Water-Soluble Pillar[7]arene: Synthesis, pH-Controlled Complexation with Paraquat, and Application in Constructing Supramolecular Vesicles

By the introduction of 14 anionic carboxylate groups at its two rims, a water-soluble pillar[7]­arene (<b>WP7</b>) was synthesized. Its pH-controlled complexation with paraquat <b>G</b>_<b>1</b> in water was investigated. Host <b>WP7</b> and guest <b>G</b>_<b>1</b> formed a 1:1 [2]­pseudorotaxane with a high association constant of (2.96 ± 0.31) × 10⁹ M^–1 in water. Furthermore, we took advantage of this novel molecular recognition motif to fabricate a supra-amphiphile based on <b>WP7</b> and an amphiphilic paraquat derivative <b>G</b>_<b>2</b>. The morphologies and sizes of self-assemblies of <b>G</b>_<b>2</b> and <b>WP7</b>⊃<b>G</b>_<b>2</b> were identified by transmission electron microscopy and dynamic light scattering

Data-Driven Deciphering of Latent Lesions in Heterogeneous Tissue Using Function-Directed <i>t</i>‑SNE of Mass Spectrometry Imaging Data

Mass spectrometry imaging (MSI), which quantifies the underlying chemistry with molecular spatial information in tissue, represents an emerging tool for the functional exploration of pathological progression. Unsupervised machine learning of MSI datasets usually gives an overall interpretation of the metabolic features derived from the abundant ions. However, the features related to the latent lesions are always concealed by the abundant ion features, which hinders precise delineation of the lesions. Herein, we report a data-driven MSI data segmentation approach for recognizing the hidden lesions in the heterogeneous tissue without prior knowledge, which utilizes one-step prediction for feature selection to generate function-specific segmentation maps of the tissue. The performance and robustness of this approach are demonstrated on the MSI datasets of the ischemic rat brain tissues and the human glioma tissue, both possessing different structural complexity and metabolic heterogeneity. Application of the approach to the MSI datasets of the ischemic rat brain tissues reveals the location of the ischemic penumbra, a hidden zone between the ischemic core and the healthy tissue, and instantly discovers the metabolic signatures related to the penumbra. In view of the precise demarcation of latent lesions and the screening of lesion-specific metabolic signatures in tissues, this approach has great potential for in-depth exploration of the metabolic organization of complex tissue

Data-Driven Deciphering of Latent Lesions in Heterogeneous Tissue Using Function-Directed <i>t</i>‑SNE of Mass Spectrometry Imaging Data

Mass spectrometry imaging (MSI), which quantifies the underlying chemistry with molecular spatial information in tissue, represents an emerging tool for the functional exploration of pathological progression. Unsupervised machine learning of MSI datasets usually gives an overall interpretation of the metabolic features derived from the abundant ions. However, the features related to the latent lesions are always concealed by the abundant ion features, which hinders precise delineation of the lesions. Herein, we report a data-driven MSI data segmentation approach for recognizing the hidden lesions in the heterogeneous tissue without prior knowledge, which utilizes one-step prediction for feature selection to generate function-specific segmentation maps of the tissue. The performance and robustness of this approach are demonstrated on the MSI datasets of the ischemic rat brain tissues and the human glioma tissue, both possessing different structural complexity and metabolic heterogeneity. Application of the approach to the MSI datasets of the ischemic rat brain tissues reveals the location of the ischemic penumbra, a hidden zone between the ischemic core and the healthy tissue, and instantly discovers the metabolic signatures related to the penumbra. In view of the precise demarcation of latent lesions and the screening of lesion-specific metabolic signatures in tissues, this approach has great potential for in-depth exploration of the metabolic organization of complex tissue

Design and Construction of <i>Endo</i>-Functionalized Multiferrocenyl Hexagons via Coordination-Driven Self-Assembly and Their Electrochemistry

The construction of a new family of <i>endo</i>-functionalized multiferrocenyl hexagons with various sizes via coordination-driven self-assembly is described. The structures of these novel metallacycles, containing several ferrocenyl moieties at their interior surface, are characterized by multinuclear NMR (³¹P and ¹H) spectroscopy, cold-spray ionization mass spectrometry (CSI-TOF-MS), elemental analysis, and molecular modeling. Insight into the structural and electrochemical properties of these <i>endo</i>-functionalized multiferrocenyl hexagons was obtained through cyclic voltammetry investigation

Design and Construction of <i>Endo</i>-Functionalized Multiferrocenyl Hexagons via Coordination-Driven Self-Assembly and Their Electrochemistry

The construction of a new family of <i>endo</i>-functionalized multiferrocenyl hexagons with various sizes via coordination-driven self-assembly is described. The structures of these novel metallacycles, containing several ferrocenyl moieties at their interior surface, are characterized by multinuclear NMR (³¹P and ¹H) spectroscopy, cold-spray ionization mass spectrometry (CSI-TOF-MS), elemental analysis, and molecular modeling. Insight into the structural and electrochemical properties of these <i>endo</i>-functionalized multiferrocenyl hexagons was obtained through cyclic voltammetry investigation

Liquid Chromatography–Tandem Mass Spectrometry-Based Plasma Metabonomics Delineate the Effect of Metabolites’ Stability on Reliability of Potential Biomarkers

Metabonomics is an important platform for investigating the metabolites of integrated living systems and their dynamic responses to changes caused by both endogenous and exogenous factors. A metabonomics strategy based on liquid chromatography–mass spectrometry/mass spectrometry in both positive and negative ion modes was applied to investigate the short-term and long-term stability of metabolites in plasma. Principal components analysis and ten types of identified metabolites were used to summarize the time-dependent change rules in metabolites systematically at different temperatures. The long-term stability of metabolites in plasma specimens stored at −80 °C for five years was also studied. Analysis of these subjects identified 36 metabolites with statistically significant changes in expression (<i>p</i> < 0.05) and found a kind of metabolite with a hundred-fold change. The stability of metabolites in blood at 4 °C for 24 h was also investigated. These studies show that a thorough understanding of the effects of metabolite stability are necessary for improving the reliability of potential biomarkers

Synthesis of Platinum Acetylide Derivatives with Different Shapes and Their Gel Formation Behavior

The synthesis of a series of platinum acetylide derivatives, featuring linear, triangular, and rectangular shapes, respectively, is described. The structures of these complexes were characterized by multinuclear NMR (¹H, ¹³C, and ³¹P), CSI-TOF mass spectrometry, and elemental analysis. It was found that these complexes exhibited unexpectedly different gel formation properties in the most common organic solvents. Moreover, the organometallic gels of <b>C1</b> and <b>C2</b> exhibited concentration- and temperature-dependent emission properties

Combination of Droplet Extraction and Pico-ESI-MS Allows the Identification of Metabolites from Single Cancer Cells

We have combined droplet extraction and a pulsed direct current electrospray ionization mass spectrometry method (Pico-ESI-MS) to obtain information-rich metabolite profiling from single cells. We studied normal human astrocyte cells and glioblastoma cancer cells. Over 600 tandem mass spectra (MS²) of metabolites from a single cell were recorded, allowing the successful identification of more than 300 phospholipids. We found the ratios of unsaturated phosphatidylcholines (PCs) to saturated PCs were significantly higher in glioblastoma cells compared to normal cells. In addition, both isomeric PC (17:1) and (phosphatidylethanolamine) PE (20:1) were found in glioblastoma cells, whereas only PC (17:1) was observed in astrocyte cells. Our method paves the way to characterize the chemical contents of single cells, providing rich metabolome information. We suggest that this technique is general and can be applied to other life science studies such as differentiation and drug resistance of individual cells

Optimization and Evaluation Strategy of Esophageal Tissue Preparation Protocols for Metabolomics by LC–MS

Sample preparation is a critical step in tissue metabolomics. Therefore, a comprehensive and systematic strategy for the screening of tissue preparation protocols is highly desirable. In this study, we developed an Optimization and Evaluation Strategy based on LC–MS to screen for a high-extractive efficiency and reproducible esophageal tissue preparation protocol for different types of endogenous metabolites (amino acids, carnitines, cholines, etc.), with a special focus on low-level metabolites. In this strategy, we first selected a large number of target metabolites based on literature survey, previous work in our lab, and known metabolic pathways. For these target metabolites, we tested different solvent extraction methods (biphasic solvent extraction, two-step [TS], stepwise [SW], all-in one [AO]; single-phase solvent extraction, SP) and esophageal tissue disruption methods (homogenized wet tissue [HW], ground wet tissue [GW], and ground dry tissue [GD]). A protocol involving stepwise addition of solvents and a homogenized wet tissue protocol (SWHW) was superior to the others. Finally, we evaluated the stability of endogenous metabolites in esophageal tissues and the sensitivity, reproducibility, and recovery of the optimal protocol. The results proved that the SWHW protocol was robust and adequate for bioanalysis. This strategy will provide important guidance for the standardized and scientific investigation of tissue metabolomics

Combination of Injection Volume Calibration by Creatinine and MS Signals’ Normalization to Overcome Urine Variability in LC-MS-Based Metabolomics Studies

It is essential to choose one preprocessing method for liquid chromatography–mass spectrometry (LC-MS)-based metabolomics studies of urine samples in order to overcome their variability. However, the commonly used normalization methods do not substantially reduce the high variabilities arising from differences in urine concentration, especially for signal saturation (abundant metabolites exceed the dynamic range of the instrumentation) or missing values. Herein, a simple preacquisition strategy based on differential injection volumes calibrated by creatinine (to reduce the concentration differences between the samples), combined with normalization to “total useful MS signals” or “all MS signals”, is proposed to overcome urine variabilities. This strategy was first systematically compared with other popular normalization methods by application to serially diluted urine samples. Then, the method has been verified using rat urine samples of pre- and postinoculation of Walker 256 carcinoma cells. The results showed that the calibration of injection volumes based on creatinine values could effectively eliminate intragroup differences caused by variations in the concentrations of urinary metabolites, thus giving better parallelism and clustering effects. In addition, peak area normalization could further eliminate intraclass differences. Therefore, the strategy of combining peak area normalization with calibration of injection volumes of urine samples based on their creatinine values is effective for solving problems associated with urinary metabolomics