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Updated: Jan 22, 2026

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
Published on: September 28, 2017
Increasing the discrimination power of rapid evaporative ionisation mass spectrometry (REIMS) in analytical control
Paul Abu-Rabie1, Denver Sheelan1, Alice Laures1
1Medicinal Science and Technology, GlaxoSmithKline R&D, Stevenage, SG1 2NY, UK.
Rationale:
Rapid Evaporative Ionisation Mass Spectrometry (REIMS) has been evaluated as a tool to improve analytical efficiency and add capability in areas within Pharmaceutical Research and Development (Pharma R&D). This article reports the comparison of single MS, and tandem MS/MS REIMS (REIMS and REIMS/MS) methodologies to investigate which mode produces maximum discrimination power for screening applications.
Methods:
Control tissue samples and cell line suspension samples were analysed using optimised REIMS and REIMS/MS to evaluate which technique produced optimal discrimination power for control tissue and cell line identification. The iKnife sampling tool and a prototype 'cell sampler' were utilised for tissue and cell analysis, respectively. The REIMS source was coupled to a hybrid Quadrupole-Time Of Flight (QTOF) mass spectrometer. Multivariate Analysis (MVA) was utilised to evaluate the resulting Mass Spectrometry (MS) data and discriminate between sample types.
Results:
Proof of concept investigations demonstrating that REIMS/MS offered increased MVA discrimination for sample identification, compared with REIMS, is presented for the first time. Control tissue data showed discrimination by timepoint classification over 0-144 h storage after removal from the host. Timepoint discrimination was optimised using REIMS/MS with a collision energy that effectively maximised ion fragmentation. Similar optimisation was observed when REIMS/MS was applied to the identification of cell lines.
Conclusions:
The proof of concept results demonstrate that REIMS/MS can offer advantages over REIMS for control tissue quality screening, and cell line identification applications in Pharma R&D. Further work following this proof of concept investigation is being undertaken to implement the technology for these applications, utilising the optimised REIMS/MS methodology. REIMS/MS will also be used as an optimised tool for other applications.
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