A Complete Pipeline for Untargeted Urinary Volatolomic Profiling with Sorptive Extraction and Dual Polar and Nonpolar Column Methodologies Coupled with Gas Chromatography Time-of-Flight Mass Spectrometry

The optimization and comparison of two high-throughput faecal headspace sampling platforms: the microchamber/thermal extractor and hi-capacity sorptive extraction probes (HiSorb)

Disease detection and monitoring using volatile organic compounds (VOCs) is becoming increasingly popular. For a variety of (gastrointestinal) diseases the microbiome should be considered. As its output is to large extent volatile, faecal volatilomics carries great potential. One technical limitation is that current faecal headspace analysis requires specialized instrumentation which is costly and typically does not work in harmony with thermal desorption units often utilized in e.g. exhaled breath studies. This lack of harmonization hinders uptake of such analyses by the Volatilomics community. Therefore, this study optimized and compared two recently harmonized faecal headspace sampling platforms:High-capacity Sorptive extraction (HiSorb) probesand theMicrochamber thermal extractor (Microchamber). Statistical design of experiment was applied to find optimal sampling conditions by maximizing reproducibility, the number of VOCs detected, and between subject variation. To foster general applicability those factors were defined using semi-targeted as well as untargeted metabolic profiles. HiSorb probes were found to result in a faster sampling procedure, higher number of detected VOCs, and higher stability. The headspace collection using the Microchamber resulted in a lower number of detected VOCs, longer sampling times and decreased stability despite a smaller number of interfering VOCs and no background signals. Based on the observed profiles, recommendations are provided on pre-processing and study design when using either one of both platforms. Both can be used to perform faecal headspace collection, but altogether HiSorb is recommended.

Investigating the effects of storage conditions on urinary volatilomes for their reliability in disease diagnosis

BACKGROUND

Cancer detection presents challenges regarding invasiveness, cost, and reliability. As a result, exploring alternative diagnostic methods holds significant clinical importance. Urinary metabolomic profiling has emerged as a promising avenue; however, its application for cancer diagnosis may be influenced by sample preparation or storage conditions.

OBJECTIVE

This study aimed to assess the impact of sample storage and processing conditions on urinary volatile organic compounds (VOCs) profiles and establish a robust standard operating procedure (SOP) for such diagnostic applications.

METHODS

Five key variables were investigated: storage temperatures, durations, freeze-thaw cycles, sample collection conditions, and sample amounts. The analysis of VOCs involved stir bar sorptive extraction coupled with thermal desorption-gas chromatography/mass spectrometry (SBSE-TD-GC-MS), with compound identification facilitated by the National Institute of Standards and Technology Library (NIST). Extensive statistical analysis, including combined scatterplot and response surface (CSRS) plots, partial least squares-discriminant analysis (PLS-DA), and probability density function plots (PDFs), were employed to study the effects of the factors.

RESULTS

Our findings revealed that urine storage duration, sample amount, temperature, and fasting/non-fasting sample collection did not significantly impact urinary metabolite profiles. This suggests flexibility in urine sample collection conditions, enabling individuals to contribute samples under varying circumstances. However, the influence of freeze-thaw cycles was evident, as VOC profiles exhibited distinct clustering patterns based on the number of cycles. This emphasizes the effect of freeze-thaw cycles on the integrity of urinary profiles.

CONCLUSIONS

The developed SOP integrating SBSE-TD-GC-MS and statistical analyses can serve as a valuable tool for analyzing urinary organic compounds with minimal preparation and sensitive detection. The findings also support that urinary VOCs for cancer screening and diagnosis could be a feasible alternative offering a robust, non-invasive, and sensitive approach for cancer screening.

Global Urinary Volatolomics with (GC×)GC-TOF-MS

Urinary volatolomics offers a noninvasive approach for disease detection and monitoring. Herein we present an improved methodology for global volatolomic profiling. Wide coverage was achieved by utilizing a multiphase sorbent for volatile organic compound (VOC) extraction. A single, midpolar column gas chromatography (GC) assay yielded substantially higher numbers of monitored VOCs compared to our previously reported single-sorbent method. Multidimensional GC (GC×GC) enhanced further biomarker discovery while data analysis was simplified by using a tile-based approach. At the same time, the required urine volume was reduced 5-fold from 2 to 0.4 mL. The applicability of the methodology was demonstrated in a pancreatic ductal adenocarcinoma cohort where previous findings were confirmed while a series of additional VOCs with diagnostic potential were discovered.

Rapid Diagnosis of Urinary Tract Cancers on a LEGO-Inspired Detection Platform via Chemiresistive Profiling of Volatile Metabolites

Rapid and in situ profiling of volatile metabolites from body fluids represents a new trend in cancer diagnosis and classification in the early stages. We report herein an on-chip strategy that combines an array of conductive nanosensors with a chaotic gas micromixer for real-time monitoring of volatiles from urine and for accurate diagnosis and classification of urinary tract cancers. By integrating a class of LEGO-inspired microchambers immobilized with MXene-based sensing nanofilms and zigzag microfluidic gas channels, it enables the intensive intermingling of volatile organic chemicals with sensor elements that tremendously facilitate their ion-dipole interactions for molecular recognition. Aided with an all-in-one, point-of-care platform and an effective machine-learning algorithm, healthy or diseased samples from subpopulations (i.e., tumor subtypes, staging, lymph node metastasis, and distant metastasis) of urinary tract cancers can be reliably fingerprinted in a few minutes with high sensitivity and specificity. The developed detection platform has proven to be a noninvasive supplement to the liquid biopsies available for facile screening of urinary tract cancers, which holds great potential for large-scale personalized healthcare in low-resource areas.