Protein Correlation
Protein Correlation - Related Videos
09:35
Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling
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Cited by 9 •
2017
Here we present protocols for affinity purification of protein complexes and their separation by blue native PAGE, followed by protein correlation profiling using label free quantitative mass spectrometry. This method is useful to resolve interactomes into distinct protein complexes.
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.
12:28
Correlative Super-resolution and Electron Microscopy to Resolve Protein Localization in Zebrafish Retina
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Cited by 11 •
2017
This protocol describes the necessary steps to obtain subcellular protein localization results on zebrafish retina by correlating super-resolution light microscopy and scanning electron microscopy images.
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.
FAQs
A positive correlation means two proteins tend to increase or decrease together across the measured samples, whereas a negative correlation means that higher abundance, expression, or activity of one tends to accompany lower values of the other. These patterns can indicate coordinated pathway activity or contrasting biological responses, but their interpretation depends on the samples and conditions being compared.
Protein correlation can highlight coordinated regulation, functional association, or activity within the same biological pathway. When several proteins vary together across cells, tissues, organisms, or experimental conditions, their shared pattern may point to a common cellular response. The result is best treated as evidence for a relationship and a way to generate hypotheses about molecular organization.
Correlation measures whether protein measurements vary together, not whether the proteins physically bind or directly control one another. A shared regulator, common pathway, or broader cellular response could produce the same pattern. Consequently, correlated proteins can serve as candidates for further investigation, while direct interaction requires evidence beyond the statistical relationship itself.
Researchers first quantify proteins across selected cells, tissues, organisms, or experimental conditions. Mass spectrometry and immunoassays are examples of methods that can provide measurements of protein abundance, expression, or activity. They then compare the measurements across samples to identify proteins that vary together or in opposite directions, producing relationships suitable for biological interpretation.
Correlated protein measurements can be organized into molecular networks in which relationships represent coordinated variation across samples. Such networks help researchers examine groups of proteins rather than isolated measurements and can reveal patterns consistent with shared regulation, functional association, or pathway activity. The resulting connections are hypothesis-generating and do not independently establish direct molecular interactions.
In biology, protein correlation analysis can help identify candidate biomarkers, interpret cellular responses, and investigate possible disease mechanisms. It can also support the generation of hypotheses about therapeutic targets by connecting protein patterns with experimental conditions or biological states. Its value lies in prioritizing relationships and pathways for additional study rather than providing definitive causal conclusions.