Data from: Evaluating single-cell variability in proteasomal decay
Data files
Aug 25, 2023 version files 45.49 GB
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06-28-22-modc-pup1.tar.gz
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10-7-22-cln2.tar.gz
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10-7-22-yeGFP.tar.gz
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4-1-22-PI-modc-pup1.tar.gz
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7-20-22-cln2.tar.gz
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7-20-22-yeGFP.tar.gz
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for_imaris_4-1-22-PI-modc-pup1.tar.gz
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for_imaris-10-7-22-pup1.tar.gz
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for_imaris-6-28-22-pup1.tar.gz
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for_imaris-7-20-22-pup1.tar.gz
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README.md
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single_cell_GFP_analysis.tar.gz
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single_cell_GFP_analyssis-PI.tar.gz
Abstract
Gene expression is a stochastic process that leads to variability in mRNA and protein abundances even within an isogenic population of cells grown in the same environment.
This variation, often called gene-expression noise, has typically been attributed to transcriptional and translational processes while ignoring the contributions of protein decay variability across cells.
Here we estimate the single-cell protein decay rates of two degron GFPs in \textit{Saccharomyces cerevisiae} using time-lapse microscopy.
We find substantial cell-to-cell variability in the decay rates of the degron GFPs.
We evaluate cellular features that explain the variability in the proteasomal decay and find that the amount of 20s catalytic beta subunit of the proteasome marginally explains the observed variability in the degron GFP half-lives.
We propose alternate hypotheses that might explain the observed variability in the decay of the two degron GFPs.
Overall, our study highlights the importance of studying the kinetics of the decay process at single-cell resolution and that decay rates vary at the single-cell level, and that the decay process is stochastic.
A complex model of decay dynamics must be included when modeling stochastic gene expression to estimate gene expression noise.
Methods
The data submitted here are raw fluorescent microscopy images from Nikon TiE.
Usage notes
Fiji to open the raw images. Imaris suite version 9.8 (or any version) to open the ".ims" files. The CSV files are to be used with RMDs on the GitHub repo mentioned in the README file.