Velocity landscape correlation resolves multiple flowing protein populations from fluorescence image time series

Area of Science:

• Cell Biology
• Biophysics
• Microscopy

Background:

• Molecular, vesicular, and organellar flows are crucial for cellular functions like motility and division.
• Advanced microscopy allows for higher resolution of these movements.
• Existing spatio-temporal image correlation spectroscopy assumes a single dominant flow component, limiting analysis when multiple flows coexist.

Purpose of the Study:

• To develop and validate a novel method for detecting and quantifying multiple molecular flow components within cellular imaging data.
• To overcome the limitations of existing methods that assume a single dominant flow.

Main Methods:

• Introduced velocity landscape correlation (VLC), an extension of correlation analysis for fluorescence intensity fluctuations.
• Theoretically demonstrated VLC's principles.
• Tested VLC performance using computer-simulated image series with diverse flow dynamics.
• Applied VLC to analyze STIM1 protein dynamics in Cystic Fibrosis Bronchial Epithelial (CFBE) cells.

Main Results:

• VLC successfully detects and extracts multiple flow components from image series.
• Simulations confirmed VLC's robustness across various flow dynamics.
• The method was applied to study variable STIM1 protein fluxing on microtubules in CFBE cells, revealing complex transport patterns.

Conclusions:

• Velocity landscape correlation (VLC) is an effective method for analyzing complex cellular transport dynamics with multiple flow components.
• VLC advances the study of intracellular transport by enabling more accurate quantification of molecular flows.
• This technique has significant potential for investigating cellular processes and diseases involving aberrant transport, such as in Cystic Fibrosis.