Bacteria live in a broad range of environmental temperatures that require adaptations of their RNA sequences to maintain function. Riboswitches are regulatory RNAs that change conformation upon binding of typical metabolite ligands to control bacterial gene expression. The paradigmatic small class-I preQ1 riboswitches from the mesophile Bacillus subtilis (Bsu) and the thermophile Thermoanaerobacter tengcongensis (Tte) adopt similar pseudoknot structures when bound to preQ1. Here, we use single-molecule detected chemical denaturation by urea to compare the thermodynamic and kinetic folding properties of the two riboswitches, and the urea-countering effects of trimethylamine N-oxide (TMAO). This data includes the experimental findings and associated analyses detailed in the research article titled "Single-molecule FRET observes opposing effects of urea and TMAO on structurally similar meso- and thermophilic riboswitch RNAs". The data consists of multiple zip files, each representing an experiment that corresponds to the key results in the publication. Each experiment includes movies, qualifying smFRET trajectories, and analysis files related to various conditions within that experimental group.
Traces from single-molecule fluorescence microscopy (SMFM) experiments exhibit photophysical artifacts that typically necessitate human expert screening, which is time-consuming and introduces potential for user-dependent expectation bias. Here, we have used deep learning to develop a rapid, automatic SMFM trace selector, termed AutoSiM, that improves the sensitivity and specificity of an assay for a DNA point mutation based on single-molecule recognition through equilibrium Poisson sampling (SiMREPS). The improved performance of AutoSiM is based on accepting both more true positives and fewer false positives than the conventional approach of hidden Markov modeling (HMM) followed by thresholding. As a second application, the selector was used for automated screening of single-molecule Förster resonance energy transfer (smFRET) data to identify high-quality traces for further analysis, and achieves ~90% concordance with manual selection while requiring less processing time. AutoSiM can be adapted readily to novel datasets, requiring only modest Transfer Learning.
Li, J., Zhang, L., Johnson-Buck, A., & Walter, N. G. (2020). Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning. Nature Communications, 11(1), 5833. https://doi.org/10.1038/s41467-020-19673-1 and Hayward, S., Lund, P., Kang, Q., Johnson-Buck, A., Tewari, M., Walter, N. (2018). Single-molecule microscopy image data and analysis files for "Ultra-specific and Amplification-free Quantification of Mutant DNA by Single-molecule Kinetic Fingerprinting" [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/Z2CZ35DF
The sensitive measurement of specific protein biomarkers is important for medical diagnostics and research. However, existing methods for quantifying proteins use antibody probes that cannot distinguish between specific and nonspecific binding, limiting their sensitivity and specificity. This work establishes a method for distinguishing between specific binding to the target protein and nonspecific binding to assay surfaces using single-molecule kinetic measurements with dynamically binding probes. This is significant because it permits extremely sensitive protein measurements without requiring a high-affinity detection antibody or any washing steps, enabling streamlined and sensitive quantification of proteins even when no pair of high-quality, tightly binding antibodies is available.
This work contains the experimental data and associated analysis that are described in the research publication entitled "Ultra-specific and Amplification-free Quantification of Mutant DNA by Single-molecule Kinetic Fingerprinting". This work contains multiple zip files, each of which represents one of the principal experiment groups presented in the publication. Each experiment group contains movie and analysis files corresponding to various experimental conditions related to that experiment group.
