MSACL 2019 EU Abstract

Untargeted Steroidomics for the Identification of Novel Steroid Profiles in Dysregulated Steroidogenesis

Paal William Wallace (1), Graeme Eisenhofer (1), Mirko Peitzsch (2)
(1) Technische Universität Dresden, Germany, (2) Universitätsklinikum Carl Gustav Carus, Dresden, Germany

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Paal William Wallace (Presenter) Technische Universität Dresden

Relevant Financial Disclosures (within past 24 months) No relevant financial relationship(s) to disclose. Discussing Products? NO, presenter will NOT present educational content related to products/services of the ineligible company(ies) listed above. Conflict mitigation NOT REQUIRED.

Introduction: There is a need to develop new diagnostic tools to diagnose and subtype patients with dysregulated steroidogenesis. Methods used today may require medication to be altered, multistep testing, imaging and even surgery to be able to make a final diagnosis. We hypothesize that it is possible to avoid many of these steps if the correct biomarkers or plasma steroid profiles are identified for patients suffering from these steroid dysregulation diseases, allowing diagnosis using only a single plasma sample.
Objective: Identify novel steroid profiles to diagnose and subtype diseases with a dysregulated steroidogenesis by the use of untargeted steroidomics on a UPLC-IMS-MS/MS platform.
Method: Steroid extraction is performed using positive pressure on a 96-well SPE column. The extracted compounds are analyzed in an untargeted approach on a Vion IMS-QTof (Waters) coupled to an Acquity I-Class UPLC (Waters). The samples are separated on a Cortex UPLC C18 column (2.1mmx100mm, 1.6µm) with a 15 min gradient. Samples are analyzed in both positive and negative mode within a mass range from 50-1000 m/z using MS-MS with a high collision energy ramping from 26eV to 56eV.
The resulting ions are searched against an internal library of 40 steroids (that is continuously being expanded), and against public databases so as to give identifications to the ions.
To be able to determine the correct identifications the method has several features allow for further refinement of the identifications. Ion mobility allows for the measurement of the cross collisional section (CCS) values of the compounds, giving us another physical parameter by which we can separate isobaric compounds from one another. The common fragmentation pattern of steroids in CID adds another feature that can be used to identify a compound as a steroid.
Conclusion: Most steroid dysregulation diseases can’t be identified or subtyped by measurement of a single compound. A plasma steroid profile is more likely to be the way forward. By measuring a large number of steroids, as this approach allows, machine learning techniques can be applied to the datasets and determine steroid profiles for the different diseases resulting in a simple diagnostic approach.
We are applying this methodology to a variety of patients with dysregulated steroidogenesis in an attempt to differentiate ACC from ACA, subtype Cushing’s and subclinical Cushing’s patients and subtype primary aldosteronism without the need for an adrenal venous sampling.