Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation.

Our current understanding of biomolecular condensate formation is largely based on observing the final near-equilibrium condensate state. Despite expectations from classical nucleation theory, pre-critical protein clusters were recently shown to form under subsaturation conditions in vitro; if similar long-lived clusters comprising more than a few molecules are also present in cells, our understanding of the physical basis of biological phase separation may fundamentally change. Here, we combine fluorescence microscopy with photobleaching analysis to quantify the formation of clusters of NELF proteins in living, stressed cells. We categorise small and large clusters based on their dynamics and their response to p38 kinase inhibition. We find a broad distribution of pre-condensate cluster sizes and show that NELF protein cluster formation can be explained as non-classical nucleation with a surprisingly flat free-energy landscape for a wide range of sizes and an inhibition of condensation in unstressed cells.

Designed membrane protein heterodimers and control of their affinity by binding domain and membrane linker properties.

Many membrane proteins utilize dimerization to transmit signals across the cell membrane via regulation of the lateral binding affinity. The complexity of natural membrane proteins hampers the understanding of this regulation on a biophysical level. We designed simplified membrane proteins from well-defined soluble dimerization domains with tunable affinities, flexible linkers, and an inert membrane anchor. Live-cell single-molecule imaging demonstrates that their dimerization affinity indeed depends on the strength of their binding domains. We confirm that as predicted, the 2-dimensional affinity increases with the 3-dimensional binding affinity of the binding domains and decreases with linker lengths. Models of extended and coiled linkers delineate an expected range of 2-dimensional affinities, and our observations for proteins with medium binding strength agree well with the models. Our work helps in understanding the function of membrane proteins and has important implications for the design of synthetic receptors.

Kappa but not delta or mu opioid receptors form homodimers at low membrane densities.

Opioid receptors (ORs) have been observed as homo- and heterodimers, but it is unclear if the dimers are stable under physiological conditions, and whether monomers or dimers comprise the predominant fraction in a cell. Here, we use three live-cell imaging approaches to assess dimerization of ORs at expression levels that are 10-100 × smaller than in classical biochemical assays. At membrane densities around 25/µm2, a split-GFP assay reveals that κOR dimerizes, while µOR and δOR stay monomeric. At receptor densities < 5/µm2, single-molecule imaging showed no κOR dimers, supporting the concept that dimer formation depends on receptor membrane density. To directly observe the transition from monomers to dimers, we used a single-molecule assay to assess membrane protein interactions at densities up to 100 × higher than conventional single-molecule imaging. We observe that κOR is monomeric at densities < 10/µm2 and forms dimers at densities that are considered physiological. In contrast, µOR and δOR stay monomeric even at the highest densities covered by our approach. The observation of long-lasting co-localization of red and green κOR spots suggests that it is a specific effect based on OR dimerization and not an artefact of coincidental encounters.

A Novel Fault Detection and Identification Framework for Rotating Machinery Using Residual Current Spectrum.

A novel framework of model-based fault detection and identification (MFDI) for induction motor (IM)-driven rotating machinery (RM) is proposed in this study. A data-driven subspace identification (SID) algorithm is employed to obtain the IM state-space model from the voltage and current signals in a quasi-steady-state condition. This study aims to improve the frequency-domain fault detection and identification (FDI) by replacing the current signal with a residual signal where a thresholding method is applied to the residual signal. Through the residual spectrum and threshold comparison, a binary decision is made to find fault signatures in the spectrum. The statistical Q-function is used to generate the fault frequency band to distinguish between the fault signature and the noise signature. The experiment in this study is performed on a wastewater pump in an existing industrial facility to verify the proposed FDI. Two faulty conditions with mathematically known and mathematically unknown faulty signatures are experimented with and diagnosed. The study results present that the residual spectrum demonstrated to be more sensitive to fault signatures compare to the current spectrum. The proposed FDI has successfully shown to identify the fault signatures even for the mathematically unknown faulty signatures.