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Spatially resolved sampling for untargeted metabolomics: A new tool for salivomics.

ABSTRACT: Saliva is a complex bodily fluid composed of metabolites secreted by major and minor glands, as well as by-products of host oral cells, oral bacteria, gingival crevicular fluid, and exogenous compounds. Major salivary glands include the paired parotid, submandibular, and sublingual glands. The secreted fluids of the salivary glands vary in composition, flow rate, site of release, and clearance suggesting that different types of saliva fulfill different functions and therefore can provide unique biological information. Consequently, for the comprehension of the functionality of the salivary components, spatially resolved investigations are warranted. To understand and comprehensively map the highly heterogeneous environment of the oral cavity, advanced spatial sampling techniques for metabolomics analysis are needed. Here, we present a systematic evaluation of collection devices for spatially resolved sampling aimed at untargeted metabolomics and propose a comprehensive and reproducible collection and analysis protocol for the spatially resolved analysis of the human oral metabolome.

SUBMITTER: Ciurli A

PROVIDER: S-EPMC8271151 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Spatially resolved sampling for untargeted metabolomics: A new tool for salivomics.

Ciurli Alessio A Liebl Maximiliam M Derks Rico J E RJE Neefjes Jacques J C JJC Giera Martin M

iScience 20210624 7

Saliva is a complex bodily fluid composed of metabolites secreted by major and minor glands, as well as by-products of host oral cells, oral bacteria, gingival crevicular fluid, and exogenous compounds. Major salivary glands include the paired parotid, submandibular, and sublingual glands. The secreted fluids of the salivary glands vary in composition, flow rate, site of release, and clearance suggesting that different types of saliva fulfill different functions and therefore can provide unique ...[more]

PMID: 34278270

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Project description:IntroductionUntargeted metabolomics holds significant promise for biomarker detection and development. In resource-limited settings, a dried blood spot (DBS)-based platform would offer significant advantages over plasma-based approaches that require a cold supply chain.ObjectivesThe primary goal of this study was to compare the ability of DBS- and plasma-based assays to characterize maternal metabolites. Utility of the two assays was also assessed in the context of a case-control predictive model in pregnant women living with HIV.MethodsUntargeted metabolomics was performed on archived paired maternal plasma and DBS from n = 79 women enrolled in a large clinical trial.ResultsA total of 984 named biochemicals were detected across both plasma and DBS samples, of which 627 (63.7%), 260 (26.4%), and 97 (9.9%) were detected in both plasma and DBS, plasma alone, and DBS alone, respectively. Variation attributable to study individual (R2 = 0.54, p < 0.001) exceeded that of the sample type (R2 = 0.21, p < 0.001), suggesting that both plasma and DBS were capable of differentiating individual metabolomic profiles. Log-transformed metabolite abundances were strongly correlated (mean Spearman rho = 0.51) but showed low agreement (mean intraclass correlation of 0.15). However, following standardization, DBS and plasma metabolite profiles were strongly concordant (mean intraclass correlation of 0.52). Random forests classification models for cases versus controls identified distinct feature sets with comparable performance in plasma and DBS (86.5% versus 91.2% mean accuracy, respectively).ConclusionMaternal plasma and DBS samples yield distinct metabolite profiles highly predictive of the individual subject. In our case study, classification models showed similar performance albeit with distinct feature sets. Appropriate normalization and standardization methods are critical to leverage data from both sample types. Ultimately, the choice of sample type will likely depend on the compounds of interest as well as logistical demands.

Dataset Information

Spatially resolved sampling for untargeted metabolomics: A new tool for salivomics.

Publications

Spatially resolved sampling for untargeted metabolomics: A new tool for salivomics.

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