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.
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.
Authors: Stanislav I Pekov; Evgeny S Zhvansky; Vasily A Eliferov; Anatoly A Sorokin; Daniil G Ivanov; Eugene N Nikolaev; Igor A Popov Journal: Molecules Date: 2022-04-18 Impact factor: 4.927