Isoporous Membrane Mediated Imprinting Mass Spectrometry Imaging for Spatially-Resolved Metabolomics and Rapid Histopathological Diagnosis.

Surface-assisted laser desorption/ionization (SALDI) mass spectrometry imaging (MSI) holds great value in spatial metabolomics and tumor diagnosis. Tissue imprinting on the SALDI target can avoid laser-induced tissue ablation and simplifies the sample preparation. However, the tissue imprinting process always causes lateral diffusion of biomolecules, thereby losing the fidelity of metabolite distribution on tissue. Herein, a membrane-mediated imprinting mass spectrometry imaging (MMI-MSI) strategy is proposed using isoporous nuclepore track-etched membrane as a mediating imprinting layer to selectively transport metabolites through uniform and vertical pores onto silicon nanowires (SiNWs) array. Compared with conventional direct imprinting technique, MMI-MSI can not only exclude the adsorption of large biomolecules but also avoid the lateral diffusion of metabolites. The whole time for MMI-based sample preparation can be reduced to 2 min, and the lipid peak number can increase from 46 to 113 in kidney tissue detection. Meanwhile, higher resolution of MSI can be achieved due to the confinement effect of the pore channel in the diffusion of metabolites. Based on MMI-MSI, the tumor margins of liver cancer can be clearly discriminated and their different subtypes can be precisely classified. This work demonstrates MMI-MSI is a rapid, highly sensitive, robust and high-resolution technique for spatially-resolved metabolomics and pathological diagnosis.

Integration of metabolomics and peptidomics reveals distinct molecular landscape of human diabetic kidney disease.

Diabetic kidney disease (DKD) is the most common microvascular complication of diabetes, and there is an urgent need to discover reliable biomarkers for early diagnosis. Here, we established an effective urine multi-omics platform and integrated metabolomics and peptidomics to investigate the biological changes during DKD pathogenesis. Methods: Totally 766 volunteers (221 HC, 198 T2DM, 175 early DKD, 125 overt DKD, and 47 grey-zone T2DM patients with abnormal urinary mALB concentration) were included in this study. Non-targeted metabolic fingerprints of urine samples were acquired on matrix-free LDI-MS platform by the tip-contact extraction method using fluorinated ethylene propylene coated silicon nanowires chips (FEP@SiNWs), while peptide profiles hidden in urine samples were uncovered by MALDI-TOF MS after capturing urine peptides by porous silicon microparticles. Results: After multivariate analysis, ten metabolites and six peptides were verified to be stepwise regulated in different DKD stages. The altered metabolic pathways and biological processes associated with the DKD pathogenesis were concentrated in amino acid metabolism and cellular protein metabolic process, which were supported by renal transcriptomics. Interestingly, multi-omics significantly increased the diagnostic accuracy for both early DKD diagnosis and DKD status discrimination. Combined with machine learning, a stepwise prediction model was constructed and 89.9% of HC, 75.5% of T2DM, 69.6% of early DKD and 75.7% of overt DKD subjects in the external validation cohort were correctly classified. In addition, 87.5% of grey-zone patients were successfully distinguished from T2DM patients. Conclusion: This multi-omics platform displayed a satisfactory ability to explore molecular information and provided a new insight for establishing effective DKD management.

Accurate Discrimination of Benign Biliary Diseases and Cholangiocarcinoma with Serum Multiomics Revealed by High-Throughput Nanoassisted Laser Desorption Ionization Mass Spectrometry.

Cholangiocarcinoma (CCA) is an aggressive malignant tumor with a poor prognosis. Carbohydrate antigen 19-9 is an essential biomarker for CCA diagnosis, but its low sensitivity (72%) makes the diagnosis unreliable. To explore potential biomarkers for the diagnosis of CCA, a high-throughput nanoassisted laser desorption ionization mass spectrometry technique was constructed. We performed serum lipidomics and peptidomics analyses from 112 patients with CCA and 123 patients with benign biliary diseases. Lipidomics analysis showed that various lipids, such as glycerophospholipids, glycerides, and sphingolipids, were perturbed. Peptidomics analysis revealed perturbations of multiple proteins involved in the coagulation cascade, lipid transport, and so on. After data mining, 25 characteristic molecules including 20 lipids and 5 peptides were identified as potential diagnostic biomarkers. After screening various machine learning algorithms, artificial neural network was selected to construct a multiomics model for CCA diagnosis with 96.5% sensitivity and 96.4% specificity. The sensitivity and specificity of the model in the independent test cohort were 93.8 and 87.5%, respectively. Furthermore, integrated analysis with transcriptomic data in the cancer genome atlas confirmed that genes altered in CCA significantly affected multiple lipid- and protein-related pathways. Data are available via MetaboLights with the identifier MTBLS6712.

A Virtual Reality Platform for Context-Dependent Cognitive Research in Rodents.

Animal survival necessitates adaptive behaviors in volatile environmental contexts. Virtual reality (VR) technology is instrumental to study the neural mechanisms underlying behaviors modulated by environmental context by simulating the real world with maximized control of contextual elements. Yet current VR tools for rodents have limited flexibility and performance (e.g., frame rate) for context-dependent cognitive research. Here, we describe a high-performance VR platform with which to study contextual behaviors immersed in editable virtual contexts. This platform was assembled from modular hardware and custom-written software with flexibility and upgradability. Using this platform, we trained mice to perform context-dependent cognitive tasks with rules ranging from discrimination to delayed-sample-to-match while recording from thousands of hippocampal place cells. By precise manipulations of context elements, we found that the context recognition was intact with partial context elements, but impaired by exchanges of context elements. Collectively, our work establishes a configurable VR platform with which to investigate context-dependent cognition with large-scale neural recording.

Dual-Mechanism-Driven Strategy for High-Coverage Detection of Serum Lipids on a Novel SALDI-MS Target.

Serum lipid metabolites have been emerging as ideal biomarkers for disease diagnosis and prediction. In the current stage, nontargeted or targeted lipidomic research mainly relies on a liquid chromatography-mass spectrometry (LC-MS) platform, but future clinical applications need more robust and high-speed platforms. Surface-assisted laser desorption ionization mass spectrometry (SALDI-MS) has shown excellent advantages in the high-speed analysis of lipid metabolites. However, the platform in the positive ion mode is more inclined to target a certain class of lipids, leading to the low coverage of lipid detection and limiting its practical translation to clinical applications. Herein, we proposed a dual-mechanism-driven strategy for high-coverage detection of serum lipids on a novel SALDI-MS target, which is a composite nanostructure comprising vertical silicon nanowires (VSiNWs) decorated with AuNPs and polydopamine (VSiNW-Au-PDA). The performance of laser desorption and ionization on the target can be enhanced by charge-driven desorption coupled with thermal-driven desorption. Simultaneous detection of 236 serum lipids (S/N ≥ 5) including neutral and polar lipids can be achieved in the positive ion mode. Among these, 107 lipid peaks were successfully identified. When combined with VSiNW-Au-PDA and VSiNW chips, 479 lipid peaks can be detected in serum samples in positive and negative ion modes, respectively. Based on the platform, serum samples from 57 hepatocellular carcinoma (HCC) patients and 76 healthy controls were analyzed. After data mining, 14 lipids containing different lipid types (TAG, CE, PC) were selected as potential lipidomic biomarkers. With the assistance of an artificial neural network, a diagnostic model with a sensitivity of 92.7% and a specificity of 96% was constructed for HCC diagnosis.

Tissue Imprinting on 2D Nanoflakes-Capped Silicon Nanowires for Lipidomic Mass Spectrometry Imaging and Cancer Diagnosis.

Spatially resolved tissue lipidomics is essential for accurate intraoperative and postoperative cancer diagnosis by revealing molecular information in the tumor microenvironment. Matrix-free laser desorption ionization mass spectrometry imaging (LDI-MSI) is an emerging attractive technology for label-free visualization of metabolites distributions in biological specimens. However, the development of LDI-MSI technology that could conveniently and authentically reveal molecular distribution on tissue samples is still a challenge. Herein, we present a tissue imprinting technology by retaining tissue lipids on 2D nanoflakes-capped silicon nanowires (SiNWs) for further mass spectrometry imaging and cancer diagnosis. The 2D nanoflakes were prepared by liquid exfoliation of molybdenum disulfide (MoS2) with nitrogen-doped graphene quantum dots (NGQDs), which serve as both intercalation agent and dispersant. The obtained NGQD@MoS2 nanoflakes were then decorated on the tip of vertical SiNWs, forming a hybrid NGQD@MoS2/SiNWs nanostructure, which display excellent lipid extraction ability, enhanced LDI efficiency and molecule imaging capability. The peak number and total ion intensity of different lipids species on animal lung tissues obtained by tissue imprinting LDI-MSI on NGQD@MoS2/SiNWs were ∼4-5 times greater than those on SiNWs substrate. As a proof-of-concept demonstration, the NGQD@MoS2/SiNWs nanostructure was further applied to visualize phospholipids on sliced non small cell lung cancer (NSCLC) tissue along with the adjacent normal tissue. On the basis of selected feature lipids and machine learning algorithm, a prediction model was constructed to discriminate NSCLC tissues from the adjacent normal tissues with an accuracy of 100% for the discovery cohort and 91.7% for the independent validation cohort.

Lipid response of hepatocellular carcinoma cells to anticancer drug detected on nanostructure-assisted LDI-MS platform.

High heterogeneity of hepatocellular carcinoma (HCC) tumor has become an obstacle to select effective therapy for the treatment of HCC patients. Methods that can guide the decision on therapy choice for HCC treatment are highly demanded. Evaluating the drug response of heterogeneous tumor cells at the molecular level can help to reveal the toxicity mechanism of anticancer drugs and provide more information than current cell-based chemosensitivity assays. In the present work, nanostructure-assisted laser desorption/ionization mass spectrometry (NALDI-MS) was used to investigate the lipid response of HCC cells to anticancer drugs. Three types of HCC cells (LM3, Hep G2, Huh7) were treated with sorafenib, doxorubicin hydro-chloride, and cisplatin. We found that the lipid profiles of HCC cells changed a lot after the drug treatment, and the degree of lipid changes was related to the cell viability. Two pairs of fatty acids C16:1/C16:0 and C18:1/C18:0 were found to be strongly related to the viability of HCC cells after drug treatment, and were more sensitive than Methyl-thiazolyl tetrazolium (MTT) assay. Accordingly, they can act as sensitive and comprehensive indexes to evaluate the drug susceptibility of HCC cells. In addition, the peak ratio of several neighboring phospholipids displayed high correlation with drug response of specific cell subtype to specific drug. The ratio of neighboring lipids may be traced back to the activity of enzyme and gene expression which regulate the lipidomic pathway. This method provides drug response of heterogenous tumor cells at molecular level and could be a potential candidate to precise tumor chemosensitivity assay.

Synergistic and On-Demand Release of Ag-AMPs Loaded on Porous Silicon Nanocarriers for Antibacteria and Wound Healing.

Due to the abuse of antibiotics, antimicrobial resistance is rapidly emerging and becoming a major global risk for public health. Thus, there is an urgent need for reducing the use of antibiotics, finding novel treatment approaches, and developing controllable release systems. In this work, a dual synergistic antibacterial platform with on-demand release ability based on silver nanoparticles (AgNPs) and antimicrobial peptide (AMP) coloaded porous silicon (PSi) was developed. The combination of AgNPs and AMPs (Tet-213, KRWWKWWRRC) exhibited an excellent synergistic antibacterial effect. As a carrier, porous silicon can efficiently load AgNPs and AMP under mild conditions and give the platform an on-demand release ability and a synergistic release effect. The AgNPs and AMP coloaded porous silicon microparticles (AgNPs-AMP@PSiMPs) exhibited an acid pH and reactive oxygen species (ROS)-stimulated release of silver ions (Ag+) and AMPs under bacterial infection conditions because of oxidation and desorption effects. Moreover, the release of the bactericide could be promoted by each other due to the interplay between AgNPs and Tet-213. In vitro antibacterial tests demonstrated that AgNPs-AMP@PSiMPs inherited the intrinsic properties and synergistic antibacterial efficiency of both bactericides. In addition, wound dressing loaded with AgNPs-AMP@PSiMPs showed outstanding in vivo bacteria-killing activity, accelerating wound-healing, and low biotoxicity in aStaphylococcus aureus-infected rat wound model. The present work demonstrated that PSiMPS might be an efficient platform for loading the antibiotic-free bactericide, which could synergistically and on-demand release to fight wound infection and promote wound healing.

Cationic cyanine chromophore-assembled upconversion nanoparticles for sensing and imaging H2S in living cells and zebrafish.

Elevated hydrogen sulfide (H2S) level is closely associated with various diseases. So the sensing of H2S is noteworthy for divulging its role in diagnosing these diseases. Herein, we proposed poly(acrylic acid)-modified upconversion nanoparticles assembled with cationic near-infrared cyanine chromophores (Cy7-Cl) as the nanoprobe (Cy7-UCNPs) for monitoring H2S based on thiolation reactions. The presence of H2S resulted into about five-fold enhancement in the luminescence intensity of Cy7-UCNPs and the nanoprobe showed a good linearity (R2 =0.9952) over the range of 1.0 - 90 µM. Furthermore, Cy7-UCNPs were successfully employed in sensing and imaging of exogenous and endogenous H2S in live cells and zebrafish. The system shows great potential in the field of nanobiomedicine because of the many excellent properties including high sensitivity, good selectivity, and low cytotoxicity.