K+-Specification with Flavone P0 Probe in a G-Quadruplex DNA.

G-quadruplex (G4) DNA is considered as a prospective therapeutic target due to its potential biological significance. To understand G4 biological roles and function, a G4-specific fluorescent probe is necessary. However, it is difficult for versatile G4 to precisely recognize without perturbing their folding dynamics. Herein, we reported that flavone P0 can be a fluorescent probe for G4 DNA-specific recognition and have developed a highly selective detection of K+ ion by dimeric G4/P0 system. When comparing various nucleic acid structures, including G4, i-motif, ss/ds-DNA, and triplex, an apparent fluorescence enhancement is observed in the presence of G4 DNA for 85-fold, but only 8-fold for non-G4 DNA. Furthermore, based on fluorescent probe of flavone P0 for G4 DNA screening, the noncovalent dimeric G4/P0 system is exploited as a K+ sensor, that selectively responds to K+ with a 513-fold fluorescence enhancement and a detection range for K+ ion concentration from 0 to 500 mM. This K+ sensor also has a remarkably anti-interference ability for other metal cations, especially for a high concentration of Na+. These results have demonstrated that flavone P0 is an efficient tool for monitoring G-quadruplex DNA and endows flavone P0 with bioanalytical and medicinal applications.

Unraveling Intracellular Protein Corona Components of Nanoplastics via Photocatalytic Protein Proximity Labeling.

Bioaccumulation of nanoplastic particles has drawn increasing attention regarding environmental sustainability and biosafety. How nanoplastic particles interact with the cellular milieu still remains elusive. Herein, we exemplify a general approach to profile the composition of a "protein corona" interacting with nanoparticles via the photocatalytic protein proximity labeling method. To enable photocatalytic proximity labeling of the proteome interacting with particles, iodine-substituted BODIPY (I-BODIPY) is selected as the photosensitizer and covalently conjugated onto amino-polystyrene nanoparticles as a model system. Next, selective proximity labeling of interacting proteins is demonstrated using I-BODIPY-labeled nanoplastic particles in both Escherichia coli lysate and live alpha mouse liver 12 cells. Mechanistic studies reveal that the covalent modifications of proteins by an aminoalkyne substrate are conducted via a reactive oxygen species photosensitization pathway. Further proteomic analysis uncovers that mitochondria-related proteins are intensively involved in the protein corona, indicating substantial interactions between nanoplastic particles and mitochondria. In addition, proteostasis network components are also identified, accompanied by consequent cellular proteome aggregation confirmed by fluorescence imaging. Together, this work exemplifies a general strategy to interrogate the composition of the protein corona of nanomaterials by endowing them with photooxidation properties to enable photocatalytic protein proximity labeling function.

Chemical sensors detect and resolve proteome aggregation in peripheral neuropathy cell model induced by chemotherapeutic agents.

As a consequence of somatosensory nervous system injury or disease, neuropathic pain is commonly associated with chemotherapies, known as chemotherapy-induced peripheral neuropathy (CIPN). However, the mechanisms underlying CIPN-induced proteome aggregation in neuronal cells remain elusive due to limited detection tools. Herein, we present series sensors for fluorescence imaging (AggStain) and proteomics analysis (AggLink) to visualize and capture aggregated proteome in CIPN neuronal cell model. The environment-sensitive AggStain imaging sensor selectively binds and detects protein aggregation with 12.3 fold fluorescence enhancement. Further, the covalent AggLink proteomic sensor captures cellular aggregated proteins and profiles their composition via LC-MS/MS analysis. This integrative sensor platform reveals the presence of proteome aggregation in CIPN cell model and highlights its potential for broader applications in assessing proteome stability under various cellular stress conditions.

[Applications and challenges of wearable electroencephalogram signals in depression recognition and personalized music intervention].

Rapid and accurate identification and effective non-drug intervention are the worldwide challenges in the field of depression. Electroencephalogram (EEG) signals contain rich quantitative markers of depression, but whole-brain EEG signals acquisition process is too complicated to be applied on a large-scale population. Based on the wearable frontal lobe EEG monitoring device developed by the authors' laboratory, this study discussed the application of wearable EEG signal in depression recognition and intervention. The technical principle of wearable EEG signals monitoring device and the commonly used wearable EEG devices were introduced. Key technologies for wearable EEG signals-based depression recognition and the existing technical limitations were reviewed and discussed. Finally, a closed-loop brain-computer music interface system for personalized depression intervention was proposed, and the technical challenges were further discussed. This review paper may contribute to the transformation of relevant theories and technologies from basic research to application, and further advance the process of depression screening and personalized intervention.

Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding Diagnosis and Cognitive Prediction of Dementia.

Exploring the spatiotemporal dynamic patterns of multi-channel electroencephalography (EEG) is crucial for interpreting dementia and related cognitive decline. Spatiotemporal patterns of EEG can be described through microstate analysis, which provides a discrete approximation of the continuous electric field patterns generated by the brain cortex. Here, we propose a novel microstate spatiotemporal dynamic indicator, termed the microstate sequence non-randomness index (MSNRI). The essence of the method lies in initially generating a sequence of microstate transition patterns through state space compression of EEG data using microstate analysis. Following this, we assess the non-randomness of these microstate patterns using information-based similarity analysis. The results suggest that this MSNRI metric is a potential marker for distinguishing between health control (HC) and frontotemporal dementia (FTD) (HC vs. FTD: 6.958 vs. 5.756, p < 0.01), as well as between HC and populations with Alzheimer's disease (AD) (HC vs. AD: 6.958 vs. 5.462, p < 0.001). Healthy individuals exhibit more complex macroscopic structures and non-random spatiotemporal patterns of microstates, whereas dementia disorders lead to more random spatiotemporal patterns. Additionally, we extend the proposed method by integrating the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method to explore spatiotemporal dynamic patterns of microstates at specific frequency scales. Moreover, we assessed the effectiveness of this innovative method in predicting cognitive scores. The results demonstrate that the incorporation of CEEMD-enhanced microstate dynamic indicators significantly improved the prediction accuracy of Mini-Mental State Examination (MMSE) scores (R2 = 0.940). The CEEMD-enhanced MSNRI method not only aids in the exploration of large-scale neural changes in populations with dementia but also offers a robust tool for characterizing the dynamics of EEG microstate transitions and their impact on cognitive function.

Dual-environment-sensitive probe to detect protein aggregation in stressed laryngeal carcinoma cells and tissues.

The interplay between protein folding and biological activity is crucial, with the integrity of the proteome being paramount to ensuring effective biological function execution. In this study, we report a dual-environment-sensitive probe A1, capable of selectively binding to protein aggregates and dynamically monitoring their formation and degradation. Through in vitro, cellular, and tissue assays, A1 demonstrated specificity in distinguishing aggregated from folded protein states, selectively partitioning into aggregated proteins. Thermal shift assays revealed A1 could monitor the process of protein aggregation upon binding to misfolded proteins and preceding to insoluble aggregate formation. In cellular models, A1 detected stress-induced proteome aggregation in TU212 cells (laryngeal carcinoma cells), revealing a less polar microenvironment within the aggregated proteome. Similarly, tissue samples showed more severe proteome aggregation in cancerous tissues compared to paracancerous tissues. Overall, A1 represents a versatile tool for probing protein aggregation with significant implications for both fundamental research and clinical diagnostics.

Isophorone-based crystallization-induced-emission sensors detect proteome aggregation in live cells and tissues with breast cancer.

BACKGROUND: Protein misfolding and aggregation can lead to various diseases. Recent studies have shed light on the aggregated protein in breast cancer pathology, which suggests that it is crucial to design chemical sensors that visualize protein aggregates in breast cancer, especially in clinical patient-derived samples. However, most reported sensors are constrained in cultured cell lines. RESULTS: In this work, we present the development of two isophorone-based crystallization-induced-emission fluorophores for detecting proteome aggregation in breast cancer cell line and tissues biopsied from diseased patients, designated as A1 and A2. These probes exhibited viscosity sensitivity and recovered their fluorescence strongly at crystalline state. Moreover, A1 and A2 exhibit selective binding capacity and strong fluorescence for various aggregated proteins. Utilizing these probes, we detect protein aggregation in stressed breast cancer cells, xenograft mouse model of human breast cancer and clinical patient-derived samples. Notably, the fluorescence intensity of both probes light up in tumor tissues. SIGNIFICANCE: The synthesized isophorone-based crystallization-induced-emission fluorophores, A1 and A2, enable sensitive detection of protein aggregation in breast cancer cells and tissues. In the future, aggregated proteins are expected to become indicators for early diagnosis and clinical disease monitoring of breast cancer.