Accurate and High-Resolution Particle Mass Measurement Using a Peak Filtering Algorithm.

Charge detection quadrupole ion trap mass spectrometry (CD-QIT MS) is an effective way of achieving the mass analysis of microparticles with ultrahigh mass. However, its mass accuracy and resolution are still poor. To enhance the performance of CD-QIT MS, the resolution Rpeak of each peak in the mass spectra resulting from an individual particle was assessed, and a peak filtering algorithm that can filter out particle adducts and clusters with a lower Rpeak was proposed. By using this strategy, more accurate mass information about the analyzed particles could be obtained, and the mass resolution of CD-QIT MS was improved by nearly 2-fold, which was demonstrated by using the polystyrene (PS) particle size standards and red blood cells (RBCs). Benefiting from these advantages of the peak filtering algorithm, the baseline separation and relative quantification of 3 and 4 µm PS particles were achieved. To prove the application value of this algorithm in a biological system, the mass of yeast cells harvested at different times was measured, and it was found that the mixed unbudded and budded yeast cells, which otherwise would not be differentiable, were distinguished and quantified with the algorithm.

Recent advances in entirely hand-held ionization sources for mass spectrometry.

Ambient ionization mass spectrometry (AIMS) has been developing explosively since its first debut. The ionization process was hence able to be achieved under atmospheric pressure, facilitating on-site field analysis in a variety of areas, such as clinical diagnosis, metabolic phenotyping, and surface analysis. As part of the ambitious goal of making MS a general device that can be used in everyday life, lots of efforts have been paid to miniaturize the ionization source. This review discusses avant-garde sources that could be entirely hand-held without any accessories. The structure and applications of the devices are described in detail as well. They could be expediently used in real-time and on-site analysis, presenting a great future potential for the routinizing of MS.

Screening of hepatocellular carcinoma via machine learning based on atmospheric pressure glow discharge mass spectrometry.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a high mortality rate. The diagnosis of HCC is currently based on alpha-fetoprotein detection, imaging examinations, and liver biopsy, which are expensive or invasive. Here, we developed a cost-effective, time-saving, and painless method for the screening of HCC via machine learning based on atmospheric pressure glow discharge mass spectrometry (APGD-MS). Ninety urine samples from HCC patients and healthy control (HC) participants were analyzed. The relative quantification data were utilized to train machine learning models. Neural network was chosen as the best classifier with a classification accuracy of 94%. Besides, the levels of eleven urinary carbonyl metabolites were found to be significantly different between HCC and HC, including glycolic acid, pyroglutamic acid, acetic acid, etc. The possible reasons for the regulation were tentatively proposed. This method realizes the screening of HCC via potential urine metabolic biomarkers based on APGD-MS, bringing a hopeful point-of-care diagnosis of HCC in a patient-friendly manner.

Rapid screening for genitourinary cancers: mass spectrometry-based metabolic fingerprinting of urine.

Genitourinary (GU) cancers are among the most common malignant diseases in men. Rapid screening is the key to GU cancer management for early diagnosis and treatment. Urine is a highly accessible specimen type and urine metabolic fingerprints (UMFs) reflect underlying metabolite signatures of GU cancers. Herein, rapid screening of GU cancers is performed using high-throughput extraction of UMFs by mass spectrometry and efficient recognition by machine learning (ML). GU cancer patients can be distinguished with an accuracy of 90.1%. Besides, key biomarkers such as citric acid were found remarkably upregulated in cancer groups, indicating the dysregulated pathways. This approach highlights the potential role of ML in clinical application and demonstrates the expanding utility of UMFs in disease screening.

Profiling of Urine Carbonyl Metabolic Fingerprints in Bladder Cancer Based on Ambient Ionization Mass Spectrometry.

The diagnosis of bladder cancer (BC) is currently based on cystoscopy, which is invasive and expensive. Here, we describe a noninvasive profiling method for carbonyl metabolic fingerprints in BC, which is based on a desorption, separation, and ionization mass spectrometry (DSI-MS) platform with N,N-dimethylethylenediamine (DMED) as a differential labeling reagent. The DSI-MS platform avoids the interferences from intra- and/or intersamples. Additionally, the DMED derivatization increases detection sensitivity and distinguishes carboxyl, aldehyde, and ketone groups in untreated urine samples. Carbonyl metabolic fingerprints of urine from 41 BC patients and 41 controls were portrayed and 9 potential biomarkers were identified. The mechanisms of the regulations of these biomarkers have been tentatively discussed. A logistic regression (LR) machine learning algorithm was applied to discriminate BC from controls, and an accuracy of 85% was achieved. We believe that the method proposed here may pave the way toward the point-of-care diagnosis of BC in a patient-friendly manner.

Comparative transcriptome analysis reveals involvement of TLR-2 signaling in the pathogenesis of intracranial aneurysm.

In this study, we collected and analyzed 15 aneurysmal and 17 superficial temporal artery (STA) specimens from 32 Chinese patients with intracranial aneurysm. Total RNA was extracted and reverse transcribed to cDNA, and genome-wide expression profiling was performed by using the Affymetrix Human Genome U133 Plus 2.0 Array which allows a total number of 38,500 genes to be analyzed at the same time. Real-time RT-PCR was performed to verify the expression level of 8 selected genes. We found significant up-regulation of the TLR-2 gene. This result suggests that TLR-2 plays a key role in the formation of intracranial aneurysm in a Chinese population. To our knowledge, this study is the first to use the Human Genome U133 Plus 2.0 Array to analyze the gene expression profiles in Chinese patients with intracranial aneurysm.

Gene expression profiles in intracranial aneurysms.

In this study, we extracted total RNA from 15 intracranial aneurysms and 17 superficial temporal artery samples, then performed genome-wide expression profiling using the Affymetrix U133 Plus 2.0 GeneChip. Genes that were differentially expressed between intracranial aneurysms and arterial samples were identified using significance analysis for microarrays, and the expression patterns of three randomly-selected genes were verified by real-time polymerase chain reaction analysis. We identified 3 736 differentially-expressed genes out of the 47,000 assayed transcripts. A total of 179 genes showed a >10-fold change in expression between the aneurysms and the arterial samples. Genes involved in the proliferation, migration, and apoptosis of vascular muscle cells, atherosclerosis, extracellular matrix disruption, and inflammatory reactions were associated with the formation of intracranial aneurysms. There were no significant differences in gene expression profile between unruptured and ruptured aneurysms.

Association between IRF-5 polymorphisms and risk of acute coronary syndrome.

Previous studies suggested that genetic polymorphisms in interferon regulatory factor 5 (IRF-5) are implicated in the susceptibility to a range of autoimmune diseases, including rheumatoid arthritis, systemic lupus erythematosus, and inflammatory bowel disease. Recently, IRF-5 has been implicated in inflammatory processes that are associated with excessive remodeling and atherosclerosis. Our purpose was to investigate the association between the IRF-5 polymorphisms and the risk of acute coronary syndrome (ACS) in a Chinese population. The 5 bp indel (insertion/deletion) (CGGGG) polymorphism, located 64 bp upstream of the alternative exon 1a of IRF-5 gene, and the deletion of 30 bp in exon 6 of IRF-5 gene were analyzed among 148 patients with ACS and 246 controls in a Chinese population, using a polymerase chain reaction-restriction fragment length polymorphism strategy and direct sequencing. The frequencies of (CGGGG)(3)(CGGGG)(4) genotype and (CGGGG)(4) allele in ACS patients were significantly higher than those in control subjects (p = 0.018, odds ratio [OR] = 1.76, 95% confidence interval [CI]: 1.10-2.81; p = 0.028, OR = 1.62, 95% CI: 1.05-2.50, respectively). However, no significant relationship between the 30 bp exon 6 polymorphism of the IRF-5 gene and the risk of ACS was observed (p = 0.770, OR = 0.96, 95% CI: 0.72-1.28). The 5 bp indel (CGGGG) polymorphism of the IRF-5 gene may be associated with susceptibility to ACS.