2D nanosheets (NSs) have been widely used in drug-related applications. However, a comprehensive investigation into the cytotoxicity mechanism linked to the redox activity is lacking. In this study, with cytochrome c (Cyt c) as the model biospecies, the cytotoxicity of 2D NSs was evaluated systematically based on their redox effect with microfluidic techniques. The interface interaction, dissolution, and redox effect of 2D NSs on Cyt c were monitored with pulsed streaming potential (SP) measurement and capillary electrophoresis (CE). The relationship between the redox activity of 2D NSs and the function of Cyt c was evaluated in vitro with Hela cells. The results indicated that the dissolution and redox activity of 2D NSs can be simultaneously monitored with CE under weak interface interactions and at low sample volumes. Both WS2 NSs and MoS2 NSs can reduce Cyt c without significant dissolution, with reduction rates measured at 6.24 × 10-5 M for WS2 NSs and 3.76 × 10-5 M for MoS2 NSs. Furthermore, exposure to 2D NSs exhibited heightened reducibility, which prompted more pronounced alterations associated with Cyt c dysfunction, encompassing ATP synthesis, modifications in mitochondrial membrane potential, and increased reactive oxygen species production. These observations suggest a positive correlation between the redox activity of 2D NSs and their redox toxicity in Hela cells. These findings provide valuable insight into the redox properties of 2D NSs regarding cytotoxicity and offer the possibility to modify the 2D NSs to reduce their redox toxicity for clinical applications.
Metabolomics and proteomics offer significant advantages in understanding biological mechanisms at two hierarchical levels. However, conventional single omics analysis faces challenges due to the high demand for specimens and the complexity of intrinsic associations. To obtain comprehensive and accurate system biological information, we developed a multiomics analytical method called Windows Scanning Multiomics (WSM). In this method, we performed simultaneous extraction of metabolites and proteins from the same sample, resulting in a 10% increase in the coverage of the identified biomolecules. Both metabolomics and proteomics analyses were conducted by using ultrahigh-performance liquid chromatography mass spectrometry (UPLC-MS), eliminating the need for instrument conversions. Additionally, we designed an R-based program (WSM.R) to integrate mathematical and biological correlations between metabolites and proteins into a correlation network. The network created from simultaneously extracted biomolecules was more focused and comprehensive compared to those from separate extractions. Notably, we excluded six pairs of false-positive relationships between metabolites and proteins in the network established using simultaneously extracted biomolecules. In conclusion, this study introduces a novel approach for multiomics analysis and data processing that greatly aids in bioinformation mining from multiomics results. This method is poised to play an indispensable role in systems biology research.
Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.
We have developed a DNA sensor that can be finalized to detect a specific target on demand. The electrode surface was modified with 2,7-diamino-1,8-naphthyridine (DANP), a small molecule with nanomolar affinity for the cytosine bulge structure. The electrode was immersed in a solution of synthetic probe-DNA that had a cytosine bulge structure at one end and a complementary sequence to the target DNA at the other end. The strong binding between the cytosine bulge and DANP anchored the probe DNAs to the electrode surface, and the electrode became ready for target DNA sensing. The complementary sequence portion of the probe DNA can be changed as requested, allowing for the detection of a wide variety of targets. Electrochemical impedance spectroscopy (EIS) with the modified electrode detected target DNAs with a high sensitivity. The charge transfer resistance (Rct) extracted from EIS showed a logarithmic relationship with the concentration of target DNA. The limit of detection (LoD) was less than 0.01 μM. By this method, highly sensitive DNA sensors for various target sequences could be easily produced.
High-resolution reconstruction has attracted increasing research interest in mass spectrometry imaging (MSI), but it remains a challenging ill-posed problem. In the present study, we proposed a deep learning model to fuse multimodal images to enhance the spatial resolution of MSI data, namely, DeepFERE. Hematoxylin and eosin (H&E) stain microscopy imaging was used to pose constraints in the process of high-resolution reconstruction to alleviate the ill-posedness. A novel model architecture was designed to achieve multi-task optimization by incorporating multi-modal image registration and fusion in a mutually reinforced framework. Experimental results demonstrated that the proposed DeepFERE model is able to produce high-resolution reconstruction images with rich chemical information and a detailed structure on both visual inspection and quantitative evaluation. In addition, our method was found to be able to improve the delimitation of the boundary between cancerous and para-cancerous regions in the MSI image. Furthermore, the reconstruction of low-resolution spatial transcriptomics data demonstrated that the developed DeepFERE model may find wider applications in biomedical fields.
Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatment. Generally, drug interactions have been studied using nonclinical pharmacokinetics, toxicology, and pharmacology. Here, we propose a complementary strategy based on metabolomics, which we call interaction metabolite set enrichment analysis, or iMSEA, to decipher drug interactions. First, a digraph-based heterogeneous network model was constructed to model the biological metabolic network based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Second, treatment-specific influences on all detected metabolites were calculated and propagated across the whole network model. Third, pathway activity was defined and enriched to quantify the influence of each treatment on the predefined functional metabolite sets, i.e., metabolic pathways. Finally, drug interactions were identified by comparing the pathway activity enriched by the drug combination treatments and the single drug treatments. A data set consisting of hepatocellular carcinoma (HCC) cells that were treated with oxaliplatin (OXA) and/or vitamin C (VC) was used to illustrate the effectiveness of the iMSEA strategy for evaluation of drug interactions. Performance evaluation using synthetic noise data was also performed to evaluate sensitivities and parameter settings for the iMSEA strategy. The iMSEA strategy highlighted synergistic effects of combined OXA and VC treatments including the alterations in the glycerophospholipid metabolism pathway and glycine, serine, and threonine metabolism pathway. This work provides an alternative method to reveal the mechanisms of drug combinations from the viewpoint of metabolomics.
A polymer inclusion membrane (PIM) based sampling probe was developed for electrokinetic extraction of drugs from biological fluids. The probe was fabricated by dip-coating a nonconductive glass capillary tube in a homogeneous PIM solution for three cycles. The PIM solution comprised cellulose triacetate (CTA), 2-nitrophenyl octyl ether (NPOE), and 1-ethyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide [EMIM][NTf2] in a ratio of 5:4:2. The developed probe electrokinetically extracted doxorubicin from human plasma, human serum, and dried blood spot (DBS). The practicability and reliability of the electrokinetic extraction were evaluated by LC-MS/MS to quantify the desorption of extracted doxorubicin. Under the optimized conditions, a quantification limit of 0.2-2 ng/mL was achieved for the three biological samples. The probe was further integrated into a portable battery-powered device for safe low-voltage (36 V) electrokinetic extraction. The developed technique is envisioned to provide a more efficient analytical workflow in the laboratory.
Since the declaration of COVID-19 as a pandemic in early 2020, multiple variants of the severe acute respiratory syndrome-related coronavirus (SARS-CoV-2) have been detected. The emergence of multiple variants has raised concerns due to their impact on public health. Therefore, it is crucial to distinguish between different viral variants. Here, we developed a machine learning web-based application for SARS-CoV-2 variant identification via duplex real-time polymerase chain reaction (PCR) coupled with high-resolution melt (qPCR-HRM) analysis. As a proof-of-concept, we investigated the platform's ability to identify the Alpha, Delta, and wild-type strains using two sets of primers. The duplex qPCR-HRM could identify the two variants reliably in as low as 100 copies/μL. Finally, the platform was validated with 167 nasopharyngeal swab samples, which gave a sensitivity of 95.2%. This work demonstrates the potential for use as automated, cost-effective, and large-scale viral variant surveillance.
Spatial segmentation is a critical procedure in mass spectrometry imaging (MSI)-based biochemical analysis. However, the commonly used unsupervised MSI segmentation methods may lead to inappropriate segmentation results as the MSI data is characterized by high dimensionality and low signal-to-noise ratio. This process can be improved by the incorporation of precise prior knowledge, which is hard to obtain in most cases. In this study, we show that the incorporation of partial or coarse prior knowledge from different sources such as reference images or biological knowledge may also help to improve MSI segmentation results. Here, we propose a novel interactive segmentation strategy for MSI data called iSegMSI, which incorporates prior information in the form of scribble-regularization of the unsupervised model to fine-tune the segmentation results. By using two typical MSI data sets (including a whole-body mouse fetus and human thyroid cancer), the present results demonstrate the effectiveness of the iSegMSI strategy in improving the MSI segmentations. Specifically, the method can be used to subdivide a region into several subregions specified by the user-defined scribbles or to merge several subregions into a single region. Additionally, these fine-tuned results are highly tolerant to the imprecision of the scribbles. Our results suggest that the proposed iSegMSI method may be an effective preprocessing strategy to facilitate the analysis of MSI data.
The onset of Covid-19 pandemic has resulted in the exponential growth of alcohol-based hand rub (ABHR)/hand sanitizer use. Reports have emerged of ABHR products containing methanol, a highly toxic compound to humans, exposing users to acute and chronic medical illnesses. While gas chromatography-mass spectrometry (GC-MS) remains the gold-standard method for the detection and identification of impurities in ABHRs, there exist limitations at widespread volume testing. This paper demonstrates the capability of an inexpensive portable pyroelectric linear array infrared spectrometer to rapidly test ABHR and compare the performance with a benchtop Fourier transform infrared spectrometer and HS-GC-MS. Multicomponent partial least square quantification models were built with performance found to be comparable between the two spectrometers and with the HS-GC-MS. Furthermore, the portable spectrometer was field-tested with real-world samples in Malaysia on both retail products (Group A) and freely deployed public dispensers (Group B) between May and November 2020. A total of 386 samples were tested. Only 75.2% of Group A met the criteria of safe and effective ABHR [no detectable methanol and alcohol concentration above 60% (v/v)], while <50% of Group B did. In addition, 7.4 and 18.8% of Group A and Group B, respectively, were found to contain methanol above permissible limits. The high percentage of sub-standard and methanol-containing samples combined with the frequent use of ABHR by the public highlights the need for and importance of a portable and rapid testing device for widespread screening of ABHR against falsified products and protects the general public.
DNAzymes have emerged as an important class of sensors for a wide variety of metal ions, with florescence DNAzyme sensors as the most widely used in different sensing and imaging applications because of their fast response time, high signal intensity, and high sensitivity. However, the requirements of an external excitation light source and its associated power increase the cost and size of the fluorometer, making it difficult to be used for portable detections. To overcome these limitations, we report herein a DNAzyme sensor that relies on chemiluminescence resonance energy transfer (CRET) without the need for external light. The sensor is constructed by combining the functional motifs from both Pb2+-dependent 8-17 DNAzyme conjugated to fluorescein (FAM) and hemin/G-quadruplex that mimics horseradish peroxidase to catalyze the oxidation of luminol by H2O2 to yield chemiluminescence. In the absence of Pb2+, the hybridization between the enzyme and substrate strands bring the FAM and hemin/G-quadruplex in close proximity, resulting in CRET. The presence of Pb2+ ions can drive the cleavage on the substrate strand, resulting in a sharp decrease in the melting temperature of hybridization and thus separation of the FAM from hemin/G-quadruplex. The liberated CRET pair causes a ratiometric increase in the donor's fluorescent signal and a decrease in the acceptor signal. Using this method, Pb2+ ions have been measured rapidly (<15 min) with a low limit of detection at 5 nM. By removing the requirement of exogenous light excitation, we have demonstrated a simple and portable detection using a smartphone, making the DNAzyme-CRET system suitable for field tests of lake water. Since DNAzymes selective for other metal ions or targets, such as bacteria, can be obtained using in vitro selection, the method reported here opens a new avenue for rapid, portable, and ratiometric detection of many targets in environmental monitoring, food safety, and medical diagnostics.
The metal-clad leaky waveguide (MCLW) is an optical biosensor consisting of a metal layer and a low index waveguide layer on a glass substrate. This label-free sensor measures refractive index (RI) changes within the waveguide layer. This work shows the development and optimization of acrylate based-hydrogel as the waveguide layer formed from PEG diacrylate (PEGDA, Mn 700), PEG methyl ether acrylate (PEGMEA, Mn 480), and acrylate-PEG2000-NHS fabricated on a substrate coated with 9.5 nm of titanium. The acrylate-based hydrogel is a synthetic polymer, so properties such as optical transparency, porosity, and hydrogel functionalization by a well-controlled reactive group can be tailored for immobilization of the bioreceptor within the hydrogel matrix. The waveguide sensor demonstrated an equal response to solutions of identical RI containing small (glycerol) and large (bovine serum albumin; BSA) analyte molecules, indicating that the hydrogel waveguide film is highly porous to both sizes of molecule, thus potentially allowing penetration of a range of analytes within the porous matrix. The final optimized MCLW chip was formed from a total hydrogel concentration of 40% v/v of PEGMEA-PEGDA (Mn 700), functionalized with 2.5% v/v of acrylate-PEG2000-NHS. The sensor generated a single-moded waveguide signal with a RI sensitivity of 128.61 ± 0.15° RIU-1 and limit of detection obtained at 2.2 × 10-6 RIU with excellent signal-to-noise ratio for the glycerol detection. The sensor demonstrated RI detection by monitoring changes in the out-coupled angle resulting from successful binding of d-biotin to streptavidin immobilized on functionalized acrylate hydrogel, generating a binding signal of (12.379 ± 0.452) × 10-3°.
We present an electrochemical long period fiber grating (LPFG) sensor for electroactive species with an optically transparent electrode. The sensor was fabricated by coating indium tin oxide onto the surface of LPFG using a polygonal barrel-sputtering method. LPFG was produced by an electric arc-induced technique. The sensing is based on change in the detection of electron density on the electrode surface during potential application and its reduction by electrochemical redox of analytes. Four typical electroactive species of methylene blue, hexaammineruthenium(III), ferrocyanide, and ferrocenedimethanol were used to investigate the sensor performance. The concentrations of analytes were determined by the modulation of the potential as the change in transmittance around the resonance band of LPFG. The sensitivity of the sensor, particularly to methylene blue, was high, and the sensor responded to a wide concentration range of 0.001 mM to 1 mM.
Rayleigh surface acoustic waves (SAWs) have been demonstrated as a powerful and effective means for driving a wide range of microfluidic actuation processes. Traditionally, SAWs have been generated on piezoelectric substrates, although the cost of the material and the electrode deposition process makes them less amenable as low-cost and disposable components. As such, a "razor-and-blades" model that couples the acoustic energy of the SAW on the piezoelectric substrate through a fluid coupling layer and into a low-cost and, hence, disposable silicon superstrate on which various microfluidic processes can be conducted has been proposed. Nevertheless, it was shown that only bulk vibration in the form of Lamb waves can be excited in the superstrate, which is considerably less efficient and flexible in terms of microfluidic functionality compared to its surface counterpart, that is, the SAW. Here, we reveal an extremely simple way that quite unexpectedly and rather nonintuitively allows SAWs to be generated on the superstrate-by coating the superstrate with a thin gold layer. In addition to verifying the existence of the SAW on the coated superstrate, we carry out finite-difference time domain numerical simulations that not only confirm the experimental observations but also facilitate an understanding of the surprising difference that the coating makes. Finally, we elucidate the various power-dependent particle concentration phenomena that can be carried out in a sessile droplet atop the superstrate and show the possibility for simply carrying out rapid and effective microcentrifugation-a process that is considerably more difficult with Lamb wave excitation on the superstrate.
There are no methods sensitive enough to detect enzymes within cells, without the use of analyte labeling. Here we show that it is possible to detect protein ion signals of three different H2S-synthesizing enzymes inside microglia after pretreatment with silver nanowires (AgNW) using time-of-flight secondary ion mass spectrometry (TOF-SIMS). Protein fragment ions, including the fragment of amino acid (C4H8N+ = 70 amu), fragments of the sulfur-producing cystathionine-containing enzymes, and the Ag+ ion signal could be detected without the use of any labels; the cells were mapped using the C4H8N+ amino acid fragment. Scanning electron microscopy imaging and energy-dispersive X-ray chemical analysis showed that the AgNWs were inside the same cells imaged by TOF-SIMS and transformed chemically into crystalline Ag2S within cells in which the sulfur-producing proteins were detected. The presence of these sulfur-producing cystathionine-containing enzymes within the cells was confirmed by Western blots and confocal microscopy images of fluorescently labeled antibodies against the sulfur-producing enzymes. Label-free TOF-SIMS is very promising for the label-free identification of H2S-contributing enzymes and their cellular localization in biological systems. The technique could in the future be used to identify which of these enzymes are most contributory.
A new sample preparation method is proposed for the extraction of pharmaceutical compounds (Metformin, Phenyl biguanide, and Phenformin) of varied hydrophilicity, dissolved in an aqueous sample. When in contact with an organic phase, an interfacial potential is imposed by the presence of an ion, tetramethylammonium (TMA+), common to each phase. The interfacial potential difference drives the transfer of ionic analytes across the interface and allows it to reach up to nearly 100% extraction efficiency and a 60-fold enrichment factor in optimized extraction conditions as determined by HPLC analysis.
Light-addressable potentiometric sensors (LAPS) are of great interest in bioimaging applications such as the monitoring of concentrations in microfluidic channels or the investigation of metabolic and signaling events in living cells. By measuring the photocurrents at electrolyte-insulator-semiconductor (EIS) and electrolyte-semiconductor structures, LAPS can produce spatiotemporal images of chemical or biological analytes, electrical potentials and impedance. However, its commercial applications are often restricted by their limited AC photocurrents and resolution of LAPS images. Herein, for the first time, the use of 1D semiconducting oxides in the form of ZnO nanorods for LAPS imaging is explored to solve this issue. A significantly increased AC photocurrent with enhanced image resolution has been achieved based on ZnO nanorods, with a photocurrent of 45.7 ± 0.1 nA at a light intensity of 0.05 mW, a lateral resolution as low as 3.0 μm as demonstrated by images of a PMMA dot on ZnO nanorods and a pH sensitivity of 53 mV/pH. The suitability of the device for bioanalysis and bioimaging was demonstrated by monitoring the degradation of a thin poly(ester amide) film with the enzyme α-chymotrypsin using LAPS. This simple and robust route to fabricate LAPS substrates with excellent performance would provide tremendous opportunities for bioimaging.
Organically (octyl amine, OA) surface modified electrocatalyst (OA-Pt/CB) was studied for its oxygen reduction reaction (ORR) activity via dc methods and its charge and mass transfer properties were studied via electrochemical impedance spectroscopy (EIS). Comparison with a commercial catalyst (TEC10V30E) with similar Pt content was also carried out. In EIS, both the catalysts showed a single time-constant with an emerging high-frequency semicircle of very small diameter which was fitted using suitable equivalent circuits. The organically modified catalyst showed lower charge-transfer resistance and hence, low polarization resistance in high potential region as compared to the commercial catalyst. The dominance of kinetic processes was observed at 0.925-1.000 V, whereas domination of diffusion based processes was observed at lower potential region for the organic catalyst. No effect due to the presence of carbon was observed in the EIS spectra. Using the hydrodynamic method, higher current penetration depth was obtained for the organically modified catalyst at 1600 rpm. Exchange current density and Tafel slopes for both the electrocatalysts were calculated from the polarization resistance obtained from EIS which was in correlation with the results obtained from dc methods.