z Scores
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2023
A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one. z scores help...
01:17
z Scores and Area Under the Curve
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2023
z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of zero.
FAQs
Probe sequences are designed to be complementary to the RNA target, allowing hybridization to identify the intended molecule within fixed cells or tissue sections. The resulting enzyme-linked chromogenic reaction creates a visible signal that can be evaluated microscopically. This molecular recognition step connects the presence of a specific transcript with the spatially localized signal used for scoring.
Scoring can consider whether a signal is present, how intense it appears, where it is distributed, and what proportion of cells is positive. These features provide complementary information rather than a single measurement. For example, two samples may contain similar numbers of positive cells but differ in signal intensity or cellular distribution, revealing different expression patterns.
Spatial heterogeneity shows that RNA expression may vary among cells or regions within the same tissue section. Because the signal remains associated with its cellular and tissue location, scoring can distinguish localized expression from a more uniform pattern. This context is valuable in genetics and molecular pathology when cellular composition may influence the apparent expression profile.
A typical workflow uses fixed cells or tissue sections, applies probes complementary to the RNA target, and allows them to hybridize within the sample. Enzyme-linked detection then generates a colored product, which is examined by light microscopy. The observer records assay-defined features such as signal presence, intensity, distribution, or the proportion of positive cells.
The assay design determines which scoring dimensions are emphasized and how the observed signal is summarized. A protocol may prioritize signal presence, intensity, distribution, or the fraction of positive cells. Consequently, results should be interpreted according to the selected scoring framework rather than treated as a single universal measurement of RNA abundance.
The method supports spatial gene-expression analysis, biomarker evaluation, and validation of molecular findings in tissue samples. It can also help assess cellular heterogeneity by showing how expression differs across a section. These applications connect molecular observations with tissue location, providing context that is relevant when interpreting genetic or pathology-related findings.