Sub-nanoliter metabolomics via mass spectrometry to characterize volume-limited samples

Critical Role of Voltage Application Points in "Analog" and "Digital" Electrospray Ionization Mass Spectrometry

In electrospray ionization (ESI) mass spectrometry (MS), an electric DC potential is often applied to a metal capillary used to infuse a liquid sample. However, in some cases, especially when employing nanoelectrospray ionization (nanoESI), it is convenient to use a nonconducting capillary for sample delivery and spraying. In these cases, the potentials can be applied, for example, using a metal union placed in the proximity of the capillary outlet or to an electrode located in the sample reservoir near the capillary inlet. The optimum potential values, which warrant high MS signals, are different in these two operational conditions. A higher potential needs to be applied when the electrode is placed further away from the capillary outlet. Moreover, sample conductivity has a strong influence on the optimum potential values. Lower potentials must be used with highly conductive electrolytes. Thus, DC voltage scans are required to determine the optimum potentials. Applying electric potential to the electrode located in the sample reservoir, rather than metal union, significantly decreases the appearance of oxidized analyte peaks. We also show that a single-polarity square AC waveform can be applied to the union or sample reservoir electrode, and if its frequency is sufficiently high, it has a similar effect as decreasing DC voltage, allowing for digital control of electrospray with square waves (by varying duty cycle). Interestingly, the liquid meniscus oscillation frequency is independent of the AC signal frequency if the frequency is sufficiently high. Applying the AC signal in certain conditions stabilizes the electrospray plume. These observations reveal the resemblance of the ESI sample line to an RC circuit.

rIDIMS: A novel tool for processing direct-infusion mass spectrometry data

Metabolomics using mass spectrometry-only (MS) analysis either by continuous or intermittent direct infusion (DIMS) and ambient ionization techniques (AMS) has grown in popularity due to their rapid, high-throughput nature and the advantage of performing fast analysis with minimal or no sample pretreatments. But currently, end-users without programming knowledge do not find applications with Graphical User Interface (GUI) specialized in processing DIMS or AMS data. Specifically, there is a lack of standardized workflow for processing data from limited sample sizes and scans from different total ion chronograms (TIC).To address this gap, we present rIDIMS, a browser-based application that offers a straightforward and fast workflow focusing on high-quality scan selection, grouping of isotopologues and adducts, data alignment, binning, and filtering. We also introduce a novel function for selecting TIC scans that is reproducible and statistically reliable, which is a feature particularly useful for studies with limited sample sizes. After processing in rIDIMS, the result is exported in an HTML report document that presents publication-quality figures, statistical data and tables, ready to be customized and exported. We demonstrate rIDIMS functionality in three cases: (i) Classification of coffee bean species through the chemical profile obtained with Mass Spec Pen; (ii) Public repository DIMS data from lipid profiling in monogenic insulin resistance syndromes, and (iii) Lipids for lung cancer classification. We show that our implementation facilitates the processing of AMS and DIMS data through an easy and intuitive interface, contributing to reproducible and reliable metabolomic investigations. Indeed, rIDIMS function asa user-friendly GUI based Shiny web application for intuitive use by end-users (available at https://github.com/BioinovarLab/rIDIMS).

Integration of metabolomics and other omics: from microbes to microbiome

Metabolomics is a cutting-edge omics technology that identifies metabolites in organisms and their environments and tracks their fluctuations. This field has been extensively utilized to elucidate previously unknown metabolic pathways and to identify the underlying causes of metabolic changes, given its direct association with phenotypic alterations. However, metabolomics inherently has limitations that can lead to false positives and false negatives. First, most metabolites function as intermediates in multiple biochemical reactions, making it challenging to pinpoint which specific reaction is responsible for the observed changes in metabolite levels. Consequently, metabolic processes that are anticipated to vary with metabolite concentrations may not exhibit significant changes, generating false positives. Second, the range of metabolites identified is contingent upon the analytical conditions employed. Until now, no analytical instrument or protocol has been developed that can capture all metabolites simultaneously. Therefore, some metabolites are changed but are not detected, generating false negatives. In this review, we offer a novel and systematic assessment of the limitations of omics technologies and propose-specific strategies to minimize false positives and false negatives through multi-omics approaches. Additionally, we provide examples of multi-omics applications in microbial metabolic engineering and host-microbiome interactions, helping other researchers gain a better understanding of these strategies. KEY POINTS: • Metabolomics identifies metabolic shifts but has inherent false positive/negatives. • Multi-omics approaches help overcome metabolomics' inherent limitations.

Charge Generation and Enhancement of Key Components of Triboelectric Nanogenerators: A Review

The past decade has witnessed remarkable progress in high-performance Triboelectric nanogenerators (TENG) with the design and synthesis of functional dielectric materials, the exploration of novel dynamic charge transport mechanisms, and the innovative design of architecture, making it one of the most crucial technologies for energy harvesting. High output charge density is fundamental for TENG to expand its application scope and accelerate industrialization; it depends on the dynamic equilibrium of charge generation, trapping, de-trapping, and migration within its core components. Here, this review classifies and summarizes innovative approaches to enhance the charge density of the charge generation, charge trapping, and charge collection layers. The milestone of high charge density TENG is reviewed based on material selection and innovative mechanisms. The state-of-the-art principles and techniques for generating high charge density and suppressing charge decay are discussed and highlighted in detail, and the distinct charge transport mechanisms, the technologies of advanced materials preparation, and the effective charge excitation strategy are emphatically introduced. Lastly, the bottleneck and future research priorities for boosting the output charge density are summarized. A summary of these cutting-edge developments intends to provide readers with a deep understanding of the future design of high-output TENG.

The involvement of oral bacteria in inflammatory bowel disease

Microorganisms play an important role in the pathogenesis of inflammatory bowel disease (IBD). The oral cavity, the second-largest microbial niche, is connected to the gastro-intestinal tract. Ectopic gut colonization by oral microbes is a signature of IBD. Current studies suggest that patients with IBD often report more oral manifestations and these oral issues are closely linked with disease activity. Murine studies have indicated that several oral microbes exacerbate intestinal inflammation. Moreover, intestinal inflammation can promote oral microbial dysbiosis and the migration of oral microbes to the gastro-intestinal tract. The reciprocal consequences of oral microbial dysbiosis and IBD, specifically through metabolic alterations, have not yet been elucidated. In this review, we summarize the relationship between oral bacteria and IBD from multiple perspectives, including clinical manifestations, microbial dysbiosis, and metabolic alterations, and find that oral pathogens increase anti-inflammatory metabolites and decrease inflammation-related metabolites.

Rapid analysis of untreated food samples by gel loading tip spray ionization mass spectrometry

Rapid, efficient, versatile, easy-to-use, and non-expensive analytical approaches are globally demanded for food analysis. Many ambient ionization approaches based on electrospray ionization (ESI) have been developed recently for the rapid molecular characterization of food products. However, those approaches mainly suffer from insufficient signal duration for comprehensive chemical characterization by tandem MS analysis. Here, a commercially available disposable gel loading tip is used as a low-cost emitter for the direct ionization of untreated food samples. The most important advantages of our approach include high stability, and durability of the signal (> 10 min), low cost (ca. 0.1 USD per run), low sample and solvent consumption, prevention of tip clogging and discharge, operational simplicity, and potential for automation. Quantitative analysis of sulfapyridine, HMF (hydroxymethylfurfural), and chloramphenicol in real sample shows the limit-of-detection 0.1 μg mL-1, 0.005 μg mL-1, 0.01 μg mL-1; the linearity range 0.1-5 μg mL-1, 0.005-0.25 μg mL-1, 0.01-1 μg mL-1; and the linear fits R2 ≥ 0.980, 0.991, 0.986. Moreover, we show that tip-ESI can also afford sequential molecular ionization of untreated viscous samples, which is difficult to achieve by conventional ESI. We conclude that tip-ESI-MS is a versatile analytical approach for the rapid chemical analysis of untreated food samples.

Rapid screening of infertility-associated gynecological conditions via ambient glow discharge mass spectrometry utilizing urine metabolic fingerprints

Infertility presents a widespread challenge for many families worldwide, often arising from various gynecological diseases (GDs) that hinder successful pregnancies. Current diagnostic methods for GDs have disadvantages such as low efficiency, high cost, misdiagnose, invasive injury and etc. This paper introduces a rapid, non-invasive, efficient, and straightforward analytical method that utilizes desorption, separation, and ionization mass spectrometry (DSI-MS) platform in conjunction with machine learning (ML) to detect urine metabolite fingerprints in patients with different GDs. We analyzed 257 samples from patients diagnosed with polycystic ovary syndrome (PCOS), premature ovarian insufficiency (POI), diminished ovarian reserve (DOR), endometriosis (EMS), recurrent pregnancy loss (RPL), recurrent implantation failure (RIF), and 87 samples from healthy control (HC) individuals. We identified metabolite differences and dysregulated pathways through dimensionality reduction methods, with the result of the discovery of 7 potential biomarkers for GDs diagnosis. The ML method effectively distinguished subtle differences in urine metabolite fingerprints. We anticipate that this innovative approach will offer a patient-friendly, rapid screening, and differentiation method for infertility-related GDs patients.

Multi-omic analysis tools for microbial metabolites prediction

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.

Triboelectric Nanogenerators for the Masses: A Low-Cost Do-It-Yourself Pulsed Ion Source for Sample-Limited Applications

Triboelectric nanogenerators (TENG) are useful devices for converting mechanical motion into electric current using readily available materials. Though the applications for these devices span across many fields, TENG can be leveraged for mass spectrometry (MS) as inexpensive and effective power supplies for pulsed nanoelectrospray ionization (nESI). The inherently discontinuous spray provided by TENG is particularly useful in scenarios where high sample economy is imperative, as in the case of ultraprecious samples. Previous work has shown the utility of TENG MS as a highly sensitive technique capable of yielding quality spectra from only a few microliters of sample at low micromolar concentrations. As the field of miniaturized, fieldable mass spectrometers grows, it remains critical to develop advanced ion sources with similarly small power requirements and footprints. Here, we present a redesigned TENG ion source with a sub-1000 USD material cost, lower power consumption, reduced footprint, and improved capabilities. We validate the performance of this new device for a diverse set of applications, including lipid double bond localization and native protein analysis.

Metabolic profiling of Oryza sativa seedlings under chilling stress using nanoliter electrospray ionization mass spectrometry

Low temperatures significantly impact on rice (Oryza sativa) yield and quality. Traditional metabolomic techniques, often involving time-consuming chromatography-mass spectrometry procedures, are currently in use. This study investigated metabolomic responses of rice seedlings under low-temperature stress using nanoliter electrospray ionization mass spectrometry (nanoESI-MS) in combination with multivariate analysis. Results revealed distinct metabolic profiles in 'Qiutianxiaoting' (japonica) and '93-11' (indica) rice seedlings. Among the 36 identified compounds in rice, seven key metabolites, comprising l-glutamic acid, asparagine, tryptophan, citric acid, α-linolenic acid, malic acid, and inositol, were identified as responsive to cold stress. Notably, malic acid content reached 1332.40 μg/g dry weight in Qiutianxiaoting and 1444.13 μg/g in 93-11. Both the qualitative and quantitative results of nanoESI-MS were further confirmed through gas chromatography-mass spectrometry validation. The findings highlight the potential of nanoESI-MS for rapidly characterizing crucial metabolites across diverse plant species under exposure to stress.

Multi-biomarker combination detection system for diagnosis and classification of dry eye disease by imaging of a multi-channel metasurface

Dry eye disease (DED) is one of the most common ocular surface diseases, characterized by unstable tear film and ocular inflammation, affecting hundreds of millions of people worldwide. Currently, the clinical diagnosis of DED mainly relies on physical methods such as optical microscopy and ocular surface interferometric imaging, but classifying DED is still difficult. Here, we propose a compact and portable immune detection system based on the direct imaging of a nanophotonic metasurface with gradient geometry, for fast and ultra-sensitive detection of multiple biomarkers (i.e. Matrix metalloproteinase-9 (MMP-9), Lipocalin-1 (LCN-1), Lactoferrin (LTF)) in tears for the diagnosis and classification of DED. This centimeter-scale concentric nanophotonic metasurface, which consists of millions of unique metallic nanostructures, was fabricated through a cost-effective nanoimprint lithography (NIL) process. The immune detection system based on the antibody-modified metasurface shows favorable detection selectivity, an ultra-high sensitivity (3350 pixels/Refractive Index Unit (RIU)) and low limit of detection (LOD) (0.3 ng/mL for MMP-9, 1 ng/mL for LTF, and 0.5 ng/mL for LCN-1). Further clinical sampling and detection results demonstrated that this multi-biomarker detection system enabled accurate determination and symptom classification of DED, manifesting high correlation and consistency with clinical diagnosis results. The advantages such as low sample consumption, one-step detection, simple operation, and simultaneous detection of multiple biomarkers make the platform promising for screening and detecting a broader range of biomarker combinations in clinical practice.

Triboelectric Nanogenerator-Coated Blade Spray Mass Spectrometry for Volume-Limited Drug Analysis

The demand for analytical tools for the analysis of low-concentration volume-limited samples has driven researchers to explore new analytical approaches. Mass spectrometry excels at trace analysis due to its high sensitivity and specificity, whereas ambient methods simplify, or completely eliminate sample preparation. Herein, we report a triboelectric nanogenerator-coated blade spray ambient mass spectrometry (TENG-CBS MS) method for the extraction, elution, and ionization of volume-limited, low-concentration small molecule drug samples with minimum sample preparation. Using a TENG device as the CBS power supply, we show it is possible to extract and analyze drug samples in a pulsed fashion at sub-nanogram to picogram levels with good stability and reproducibility. A wide range of analytes polarities were tested. Results indicated this method could also be useful for the analysis of low-level analytes in precious, volume limited samples in a simple single step.

Triboelectric Nanogenerators: Low-Cost Power Supplies for Improved Electrospray Ionization

Electrospray ionization (ESI) is one of the most popular methods to generate ions for mass spectrometry (MS). When compared with other ionization techniques, it can generate ions from liquid-phase samples without additives, retaining covalent and non-covalent interactions of the molecules of interest. When hyphenated to liquid chromatography, it greatly expands the versatility of MS analysis of complex mixtures. However, despite the extensive growth in the application of ESI, the technique still suffers from some drawbacks when powered by direct current (DC) power supplies. Triboelectric nanogenerators promise to be a new power source for the generation of ions by ESI, improving on the analytical capabilities of traditional DC ESI. In this review we highlight the fundamentals of ESI driven by DC power supplies, its contrasting qualities to triboelectric nanogenerator power supplies, and its applications to three distinct fields of research: forensics, metabolomics, and protein structure analysis.

Novel Isotope-Labeled Derivatization Strategy for the Simultaneous Analysis of Fatty Acids and Fatty Alcohols and Its Application in Idiopathic Inflammatory Myopathies and Pancreatic Cancer

Fatty acids (FAs) and fatty alcohols (FOHs) are essential compounds for maintaining life. Due to the inherent poor ionization efficiency, low abundance, and complex matrix effect, such metabolites are challenging to precisely quantify and explore deeply. In this study, a pair of novel isotope derivatization reagents known as d0/d5-1-(2-oxo-2-(piperazin-1-yl) ethyl) pyridine-1-ium (d0/d5-OPEPI) were designed and synthesized, and an in-depth screening strategy for FAs and FOHs was established based on d0/d5-OPEPI coupled with liquid chromatography-tandem high-resolution mass spectrometry (LC-HRMS/MS). Using this approach, a total of 332 metabolites were identified and annotated (some of the FAs and FOHs were reconfirmed by standards). Our results demonstrated that OPEPI labeling could significantly enhance the MS response of FAs and FOHs via the introduction of permanently charged tags. The detection sensitivities of FAs were increased by 200-2345-fold compared with the nonderivatization method. At the same time, for FOHs, due to the absence of ionizable functional groups, sensitive detection was achieved utilizing OPEPI derivatization. One-to-one internal standards were provided by using d5-OPEPI labeling to minimize the errors in quantitation. Moreover, the method validation results showed that the method was stable and reliable. Finally, the established method was successfully applied to the study of the FA and FOH profiles of two heterogeneous severe clinical disease tissues. This study would improve our understanding of the pathological and metabolic mechanisms of FAs and FOHs for inflammatory myopathies and pancreatic cancer and also prove the generality and accuracy of the developed analytical method for complex samples.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Machine learning-empowered cis-diol metabolic fingerprinting enables precise diagnosis of primary liver cancer

Cis-diol metabolic reprogramming evolves during primary liver cancer (PLC) initiation and progression. However, owing to the low concentrations and highly structural heterogeneity of cis-diols in vivo, severe interference from complex biofluids and limited profiling coverage of existing methods, in-depth profiling of cis-diol metabolites and linking their specific changes with PLC remain challenging. Besides, due to the low specificity of widely used protein biomarkers, accurate classification of PLC from hepatitis still represents an unmet need in clinical diagnostics. Herein, to high-coverage profile cis-diols and explore the translational potential of them as biomarkers, a machine learning-empowered boronate affinity extraction-solvent evaporation assisted enrichment-mass spectrometry (MLE-BESE-MS) was developed. A single analytical platform integrated with multiple complementary functions, including pH-controlled boronate affinity extraction, solvent evaporation-assisted enrichment and nanoelectrospray ionization-based cis-diol identification, was constructed, which significantly improved the metabolite coverage. Meanwhile, by virtue of machine learning (principal components analysis, orthogonal partial least-squares discrimination analysis and random forest), collected cis-diols were statistically screened to extract efficient features for precise PLC diagnosis, and the results outperform the routinely used protein biomarker-based methods both in sensitivity (87.5% vs. less than 70%) and specificity (85.7% vs. ca. 80%). This machine learning-empowered integrated MS platform advanced the targeted metabolic analysis for early cancer diagnosis, rendering great promise for clinical translation.

Metabolomic analysis of circulating tumor cells derived liver metastasis of colorectal cancer

Metabolic reprogramming is one of the essential features of tumor that may dramatically contribute to metastasis and collapse. The metabolic profiling is investigated on the patient derived tissue and cancer cell line derived mouse metastasis xenograft. As well-recognized "seeds" for remote metastasis of tumor, role of circulating tumor cells (CTCs) in the study of metabolic reprogramming feature of tumor is yet to be elucidated. More specifically, whether there is difference of metabolic features of liver metastasis in colorectal cancer (CRC) derived from either CTCs or cancer cell line is still unknown. In this study, comprehensive untargeted metabolomics was performed using high performance liquid chromatography-mass spectrometry (HPLC-MS) in liver metastasis tissues from CT26 cells and CTCs derived mouse models. We identified 288 differential metabolites associated with the pathways such as one carbon pool by folate, folate biosynthesis and histidine metabolism through bioinformation analysis. Multiple gene expression was upregulated in the CTCs derived liver metastasis, specifically some specific enzymes. These results indicated that the metabolite phenotype and corresponding gene expression in the CTCs derived liver metastasis tissues was different from the parental CT26 cells, displaying a specific up-regulation of mRNAs involved in the above metabolism-related pathways. The metabolic profile of CTCs was characterized on the liver metastatic process in colorectal cancer. The invasion ability and chemo drug tolerance of the CTCs derived tumor and metastasis was found to be overwhelming higher than cell line derived counterpart. Identification of the differential metabolites will lead to a better understanding of the hallmarks of the cancer progression and metastasis, which may suggest potential attractive target for treating metastatic CRC.

Fine optimization of a dissolution dynamic nuclear polarization experimental setting for 13C NMR of metabolic samples

NMR-based analysis of metabolite mixtures provides crucial information on biological systems but mostly relies on 1D 1 H experiments for maximizing sensitivity. However, strong peak overlap of 1 H spectra often is a limitation for the analysis of inherently complex biological mixtures. Dissolution dynamic nuclear polarization (d-DNP) improves NMR sensitivity by several orders of magnitude, which enables 13 C NMR-based analysis of metabolites at natural abundance. We have recently demonstrated the successful introduction of d-DNP into a full untargeted metabolomics workflow applied to the study of plant metabolism. Here we describe the systematic optimization of d-DNP experimental settings for experiments at natural 13 C abundance and show how the resolution, sensitivity, and ultimately the number of detectable signals improve as a result. We have systematically optimized the parameters involved (in a semi-automated prototype d-DNP system, from sample preparation to signal detection, aiming at providing an optimization guide for potential users of such a system, who may not be experts in instrumental development). The optimization procedure makes it possible to detect previously inaccessible protonated 13 C signals of metabolites at natural abundance with at least 4 times improved line shape and a high repeatability compared to a previously reported d-DNP-enhanced untargeted metabolomic study. This extends the application scope of hyperpolarized 13 C NMR at natural abundance and paves the way to a more general use of DNP-hyperpolarized NMR in metabolomics studies.

A Direct Infusion Probe for Rapid Metabolomics of Low-Volume Samples

Targeted and nontargeted metabolomics has the potential to evaluate and detect global metabolite changes in biological systems. Direct infusion mass spectrometric analysis enables detection of all ionizable small molecules, thus simultaneously providing information on both metabolites and lipids in chemically complex samples. However, to unravel the heterogeneity of the metabolic status of cells in culture and tissue a low number of cells per sample should be analyzed with high sensitivity, which requires low sample volumes. Here, we present the design and characterization of the direct infusion probe, DIP. The DIP is simple to build and position directly in front of a mass spectrometer for rapid metabolomics of chemically complex biological samples using pneumatically assisted electrospray ionization at 1 μL/min flow rate. The resulting data is acquired in a square wave profile with minimal carryover between samples that enhances throughput and enables several minutes of uniform MS signal from 5 μL sample volumes. The DIP was applied to study the intracellular metabolism of insulin secreting INS-1 cells and the results show that exposure to 20 mM glucose for 15 min significantly alters the abundance of several small metabolites, amino acids, and lipids.

An injectable thermosensitive Pluronic F127/hyaluronic acid hydrogel loaded with human umbilical cord mesenchymal stem cells and asiaticoside microspheres for uterine scar repair

Uterine scar was one of the long-term complications cesarean section. In this study, an thermo-responsive injectable hydrogel loaded with human umbilical cord mesenchymal stem cells (UCMSCs) and asiaticoside microspheres (AMs) was used for uterine scar repair, which was prepared by optimizing the mixed ratio of aldehyde-functionalized Pluronic F127 (F127-CHO) and adipic dihydrazide-modified hyaluronic acid (AHA). The asiaticoside was loaded in Poly (DL-lactide-co-gycolide) (PLGA) by emulsion- diffusion-evaporation method. The hydrogel had appropriate pore size, good mechanical property, and slow release ability of asiaticoside. In vitro cell experiments demonstrated that F127-CHO/AHA/AMs could effectively promote stem cell adhesion and proliferation, promote angiogenesis, and provide a suitable microenvironment for cell survival. The F127-CHO/AHA/AMs/UCMSCs hydrogel was further used to repair uterine scar in female SD rats. The results showed that the prepared hydrogel could promote the proliferation of rat endometrial cells, promote the regeneration of glands, reduce the degree of endometrial fibrosis and restore the morphology of uterine cavity. The hydrogel could upregulate expression of Ki67 and IGF-1, downregulate TGF-β1 expression and promote M1-M2 transition of macrophages. This study confirmed that the prepared hydrogel could be used as an effective transplantation strategy, which could be expected to achieve clinical transformation of uterine scar repair.

Coral-like Magnetic Particles for Chemoselective Extraction of Anionic Metabolites

To date, advanced chemical biology tools for chemoselective extraction of metabolites are limited. In this study, unique coral-like polymer particles were synthesized via high concentrations of 1-ethyl-3-(3-(dimethylamino)propyl) carbodiimide hydrochloride (EDC)/N-hydroxysuccinimide (NHS), which are usually used as condensation agents. The polymers can wrap or adhere Fe3O4 nanoparticles (Fe3O4-NPs) to form polymer magnetic microparticles (PMMPs). With abundant NHS-activated moieties on their surface, the coral-like PMMPs could be modified by cystamine for the chemoselective extraction of phosphate/carboxylate anion metabolites from complex biological samples. Finally, 97 metabolites including nucleotides, phosphates, phosphate sugars, carboxylate sugars, and organic acids were extracted and identified from serum, tissues, and cells. These metabolites are involved in four major metabolic pathways including glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, and nucleotide metabolism. This study has provided a cost-effective and easy-to-implement preparation of PMMPs with a robust chemoselective extraction ability and versatile applications.

Nanomaterials-assisted metabolic analysis toward in vitro diagnostics

In vitro diagnostics (IVD) has played an indispensable role in healthcare system by providing necessary information to indicate disease condition and guide therapeutic decision. Metabolic analysis can be the primary choice to facilitate the IVD since it characterizes the downstream metabolites and offers real-time feedback of the human body. Nanomaterials with well-designed composition and nanostructure have been developed for the construction of high-performance detection platforms toward metabolic analysis. Herein, we summarize the recent progress of nanomaterials-assisted metabolic analysis and the related applications in IVD. We first introduce the important role that nanomaterials play in metabolic analysis when coupled with different detection platforms, including electrochemical sensors, optical spectrometry, and mass spectrometry. We further highlight the nanomaterials-assisted metabolic analysis toward IVD applications, from the perspectives of both the targeted biomarker quantitation and untargeted fingerprint extraction. This review provides fundamental insights into the function of nanomaterials in metabolic analysis, thus facilitating the design of next-generation diagnostic devices in clinical practice.

Ultra-trace enriching biosensing in nanoliter sample

Rapid detection and accurate analysis of trace samples is an important prerequisite for precision medicine. Here we integrated capillary with ultrasound to induce biomarkers enrichment in nanoliter samples, and developed a nanoliter sample enrichment analysis method for ultra-trace miRNA biosensing. The interaction between ultrasonic field and capillary provides a gradient ultrasound field, which is essential for the aggregation of functionalized microspheres along with the enrichment of specific biomarkers. The results indicated that the enrichment of the biomarkers effectively enhanced the fluorescence intensity, and the limit of detection reaches 7.8✕10^-12 M in 100 nL. Such integrated device can realize ultrasonic enrichment and visual analysis of target samples, and provides a new idea for rapid and highly sensitive detection of ultra-trace biomarkers in clinical diagnosis.

The Potential of Metabolomics in Biomedical Applications

The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed the discovery of relevant metabolites that characterize a wide variety of human phenotypes with respect to health, disease, drug monitoring, and even aging. Metabolomics, parallel to genomics, has led to the discovery of biomarkers and has aided in the understanding of a diversity of molecular mechanisms, highlighting its application in precision medicine. This review focuses on the metabolomics that can be applied to improve human health, as well as its trends and impacts in metabolic and neurodegenerative diseases, cancer, longevity, the exposome, liquid biopsy development, and pharmacometabolomics. The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development. Biomedical applications of metabolomics can already be foreseen to monitor the progression of metabolic diseases, such as obesity and diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, and genomics; these can assess disease severity and predict a potential treatment. Future endeavors should focus on determining the applicability and clinical utility of metabolomic-derived markers and their appropriate implementation in large-scale clinical settings.

Metabolic phenotyping of tear fluid as a prognostic tool for personalised medicine exemplified by T2DM patients

Background/aims

Concerning healthcare approaches, a paradigm change from reactive medicine to predictive approaches, targeted prevention, and personalisation of medical services is highly desirable. This raises demand for biomarker signatures that support the prediction and diagnosis of diseases, as well as monitoring strategies regarding therapeutic efficacy and supporting individualised treatments. New methodological developments should preferably rely on non-invasively sampled biofluids like sweat and tears in order to provide optimal compliance, reduce costs, and ensure availability of the biomaterial. Here, we have thus investigated the metabolic composition of human tears in comparison to finger sweat in order to find biofluid-specific marker molecules derived from distinct secretory glands. The comprehensive investigation of numerous biofluids may lead to the identification of novel biomarker signatures. Moreover, tear fluid analysis may not only provide insight into eye pathologies but may also be relevant for the prediction and monitoring of disease progression and/ or treatment of systemic disorders such as type 2 diabetes mellitus.

Methods

Sweat and tear fluid were sampled from 20 healthy volunteers using filter paper and commercially available Schirmer strips, respectively. Finger sweat analysis has already been successfully established in our laboratory. In this study, we set up and evaluated methods for tear fluid extraction and analysis using high-resolution mass spectrometry hyphenated with liquid chromatography, using optimised gradients each for metabolites and eicosanoids. Sweat and tears were systematically compared using statistical analysis. As second approach, we performed a clinical pilot study with 8 diabetic patients and compared them to 19 healthy subjects.

Results

Tear fluid was found to be a rich source for metabolic phenotyping. Remarkably, several molecules previously identified by us in sweat were found significantly enriched in tear fluid, including creatine or taurine. Furthermore, other metabolites such as kahweol and various eicosanoids were exclusively detectable in tears, demonstrating the orthogonal power for biofluid analysis in order to gain information on individual health states. The clinical pilot study revealed that many endogenous metabolites that have previously been linked to type 2 diabetes such as carnitine, tyrosine, uric acid, and valine were indeed found significantly up-regulated in tears of diabetic patients. Nicotinic acid and taurine were elevated in the diabetic cohort as well and may represent new biomarkers for diabetes specifically identified in tear fluid. Additionally, systemic medications, like metformin, bisoprolol, and gabapentin, were readily detectable in tears of patients.

Conclusions

The high number of identified marker molecules found in tear fluid apparently supports disease development prediction, developing preventive approaches as well as tailoring individual patients’ treatments and monitoring treatment efficacy. Tear fluid analysis may also support pharmacokinetic studies and patient compliance control.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00272-7.

Omics Technologies for High-Throughput-Screening of Cell-Biomaterial Interactions

Recent research effort in biomaterial development has largely focused on engineering bio-instructive materials to stimulate specific cell signaling. Assessing the biological performance of these materials using time-consuming and trial-and-error traditional...

Triboelectric Nanogenerator-Based Sensor Systems for Chemical or Biological Detection

The rapid advances in the Internet of things and wearable devices have created a massive platform for sensor systems that detect chemical or biological agents. The accelerated development of these devices in recent years has simultaneously aggravated the power supply problems. Triboelectric nanogenerators (TENGs) represent a thriving renewable energy technology with the potential to revolutionize this field. In this review, the significance of TENG-based sensor systems in chemical or biological detection from the perspective of the development of power supply for biochemical sensors is discussed. Further, a range of TENGs are classified according to their roles as power supplies and/or self-powered active sensors. The TENG powered sensor systems are further discussed on the basis of their framework and applications. The working principles and structures of different TENG-based self-powered active sensors are presented, along with the classification of the sensors based on these factors. In addition, some representative applications are introduced, and the corresponding challenges are discussed. Finally, some perspectives for the future innovations of TENG-based sensor systems for chemical/biological detection are discussed.

Simultaneous 3-Nitrophenylhydrazine Derivatization Strategy of Carbonyl, Carboxyl and Phosphoryl Submetabolome for LC-MS/MS-Based Targeted Metabolomics with Improved Sensitivity and Coverage

Metabolomics is a powerful and essential technology for profiling metabolic phenotypes and exploring metabolic reprogramming, which enables the identification of biomarkers and provides mechanistic insights into physiology and disease. However, its applications are still limited by the technical challenges particularly in its detection sensitivity for the analysis of biological samples with limited amount, necessitating the development of highly sensitive approaches. Here, we developed a highly sensitive liquid chromatography tandem mass spectrometry method based on a 3-nitrophenylhydrazine (3-NPH) derivatization strategy that simultaneously targets carbonyl, carboxyl, and phosphoryl groups for targeted metabolomic analysis (HSD^ccp-TM) in biological samples. By testing 130 endogenous metabolites including organic acids, amino acids, carbohydrates, nucleotides, carnitines, and vitamins, we showed that the derivatization strategy resulted in significantly improved detection sensitivity and chromatographic separation capability. Metabolic profiling of merely 60 oocytes and 5000 hematopoietic stem cells primarily isolated from mice demonstrated that this method enabled routine metabolomic analysis in trace amounts of biospecimens. Moreover, the derivatization strategy bypassed the tediousness of inferring the MS fragmentation patterns and simplified the complexity of monitoring ion pairs of metabolites, which greatly facilitated the metabolic flux analysis (MFA) for glycolysis, the tricarboxylic acid (TCA) cycle, and pentose phosphate pathway (PPP) in cell cultures. In summary, the novel 3-NPH derivatization-based method with high sensitivity, good chromatographic separation, and broad coverage showed great potential in promoting metabolomics and MFA, especially in trace amounts of biospecimens.

Advances in Smart Sensing and Medical Electronics by Self-Powered Sensors Based on Triboelectric Nanogenerators

With the rapid progress of artificial intelligence, humans are moving toward the era of the intelligent connection of all things. Therefore, the demand for sensors is drastically increasing with developing intelligent social applications. Traditional sensors must be triggered by an external power source and the energy consumption is high for equipment that is widely distributed and working intermittently, which is not conducive to developing sustainable green and healthy applications. However, self-powered sensors based on triboelectric nanogenerators (TENG) can autonomously harvest energy from the surrounding environment and convert this energy into electrical energy for storage. Sensors can also be self-powered without an external power supply, which is vital for smart cities, smart homes, smart transportation, environmental monitoring, wearable devices, and bio-medicine. This review mainly summarizes the working mechanism of TENG and the research progress of self-powered sensors based on TENG about the Internet of Things (IoT), robotics, human–computer interaction, and intelligent medical fields in recent years.

New software tools, databases, and resources in metabolomics: updates from 2020

BACKGROUND

Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success.

AIM OF THE REVIEW

In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community.

KEY SCIENTIFIC CONCEPTS OF REVIEW

In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.

Triboelectric Nanogenerator Ion Mobility-Mass Spectrometry for In-Depth Lipid Annotation

Lipids play a critical role in cell membrane integrity, signaling, and energy storage. However, in-depth structural characterization of lipids is still challenging and not routinely possible in lipidomics experiments. Techniques such as collision-induced dissociation (CID) tandem mass spectrometry (MS/MS), ion mobility (IM) spectrometry, and ultrahigh-performance liquid chromatography are not yet capable of fully characterizing double-bond and sn-chain position of lipids in a high-throughput manner. Herein, we report on the ability to structurally characterize lipids using large-area triboelectric nanogenerators (TENG) coupled with time-aligned parallel (TAP) fragmentation IM-MS analysis. Gas-phase lipid epoxidation during TENG ionization, coupled to mobility-resolved MS3 via TAP IM-MS, enabled the acquisition of detailed information on the presence and position of lipid C═C double bonds, the fatty acyl sn-chain position and composition, and the cis/trans geometrical C═C isomerism. The proposed methodology proved useful for the shotgun lipidomics analysis of lipid extracts from biological samples, enabling the detailed annotation of numerous lipid isobars.

Understanding Ustilago maydis Infection of Multiple Maize Organs

Ustilago maydis is a smut fungus that infects all aerial maize organs, namely, seedling leaves, tassels, and ears. In all organs, tumors are formed by inducing hypertrophy and hyperplasia in actively dividing cells; however, the vast differences in cell types and developmental stages for different parts of the plant requires that U. maydis have both general and organ-specific strategies for infecting maize. In this review, we summarize how the maize–U. maydis interaction can be studied using mutant U. maydis strains to better understand how individual effectors contribute to this interaction, either through general or specific expression in a cell type, tissue, or organ. We also examine how male sterile maize mutants that do not support tumor formation can be used to explore key features of the maize anthers that are required for successful infection. Finally, we discuss key unanswered questions about the maize–U. maydis interaction and how new technologies can potentially be used to answer them.

Nested-channel for on-demand alternation between electrospray ionization regimes

On-demand electrospray ionization from different liquid channels in the same emitter was realized using filamented capillary and gas phase charge supply. The solution sub-channel was formed when back-filling solution to the emitter tip by capillary action along the filament. Gas phase charge carriers were used to trigger electrospray ionization from the solution meniscus at the tip. The meniscus at the tip opening may be fully filled or partially empty to generate electrospray ionization in main-channel regime and sub-channel regime, respectively. For emitters with 4 μm tip opening, the two nested electrospray (nested-ESI) channels accommodated ESI flow rates ranging from 50 pL min-1 to 150 nL min-1. The platform enabled on-demand regime alternations within one sample run, in which the sub-channel regime generated smaller charged droplets. Ionization efficiencies for saccharides, glycopeptide, and proteins were enhanced in the sub-channel regime. Non-specific salt adducts were reduced and identified by regime alternation. Surprisingly, the sub-channel regime produced more uniform responses for a peptide mixture whose relative ionization efficiencies were insensitive to ESI conditions in previous picoelectrospray study. The nested channels also allowed effective washing of emitter tip for multiple sampling and analysis operations.