Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Statistical Software for Data Analysis and Clinical Trials

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

Bioequivalence Data: Statistical Interpretation

Bioequivalence Data: Statistical Interpretation

Body:The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...

Correlation of Experimental Data

Correlation of Experimental Data

Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

Correlations

Correlations

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...

Statistical Analysis: Overview

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

K-Ras controls asymmetric cell divisions from the primary cilium.

Cell death & disease·2026

Same author

Direct visualization of native GSDMD pores reveals lipid-driven stabilization during pyroptosis.

Science advances·2026

Same author

Ethanol-driven structural transitions of human serum albumin and surface-assisted 2D network formation on Ti-6Al-4V: A kinetic-morphological map.

Colloids and surfaces. B, Biointerfaces·2026

Same author

Smart event-triggered MINFLUX microscopy to catch and follow rare events.

Nature communications·2026

Same author

Protocol for microfluidic-based high-precision general polarization fluorescence microscopy of lipid packing in membrane vesicles.

STAR protocols·2026

Same author

Metabolic profiling of steatotic liver disease by fluorescence lifetime imaging microscopy.

Communications medicine·2026

Search research articles

Related Experiment Video

Updated: Feb 7, 2026

Spot Variation Fluorescence Correlation Spectroscopy for Analysis of Molecular Diffusion at the Plasma Membrane of Living Cells

Spot Variation Fluorescence Correlation Spectroscopy for Analysis of Molecular Diffusion at the Plasma Membrane of Living Cells

Published on: November 12, 2020

Statistical Analysis of Scanning Fluorescence Correlation Spectroscopy Data Differentiates Free from Hindered

Falk Schneider, Dominic Waithe, B Christoffer Lagerholm

¹Institute of Applied Optics, Friedrich-Schiller-University and Leibniz Institute of Photonic Technology, Helmholtzweg 4 , 07743 Jena , Germany.

|July 21, 2018

Summary

This study introduces a new analysis method for scanning fluorescence correlation spectroscopy data. It reveals nanoscale diffusion heterogeneity in living cell plasma membranes, distinguishing between free and hindered movement.

Keywords:

Brownian actin cytoskeleton diffusion modes free and trapped diffusion hindered diffusion dynamics scanning FCS

More Related Videos

Molecular Diffusion in Plasma Membranes of Primary Lymphocytes Measured by Fluorescence Correlation Spectroscopy

Molecular Diffusion in Plasma Membranes of Primary Lymphocytes Measured by Fluorescence Correlation Spectroscopy

Published on: February 1, 2017

Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy

Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy

Published on: April 25, 2021

Related Experiment Videos

Last Updated: Feb 7, 2026

Spot Variation Fluorescence Correlation Spectroscopy for Analysis of Molecular Diffusion at the Plasma Membrane of Living Cells

Spot Variation Fluorescence Correlation Spectroscopy for Analysis of Molecular Diffusion at the Plasma Membrane of Living Cells

Published on: November 12, 2020

Molecular Diffusion in Plasma Membranes of Primary Lymphocytes Measured by Fluorescence Correlation Spectroscopy

Molecular Diffusion in Plasma Membranes of Primary Lymphocytes Measured by Fluorescence Correlation Spectroscopy

Published on: February 1, 2017

Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy

Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy

Published on: April 25, 2021

Area of Science:

Cell Biology
Biophysics
Statistical Analysis

Background:

Cellular plasma membranes exhibit complex diffusion dynamics crucial for cell regulation.
Quantifying this membrane diffusion is challenging due to low throughput and sensitivity limitations.

Purpose of the Study:

To develop a robust statistical analysis pipeline for large scanning fluorescence correlation spectroscopy (sFCS) datasets.
To uncover nanoscale heterogeneity in living cell plasma membranes.

Main Methods:

Utilized a new statistical analysis pipeline for processing large sFCS data.
Applied the method to fluorescent lipid and protein analogues in living cells.

Main Results:

Successfully uncovered nanoscale heterogeneity within the plasma membrane.
Differentiated between free and hindered diffusion modes of membrane components.
Demonstrated broad applicability for analyzing sFCS data.

Conclusions:

The developed pipeline offers a broadly applicable solution for quantifying plasma membrane diffusion heterogeneity.
Enables deeper understanding of cell regulation and function through detailed diffusion analysis.