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.

Photoemission is a promising approach to electron sources for electron beam lithography because of the ease with which various shapes or arrays of independently modulated sources can be fabricated. However, most high-quantum-efficiency photoemitters are extremely sensitive to even partial monolayers of contamination, and therefore require some combination of differential pumping systems and photoemitter surface protection after activation. Here we propose to use a high-power 257 nm laser in combination with the relatively high work function and low quantum efficiency of gold films to produce practical multicathode electron sources for electron beam lithography. Gold films have the offsetting advantages that their photoemission characteristics are relatively reproducible and stable even in contaminating environments. It is possible, therefore, to prepare and handle them in air as well as operate them in less demanding vacuum environments. It is shown that a back-illuminated 15 nm gold film on a quartz or sapphire substrate exhibits a quantum efficiency of approximately 10−4 at 257 nm, producing photocurrents greater than 1 μA at a laser power of under 200 mW. The quantum efficiency is very reproducible and relatively stable under a variety of environmental and operational conditions. Slow changes, by as much as a factor of 3, over time periods of several weeks following sample preparation have been observed, consistent with variations of the gold work function of approximately 0.2 eV. The results are in good agreement with a straightforward extension of existing photoemission models for bulk material.

Lung cancer (LC) is a widespread cancer that is the cause of the highest mortality rate accounting for 25% of all cancer deaths. To date, most LC patients are diagnosed at the advanced stage owing to the lack of obvious symptoms in the early stage and the limitations of current clinical diagnostic techniques. Therefore, developing a high throughput technique for early screening is of great importance. In this work, we established an effective and rapid salivary metabolic analysis platform for early LC diagnosis and combined metabolomics and transcriptomics to reveal the metabolic fluctuations correlated to LC. Saliva samples were collected from a total of 150 volunteers including 89 patients with early LC, 11 patients with advanced LC, and 50 healthy controls. The metabolic profiling of noninvasive samples was investigated on an ultralow noise TELDI-MS platform. In addition, data normalization methods were screened and assessed to overcome the MS signal variation caused by individual difference for biomarker mining. For untargeted metabolic profiling of saliva samples, around 264 peaks could be reliably detected in each sample. After multivariate analysis, 23 metabolites were sorted out and verified to be related to the dysfunction of the amino acid and nucleotide metabolism in early LC. Notably, transcriptomic data from online TCGA repository were utilized to support findings from the salivary metabolomics experiment, including the disorder of amino acid biosynthesis and amino acid metabolism. Based on the verified differential metabolites, early LC patients could be clearly distinguished from healthy controls with a sensitivity of 97.2% and a specificity of 92%. The ultralow noise TELDI-MS platform displayed satisfactory ability to explore salivary metabolite information and discover potential biomarkers that may help develop a noninvasive screening tool for early LC.

Adhesion related activities of six lactic acid bacteria were detected. This study will be beneficial to examine the characteristics of these strains used as probiotics in dairy products.