Single-molecule Förster resonance energy transfer (smFRET) is increasingly being used to determine distances, structures, and dynamics of biomolecules in vitro and in vivo. However, generalized protocols and FRET standards to ensure the reproducibility and accuracy of measurements of FRET efficiencies are currently lacking. Here we report the results of a comparative blind study in which 20 labs determined the FRET efficiencies (E) of several dye-labeled DNA duplexes. Using a unified, straightforward method, we obtained FRET efficiencies with s.d. between ±0.02 and ±0.05. We suggest experimental and computational procedures for converting FRET efficiencies into accurate distances, and discuss potential uncertainties in the experiment and the modeling. Our quantitative assessment of the reproducibility of intensity-based smFRET measurements and a unified correction procedure represents an important step toward the validation of distance networks, with the ultimate aim of achieving reliable structural models of biomolecular systems by smFRET-based hybrid methods.

Acquisition of cell fate is thought to rely on the specific interaction of remote cis-regulatory modules (CRMs), e.g. enhancers, and target promoters. However, the precise interplay between chromatin structure and gene expression is still unclear, particularly within multicellular developing organisms. Here we employ Hi-M, a single-cell spatial genomics approach, to detect CRM-promoter looping interactions within topologically associating domains (TADs) during early Drosophila development. By comparing cis-regulatory loops in alternate cell types, we show that physical proximity does not necessarily instruct transcriptional states. Moreover, multi-way analyses reveal multiple CRMs spatially coalesce to form hubs. Loops and CRM hubs are established early during development, prior to the emergence of TADs. Moreover, CRM hubs are formed, in part, via the action of the pioneer transcription factor Zelda and precede transcriptional activation. Our approach provides insight into the role of CRM-promoter interactions in defining transcriptional states, as well as distinct cell types.

We present a simple and robust technique for extracting kinetic rate models and thermodynamic quantities from single-molecule time traces. Single-molecule analysis of complex kinetic sequences (SMACKS) is a maximum-likelihood approach that resolves all statistically relevant rates and also their uncertainties. This is achieved by optimizing one global kinetic model based on the complete data set while allowing for experimental variations between individual trajectories. In contrast to dwell-time analysis, which is the current standard method, SMACKS includes every experimental data point, not only dwell times. As a result, it works as well for long trajectories as for an equivalent set of short ones. In addition, the previous systematic overestimation of fast over slow rates is solved. We demonstrate the power of SMACKS on the kinetics of the multidomain protein Hsp90 measured by single-molecule Förster resonance energy transfer. Experiments in and out of equilibrium are analyzed and compared to simulations, shedding new light on the role of Hsp90's ATPase function. SMACKS resolves accurate rate models even if states cause indistinguishable signals. Thereby, it pushes the boundaries of single-molecule kinetics beyond those of current methods.

We use plasmon rulers to follow the conformational dynamics of a single protein for up to 24 h at a video rate. The plasmon ruler consists of two gold nanospheres connected by a single protein linker. In our experiment, we follow the dynamics of the molecular chaperone heat shock protein 90 (Hsp90), which is known to show “open” and “closed” conformations. Our measurements confirm the previously known conformational dynamics with transition times in the second to minute time scale and reveals new dynamics on the time scale of minutes to hours. Plasmon rulers thus extend the observation bandwidth 3–4 orders of magnitude with respect to single-molecule fluorescence resonance energy transfer and enable the study of molecular dynamics with unprecedented precision.

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The iap gene of Listeria monocytogenes encodes the extracellular protein p60, which possesses a murein hydrolase activity necessary for septum separation. We constructed L. monocytogenes EGD strains harbouring plasmids that carry the iap gene under the control of the PrfA-regulated promoters of the L. monocytogenes genes hly, mpl, and actA. After insertional inactivation of the chromosomal iap gene in L. monocytogenes EGD, p60 synthesis was strictly dependent on PrfA. Elevated temperature (40 degrees C) enhanced synthesis of p60 in L. monocytogenes when the iap gene was under the control of the hly promoter; this appeared to be associated with increased synthesis of PrfA at this temperature. Synthesis of p60 in L. monocytogenes was significantly lower when the iap gene was placed under the control of the actA or the mpl promoter. Transcription of the iap gene was repressed in L. monocytogenes in the presence of PrfA when iap expression was under the control of the prfA promoter P2. Under the control of the hly promoter the gene produced low levels of secreted p60 in the presence of low amounts of PrfA, and this in turn led to the generation of long listerial cell filaments consisting of bacteria that had failed to separate. Overexpression of p60 in the presence of high levels of PrfA caused formation of single cells, which showed reduced viability depending on the level of secreted p60. These data suggest that the iap gene may be a valuable tool for monitoring virulence gene regulation by PrfA under in vivo conditions, without disturbing the integrity of the infected host cells.

Single-molecule FRET (smFRET) is a versatile technique to study the dynamics and function of biomolecules since it makes nanoscale movements detectable as fluorescence signals. The powerful ability to infer quantitative kinetic information from smFRET data is, however, complicated by experimental limitations. Diverse analysis tools have been developed to overcome these hurdles but a systematic comparison is lacking. Here, we report the results of a blind benchmark study assessing eleven analysis tools used to infer kinetic rate constants from smFRET trajectories. We test them against simulated and experimental data containing the most prominent difficulties encountered in analyzing smFRET experiments: different noise levels, varied model complexity, non-equilibrium dynamics, and kinetic heterogeneity. Our results highlight the current strengths and limitations in inferring kinetic information from smFRET trajectories. In addition, we formulate concrete recommendations and identify key targets for future developments, aimed to advance our understanding of biomolecular dynamics through quantitative experiment-derived models.