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.
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.
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.
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.
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.
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.
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.
In nucleic acid testing (NAT), gold nanoparticle (AuNP)-based lateral flow assays (LFAs) have received significant attention due to their cost-effectiveness, rapidity, and the ability to produce a simple colorimetric readout. However, the poor sensitivity of AuNP-based LFAs limits its widespread applications. Even though various efforts have been made to improve the assay sensitivity, most methods are inappropriate for integration into LFA for sample-to-answer NAT at the point-of-care (POC), usually due to the complicated fabrication processes or incompatible chemicals used. To address this, we propose a novel strategy of integrating a simple fluidic control strategy into LFA. The strategy involves incorporating a piece of paper-based shunt and a polydimethylsiloxane (PDMS) barrier to the strip to achieve optimum fluidic delays for LFA signal enhancement, resulting in 10-fold signal enhancement over unmodified LFA. The phenomena of fluidic delay were also evaluated by mathematical simulation, through which we found the movement of fluid throughout the shunt and the tortuosity effects in the presence of PDMS barrier, which significantly affect the detection sensitivity. To demonstrate the potential of integrating this strategy into a LFA with sample-in-answer-out capability, we further applied this strategy into our prototype sample-to-answer LFA to sensitively detect the Hepatitis B virus (HBV) in clinical blood samples. The proposed strategy offers great potential for highly sensitive detection of various targets for wide application in the near future.