Analytical and Bioanalytical Chemistry最新文献

Biomarkers and their concentration levels are critical indicators of metabolomics for clinical applications. Rapid and sensitive analysis methods are essential for realizing timely and efficient quantitation of those significant biomarkers. In this work, a self-driven microfluidic immunosensor was developed for rapid all-in-one separation, enrichment, and detection of biomarkers. This immunosensor was constructed from a cyclic olefin copolymer (COC) channel layer and a polydimethylsiloxane (PDMS) sensing layer. The COC channel layer was modified through protein adsorption, immobilization, and remaining active site blocking. The obtained hydrophilic microchannels not only reduce the nonspecific adsorption, but also provide stable capillary-driven flow generation with linear velocities up to 20 mm/s for aqueous solution auto-injection. The PDMS sensing layer was modified using capture antibodies to accomplish affinity recognition of target biomarkers. Procalcitonin (PCT) and serum amyloid A (SAA) were selected as model biomarkers in the feasibility study on applying the self-driven microfluidic immunosensor to bioassay. The limits of detection of PCT and SAA were 7.9 ng/L and 7.6 μg/L, respectively. Moreover, the whole process can be accomplished within 60 min with excellent selectivity and reproducibility. In clinical serum sample analysis, satisfactory recoveries were achieved for PCT and SAA in the ranges of 85.0-103.0% and 95.5-106.0%, respectively, with relative standard deviations less than 5.3%. The method accuracy was further confirmed by the results of commercial immunoassay kits. This simple and easily operated immunosensor provides a rapid and sensitive biomarker analysis tool, and promotes the further development of automated and easy-to-use microfluidic immunoassays.

Listeria monocytogenes (L. monocytogenes) is a prevalent food-borne pathogen that can cause listeriosis, which manifests as meningitis and other symptoms, potentially leading to fatal outcomes in severe cases. In this study, we developed an aptasensor utilizing carboxylated magnetic beads and Cas12a to detect L. monocytogenes. In the absence of L. monocytogenes, the aptamer maintains its spatial configuration, keeping the double-stranded DNA attached and preventing the release of a startup template and activation of Cas12a's trans-cleavage capability. Conversely, in the presence of L. monocytogenes, the aptamer undergoes a conformational change, releasing the double-stranded DNA to serve as a startup template, thereby activating the trans-cleavage capability of Cas12a. Consequently, as the concentration of L. monocytogenes increases, the observable brightness in a blue light gel cutter intensifies, leading to a rise in fluorescence intensity difference compared to the control. This Cas12a aptasensor demonstrates excellent sensitivity towards L. monocytogenes, with a lowest detection limit (LOD) of 57.15 CFU/mL and a linear range of 4×10² to 4×10⁷ CFU/mL (R²=0.9858). Notably, the proposed Cas12a aptasensor exhibited outstanding selectivity and recovery in beef samples, and could be employed for precise monitoring. This Cas12a aptasensor not only provides a novel fluorescent and visual rapid detection method for L. monocytogenes but also offers simplicity, speed, and stability compared to previous detection methods. Furthermore, it is suitable for on-site detection of beef samples.

Glycosaminoglycans (GAGs), including hyaluronic acid (HA), chondroitin sulfate (CS)/dermatan sulfate (DS), heparan sulfate (HS)/heparin (HP), and keratan sulfate (KS), play pivotal roles in living organisms. Generally, GAGs are analyzed after enzymatic digestion into unsaturated or saturated disaccharides. Due to high structural similarity between disaccharides, however, separation during analysis is challenging. Additionally, little is known about the structures of GAGs and their functional relationships. Elucidating the function of GAGs requires highly sensitive quantitative analytical methods. We developed a method for the simultaneous analysis of 18 types of disaccharides derived from HA (1 type), CS/DS (7 types), HS/HP (8 types), and KS (2 types) potentially detectable in analyses of human urine. The simple method involves HPLC separation with fluorescence detection following derivatization of GAG-derived disaccharides using 4-aminobenzoic acid ethyl ester (ABEE) as a pre-labeling agent and 2-picoline borane as a reductant. The ABEE derivatization reaction can be performed under aqueous conditions, and excess derivatization reagents can be easily, rapidly, and safely removed. This method enables highly sensitive simultaneous analysis of the 18 abovementioned types of GAG-derived disaccharides using HPLC with fluorescence detection in small amounts of urine (1 mL) in a single run. The versatile method described here could be applied to the analysis of GAGs in other biological samples.

Integration of glycan-related databases between different research fields is essential in glycoscience. It requires knowledge across the breadth of science because most glycans exist as glycoconjugates. On the other hand, especially between chemistry and biology, glycan data has not been easy to integrate due to the huge variety of glycan structure representations. We have developed WURCS (Web 3.0 Unique Representation of Carbohydrate Structures) as a notation for representing all glycan structures uniquely for the purpose of integrating data across scientific data resources. While the integration of glycan data in the field of biology has been greatly advanced, in the field of chemistry, progress has been hampered due to the lack of appropriate rules to extract sugars from chemical structures. Thus, we developed a unique algorithm to determine the range of structures allowed to be considered as sugars from the structural formulae of compounds, and we developed software to extract sugars in WURCS format according to this algorithm. In this manuscript, we show that our algorithm can extract sugars from glycoconjugate molecules represented at the molecular level and can distinguish them from other biomolecules, such as amino acids, nucleic acids, and lipids. Available as software, MolWURCS is freely available and downloadable ( https://gitlab.com/glycoinfo/molwurcs ).

The preparation of histology slides is a critical step in histopathology, and poor-quality histology slides with weak adhesion of tissue sections to the substrate often affect diagnostic accuracy and sometimes lead to diagnostic failure due to tissue section detachment. This issue has been of concern and some methods have been proposed to enhance tissue-substrate adhesion. Unfortunately, quantitative analysis of the adhesion between tissue sections and glass slides is still challenging. In this work, the adhesion of mouse brain tissue sections on gold-coated glass slides was analyzed using a laboratory-fabricated hyperspectral surface plasmon resonance microscopy (HSPRM) system that enabled single-pixel spectral SPR sensing and provided two-dimensional (2D) distribution of resonance wavelengths (RWs). The existence of the nanoscale water gap between the tissue section and the substrate was verified by fitting the RW measured in each pixel using the five-layer Fresnel reflection model. In addition, a 2D image of the tissue-substrate adhesion distance (AD) was obtained from the measured 2D distribution of RWs. The results showed that tissue-substrate AD was 20-35 nm in deionized water and 4-24 nm in saline solution. The HSPRM system used in this work has a wide wavelength range of 400-1000 nm and can perform highly sensitive and label-free detection over a large dynamic detection range with high spectral and spatial resolutions, showing significant potential applications in stain-free tissue imaging, quantitative analysis of tissue-substrate adhesion, accurate identification of tumor cells, and rapid histopathological diagnosis.

Being a widely occurring protein post-translational modification, N-glycosylation features unique multi-dimensional structures including sequence and linkage isomers. There have been successful bioinformatics efforts in N-glycan structure identification using N-glycoproteomics data; however, symmetric "mirror" branch isomers and linkage isomers are largely unresolved. Here, we report deep structure-level N-glycan identification using feature-induced structure diagnosis (FISD) integrated with a deep learning model. A neural network model is integrated to conduct the identification of featured N-glycan motifs and boosts the process of structure diagnosis and distinction for linkage isomers. By adopting publicly available N-glycoproteomics datasets of five mouse tissues (17,136 intact N-glycopeptide spectrum matches) and a consideration of 23 motif features, a deep learning model integrated with a convolutional autoencoder and a multilayer perceptron was trained to be capable of predicting N-glycan featured motifs in the MS/MS spectra with previously identified compositions. In the test of the trained model, a prediction accuracy of 0.8 and AUC value of 0.95 were achieved; 5701 previously unresolved N-glycan structures were assigned by matched structure-diagnostic ions; and by using an explainable learning algorithm, two new fragmentation features of m/z = 674.25 and m/z = 835.28 were found to be significant to three N-glycan structure motifs with fucose, NeuAc, and NeuGc, proving the capability of FISD to discover new features in the MS/MS spectra.

Chromene as the efficient biothiol recognition site was widely used to develop fluorescent probes based on thiol-chromene click reaction. However, chromene-based fluorescent probes with the both properties of ratiometric measurement and mitochondria-targeted function have not been reported and remain challenging. In this paper, we skillfully designed and synthesized the first mitochondria-targeted ratiometric fluorescent probe (Probe 1) for biothiols based on chromene. Upon addition of biothiols (Cys, Hcy, and GSH), the absorption and fluorescence spectra of Probe 1 changed from 490 to 426 nm and from 567 to 498 nm respectively, accompanied by color changes from orange to pale yellow under natural light and from orange to blue under a 365-nm UV lamp, which can be attributed to the click reaction of biothiols with α,β-unsaturated ketone of chromene moiety, subsequent pyran ring-opening, and phenol formation as well as 1,6-elimination of p-hydroxybenzyl moiety. Probe 1 not only exhibited high sensitivity (LODs of 149 nM, 133 nM, and 116 nM for Cys, GSH, and Hcy respectively), rapid response, and excellent selectivity for biothiols (Cys, Hcy, and GSH), but also could target in mitochondria and ratiometrically image the fluctuation of intracellular biothiols. Moreover, the novel design strategy of modifying chromene to the N atom of pyridine was proposed for the first time.

The current paper reports two new, robust, and efficient conditions for electromembrane extraction of acidic substances from human plasma. Two systems were developed based on eutectic solvents: A1 ("A" for acid) comprised dodecyl methyl sulfoxide and thymol in 1:2 ratio (w/w) as liquid membrane, while A2 used [6-methylcoumarin:thymol (1:2)]:2-nitrophenyl octyl ether in 2:1 ratio (w/w). The performance of A1 and A2 was characterized by extraction of 31 acidic model analytes (pharmaceutical drugs and nutrients) spiked into 100 µL human plasma diluted 1:1 (v/v) with phosphate buffer pH 7.4. The acceptor solution was 50 mM NH₄HCO₃ buffer pH 10.0, and extraction was performed at an agitation rate of 750 RPM. Voltage and extraction time were 30 V for 30 min and 10 V for 20 min for A1 and A2, respectively. Under optimal conditions, A1 extracted analytes with 1.8 ≤ log P ≤ 6.0 with an average recovery (R) of 85.1%, while A2 extracted in a range of 0.5 ≤ log P ≤ 6.0 with an average recovery of 79.9%. Meanwhile, extraction current was low at 9 and 26 µA, respectively, which is indicative of good system robustness. Using UHPLC-MS/MS analysis of the acceptor solution, repeatability of the A1 and A2 methods was determined to be 2.8-7.7% and 3.3-9.4% for R > 40%, matrix effects were 82-117% and 84-112%, respectively, and linear calibration curves were obtained. The performance and compatibility with human plasma represent a major improvement over previous state-of-the-art liquid membranes for acidic analytes, namely 1-octanol.

Protein glycosylation is a highly heterogeneous post-translational modification that has been demonstrated to exhibit significant variations in various diseases. Due to the differential patterns observed in disease and healthy populations, the glycosylated proteins hold promise as early indicators for multiple diseases. With the continuous development of liquid chromatography-mass spectrometry (LC-MS) technology and spectrum analysis software, the sensitivity for the decipher of the tandem mass spectra of the glycopeptides carrying intact glycans, i.e., intact glycopeptides, enzymatic hydrolyzed from glycoproteins has been significantly improved. From quantified intact glycopeptides, the difference of protein glycosylation at multiple levels, e.g., glycoprotein, glycan, glycosite, and site-specific glycans, could be obtained for different samples. However, the manual analysis of the intact glycopeptide quantitative data at multiple levels is tedious and time consuming. In this study, we have developed a software tool named "GP-Marker" to facilitate large-scale data mining of spectra dataset of intact N-glycopeptide at multiple levels. This software provides a user-friendly and interactive interface, offering operational tools for machine learning to researchers without programming backgrounds. It includes a range of visualization plots displaying differential glycosylation and provides the ability to extract multi-level data analysis from intact glycopeptide data quantified by Glyco-Decipher.