Single-molecule tracking dataset of histone H3 (Hht1) in Saccharomyces cerevisiae.

Single-Molecule Tracking (SMT) is a powerful method to quantify protein dynamics in live cells. Recently, we have established a data analysis pipeline for estimating various biophysical parameters (mean squared displacement, diffusion coefficient, bound fraction, residence time, jump distances, jump angles, and track statistics) from the single-molecule time-lapse movies acquired from yeast Saccharomyces cerevisiae. We acquired the time-lapse movies using different time intervals (i.e. 15 ms, 200 ms, and 1000 ms) to extract the diffusion parameters (from 15 ms time interval movies) and residence time (from 200 ms and 1000 ms time interval movies). We tracked the single molecules from these movies using three MATLAB-based software packages (MatlabTrack, TrackIT, DiaTrack (Sojourner, and Spot-On)) to quantify various biophysical parameters. In this article, we have quantified the biophysical parameters of chromatin-bound histone H3 (Hht1), labeled using JF646 HaloTag Ligand (HTL), and shared a few raw time-lapse SMT movies for the same. Histone H3 is a chromatin-bound protein and it serves as a benchmark for the stably bound molecules for the SMT experiments. Hence, this dataset can be used by various researchers to quantify the biophysical parameters of chromatin-bound molecules (Histone H3). Any newly developed tracking software can use this dataset to validate the accuracy of its tracking algorithms.

Single-molecule tracking for studying protein dynamics and target-search mechanism in live cells of S. cerevisiae.

Single-molecule tracking (SMT) is a powerful approach to quantify the biophysical parameters of protein dynamics in live cells. Here, we describe a protocol for SMT in live cells of the budding yeast Saccharomyces cerevisiae. We detail how to genetically engineer yeast strains for SMT, how to set up image acquisition parameters, and how different software programs can be used to quantify a variety of biophysical parameters such as diffusion coefficient, residence time, bound fraction, jump angles, and target-search parameters. For complete details on the use and execution of this protocol, please refer to Mehta et al. 1 and Ball et al..2.

In-vivo Single-Molecule Imaging in Yeast: Applications and Challenges.

Single-molecule imaging has gained momentum to quantify the dynamics of biomolecules in live cells, as it provides direct real-time measurements of various cellular activities under their physiological environment. Yeast, a simple and widely used eukaryote, serves as a good model system to quantify single-molecule dynamics of various cellular processes because of its low genomic and cellular complexities, as well as its facile ability to be genetically manipulated. In the past decade, significant developments have been made regarding the intracellular labeling of biomolecules (proteins, mRNA, fatty acids), the microscopy setups to visualize single-molecules and capture their fast dynamics, and the data analysis pipelines to interpret such dynamics. In this review, we summarize the current state of knowledge for the single-molecule imaging in live yeast cells to provide a ready reference for beginners. We provide a comprehensive table to demonstrate how various labs tailored the imaging regimes and data analysis pipelines to estimate various biophysical parameters for a variety of biological processes. Lastly, we present current challenges and future directions for developing better tools and resources for single-molecule imaging in live yeast cells.