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.
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.
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.
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.
We present a compact, cost-effective, label-free, real-time biosensor based on long-range surface plasmon polariton (LRSPP) gold (Au) waveguides for the detection of dengue-specific immunoglobulin M (IgM) antibody, and we demonstrate detection in actual patient blood plasma samples. Two surface functionalization approaches are proposed and demonstrated: a dengue virus serotype 2 (DENV-2) functionalized surface to capture dengue-specific IgM antibody in blood plasma and the reverse, a blood plasma functionalized surface to capture DENV-2. The results obtained via these two surface functionalization approaches are comparable to, or of greater quality, than those collected by conventional IgM antibody capture enzyme linked immunosorbent assay (MAC-ELISA). Our second functionalization approach was found to minimize nonspecific binding, thus improving the sensitivity and accuracy of the test. We also demonstrate reuse of the biosensors by regenerating the sensing surface down to the virus (or antibody) level or down to the bare Au.
In this work, independent radial diffusion at arrayed nanointerfaces between two immiscible electrolyte solutions (nanoITIES) was achieved. The arrays were formed at nanopores fabricated by focused ion beam milling of silicon nitride (SiN) membranes, enabling the reproducible and systematic design of five arrays with different ratios of pore center-to-center distance (rc) to pore radius (ra). Voltammetry across water-1,6-dichlorohexane nanoITIES formed at these arrays was examined by the interfacial transfer of tetrapropylammonium ions. The diffusion-limited ion-transfer current increased with the ratio rc/ra, reaching a plateau for rc/ra ≥ 56, which was equivalent to the theoretical current for radial diffusion to an array of independent nanoITIES. As a result, mass transport to the nanoITIES arrays was greatly enhanced due to the decreased overlap of diffusion zones at adjacent nanoITIES, allowing each interface in the array to behave independently. When the rc/ra ratio increased from 13 to 56, the analytical performance parameters of sensitivity and limit of detection were improved from 0.50 (±0.02) A M(-1) to 0.76 (±0.02) A M(-1) and from 0.101 (±0.003) μM to 0.072 (±0.002) μM, respectively. These results provide an experimental basis for the design of arrayed nanointerfaces for electrochemical sensing.