Multi-wavelength Cell Scoring
Multi-wavelength Cell Scoring - Related Videos
02:32
The de Broglie Wavelength
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2020
In the macroscopic world, objects that are large enough to be seen by the naked eye follow the rules of classical physics. A billiard ball moving on a table will behave like a particle; it will continue traveling in a straight line unless it collides with another ball, or it is acted on by some other force, such as friction. The ball has a well-defined position and velocity or well-defined momentum, p = mv, which is defined by mass m and velocity v at any given moment. This is the typical...
01:33
Synergistic Photobiomodulation with a Dual-Wavelength Laser in an In Vitro Disease Model
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2025
Divide the transwell system into five groups that undergo various treatments.The first group serves as the control and consists of a co-culture of neuroblastoma and microglia cells.In the second group, treat the co-culturer with fibrillar β-amyloid (fAβ) alone. fAβ induces damage in neuroblastoma cells.In the third group, treat neuroblastoma cells with an 808-nanometer laser, then co-culture them with untreated microglia.The laser reduces oxidative stress, improving neuronal survival.In the...
07:35
The Multi-group Experiment
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2023
Source: Laboratories of Gary Lewandowski, Dave Strohmetz, and Natalie Ciarocco—Monmouth University A multi-group design is an experimental design that has 3 or more conditions/groups of the same independent variable. This video demonstrates a multi-group experiment that examines how different interethnic ideologies (multiculturalism and color-blind) influence feelings about diversity and actions toward and out-group member. In providing an overview of how a researcher conducts a multi-group...
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
Different wavelengths can provide separate optical signatures from the same cell, allowing analysis to combine several sources of evidence rather than relying on one signal. Integrating intensity, morphology, and marker co-expression helps relate a cell’s appearance to its biological state. This broader feature set is particularly useful when a population contains multiple cellular phenotypes.
Segmentation assigns measured signals to individual cells instead of treating the entire image as one blended population. Once cells are separated computationally, their wavelength-specific intensities, morphology, and marker co-expression can be integrated into cell-level scores. This supports identification and classification of heterogeneous populations, where averaged image-level measurements could obscure differences between cells.
Marker co-expression indicates that multiple optical signals occur within the same segmented cell. Including this relationship in scoring adds context beyond the strength of any single marker and can help distinguish cells with different phenotypes. In biological studies, that distinction supports more detailed characterization of cell identity, cellular state, and variation within a population.
The workflow begins by selecting wavelengths that excite or detect the relevant fluorescent, absorbance, or other optical signals. Images are then acquired across those signals, and image-processing algorithms segment individual cells. For each cell, the analysis integrates features such as intensity, morphology, and marker co-expression, producing scores that can support identification, phenotyping, viability assessment, or classification.
Researchers can apply the method when they need to examine disease mechanisms, drug responses, or cellular interactions in populations containing varied cell states. Its ability to combine several optical signatures with cell-level features makes it suitable for identifying and phenotyping cells, assessing viability, and classifying heterogeneous populations. The resulting measurements can strengthen comparisons across biological conditions.
The analysis converts optical measurements into quantitative cell-level information, including integrated scores based on signal intensity, morphology, and marker co-expression. These results can support cell identification, phenotyping, viability assessment, and classification rather than leaving interpretation at the image-signal level. Because multiple signatures contribute to the assessment, the approach can also improve reproducibility in biological experiments.