Metabolic Fingerprinting on a Plasmonic Gold Chip for Mass Spectrometry Based in Vitro Diagnostics

Nanoplasmonic sensors for extracellular vesicles and bacterial membrane vesicles

Extracellular vesicles (EVs) are promising tools for the early diagnosis of diseases, and bacterial membrane vesicles (MVs) are especially important in health and environment monitoring. However, detecting EVs or bacterial MVs presents significant challenges for the clinical translation of EV-based diagnostics. In this Review, we provide a comprehensive discussion on the basics of nanoplasmonic sensing and emphasize recent developments in nanoplasmonics-based optical sensors to effectively identify EVs or bacterial MVs. We explore various nanoplasmonic sensors tailored for EV or bacterial MV detection, emphasizing the application of localized surface plasmon resonance through gold nanoparticles and their multimers. Additionally, we highlight advanced EV detection techniques based on surface plasmon polaritons using plasmonic thin film and nanopatterned structures. Furthermore, we evaluate the improved detection capability of surface-enhanced Raman spectroscopy in identifying and classifying these vesicles, aided by plasmonic nanostructures. Nanoplasmonic sensing techniques have remarkable precision and sensitivity, making them a potential tool for accurate EV detection in clinical applications, facilitating point-of-care molecular diagnostics. Finally, we summarize the challenges associated with nanoplasmonic EV or bacterial MV sensors and offer insights into potential future directions for this evolving field.

A single atom cobalt anchored MXene bifunctional platform for rapid, label-free and high-throughput biomarker analysis and tissue imaging

Few of single-atom materials have been served as platform to analyze small molecules for surface assisted laser desorption/ionization mass spectrometry (SALDI-MS). Herein, a novel single Co atom-anchored MXene (Co-N-Ti3C2) is prepared to achieve enhanced SALDI-MS and mass spectrometry imaging (MSI) performance for the first time. The Co-N-Ti3C2 films were prepared by a simple in situ self-assembly strategy to generate an efficient SALDI-MS platform. Compared to typical inorganic/organic matrices, Co-N-Ti3C2 films exhibit superior performance in small molecules detection with ultra-high sensitivity (LOD at amol level), excellent repeatability (CV <4%), clean background and wide analyte coverage, enabling accurate quantitative analysis of various low-concentration metabolites from 1 μL biofluid in seconds. Its usage efficiently enhanced SALDI-MS detection of various small-molecule biomarkers such as amino acids, succinic acid, itaconic acid, arachidonic acid, citrulline, prostaglandin E2, creatinine, uric acid, glutamine, D-mannose, cholesterol and inositol in positive ion mode. The blood glucose level in humans was successfully determined from a linearity concentration range (0.25-10 mM). Notably, the Co-N-Ti3C2 assisted SALDI-MSI enables study the spatial distribution of small molecules covering the range central to metabolomics at a high resolution on a tissue section. Furthermore, Co-N-Ti3C2 platform revealed a specific peak profile that distinguishes osteoarthritis (OA) from rheumatoid arthritis (RA) tissue. Density functional theory theoretical investigation revealed that single Co atoms anchored on Ti3C2 could highly enhanced the ionization ability of metabolites, resulting in high-sensitivity and heterogeneous metabolome coverage.

Metabolic profiling of urinary exosomes for systemic lupus erythematosus discrimination based on HPL-SEC/MALDI-TOF MS

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease which leads to the formation of immune complex deposits in multiple organs and has heterogeneous clinical manifestations. Currently, exosomes for liquid biopsy have been applied in diagnosis and monitoring of diseases, whereas SLE discrimination based on exosomes at the metabolic level is rarely reported. Herein, we constructed a protocol for metabolomic study of urinary exosomes from SLE patients and healthy controls (HCs) with high efficiency and throughput. Exosomes were first obtained by high-performance liquid size-exclusion chromatography (HPL-SEC), and then metabolic fingerprints of urinary exosomes were extracted by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with high throughput and high efficency. With the statistical analysis by orthogonal partial least-squares discriminant analysis (OPLS-DA) model, SLE patients were efficiently distinguished from HCs, the area under the curve (AUC) of the receiver characteristic curve (ROC) was 1.00, and the accuracy of the unsupervised clustering heatmap was 90.32%. In addition, potential biomarkers and related metabolic pathways were analyzed. This method, with the characteristics of high throughput, high efficiency, and high accuracy, will provide the broad prospect of exosome-driven precision medicine and large-scale screening in clinical applications.

Designed Concave Octahedron Heterostructures Decode Distinct Metabolic Patterns of Epithelial Ovarian Tumors

Epithelial ovarian cancer (EOC) is a polyfactorial process associated with alterations in metabolic pathways. A high-performance screening tool for EOC is in high demand to improve prognostic outcome but is still missing. Here, a concave octahedron Mn₂ O₃ /(Co,Mn)(Co,Mn)₂ O₄ (MO/CMO) composite with a heterojunction, rough surface, hollow interior, and sharp corners is developed to record metabolic patterns of ovarian tumors by laser desorption/ionization mass spectrometry (LDI-MS). The MO/CMO composites with multiple physical effects induce enhanced light absorption, preferred charge transfer, increased photothermal conversion, and selective trapping of small molecules. The MO/CMO shows ≈2-5-fold signal enhancement compared to mono- or dual-enhancement counterparts, and ≈10-48-fold compared to the commercialized products. Subsequently, serum metabolic fingerprints of ovarian tumors are revealed by MO/CMO-assisted LDI-MS, achieving high reproducibility of direct serum detection without treatment. Furthermore, machine learning of the metabolic fingerprints distinguishes malignant ovarian tumors from benign controls with the area under the curve value of 0.987. Finally, seven metabolites associated with the progression of ovarian tumors are screened as potential biomarkers. The approach guides the future depiction of the state-of-the-art matrix for intensive MS detection and accelerates the growth of nanomaterials-based platforms toward precision diagnosis scenarios.

SiO2@Au nanoshell-assisted laser desorption/ionization mass spectrometry for coronary heart disease diagnosis

Cardiovascular diseases have threatened human health, amongst which coronary heart disease (CHD) is the third most common cause of death. CHD is considered to be a metabolic disease; however, there is little research on the CHD metabolism. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has enabled the development of a suitable nanomaterial that can be used to obtain considerable high-quality metabolic information without complex pretreatment of biological fluid samples. This study combines SiO₂@Au nanoshells with minute plasma to obtain metabolic fingerprints of CHD. The thickness of the SiO₂@Au shell was also optimized to maximize the laser desorption/ionization effect. The results demonstrated 84% sensitivity at 85% specificity for distinguishing CHD patients from controls in the validation cohort.

Advanced technologies for molecular diagnosis of cancer: State of pre-clinical tumor-derived exosome liquid biopsies

Exosomes are membrane-defined extracellular vesicles (EVs) approximately 40-160 ​nm in diameter that are found in all body fluids including blood, urine, and saliva. They act as important vehicles for intercellular communication between both local and distant cells and can serve as circulating biomarkers for disease diagnosis and prognosis. Exosomes play a key role in tumor metastasis, are abundant in biofluids, and stabilize biomarkers they carry, and thus can improve cancer detection, treatment monitoring, and cancer staging/prognosis. Despite their clinical potential, lack of sensitive/specific biomarkers and sensitive isolation/enrichment and analytical technologies has posed a barrier to clinical translation of exosomes. This review presents a critical overview of technologies now being used to detect tumor-derived exosome (TDE) biomarkers in clinical specimens that have potential for clinical translation.

Construction of a ternary component chip with enhanced desorption efficiency for laser desorption/ionization mass spectrometry based metabolic fingerprinting

Introduction: In vitro metabolic fingerprinting encodes diverse diseases for clinical practice, while tedious sample pretreatment in bio-samples has largely hindered its universal application. Designed materials are highly demanded to construct diagnostic tools for high-throughput metabolic information extraction. Results: Herein, a ternary component chip composed of mesoporous silica substrate, plasmonic matrix, and perfluoroalkyl initiator is constructed for direct metabolic fingerprinting of biofluids by laser desorption/ionization mass spectrometry. Method: The performance of the designed chip is optimized in terms of silica pore size, gold sputtering time, and initiator loading parameter. The optimized chip can be coupled with microarrays to realize fast, high-throughput (∼second/sample), and microscaled (∼1 μL) sample analysis in human urine without any enrichment or purification. On-chip urine fingerprints further allow for differentiation between kidney stone patients and healthy controls. Discussion: Given the fast, high throughput, and easy operation, our approach brings a new dimension to designing nano-material-based chips for high-performance metabolic analysis and large-scale diagnostic use.

Diagnosing, Typing, and Staging of Renal Cell Carcinoma by Designer Matrix-Based Urinary Metabolic Analysis

Molecular diagnosing, typing, and staging have been considered to be the ideal alternatives of imaging-based detection methods in clinics. Designer matrix-based analytical tools, with high speed, throughout, efficiency and low/noninvasiveness, have attracted much attention recently for in vitro metabolite detection. Herein, we develop an advanced metabolic analysis tool based on highly porous metal oxides derived from available metal-organic frameworks (MOFs), which elaborately inherit the morphology and porosity of MOFs and newly incorporate laser adsorption capacity of metal oxides. Through optimized conditions, direct high-quality fingerprinting spectra in 0.5 μL of urine are acquired. Using these fingerprinting spectra, we can discriminate the renal cell carcinoma (RCC) from healthy controls with higher than 0.99 of area under the curve (AUC) values (R²Y(cum) = 0.744, Q² (cum) = 0.880), as well, from patients with other tumors (R²Y(cum) = 0.748, Q²(cum) = 0.871). We also realize the typing of three RCC subtypes, including clear cell RCC, chromophobe RCC (R²Y(cum) = 0.620, Q²(cum) = 0.656), and the staging of RCC (R²Y(cum) = 0.755, Q²(cum) = 0.857). Moreover, the tumor sizes (threshold value is 3 cm) can be remarkably recognized by this advanced metabolic analysis tool (R²Y(cum) = 0.710, Q²(cum) = 0.787). Our work brings a bright prospect for designer matrix-based analytical tools in disease diagnosis, typing and staging.

All-in-One Strategy for Downstream Molecular Profiling of Tumor-Derived Exosomes

In light of the significance of exosomes in cancer diagnosis and treatment, it is important to understand the components and functions of exosomes. Herein, an all-in-one strategy has been proposed for comprehensive characterization of exosomal proteins based on nanoporous TiO₂ clusters acting as both an extractor for exosome isolation and a nanoreactor for downstream molecular profiling. With the improved hydrophilicity and inherent properties of TiO₂, exosomes can be captured by a versatile nanodevice through the specific binding and hydrophilicity interaction synergistically. The strong concerted effect between exosomes and nanodevices ensured high efficiency and specificity of exosome isolation with high recovery and low contaminations. Meanwhile, highly efficient downstream proteomic analysis of the purified exosomes was also enabled by the nanoporous TiO₂ clusters. Benefiting from the porous structure of the nanodevice, the lysed exosomal proteins are highly concentrated in the nanopore to achieve high-efficiency in situ proteolytic digestion. Therefore, the unique features of the TiO₂ clusters ensured that all the complex steps about isolation and analysis of exosomes were completed efficiently in one simple nanodevice. The concept was first proved with exosomes from cell culture medium, where a high number of identified total proteins and protein groups in exosomes were obtained. Taking advantage of these attractive merits, the first example of the integrated platform has been successfully applied to the analysis of exosomes in complex real-case samples. Not only 196 differential protein biomarker candidates were discovered, but also many more significant cellular components and functions related to gastric cancer were found. These results suggest that the nanoporous TiO₂ cluster-based all-in-one strategy can serve as a simple, cost-effective, and integrated platform to facilitate comprehensive analysis of exosomes. Such an approach will provide a valuable tool for the study of exosome markers and their functions.

Metabolic Molecular Diagnosis of Inflammatory Bowel Disease by Synergistical Promotion of Layered Titania Nanosheets with Graphitized Carbon

Due to inefficient diagnostic methods, inflammatory bowel disease (IBD) normally progresses into severe complications including cancer. Highly efficient extraction and identification of metabolic fingerprints are of significance for disease surveillance. In this work, we synthesized a layered titania nanosheet doped with graphitized carbon (2D-GC-mTNS) through a simple one-step assembly process for assisting laser desorption ionization mass spectrometry (LDI-MS) for metabolite analysis. Based on the synergistic effect of graphitized carbon and mesoporous titania, 2D-GC-mTNS exhibits good extraction ability including high sensitivity (< 1 fmol µL^-1) and great repeatability toward metabolites. A total of 996 fingerprint spectra were collected from hundreds of native urine samples (including IBD patients and healthy controls), each of which contained 1220 m/z metabolite features. Diagnostic model was further established for precise discrimination of patients from healthy controls, with high area under the curve value of 0.972 and 0.981 toward discovery cohort and validation cohort, respectively. The 2D-GC-mTNS promotes LDI-MS to be close to clinical application, with rapid speed, minimum sample consumption and free of sample pretreatment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00055-0.

Biofluids Metabolic Profiling Based on PS@Fe3O4-NH2 Magnetic Beads-Assisted LDI-MS for Liver Cancer Screening

Liver cancer (LC) is the third frequent cause of death worldwide, so early diagnosis of liver cancer patients is crucial for disease management. Herein, we applied NH₂-coated polystyrene@Fe₃O₄ magnetic beads (PS@Fe₃O₄-NH₂ MBs) as a matrix material in laser desorption/ionization mass spectrometry (LDI-MS). Rapid, sensitive, and selective metabolic profiling of the native biofluids was achieved without any inconvenient enrichment or purification. Then, based on the selected m/z features, LC patients were discriminated from healthy controls (HCs) by machine learning, with the high area under the curve (AUC) values for urine and serum assessments (0.962 and 0.935). Moreover, initial-diagnosed and subsequent-visited LC patients were also differentiated, which indicates potential applications of this method in early diagnosis. Furthermore, among these identified compounds by FT-ICR MS, the expression level of some metabolites changed from HCs to LCs, including 29 and 12 characteristic metabolites in human urine and serum samples, respectively. These results suggest that PS@Fe₃O₄-NH₂ MBs-assisted LDI-MS coupled with machine learning is feasible for LC clinical diagnosis.

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.

Diagnosis and prognosis of breast cancer by high-performance serum metabolic fingerprints

Breast cancer (BrCa) is the most common cancer worldwide, and high-performance metabolic analysis is emerging in diagnosis and prognosis of BrCa. Here, we used nanoparticle-enhanced laser desorption/ionization mass spectrometry to record serum metabolic fingerprints of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection. Our analytical method, combined with the aid of machine learning algorithms, was demonstrated to provide high diagnostic efficiency with accuracy of 88.8% and desirable prognostic prediction (P < 0.005). Furthermore, seven metabolic biomarkers differentially enriched in BrCa serum and their related pathways were identified. Together, our findings provide a tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.

High-performance metabolic analysis is emerging in the diagnosis and prognosis of breast cancer (BrCa). Still, advanced tools are in demand to deliver the application potentials of metabolic analysis. Here, we used fast nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI-MS) to record serum metabolic fingerprints (SMFs) of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection without treatment. Subsequently, machine learning of SMFs generated by NPELDI-MS functioned as an efficient readout to distinguish BrCa from non-BrCa with an area under the curve of 0.948. Furthermore, a metabolic prognosis scoring system was constructed using SMFs with effective prediction performance toward BrCa (P < 0.005). Finally, we identified a biomarker panel of seven metabolites that were differentially enriched in BrCa serum and their related pathways. Together, our findings provide an efficient serum metabolic tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.

Hollow Cobalt Oxide/Carbon Hybrids Aid Metabolic Encoding for Active Systemic Lupus Erythematosus during Pregnancy

A noninvasive, easy operation, and accurate diagnostic protocol is highly demanded to assess systemic lupus erythematosus (SLE) activity during pregnancy, promising real-time activity monitoring during the whole gestational period to reduce adverse pregnancy outcomes. Here, machine learning of serum metabolic fingerprints (SMFs) is developed to assess the SLE activity for pregnant women. The SMFs are directly extracted through a hollow-cobalt oxide/carbon (Co₃ O₄ /C)-composite-assisted laser desorption/ionization mass spectrometer (LDI MS) platform. The Co₃ O₄ /C composite owns enhanced light absorption, size-selective trapping, and better charge-hole separation, enabling improved ionization efficiency and selectivity for LDI MS detection toward small molecules. Metabolic fingerprints are collected from ≈0.1 µL serum within 1 s without enrichment and encoded by the optimized elastic net algorithm. The averaged area under the curve (AUC) value in the differentiation of active SLE from inactive SLE and healthy controls reaches 0.985 and 0.990, respectively. Further, a simplified panel based on four identified metabolites is built to distinguish SLE flares in pregnant women with the highest AUC value of 0.875 for the blind test. This work sets an accurate and practical protocol for SLE activity assessment during pregnancy, promoting precision diagnosis of disease status transitions in clinics.

Serum fingerprinting by slippery liquid-infused porous SERS for non-invasive lung cancer detection

Direct and label-free analysis of clinical serum samples using slippery liquid-infused porous-enhanced Raman spectroscopy (SLIPSERS) enables the rapid non-invasive identification of lung cancer.

Efficient exosome subpopulation isolation and proteomic profiling using a Sub-ExoProfile chip towards cancer diagnosis and treatment

Deconstruction of the heterogeneity of surface marker-dependent exosome subpopulations by the Sub-ExoProfile chip.

Inorganic Matrices Assisted Laser Desorption/Ionization Mass Spectrometry for Metabolic Analysis in Bio-fluids

Metabolic analysis in bio-fluids interprets the end products in the bio-process, emerging as an irreplaceable disease diagnosis and monitoring platform. Laser desorption/ionization mass spectrometry (LDI MS) based metabolic analysis exhibits great potential for clinical applications in terms of high throughput, rapid signal readout, and minimal sample preparation. There are two essential elements to construct the LDI MS-based metabolic analysis: 1) well-designed nanomaterials as matrices; 2) machine learning algorithms for data analysis. This review highlights the development of various inorganic matrices to comprehend the advantages of LDI MS in metabolite detection and the recent diagnostic applications based on target metabolite detection and untargeted metabolic fingerprints in biological fluids.

Label-Free Electrochemical Detection of the Cancer Biomarker Platelet-Derived Growth Factor Receptor in Human Serum and Cancer Cells

Overexpression of the platelet-derived growth factor receptor (PDGFR) was already associated with the loss of p53 function as cancer progresses in lung, breast, and cervical cancers. Cancer biomarker detection has faced challenges and barriers due to various limitations, including a high limit of detection, low sensitivity, time-consuming techniques, and expensive equipment. Hence, the present investigation is designed to develop a cost-effective novel biosensor based on a charge-based affinity bait molecule to detect the PDGFR, thus overcoming the limitations and challenges with an immune technique based on antigen-antibody interactions. We employed EDC-NHS coupling between poly (diallyl dimethylammonium chloride) and poly(acrylic acid) to attach the multiwall carbon nanotube surface. As a result, we performed electrochemical PDGFR conversion sensing with a dynamic range of 1-10,000 ng/mL and a detection limit of 1.5 pg/mL, which is comparable to the best current results. The biosensor also displayed good selectivity, 2.51% repeatability (RSD, n = 5), and 30 days of stability. Our study provides a pathway for the design of diagnostic interfaces in biosystems, as well as the emergence of new sensor types based on ligand-receptor interactions.

Nanostructured Substrates as Matrices for Surface Assisted Laser Desorption/Ionization Mass Spectrometry: A Progress Report from Material Research to Biomedical Applications

Within the past two decades, the escalation of research output in nanotechnology fields has boosted the development of novel nanoparticles and nanostructured substrates for use as matrices in surface assisted laser desorption/ionization mass spectrometry (SALDI-MS). The application of nanomaterials as matrices, rather than organic matrices, offers remarkable characteristics that allow the analysis of small molecules with fewer matrix interfering peaks, and share higher detection sensitivity, specificity, and reproducibility. The technological advancement of SALDI-MS has in turn, propelled the application of the analytical technique in the field of biomedical analysis. In this review, the properties and fabrication methods of nanostructured substrates in SALDI-MS such as metallic-, carbon-, and silicon-based nanostructures, quantum dots, metal-organic frameworks, and covalent-organic frameworks are described. Additionally, the latest progress (most within 5 years) of biomedical applications in small molecule, large biomolecule, and MS imaging analysis including metabolite profiling, drug monitoring, bacteria identification, disease diagnosis, and therapeutic evaluation are demonstrated. Key parameters that govern nanomaterial's SALDI efficiency in biomolecule analysis are also discussed. Finally, perspectives of the future development are given to provide a better advancement and promote practical application in clinical MS.

Porous Inorganic Materials for Bioanalysis and Diagnostic Applications

Porous inorganic materials play an important role in adsorbing targeted analytes and supporting efficient reactions in analytical science. The detection performance relies on the structural properties of porous materials, considering the tunable pore size, shape, connectivity, etc. Herein, we first clarify the enhancement mechanisms of porous materials for bioanalysis, concerning the detection sensitivity and selectivity. The diagnostic applications of porous material-assisted platforms by coupling with various analytical techniques, including electrochemical sensing, optical spectrometry, and mass spectrometry, etc., are then reviewed. We foresee that advanced porous materials will bring far-reaching implications in bioanalysis toward real-case applications, especially as diagnostic assays in clinical settings.

Ultra-Fast Label-Free Serum Metabolic Diagnosis of Coronary Heart Disease via a Deep Stabilizer

Although mass spectrometry (MS) of metabolites has the potential to provide real-time monitoring of patient status for diagnostic purposes, the diagnostic application of MS is limited due to sample treatment and data quality/reproducibility. Here, the generation of a deep stabilizer for ultra-fast, label-free MS detection and the application of this method for serum metabolic diagnosis of coronary heart disease (CHD) are reported. Nanoparticle-assisted laser desorption/ionization-MS is used to achieve direct metabolic analysis of trace unprocessed serum in seconds. Furthermore, a deep stabilizer is constructed to map native MS results to high-quality results obtained by established methods. Finally, using the newly developed protocol and diagnosis variation characteristic surface to characterize sensitivity/specificity and variation, CHD is diagnosed with advanced accuracy in a high-throughput/speed manner. This work advances design of metabolic analysis tools for disease detection as it provides a direct label-free, ultra-fast, and stabilized platform for future protocol development in clinics.

Material-assisted mass spectrometric analysis of low molecular weight compounds for biomedical applications

Low molecular weight compounds play an important role in encoding the current physiological state of an individual. Laser desorption/ionization mass spectrometry (LDI MS) offers high sensitivity with low cost for molecular detection, but it is not able to cover small molecules due to the drawbacks of the conventional matrix. Advanced materials are better alternatives, showing little background interference and high LDI efficiency. Herein, we first classify the current materials with a summary of compositions and structures. Matrix preparation protocols are then reviewed, to enhance the selectivity and reproducibility of MS data better. Finally, we highlight the biomedical applications of material-assisted LDI MS, at the tissue, bio-fluid, and cellular levels. We foresee that the advanced materials will bring far-reaching implications in LDI MS towards real-case applications, especially in clinical settings.

Ultrasound-Targeted Microbubble Destruction Mediates Gene Transfection for Beta-Cell Regeneration and Glucose Regulation

Ultrasound-targeted microbubble destruction (UTMD) mediates gene transfection with high biosafety and thus has been promising toward treatment of type 1 diabetes. However, the potential application of UTMD in type 2 diabetes (T2D) is still limited, due to the lack of systematic design and dynamic monitoring. Herein, an efficient gene delivery system is constructed by plasmid deoxyribonucleic acid (DNA) encoding glucagon-like peptide 1 (GLP-1) in ultrasound-induced microbubbles, toward treatment of T2D in macaque. The as designed UTMD afforded enhancement of cell membrane penetration and GLP-1 expression in macaque, which is characterized by ultrasound-guided biopsy to monitor the dynamic process of islet cells for 6 months. Also, improvement of pancreatic beta cell regeneration, and regulation of plasma glucose in macaque with T2D is achieved. The approach would serve as promising alternatives for the treatment of T2D.

Nanocomposite-Based Matrices in Laser Desorption/Ionization Mass Spectrometry for Small-Molecule Analysis

The efficient detection of small molecules is of significance for environmental monitoring, pharmacology, metabolomics, and lipidomics. The laser desorption/ionization mass spectrometry (LDI MS) platform enables high sensitivity, accuracy, resolution, and throughput in molecular analysis, but its analytical capability with respect to small molecules is limited due to inherent drawbacks arising from conventional organic matrices. The selection of an appropriate matrix is thus a precondition for small molecule detection by LDI MS. To date, various inorganic matrices have been developed, with a growing interest in composite materials displaying synergetic effects. This Minireview highlights the development of nanocomposites as LDI matrices driven by numerous innovations in material science, and their emerging use in small-molecule analysis.

Cascade signal amplification for sensitive detection of exosomes by integrating tyramide and surface-initiated enzymatic polymerization

A novel cascade signal amplification based on tyramide signal amplification (TSA) and surface-initiated enzymatic polymerization (SIEP) was first reported for the sensitive and template-free detection of colorectal cancer (CRC) exosomes. This assay exhibited 20.9-fold signal amplification with a low detection limit of 12.8 particles per μL. Furthermore, accurate and reproducible results were obtained for detecting exosomes in serum samples, suggesting its potential application in exosomes analysis and clinical diagnostics.

Metal-Organic Framework Hybrids Aid Metabolic Profiling for Colorectal Cancer

Colorectal cancer (CRC) is the third most common fatal cancer worldwide, accounting for ≈10% of cancer-related mortality. Metabolic shift occurs from the very early stage during the development of CRC, which is of significant etiological and diagnostic importance toward precision medicine. Here, an advanced molecular tool to characterize the metabolic alterations in CRC, based on metal-organic framework (MOF) hybrids is reported. Consuming only 500 nL of plasma without any sample pretreatment, MOF hybrids yield direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds. A diagnostic prediction model by a machine learning algorithm is constructed, to discriminate CRC patients from normal controls with an average area under the curve of 0.947 for the discovery cohort and 0.912 for the independent validation cohort. In addition, CRC-specific metabolic signature consisting of 34 potential biomarkers, based on the aforementioned diagnostic model is identified. The results advance the design of nanomaterial-based platforms for metabolic analysis and establish a new liquid biopsy tool for CRC screening compatible with the current clinical workflow in practice.

Plasmonic Alloys Reveal a Distinct Metabolic Phenotype of Early Gastric Cancer

Gastric cancer (GC) is a multifactorial process, accompanied by alterations in metabolic pathways. Non-invasive metabolic profiling facilitates GC diagnosis at early stage leading to an improved prognostic outcome. Herein, mesoporous PdPtAu alloys are designed to characterize the metabolic profiles in human blood. The elemental composition is optimized with heterogeneous surface plasmonic resonance, offering preferred charge transfer for photoinduced desorption/ionization and enhanced photothermal conversion for thermally driven desorption. The surface structure of PdPtAu is further tuned with controlled mesopores, accommodating metabolites only, rather than large interfering compounds. Consequently, the optimized PdPtAu alloy yields direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds, consuming 500 nL of native plasma. A distinct metabolic phenotype is revealed for early GC by sparse learning, resulting in precise GC diagnosis with an area under the curve of 0.942. It is envisioned that the plasmonic alloy will open up a new era of minimally invasive blood analysis to improve the surveillance of cancer patients in the clinical setting.

High temperature stable conjugated polyazomethines containing naphthalene moiety: Synthesis, characterization, optical, electrical and thermal properties

A series of azomethine diol monomers containing naphthalene moiety were synthesized and oxidatively polymerized in aqueous alkaline medium using NaOCl as oxidant. The structure of the monomers and polymers was characterized by using various spectroscopic techniques. PL spectra of polymers are exhibiting good emission with high Stokes shift values than the monomers due to their polyconjugated structure. The electrical conductivity of polymers was measured by a two-point probe technique, and it increases with an increase in iodine vapour contact time up to 144 h. The electrical conductivity values were correlated with the charge density on azomethine nitrogen obtained from the Hückel calculation method. The dielectric properties of polymers were studied at different temperatures in the frequency range from 50 Hz to 5 MHz. The synthesized polyazomethines have recorded high thermal stability and are shown by carbon residue of around 50% at 800 °C in the thermogravimetric analysis.

High throughput analysis and quantitation of α-dicarbonyls in biofluid by plasmonic nanoshells enhanced laser desorption/ionization mass spectrometry

Advanced analytical platforms are required for accurate detection and quantification of small molecular substances exhibiting certain toxicity. Small molecules detection in complex biological fluids are challenged by the complexity of the samples and the low throughput of the existing methods. In the present study, to detect a batch of samples (50) in 1 h, the plasmonic nanoshell enhanced matrix-assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS) was tested. The limit of quantification (LOQ) was determined as 0.01 μg/mL (for α-dicarbonyl compounds) by vortex-assisted liquid-liquid microextraction (VALLME). The developed method can be adopted to study the high-throughput metabolomics and employed for clinical precision diagnosis with MALDI-TOF MS.

Self-Assembled Au Nanoparticle Arrays for Precise Metabolic Assay of Cerebrospinal Fluid

Precise and rapid monitoring of metabolites in biofluids is a desirable but unmet goal for disease diagnosis and management. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) exhibits advantages in metabolite analysis. However, the low accuracy in quantification of the technique limits its transformation to clinical usage. We report herein the use of Au nanoparticle arrays self-assembled at liquid-liquid interfaces for mass spectrometry (MS)-based quantitative biofluids metabolic profiling. The two-dimensional arrays feature uniformly and closely packed Au nanoparticles with 3 nm interparticle gaps. The experimental study and theoretical simulation show that the arrays exhibit high photothermal conversion and heat confinement effects, which enhance the laser desorption/ionization efficacy. With the nanoscale roughness, the AuNP arrays as laser desorption/ionization substrates can interrupt the coffee-ring effect during droplet evaporation. Therefore, high reproducibility (RSD <5%) is obtained, enabling accurate quantitative analysis of diverse metabolites from 1 μL of biofluids in seconds. By quantifying glucose in the cerebrospinal fluid (CSF), it allows us to identify patients with brain infection and rapidly evaluate the clinical therapy response. Consequently, the method shows potential in advanced metabolite analysis and biomedical diagnostics.

Plasmonic Sensors for Extracellular Vesicle Analysis: From Scientific Development to Translational Research

Extracellular vesicles (EVs), actively shed from a variety of neoplastic and host cells, are abundant in blood and carry molecular markers from parental cells. For these reasons, EVs have gained much interest as biomarkers of disease. Among a number of different analytical methods that have been developed, surface plasmon resonance (SPR) stands out as one of the ideal techniques given its sensitivity, robustness, and ability to miniaturize. In this Review, we compare different SPR platforms for EV analysis, including conventional SPR, nanoplasmonic sensors, surface-enhanced Raman spectroscopy, and plasmonic-enhanced fluorescence. We discuss different surface chemistries used to capture targeted EVs and molecularly profile their proteins and RNAs. We also highlight these plasmonic platforms' clinical applications, including cancers, neurodegenerative diseases, and cardiovascular diseases. Finally, we discuss the future perspective of plasmonic sensing for EVs and their potentials for commercialization and clinical translation.

Rapid Computer-Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi-Modal Recognition

Stroke is a leading cause of mortality and disability worldwide, expected to result in 61 million disability-adjusted life-years in 2020. Rapid diagnostics is the core of stroke management for early prevention and medical treatment. Serum metabolic fingerprints (SMFs) reflect underlying disease progression, predictive of patient phenotypes. Deep learning (DL) encoding SMFs with clinical indexes outperforms single biomarkers, while posing challenges with poor prediction to interpret by feature selection. Herein, rapid computer-aided diagnosis of stroke is performed using SMF based multi-modal recognition by DL, to combine adaptive machine learning with a novel feature selection approach. SMFs are extracted by nano-assisted laser desorption/ionization mass spectrometry (LDI MS), consuming 100 nL of serum in seconds. A multi-modal recognition is constructed by integrating SMFs and clinical indexes with an enhanced area under curve (AUC) up to 0.845 for stroke screening, compared to single-modal diagnosis by only SMFs or clinical indexes. The prediction of DL is addressed by selecting 20 key metabolite features with differential regulation through a saliency map approach, shedding light on the molecular mechanisms in stroke. The approach highlights the emerging role of DL in precision medicine and suggests an expanding utility for computational analysis of SMFs in stroke screening.

Zirconia Hybrid Nanoshells for Nutrient and Toxin Detection

Monitoring milk quality is of fundamental importance in food industry, because of the nutritional value and resulting position of milk in daily diet. The detection of small nutrients and toxins in milk is challenging, considering high sample complexity and low analyte abundance. In addition, the slow analysis and tedious sample preparation hinder the large-scale application of conventional detection techniques. Herein, zirconia hybrid nanoshells are constructed to enhance the performance of laser desorption/ionization mass spectrometry (LDI MS). Zirconia nanoshells with the optimized structures and compositions are used as matrices in LDI MS and achieve direct analysis of small molecules from 5 nL of native milk in ≈1 min, without any purification or separation. Accurate quantitation of small nutrient is achieved by introducing isotope into the zirconia nanoshell-assisted LDI MS as the internal standard, offering good consistency to biochemical analysis (BCA) with R²  = 0.94. Further, trace toxin is enriched and identified with limit-of-detection (LOD) down to 4 pm, outperforming the current analytical methods. This work sheds light on the personalized design of material-based tool for real-case bioanalysis and opens up new opportunities for the simple, fast, and cost-effective detection of various small molecules in a broad field.

Rapid detection of perfluorinated sulfonic acids through preconcentration by bubble bursting and surface-assisted laser desorption/ionization

We developed a preconcentration method in which aerosol droplets containing enriched perfluorinated sulfonic acids (PFSs) are generated through bubble bursting and collected. The droplets were subjected to PFS analysis of perfluorohexane sulfonic acid (PFHxS) and perfluorooctanesulfonic acid (PFOS) through surface-assisted laser desorption/ionization-time-of-flight mass spectrometry; silver nanoplates (AgNPts) were assisting materials. The method was highly efficient, with an approximately three-order magnitude enhancement (5 × 10-13 to 1 × 10-11 M). Ultralow PFS concentrations (0.5 ng/L of PFOS; 0.4 ng/L of PFHxS) were detected in preconcentrated tap water containing PFSs. Our method has potential for rapid real-world PFS detection in water.

Shrimp shells extracted chitin in silver nanoparticle synthesis: Expanding its prophecy towards anticancer activity in human hepatocellular carcinoma HepG2 cells

In this study, a well-organized, simplistic, and biological route of AgNPs (AgNPs) was synthesized using shrimp shell extracted chitin as reducing, capping and stabilizing factor under the optimized conditions. Also, the anticancer potential of synthesized biogenic AgNPs was evaluated against human hepatocarcinoma (HepG2) cells. Ultraviolet visible spectroscopy (UV-Vis spec) study indicated that the development of AgNPs present in the colloidal solution was single peak at 446 nm. FTIR results showed a strong chemical interaction between the chitin and biogenic AgNPs; whereas, XRD studies confirmed AgNPs presence in the composites. The SEM TEM analytical studies confirmed the synthesized AgNPs had a spherical shape crystalline structure with size ranges from 17 to 49 nm; EDX study also confirmed the percentage of weight and atomic elements available in the colloidal mixture. Furthermore, the synthesized AgNPs showed significant cytotoxic effect on the HepG2 cells with an IC₅₀ value shown at 57 ± 1.5 μg/ml. The apoptotic and necrotic cell death effects of AgNPs were also confirmed by flow cytometry. The upregulated apoptotic related proteins Bax, cytochrome-c, caspase-3, caspase-9, PARP and downregulated anti-apoptotic related proteins Bcl-2 and Bcl-xl in cancer cells, confirmed the anticancer potential of AgNPs. These findings suggest that the AgNPs possess significant anticancer activity against HepG2 cells which could play major role in the therapeutic drug development to treat cancer in future.

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma

Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 s using only 50 nL of serum. We define a metabolic range of 100–400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70–90% and specificity~90–93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics.

Early diagnosis significantly improves the probability of successful cancer therapy. Here, the authors develop a technique to analyse serum metabolites and define a biomarker panel for early-stage lung adenocarcinoma diagnosis.

A high-throughput SAMDI-mass spectrometry assay for isocitrate dehydrogenase 1

The enzyme isocitrate dehydrogenase 1 (IDH1) catalyzes the conversion of isocitrate to alpha-ketoglutarate (αKG) and has emerged as an important therapeutic target for glioblastoma multiforme (GBM). Current methods for assaying IDH1 remain poorly suited for high-throughput screening of IDH1 antagonists. This paper describes a high-throughput and quantitative assay for IDH1 that is based on the self-assembled monolayers for matrix-assisted laser desorption/ionization-mass spectrometry (SAMDI-MS) method. The assay uses a self-assembled monolayer presenting a hydrazide group that covalently captures the αKG product of IDH1, where it can then be detected by MALDI-TOF mass spectrometry. Co-capture of an isotopically-labeled αKG internal standard allows the αKG concentration to be quantitated. The assay was used to analyze a series of standard αKG solutions and produced minimal error in measured αKG concentration values. The suitability of the assay for high-throughput analysis was evaluated in a 384-sample biochemical IDH1 screen. Cells expressing IDH1 were lysed and the lysate was applied to the monolayer to capture αKG, which was then quantitated using the SAMDI-MS assay. Cells in which IDH1 expression was reduced by small-interfering RNA exhibited a corresponding decrease in αKG concentration as measured by the assay. Application of the assay toward the high-throughput screening of IDH1 inhibitors or knockdown agents may facilitate the discovery of treatments for GBM.

Metabolic Fingerprinting on Synthetic Alloys for Medulloblastoma Diagnosis and Radiotherapy Evaluation

Diagnostics is the key in screening and treatment of cancer. As an emerging tool in precision medicine, metabolic analysis detects end products of pathways, and thus is more distal than proteomic/genetic analysis. However, metabolic analysis is far from ideal in clinical diagnosis due to the sample complexity and metabolite abundance in patient specimens. A further challenge is real-time and accurate tracking of treatment effect, e.g., radiotherapy. Here, Pd-Au synthetic alloys are reported for mass-spectrometry-based metabolic fingerprinting and analysis, toward medulloblastoma diagnosis and radiotherapy evaluation. A core-shell structure is designed using magnetic core particles to support Pd-Au alloys on the surface. Optimized synthetic alloys enhance the laser desorption/ionization efficacy and achieve direct detection of 100 nL of biofluids in seconds. Medulloblastoma patients are differentiated from healthy controls with average diagnostic sensitivity of 94.0%, specificity of 85.7%, and accuracy of 89.9%, by machine learning of metabolic fingerprinting. Furthermore, the radiotherapy process of patients is monitored and a preliminary panel of serum metabolite biomarkers is identified with gradual changes. This work will lead to the application-driven development of novel materials with tailored structural design and establishment of new protocols for precision medicine in near future.

Sensing Biomarkers with Plasmonics

The detection of biomarkers is critical for enabling early disease diagnosis, monitoring the progression, and tracking the effectiveness of therapeutic intervention. Plasmonic sensors exhibit a broad range of analytical capabilities, from the rapid generation of colorimetric readouts to single-molecule sensitivity in ultralow sample volumes, which have led to their increased exploration in bioanalysis and point-of-care applications. This perspective presents selected accounts of recent developments on the different types of plasmonic sensing platforms, the pervasive challenges, and outlook on the pathway to translation. We highlight the sensing of upcoming biomarkers, including microRNA, circulating tumor cells, exosomes, and cell-free DNA, and discuss the opportunity of utilizing plasmonic nanomaterials and tools for biomarker detection beyond biofluids, such as in tissues, organs, and disease sites. The integration of plasmonic biosensors with established and upcoming technologies of instrumentation, sample pretreatment, and data analysis will help realize their translation to clinical settings for improving healthcare and enhancing the quality of life.

FeOOH@Metal-Organic Framework Core-Satellite Nanocomposites for the Serum Metabolic Fingerprinting of Gynecological Cancers

High-throughput metabolic analysis is of significance in diagnostics, while tedious sample pretreatment has largely hindered its clinic application. Herein, we designed FeOOH@ZIF-8 composites with enhanced ionization efficiency and size-exclusion effect for laser desorption/ionization mass spectrometry (LDI-MS)-based metabolic diagnosis of gynecological cancers. The FeOOH@ZIF-8-assisted LDI-MS achieved rapid, sensitive, and selective metabolic fingerprints of the native serum without any enrichment or purification. Further analysis of extracted serum metabolic fingerprints successfully discriminated patients with gynecological cancers (GCs) from healthy controls and also differentiated three major subtypes of GCs. Given the low cost, high-throughput, and easy operation, our approach brings a new dimension to disease analysis and classification.