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Updated: Jul 27, 2025

Labelling and Visualization of Mitochondrial Genome Expression Products in Baker's Yeast Saccharomyces cerevisiae
Published on: April 11, 2021
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.
Abstract:
We present and analyze video-microscopy-based single-particle-tracking measurements of the budding yeast (Saccharomyces cerevisiae) membrane protein, Pma1, fluorescently labeled either by direct fusion to the switchable fluorescent protein, mEos3.2, or by a novel, light-touch, labeling scheme, in which a 5 amino acid tag is directly fused to the C-terminus of Pma1, which then binds mEos3.2. The track diffusivity distributions of these two populations of single-particle tracks differ significantly, demonstrating that labeling method can be an important determinant of diffusive behavior. We also applied perturbation expectation maximization (pEMv2) (Koo and Mochrie in Phys Rev E 94(5):052412, 2016), which sorts trajectories into the statistically optimum number of diffusive states. For both TRAP-labeled Pma1 and Pma1-mEos3.2, pEMv2 sorts the tracks into two diffusive states: an essentially immobile state and a more mobile state. However, the mobile fraction of Pma1-mEos3.2 tracks is much smaller ([Formula: see text]) than the mobile fraction of TRAP-labeled Pma1 tracks ([Formula: see text]). In addition, the diffusivity of Pma1-mEos3.2's mobile state is several times smaller than the diffusivity of TRAP-labeled Pma1's mobile state. Thus, the two different labeling methods give rise to very different overall diffusive behaviors. To critically assess pEMv2's performance, we compare the diffusivity and covariance distributions of the experimental pEMv2-sorted populations to corresponding theoretical distributions, assuming that Pma1 displacements realize a Gaussian random process. The experiment-theory comparisons for both the TRAP-labeled Pma1 and Pma1-mEos3.2 reveal good agreement, bolstering the pEMv2 approach.
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.
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