Electrospray-assisted cryo-EM sample preparation to mitigate interfacial effects.

Addressing interfacial effects during specimen preparation in cryogenic electron microscopy remains challenging. Here we introduce ESI-cryoPrep, a specimen preparation method based on electrospray ionization in native mass spectrometry, designed to alleviate issues associated with protein denaturation or preferred orientation induced by macromolecule adsorption at interfaces. Through fine-tuning spraying parameters, we optimized protein integrity preservation and achieved the desired ice thickness for analyzing target macromolecules. With ESI-cryoPrep, we prepared high-quality cryo-specimens of five proteins and obtained three-dimensional reconstructions at near-atomic resolution. Our findings demonstrate that ESI-cryoPrep effectively confines macromolecules within the middle of the thin layer of amorphous ice, facilitating the preparation of blotting-free vitreous samples. The protective mechanism, characterized by the uneven distribution of charged biomolecules of varying sizes within charged droplets, prevents the adsorption of target biomolecules at air-water or graphene-water interfaces, thereby avoiding structural damage to the protein particles or the introduction of dominant orientation issues.

Application of Residual Structure Time Convolutional Network Based on Attention Mechanism in Remaining Useful Life Interval Prediction of Bearings.

In the context of Industry 4.0, bearings, as critical components of machinery, play a vital role in ensuring operational reliability. The detection of their health status is thus of paramount importance. Existing predictive models often focus on point predictions of bearing lifespan, lacking the ability to quantify uncertainty and having room for improvement in accuracy. To accurately predict the long-term remaining useful life (RUL) of bearings, a novel time convolutional network model with an attention mechanism-based soft thresholding decision residual structure for quantifying the lifespan interval of bearings, namely TCN-AM-GPR, is proposed. Firstly, a spatio-temporal graph is constructed from the bearing sensor signals as the input to the prediction model. Secondly, a residual structure based on a soft threshold decision with a self-attention mechanism is established to further suppress noise in the collected bearing lifespan signals. Thirdly, the extracted features pass through an interval quantization layer to obtain the RUL and its confidence interval of the bearings. The proposed methodology has been verified using the PHM2012 bearing dataset, and the comparison of simulation experiment results shows that TCN-AM-GPR achieved the best point prediction evaluation index, with a 2.17% improvement in R2 compared to the second-best performance from TCN-GPR. At the same time, it also has the best interval prediction comprehensive evaluation index, with a relative decrease of 16.73% in MWP compared to the second-best performance from TCN-GPR. The research results indicate that TCN-AM-GPR can ensure the accuracy of point estimates, while having superior advantages and practical significance in describing prediction uncertainty.

Ion Mobility Separation Using a Dual-LIT Miniature Mass Spectrometer.

Ion mobility (IM) has been increasingly used in combination with mass spectrometry (MS) for chemical and biological analysis. While implementation of IM with MS usually requires complex instrumentation with delicate controls, in this study we explored the potential of performing IM separation using dual-linear ion traps (LITs) in a miniature mass spectrometer, which was originally developed for performing comprehensive MS/MS scan functions with a simple instrumentation configuration. The IM separation was achieved by ion transfer between the LITs with dynamic gas flow. Its performance was characterized for analysis of a broad range of chemical and biological compounds including small organic compounds such as trisaccharides, raffinose, cellotriose, and melezitose, as well as protein conformers. The demonstrated technique serves as another example of developing powerful hybrid instrument functions with simple configurations and miniaturized sizes.

Accelerating protein aggregation and amyloid fibrillation for rapid inhibitor screening.

The accumulation and deposition of amyloid fibrils, also known as amyloidosis, in tissues and organs of patients has been found to be linked to numerous devastating neurodegenerative diseases. The aggregation of proteins to form amyloid fibrils, however, is a slow pathogenic process, and is a major issue for the evaluation of the effectiveness of inhibitors in new drug discovery and screening. Here, we used microdroplet reaction technology to accelerate the amyloid fibrillation process, monitored the process to shed light on the fundamental mechanism of amyloid self-assembly, and demonstrated the value of the technology in the rapid screening of potential inhibitor drugs. Proteins in microdroplets accelerated to form fibrils in milliseconds, enabling an entire cycle of inhibitor screening for Aß40 within 3 minutes. The technology would be of broad interest to drug discovery and therapeutic design to develop treatments for diseases associated with protein aggregation and fibrillation.

High-resolution separation of bioisomers using ion cloud profiling.

Elucidation of complex structures of biomolecules plays a key role in the field of chemistry and life sciences. In the past decade, ion mobility, by coupling with mass spectrometry, has become a unique tool for distinguishing isomers and isoforms of biomolecules. In this study, we develop a concept for performing ion mobility analysis using an ion trap, which enables isomer separation under ultra-high fields to achieve super high resolutions over 10,000. The potential of this technology has been demonstrated for analysis of isomers for biomolecules including disaccharides, phospholipids, and peptides with post-translational modifications.