Abstract
Transcript levels powerfully influence cell behavior and phenotype and are carefully regulated at several steps. Recently developed single cell approaches such as RNA single molecule fluorescence in-situ hybridization (smFISH) have produced advances in our understanding of how these steps work within the cell. In comparison to single-cell sequencing, smFISH provides more accurate quantification of RNA levels. Additionally, transcript subcellular localization is directly visualized, enabling the analysis of transcription (initiation and elongation), RNA export and degradation. As part of our efforts to investigate how this type of analysis can generate improved models of gene expression, we used smFISH to quantify the kinetic expression of STL1 and CTT1 mRNAs in single Saccharomyces cerevisiae cells upon 0.2 and 0.4 M NaCl osmotic stress. In this Data Descriptor, we outline our procedure along with our data in the form of raw images and processed mRNA counts. We discuss how these data can be used to develop single cell modelling approaches, to study fundamental processes in transcription regulation and develop single cell image processing approaches.
Highlights
Background & SummaryTranscript levels are regulated by essential biological processes[1,2]
Cell populated methods are unable to address whether two different mRNA species are expressed in the same cell or in different cells which has hampered the identification of distinct cell types
We present our information rich RNA single molecule fluorescence in-situ hybridization (smFISH) data set in this Data Descriptor which is an extension to our previous publication[26,28]
Summary
Transcript levels are regulated by essential biological processes[1,2]. Traditionally, investigations of how these processes are regulated within the cell have relied on cell populated based approaches[3,4,5]. Different osmotic stresses (0.2 M and 0.4 M NaCl) and monitored over sixteen time points in biological duplicate or triplicate[26,29] Because this data set contains both 3D spatial and temporal information on the expression of these RNAs, it can be used to simultaneously investigate many different important processes regulating transcription levels as they occur in the cell. Such analyses are not possible in cell population-based or single cell sequencing experiments[1,2,3,4,5,27]. We expect that such a dataset could be re-used by labs interested in studying transcription, RNA export, and RNA degradation as these processes have not yet been thoroughly investigated using single cell-based approaches
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