Fluorescence Correlation
Fluorescence Correlation - Related Videos
10:59
Determination of Lipid Raft Partitioning of Fluorescently-tagged Probes in Living Cells by Fluorescence Correlation Spectroscopy (FCS)
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Cited by 9 •
2012
A technique to probe the lipid raft partitioning of fluorescent proteins at the plasma membrane of living cells is described. It takes advantage of the disparity in diffusion times of proteins located inside or outside of lipid rafts. Acquisition can be performed dynamically in control conditions or after drug addition.
02:20
Correlations
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2020
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
14:04
Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy
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2021
We here introduce a procedure to measure protein oligomers and aggregation in cell lysate and live cells using fluorescence correlation spectroscopy.
11:16
Correlative Light- and Electron Microscopy Using Quantum Dot Nanoparticles
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Cited by 12 •
2016
A method is described whereby quantum dot (QD) nanoparticles can be used for correlative immunocytochemical studies of epoxy embedded human pathology tissue. We employ commercial antibody fragment conjugated QDs that are visualized by widefield fluorescence light microscopy and transmission electron microscopy.
01:09
Correlation
0 Views •
2023
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation. Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if: Variable a...
FAQs
Autocorrelation compares fluorescence intensity changes with themselves across different time intervals. The resulting pattern shows how quickly signal fluctuations recur, linking temporal behavior to molecular motion within the observation volume. Analysis can therefore estimate diffusion, concentration, and molecular mobility rather than treating fluorescence intensity as a static measurement. These parameters help characterize molecular behavior over time.
The observation volume determines the spatial region from which the changing fluorescence signal is collected. As labeled molecules move through this small region, their motion produces time-dependent fluctuations that can be analyzed rather than averaged away. This links local measurements to estimates of diffusion and mobility, while also supporting concentration analysis in living cells or purified samples.
Binding, aggregation, and molecular interactions can change how fluorescently labeled species move and how their signals fluctuate. Fluorescence correlation analysis uses those temporal patterns as evidence of altered molecular behavior, alongside diffusion and mobility estimates. In biology, this makes it possible to examine interactions without relying only on bulk fluorescence intensity, particularly when studying protein behavior or intracellular organization.
An experiment begins with fluorescently labeled molecules in either a living-cell or purified-sample setting. The measurement records intensity fluctuations as those molecules move through a small observation volume. Researchers then apply autocorrelation analysis to the time-dependent signal and interpret the resulting measurements in terms of diffusion, concentration, and mobility. This workflow converts dynamic fluorescence into quantitative molecular information.
The approach can characterize molecular behavior without requiring large quantities of material. That advantage supports measurements in purified samples as well as living cells, where sample availability may be limited. Because the analysis focuses on fluctuations from a small observation volume, it can connect local molecular dynamics with biological questions about organization, transport, membrane processes, and protein behavior.
In biology, measurements can be applied to intracellular organization, membrane processes, protein interactions, and molecular transport. They can also assess binding or aggregation and examine how genetic or chemical perturbations affect molecular behavior. The resulting diffusion, concentration, and mobility estimates provide quantitative context for comparing molecular dynamics across these biological situations.