A Fully Automated Online Enrichment and Separation System for Highly Reproducible and In-Depth Analysis of Intact Glycopeptide.

A fully automated online enrichment and separation system for intact glycopeptides, named AutoGP, was developed in this study by integrating three different columns in a nano-LC system. Specifically, the peptide mixture from the enzymatic digestion of a complex biological sample was first loaded on a hydrophilic interaction chromatography (HILIC) column. The nonglycopeptides in the sample were washed off the column, and the glycopeptides retained by the HILIC column were eluted to a C18 trap column to achieve an automated glycopeptide enrichment. The enriched glycopeptides were further eluted to a C18 column for separation, and the separated glycopeptides were eventually analyzed by using an orbitrap mass spectrometer (MS). The optimal operating conditions for AutoGP were systemically studied, and the performance of the fully optimized AutoGP was compared with a conventional manual system used for glycopeptide analysis. The experimental evaluation shows that the total number of glycopeptides identified is at least 1.5-fold higher, and the median coefficient of variation for the analyses is at least 50% lower by using AutoGP, as compared to the results acquired by using the manual system. In addition, AutoGP can perform effective analysis even with a 1-µg sample amount, while a 10-µg sample at least will be needed by the manual system, implying an order of magnitude better sensitivity of AutoGP. All the experimental results have consistently proven that AutoGP can be used for much better characterization of intact glycopeptides.

Intelligent Biogenic Missile for Two-Photon Fluorescence Imaging-Guided Combined Photodynamic Therapy and Chemotherapy in Tumors.

Photodynamic therapy (PDT) is a significant noninvasive therapeutic modality, but it is often limited in its application due to the restricted tissue penetration depth caused by the wavelength limitations of the light source. Two-photon (TP) fluorescence techniques are capable of having an excitation wavelength in the NIR region by absorbing two NIR photons simultaneously, which offers the potential to achieve higher spatial resolution for deep tissue imaging. Thus, the adoption of TP fluorescence techniques affords several discernible benefits for photodynamic therapy. Organic TP dyes possess a high fluorescence quantum yield. However, the biocompatibility of organic TP dyes is poor, and the method of coating organic TP dyes with silica can effectively overcome the limitations. Herein, based on the TP silica nanoparticles, a functionalized intelligent biogenic missile TP-SiNPs-G4(TMPyP4)-dsDNA(DOX)-Aptamer (TGTDDA) was developed for effective TP bioimaging and synergistic targeted photodynamic therapy and chemotherapy in tumors. First, the Sgc8 aptamer was used to target the PTK7 receptor on the surface of tumor cells. Under two-photon light irradiation, the intelligent biogenic missile can be activated for TP fluorescence imaging to identify tumor cells and the photosensitizer assembled on the nanoparticle surface can be activated for photodynamic therapy. Additionally, this intelligent biogenic missile enables the controlled release of doxorubicin (DOX). The innovative strategy substantially enhances the targeted therapeutic effectiveness of cancer cells. The intelligent biogenic missile provides an effective method for the early detection and treatment of tumors, which has a good application prospect in the real-time high-sensitivity diagnosis and treatment of tumors.

The occurrence and development mechanisms of esophageal stricture: state of the art review.

BACKGROUND: Esophageal strictures significantly impair patient quality of life and present a therapeutic challenge, particularly due to the high recurrence post-ESD/EMR. Current treatments manage symptoms rather than addressing the disease's etiology. This review concentrates on the mechanisms of esophageal stricture formation and recurrence, seeking to highlight areas for potential therapeutic intervention. METHODS: A literature search was conducted through PUBMED using search terms: esophageal stricture, mucosal resection, submucosal dissection. Relevant articles were identified through manual review with reference lists reviewed for additional articles. RESULTS: Preclinical studies and data from animal studies suggest that the mechanisms that may lead to esophageal stricture include overdifferentiation of fibroblasts, inflammatory response that is not healed in time, impaired epithelial barrier function, and multimethod factors leading to it. Dysfunction of the epithelial barrier may be the initiating mechanism for esophageal stricture. Achieving perfect in-epithelialization by tissue-engineered fabrication of cell patches has been shown to be effective in the treatment and prevention of esophageal strictures. CONCLUSION: The development of esophageal stricture involves three stages: structural damage to the esophageal epithelial barrier (EEB), chronic inflammation, and severe fibrosis, in which dysfunction or damage to the EEB is the initiating mechanism leading to esophageal stricture. Re-epithelialization is essential for the treatment and prevention of esophageal stricture. This information will help clinicians or scientists to develop effective techniques to treat esophageal stricture in the future.

Radial basis function neural network optimization algorithm based on dynamic inertial weight particle swarm optimization for separating overlapping peaks in ion mobility spectrometry.

RATIONALE: Ion mobility spectrometry (IMS), as a promising analytical tool, has been widely employed in the structural characterization of biomolecules. Nevertheless, the inherent limitation in the structural resolution of IMS frequently results in peak overlap during the analysis of isomers exhibiting comparable structures. METHODS: The radial basis function (RBF) neural network optimization algorithm based on dynamic inertial weight particle swarm optimization (DIWPSO) was proposed for separating overlapping peaks in IMS. The RBF network structure and parameters were optimized using the DIWPSO algorithm. By extensively training using a large dataset, an adaptive model was developed to effectively separate overlapping peaks in IMS data. This approach successfully overcomes issues related to local optima, ensuring efficient and precise separation of overlapping peaks. RESULTS: The method's performance was evaluated using experimental validation and analysis of overlapping peaks in the IMS spectra of two sets of isomers: 3'/6'-sialyllactose; fructose-6-phosphate, glucose-1-phosphate, and glucose-6-phosphate. A comparative analysis was conducted using other algorithms, including the sparrow search algorithm, DIWPSO algorithm, and multi-objective dynamic teaching-learning-based optimization algorithm. The comparison results show that the DIWPSO-RBF algorithm achieved remarkably low maximum relative errors of only 0.42%, 0.092%, and 0.41% for ion height, mobility, and half peak width, respectively. These error rates are significantly lower than those obtained using the other three algorithms. CONCLUSIONS: The experimental results convincingly demonstrate that this method can adaptively, rapidly, and accurately separate overlapping peaks of multiple components, improving the structural resolution of IMS.

Electron ionization mass spectrometry feature peak relationships combined with deep classification model to assist similarity algorithm for fast and accurate identification of compounds.

RATIONALE: Gas chromatography-mass spectrometry (GC-MS) combines chromatography and MS, providing full play to the advantages of high separation efficiency of GC, strong qualitative ability of MS, and high sensitivity of detector. In GC-MS data processing, determining the experimental compounds is one of the most important analytical steps, which is usually realized by one-to-one similarity calculations between the experimental mass spectrum and the standard mass spectrum library. Although the accuracy of the algorithm has been improved in recent years, it is still difficult to distinguish structurally similar mass spectra, especially isomers. At the same time, the library capacity is very large and increasing every year, and the algorithm needs to perform large numbers of calculations with irrelevant compounds in the library to recognize unknown compounds, which leads to a significant reduction in efficiency. METHODS: This work proposed to exclude a large number of irrelevant mass spectra by presearching, perform preliminary similarity calculations using similarity algorithms, and finally improve the accuracy of similarity calculations using deep classification models. The replica library of NIST17 is used as the query data, and the master library is used as the reference database. RESULTS: Compared with the traditional recognition algorithm, the preprocessing algorithm has reduced the time by 4.2 h, and by adding the deep learning models 1 and 2 as the final determination, the recognition accuracy has been improved by 1.9% and 6.5%, respectively, based on the original algorithm. CONCLUSIONS: This method improves the recognition efficiency compared to conventional algorithms and at the same time has better recognition accuracy for structurally similar mass spectra and isomers.

Lighting up endogenous H2O2 in the tumor microenvironment using a dual-mode nanoprobe for long afterglow and MR bioimaging.

Persistent luminescent nanoparticles (PLNPs) are excellent luminescent materials, and near-infrared PLNPs are efficiently applied for biosensing and bioimaging due to their advantages of no excitation, excellent light stability and long afterglow. However, due to interference from the complex environment within organisms, single-mode imaging methods often face limitations in selectivity, sensitivity, and accuracy. Therefore, it is desirable to construct a dual-mode imaging probe strategy with higher specificity and sensitivity for bioimaging. Magnetic resonance imaging (MRI) has been widely used in the field of bioimaging due to its advantages of high resolution, non-radiation and non-invasiveness. Here, by combining near-infrared PLNPs and manganese dioxide (MnO2) nanosheets, a sensitive and convenient dual-mode "turn on" bioimaging nanoprobe ZGC@MnO2 has been developed for long afterglow imaging and MRI of endogenous hydrogen peroxide (H2O2) in the tumor microenvironment (TME). The monitoring of H2O2 has garnered significant attention due to its crucial role in human pathologies. For the dual-mode "turn on" bioimaging nanoprobe, the near-infrared PLNPs of quasi-spherical ZnGa2O4:Cr (ZGC) nanoparticles were synthesized as luminophores, and MnO2 nanosheets were utilized as a fluorescence quencher, carrier and H2O2 recognizer. H2O2 in the TME could reduce MnO2 nanosheets to Mn2+ for MRI, and ZGC nanoparticles were released for long afterglow imaging. Finally, the ZGC@MnO2 nanoprobe exhibited a rapid response, an excellent signal-to-noise ratio and a limit of detection of 3.67 nM for endogenous H2O2 in the TME. This dual-mode approach enhances the detection sensitivity for endogenous H2O2, thereby facilitating the research of endogenous H2O2-associated diseases and clinical diagnostics.

An efficient strategy with a synergistic effect of hydrophilic and electrostatic interactions for simultaneous enrichment of N- and O-glycopeptides.

N- and O-glycosylation modifications of proteins are closely linked to the onset and development of many diseases and have gained widespread attention as potential targets for therapy and diagnosis. However, the low abundance and low ionization efficiency of glycopeptides as well as the high heterogeneity make glycosylation analysis challenging. Here, an enrichment strategy, using Knoevenagel copolymers modified with polydopamine-adenosine (denoted as PDA-ADE@KCP), was firstly proposed for simultaneous enrichment of N- and O-glycopeptides through the synergistic effects of hydrophilic and electrostatic interactions. The adjustable charged surface and hydrophilic properties endow the material with the capability to achieve effective enrichment of intact N- and O-glycopeptides. The experimental results exhibited excellent selectivity (1 : 5000) and sensitivity (0.1 fmol µL-1) of the prepared material for N-glycopeptides from standard protein digest samples. Moreover, it was further applied to simultaneous capturing of N- and O-glycopeptides from mouse liver protein digests. Compared to the commercially available zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) material, the number of glycoproteins corresponding to all N- and O-glycopeptides enriched with PDA-ADE@KCP was much more than that with ZIC-HILIC. Furthermore, PDA-ADE@KCP captured more O-glycopeptides than ZIC-HILIC, revealing its superior performance in O-glycopeptide enrichment. All these results indicated that the strategy holds immense potential in characterizing N- and O-intact glycopeptides in the field of proteomics.

A DNA octahedral amplifier for endogenous circRNA detection and bioimaging in living cells and its biomarker study.

The discovery of reliable biomarkers is essential for early diagnosis, treatment, and prognosis assessment of diseases. Many research studies have shown that circRNA is a potential biomarker for diagnosis and prognosis of diseases. However, in situ monitoring circRNA in live cells is still a challenge at present, which brings a major limitation to the development and verification of circRNA as a disease biomarker. In this study, a catalytic hairpin assembly (CHA) reaction-based DNA octahedral amplifier (DOA) was developed for fluorescence resonance energy transfer (FRET) detection and bioimaging of circRNA in living cells. The DOA was first produced by self-assembling a DNA octahedron with six customized single-stranded DNAs, and two hairpins H1 (Cy3) and H2 (Cy5) were then hybridized to four vertices of the DNA octahedron. Idiopathic pulmonary fibrosis (IPF)-related circHIPK3 was used as the target. Once the CHA reaction from H1 and H2 on DOA was activated by a sequence-specific back-splice junction (BSJ) of circHIPK3, a significant FRET signal can be obtained from Cy3 to Cy5. The circHIPK3 was subsequently released to cause the next CHA reaction. Because the DOA has the advantages of the spatial-confinement effect, resistance to nuclease degradation and easy penetration into cells, rapid and excellent signal amplification FRET detection and bioimaging of endogenous circHIPK3 can be achieved in various cells. This study provides a high-precision assay platform to explore the possibility of using circRNA as a biomarker, and it is valuable for circRNA-related early diagnosis and treatment of diseases.

Dual-Mode Gold Nanocluster-Based Nanoprobe Platform for Two-Photon Fluorescence Imaging and Fluorescence Lifetime Imaging of Intracellular Endogenous miRNA.

Bioimaging is widely used in various fields of modern medicine. Fluorescence imaging has the advantages of high sensitivity, high selectivity, noninvasiveness, in situ imaging, and so on. However, one-photon (OP) fluorescence imaging has problems, such as low tissue penetration depth and low spatiotemporal resolution. These disadvantages can be solved by two-photon (TP) fluorescence imaging. However, TP imaging still uses fluorescence intensity as a signal. The complexity of organisms will inevitably affect the change of fluorescence intensity, cause false-positive signals, and affect the accuracy of the results obtained. Fluorescence lifetime imaging (FLIM) is different from other kinds of fluorescence imaging, which is an intrinsic property of the material and independent of the material concentration and fluorescence intensity. FLIM can effectively avoid the fluctuation of TP imaging based on fluorescence intensity and the interference of autofluorescence. Therefore, based on silica-coated gold nanoclusters (AuNCs@SiO2) combined with nucleic acid probes, the dual-mode nanoprobe platform was constructed for TP and FLIM imaging of intracellular endogenous miRNA-21 for the first time. First, the dual-mode nanoprobe used a dual fluorescence quencher of BHQ2 and graphene oxide (GO), which has a high signal-to-noise ratio and anti-interference. Second, the dual-mode nanoprobe can detect miR-21 with high sensitivity and selectivity in vitro, with a detection limit of 0.91 nM. Finally, the dual-mode nanoprobes performed satisfactory TP fluorescence imaging (330.0 µm penetration depth) and FLIM (τave = 50.0 ns) of endogenous miR-21 in living cells and tissues. The dual-mode platforms have promising applications in miRNA-based early detection and therapy and hold much promise for improving clinical efficacy.

Two-step particle swarm optimization algorithm for effective deconvolution and resolution enhancement of various overlapping peaks.

RATIONALE: The existing particle swarm optimization (PSO) algorithms are only effective in deconvoluting the overlapping peaks in ion mobility spectra with fewer than four component peaks, which limits the applicability of these algorithms. METHODS: A high-performance two-step particle swarm optimization (TSPSO) algorithm was developed. Compared to the existing PSO algorithms, TSPSO can narrow the search ranges of all coefficients for the overlapping peaks through Gaussian model calculation, and thus can deconvolute various overlapping peaks with high accuracy, even for 30-component overlapping peaks. In addition, the TSPSO could be further applied to enhance the resolution of the spectra by narrowing the peak widths after the peak deconvolution. RESULTS: Simulated overlapping peaks were first used to evaluate the performance of TSPSO as compared to the dynamic inertia weight particle swarm optimization (DIWPSO) algorithm. The results showed that the profiles of the peaks deconvoluted by using TSPSO were more consistent with the original ones. The fitness values and the standard deviations of the fitness values from TSPSO were also at least an order of magnitude less than those from DIWPSO. By applying TSPSO, the overlapping peaks from both mass spectrometry (MS) and field asymmetric waveform ion mobility spectrometry (FAIMS) spectra can also be well deconvoluted. In addition, the resolutions of the MS and FAIMS spectra can be effectively enhanced after peak deconvolution. The enhanced spectra matched excellently with the experimental ones acquired at high-resolution modes. CONCLUSIONS: The experiment results convincingly demonstrate that the TSPSO algorithm is capable of both deconvoluting complex overlapping peaks and enhancing the spectrum resolution with high accuracy.

Deconvolution of overlapping peaks in ion mobility spectrometry based on a multiobjective dynamic teaching-learning-based optimization.

RATIONALE: Because of its powerful analytical ability, ion mobility spectrometry (IMS) plays an important role in the field of mass spectrometry. However, one of the main defects of IMS is its low structural resolution, which leads to the phenomenon of peak overlap in the analysis of compounds with similar mass charge ratio. METHODS: A multiobjective dynamic teaching-learning-based optimization (MDTLBO) method was proposed to separate IMS overlapping peaks. This method prevents local optimization and identifies peak model coefficients efficiently. In addition, the position information of particles largely reflects the half-peak width of IMS, which makes single peaks difficult to appear and coefficient identification easier. RESULTS: The performance comparison of MDTLBO with other deconvolution methods (genetic algorithm, improved particle swarm optimization algorithm, and dynamic inertia weight particle swarm optimization algorithm) shows that the maximum deconvolution error of MDTLBO is only 0.7%, which is much lower than that for the other three methods. In addition, robustness is a performance index that reflects the advantages and disadvantages of the algorithm. CONCLUSION: MBTLBO is more robust than other algorithms for separating overlapping peaks. The algorithm can separate the heavily overlapped mobility peaks, produce better analysis results, and improve the resolution of IMS.

Overlapping peak separation algorithm for ion mobility spectra based on multistrategy JAYA.

RATIONALE: In the field of separation science, ion mobility spectrometry (IMS) plays an important role as an analytical tool. However, the lack of sufficient structural resolution is a common problem in qualitative and quantitative analysis using IMS. A method is needed to solve the problem of overlapping peaks caused by insufficient resolution. METHODS: The method uses multiple strategies to more effectively use population information to balance exploration and exploitation capabilities, prevent local optimization, accurately resolve overlapping peaks, quickly obtain optimal spectral peak model coefficients, and accurately identify compounds. RESULTS: Multistrategy JAYA algorithm's (MSJAYA) performance is compared with improved particle swarm optimization (IPSO), dynamic inertia weight particle swarm optimization (DIWPSO), and multiobjective dynamic teaching-learning-based optimization (MDTLBO). The analysis shows that MSJAYA's maximum separation error is within 0.6%, a level of accuracy not guaranteed by the other algorithms. In addition, the separation error fluctuates within a much smaller range, demonstrating MSJAYA's superior robustness. CONCLUSIONS: Compared with other overlapping peak separation algorithms, MSJAYA is more applicable because no special parameters are used. The method allows fast deconvolution analysis of strong overlapping peaks with multiple components, which greatly improves the resolution of IMS.

An improved algorithm for resolving overlapping peaks in ion mobility spectrometry and its application to the separation of glycan isomers.

Despite the popularity of ion mobility spectrometry (IMS) for glycan analysis, its limited structural resolution hinders the effective separation of many glycan isomers. This leads to the overlap of IMS peaks, consequently impacting the accurate identification of glycan compositions. To this end, an improved algorithm, namely second-order differentiation combined with a simulated annealing particle swarm optimization algorithm based on sine adaptive weights (DWSA-PSO), was proposed for the separation of overlapping IMS peaks formed by glycan isomers. DWSA-PSO first performed second-order differentiation to automatically determine the number of components in overlapping peaks and exclude impossible single-peak combinations. It then introduced sinusoidal adaptive weights and a simulated annealing mechanism to improve the algorithm's search capability and global optimization performance, thereby enabling accurate and efficient separation of individual peaks. To evaluate the performance of DWSA-PSO and its application to the separation of glycan isomers, multiple sets of overlapping peaks with different degrees of overlap were simulated, and various types of multi-component overlapping peaks were formed using six disaccharide and four trisaccharide isomers. The experimental results consistently demonstrated that the DWSA-PSO algorithm outperformed both the improved particle swarm optimization (IPSO) algorithm and the dynamic inertia weight particle swarm optimization (DIWPSO) algorithm in terms of separation accuracy, running time, and fitness values. In addition, the DWSA-PSO algorithm was successfully applied to the separation of glycan isomers in malt milk beverage. All these results reveal the capability of the DWSA-PSO algorithm to facilitate the accurate identification of glycan isomers.

Target-triggered enzyme-free amplification for highly efficient AND-gated bioimaging in living cells.

Rapid, simultaneous, and sensitive detection of biomolecules has important application prospects in disease diagnosis and biomedical research. However, because the content of intracellular endogenous target biomolecules is usually very low, traditional detection methods can't be used for effective detection and imaging, and to enhance the detection sensitivity, signal amplification strategies are frequently required. The hybridization chain reaction (HCR) has been used to detect many disease biomarkers because of its simple operation, good reproducibility, and no enzyme involvement. Although HCR signal amplification methods have been employed to detect and image intracellular biomolecules, there are still false positive signals. Therefore, a target-triggered enzyme-free amplification system (GHCR system) was developed, as a fluorescent AND-gated sensing platform for intracellular target probing. The false positive signals can be well avoided and the accuracy of detection and imaging can be improved by using the design of the AND gate. Two cancer markers, GSH and miR-1246, were used as two orthogonal inputs for the AND gated probe. The AND-gated probe only works when GSH and miR-1246 are the inputs at the same time, and FRET signals can be the output. In addition to the use of AND-gated imaging, FRET-based high-precision ratiometric fluorescence imaging was employed. FRET-based ratiometric fluorescent probes have a higher ability to resist interference from the intracellular environment, they can avoid false positive signals well, and they are expected to have good specificity. Due to the advantages of HCR, AND-gated, and FRET fluorescent probes, the GHCR system exhibited highly efficient AND-gated FRET bioimaging for intracellular endogenous miRNAs with a lower detection limit of 18 pM, which benefits the applications of ratiometric intracellular biosensing and bioimaging and offers a novel concept for advancing the diagnosis and therapeutic strategies in the field of cancer.

Two-Dimensional FAIMS-IMS Characterization of Peptide Conformers with Resolution Exceeding 1000.

A high-performance field asymmetric waveform ion mobility spectrometry (FAIMS)-IMS-MS platform was developed and applied to explore the conformational diversity of the singly and doubly charged bradykinin (BK + H+)+ and (BK + 2H+)2+ ions. With pure N2 as the FAIMS carrier gas, more than ten conformers of (BK + H+)+ can be resolved using FAIMS-IMS, as compared to only four conformers resolved using either FAIMS or IMS alone. Interestingly, multiple conformers of (BK + H+)+ were found to have completely different values of FAIMS compensation voltage (CV), while their IMS drift times were essentially the same, which were also proven experimentally to not result from the structural annealing by the collisional heating in the ion funnel. The separations in the FAIMS and IMS dimensions are substantially orthogonal, and the overall resolving power of two-dimensional FAIMS-IMS separation is largely proportional to the product of the separation resolving powers of FAIMS and IMS. Using a gas mixture of N2/He to further improve the resolving power of the FAIMS separation, the total resolving powers of the combined FAIMS and IMS separation were estimated to be about 1020 and 1400 for (BK + H+)+ and (BK + 2H+)2+ ions, respectively, which are significantly higher than the resolving power of any ion mobility-based separation techniques demonstrated so far. The combined FAIMS-IMS can thus be a much more powerful technique to explore the structural diversity of biomolecules.

Combination of continuous wavelet transform and genetic algorithm-based Otsu for efficient mass spectrometry peak detection.

Mass spectrometry (MS) data is susceptible to random noises and alternating baseline, posing great challenges to spectral peak detection, especially for weak peaks and overlapping peaks. Herein, an efficient peak detection algorithm combining continuous wavelet transform (CWT) and genetic algorithm-based threshold segmentation (denoted as WSTGA) for mass spectrometry was proposed. Firstly, Mexican Hat wavelet was selected as the mother wavelet by comparing the matching degree between the difference of Gaussian (DOG) and different wavelets. Subsequently, the ridges and valleys were identified from 2D wavelet coefficient matrix. Afterward, an improved threshold segmentation method, Otsu method based on genetic algorithm, was introduced to find optimal segmentation threshold and achieve better image segmentation, overcoming the deficiency of traditional Otsu method that cannot handle long-tailed unimodal histograms. Finally, the characteristic peaks were successfully identified by utilizing the ridge-valley lines in wavelet space and original spectrum. Receiver operating characteristic (ROC) curve, area under curve (AUC) and F1 measure are used as criterions to evaluate performance of peak detection algorithms. Compared with multi-scale peak detection (MSPD) and CWT and image segmentation (CWT-IS) methods, all the results showed that WSTGA can achieve better peak detection. More importantly, the experimental results from MALDI-TOF spectra demonstrated that WSTGA can effectively detect more weak peaks and overlapping peaks while maintaining a lower false peak detection rate than MSPD and CWT-IS methods, indicating its great advantages in characteristic peak identification.

Deep learning under mass-to-charge ratio pre-retrieval to realize electron ionization mass spectrometry library retrieval.

RATIONALE: Gas chromatography-mass spectrometry (GC-MS) is an analytical technique widely used in materials science, biomedicine, and other fields. The target compound in the experiment is identified by searching for its mass spectrum in a large mass spectrum database using some algorithms. This work introduces the use of deep learning ranking for the identification of small molecules using low-resolution electron ionization MS. Because different spectra are often very similar, the algorithm produces wrong search results, and the search accuracy needs improvement. Due to the library's large amount of data, the algorithm sometimes requires a large amount of calculation and is very time consuming. METHODS: Given these two problems, this work aims to develop a model for ranking based on mass-to-charge ratio (m/z) pre-retrieval method combined with deep learning to improve search accuracy and reduce the algorithm's computational time. The master spectral library maintained by the National Institute of Standards and Technology is used as the reference library for all the experiments, and the replicate library is used as the query library to evaluate the method's performance. RESULTS: Compared with non-machine learning algorithms, the combination of m/z matching pre-retrieval and deep learning significantly improves library retrieval accuracy by about 4%. Moreover, compared with the deep learning sorting algorithm that does not use the pre-retrieval process, it improves the accuracy of spectral library retrieval by about 0.1% and reduces the computational time of the algorithm by more than 2 h. CONCLUSIONS: This method identifies compounds more efficiently and accurately than non-machine learning and deep learning algorithms without a pre-retrieval process.

Application of zero-phase digital filtering for effective denoising of field asymmetric waveform ion mobility spectrometry signal.

RATIONALE: Field asymmetric waveform ion mobility spectrometry (FAIMS) has a great potential to become a portable technology for rapid detection of chemical and biological agents. However, the ion current signals, measured at the exit of the planar FAIMS directly, may contain different types of noises. The peak information in the FAIMS spectrum, such as the compensation voltage (CV) value at the maximum peak intensity (CVP ) and the peak width at half maximum (Wh ), could not be accurately determined under the weak signal condition, which significantly limits the achievable instrument sensitivity, and there are no existing solutions to the problem. METHODS: This study analyzed the noise type of FAIMS signal in detail, and three different signal processing algorithms, such as median filtering (MF), discrete wavelet transform (DWT), and zero-phase digital filtering (ZDF), were evaluated for their performance in denoising the FAIMS signal. RESULTS: The results show that the standard deviation of CVp obtained from the signal denoised using ZDF algorithm is at least 31.82% smaller as compared to using MF and DWT algorithms. The standard deviation of Wh is at least 45.45% smaller using ZDF algorithm. Moreover, only ZDF algorithm can keep the percentage error for the CV value of the denoised signal to be within 0.50 ± 0.47% of the true CV value, implying the effectiveness of ZDF algorithm in denoising while retaining the integrity of the signal. CONCLUSIONS: The ZDF algorithm greatly reduces the analyte peak extraction error and improves the limit of detection in FAIMS measurements.

A novel hydrophilic hydrogel with a 3D network structure for the highly efficient enrichment of N-glycopeptides.

Protein glycosylation is of great significance in various physiological processes. Nevertheless, it remains a huge challenge to identify glycopeptides in complex biosamples by the direct mass spectrometry analysis due to the low ion efficiency and low abundance of glycopeptides. In this study, a novel hydrogel (denoted as ZIF-8/SAP) with a stable three-dimensional (3D) network structure and excellent hydrophilicity was successfully fabricated to capture glycopeptides with high efficiency. Owing to the unique characteristics, ZIF-8/SAP exhibited great selectivity (1 : 1000), great sensitivity (1 fmol µL-1), large binding capacity (300 mg g-1) and satisfactory reusability (seven cycles). Inspired by the great enrichment performance of the as-prepared material toward glycopeptides, ZIF-8/SAP was further applied to capture glycopeptides from a real human serum sample. The experimental results demonstrated that 217 N-glycosylation sites were identified in 283 N-glycopeptides, corresponding to 95 glycoproteins identified from 10 µL human serum by the nano-LC-MS/MS analysis, revealing the great potential of the novel ZIF-8/SAP hydrogel for glycopeptide enrichment and glycoproteomic research.

A novel graphene oxide/chitosan foam incorporated with metal-organic framework stationary phase for simultaneous enrichment of glycopeptide and phosphopeptide with high efficiency.

A novel hydrophilic porous biocomposite was fabricated by incorporating graphene oxide (GO) @chitosan (CS) foam substrate (GO@CS@ZIF-8 foam) with ZIF-8 crystals in situ via a facile stirring method for simultaneous enrichment of glycopeptides and phosphopeptides from complex biological samples. The experimental results demonstrated that GO@CS@ZIF-8 foam exhibited favorable specificity for simultaneous enrichment of N-glycopeptides and phosphopeptides under the same condition for HRP and ß-casein tryptic digest mixtures. The novel material was further applied to enriching both glycopeptides and phosphopeptides simultaneously from 4 µL complex human serum, and 423 N-glycopeptides and 40 phosphopeptides corresponding to 133 glycoproteins and 29 phosphoproteins were identified, respectively.