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.
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.
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.
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.
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.
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.