Uncovering diffusive states of the yeast membrane protein, Pma1, and how labeling method can change diffusive

Mary Lou P Bailey1,2, Susan E Pratt1,3, Michael Hinrichsen4

  • 1Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, CT, 06511, USA.

Insights

Labeling methods significantly impact yeast membrane protein Pma1

Area of Science:

  • Biophysics
  • Cell Biology
  • Microscopy

Background:

  • The budding yeast (Saccharomyces cerevisiae) membrane protein Pma1 is crucial for cellular function.
  • Understanding Pma1's diffusive behavior is key to elucidating its role in membrane dynamics.
  • Fluorescent labeling techniques are essential for tracking protein movement at the single-particle level.

Purpose of the Study:

  • To investigate how different fluorescent labeling strategies affect the measured diffusive behavior of Pma1.
  • To compare the efficacy of direct fusion labeling with mEos3.2 versus a novel tag-binding approach.
  • To validate the performance of the perturbation expectation maximization (pEMv2) algorithm in analyzing single-particle tracking data.

Main Methods:

  • Video-microscopy-based single-particle tracking (SPT) of fluorescently labeled Pma1.
  • Utilizing two labeling methods: direct fusion to mEos3.2 and a novel tag-binding scheme.
  • Applying the perturbation expectation maximization (pEMv2) algorithm to sort trajectories into diffusive states.

Main Results:

  • Different labeling methods yielded significantly different Pma1 diffusivity distributions.
  • pEMv2 identified two diffusive states (immobile and mobile) for both labeling methods.
  • The mEos3.2 direct fusion resulted in a smaller mobile fraction and lower diffusivity compared to the tag-binding method.
  • Experimental results showed good agreement with theoretical predictions for a Gaussian random process.

Conclusions:

  • The choice of fluorescent labeling method critically influences the observed diffusive behavior of Pma1.
  • The novel tag-binding method offers a less perturbative approach to studying Pma1 dynamics.
  • The pEMv2 algorithm is a robust tool for analyzing SPT data and resolving distinct diffusive states.