Proteomics Profiling of Stool Samples from Preterm Neonates with SWATH/DIA Mass Spectrometry for Predicting Necrotizing Enterocolitis.

Necrotizing enterocolitis (NEC) is a life-threatening condition for premature infants in neonatal intensive care units. Finding indicators that can predict NEC development before symptoms appear would provide more time to apply targeted interventions. In this study, stools from 132 very-low-birth-weight (VLBW) infants were collected daily in the context of a multi-center prospective study aimed at investigating the potential of fecal biomarkers for NEC prediction using proteomics technology. Eight of the VLBW infants received a stage-3 NEC diagnosis. Stools collected from the NEC infants up to 10 days before their diagnosis were available for seven of them. Their samples were matched with those from seven pairs of non-NEC controls. The samples were processed for liquid chromatography-tandem mass spectrometry analysis using SWATH/DIA acquisition and cross-compatible proteomic software to perform label-free quantification. ROC curve and principal component analyses were used to explore discriminating information and to evaluate candidate protein markers. A series of 36 proteins showed the most efficient capacity with a signature that predicted all seven NEC infants at least a week in advance. Overall, our study demonstrates that multiplexed proteomic signature detection constitutes a promising approach for the early detection of NEC development in premature infants.

A colorimetric nano-biosensor for simultaneous detection of prevalent cancers using unamplified cell-free ribonucleic acid biomarkers.

Early detection of cancer increases the chance of effective treatment and survival rates. The aim of this study is to develop a rapid and non-invasive nano-biosensing method to screen common lethal cancers in their early stages. In that regard, two circulating microRNA (miR-21, miR-155) biomarkers, which are upregulated in plasma in prevalent cancers, were targeted by a rapid and colorimetric nano-biosensor based on non-crosslinking Au-nanoprobes without amplification requirement. Multiple cancerous cell lines, including A549, MCF7, HT-29, A2780, AGS, MKN-45, and SW-1736 and the primary fibroblast were examined with naked eyes after the hybridization assay using exogenous biomarkers. The results were also confirmed by spectroscopy analysis. The upregulated miRNAs in cancerous cell lines caused a significant blue shift in the Au-nanoprobe absorbance spectrum while the samples isolated from normal cells remained intact red. The limit of detection (LOD) of the method was determined to be less than one ng/µL of total isolated miRNA using an instrument-free visual method. The developed geno-sensing method could serve as a simple, point-of-care platform for cancer prognosis and diagnosis, leading to operative nano-theranostics.

Ribonucleic-acid-biomarker candidates for early-phase group detection of common cancers.

Cancer is considered as a challenging lethal agent around the world and its detection at early stages would help prevention of the high mortality. Among the widely used biomarkers in clinical diagnosis of cancer, extracellular non-coding RNAs as ribonucleic acid biomarkers serve as state-of-the-art candidates for molecular diagnosis. In that regard, microRNAs are of great priority mainly because of high variety and stability in body fluids. Accordingly, common miRNAs among most prevalent cancers could help us (pre)diagnose cancer with high accuracy in target samples. In this study, common lethal cancers to humans were investigated in case of miRNA profiles to determine the possible common correlation between miRNA up-regulation or down-regulation (as a ribonucleic acid biomarker) and developing the cancers. It was shown that among the investigated miRNAs, five typical extracellular miRNAs (miR-18a, miR-21, miR-155, miR-221, and miR-375) dysregulation are predominant in most cancer varieties comprising breast, colon, lung, prostate, pancreas, gastric, ovarian, esophagus and liver. This could serve as an appropriate target site for developing point-of-care approaches for cancer detection e.g. designing diagnostic biosensor-based microarrays or kits for both quantification and qualification of the biomarkers. Besides, the miRNA candidates could be efficiently applied to cancer therapeutic approaches.

Application of SWATH Mass Spectrometry and Machine Learning in the Diagnosis of Inflammatory Bowel Disease Based on the Stool Proteome.

Inflammatory bowel disease (IBD) flare-ups exhibit symptoms that are similar to other diseases and conditions, making diagnosis and treatment complicated. Currently, the gold standard for diagnosing and monitoring IBD is colonoscopy and biopsy, which are invasive and uncomfortable procedures, and the fecal calprotectin test, which is not sufficiently accurate. Therefore, it is necessary to develop an alternative method. In this study, our aim was to provide proof of concept for the application of Sequential Window Acquisition of All Theoretical Mass Spectra-Mass spectrometry (SWATH-MS) and machine learning to develop a non-invasive and accurate predictive model using the stool proteome to distinguish between active IBD patients and symptomatic non-IBD patients. Proteome profiles of 123 samples were obtained and data processing procedures were optimized to select an appropriate pipeline. The differentially abundant analysis identified 48 proteins. Utilizing correlation-based feature selection (Cfs), 7 proteins were selected for proceeding steps. To identify the most appropriate predictive machine learning model, five of the most popular methods, including support vector machines (SVMs), random forests, logistic regression, naive Bayes, and k-nearest neighbors (KNN), were assessed. The generated model was validated by implementing the algorithm on 45 prospective unseen datasets; the results showed a sensitivity of 96% and a specificity of 76%, indicating its performance. In conclusion, this study illustrates the effectiveness of utilizing the stool proteome obtained through SWATH-MS in accurately diagnosing active IBD via a machine learning model.