ArticleAnalytical and bioanalytical chemistry2025
Evaluating rapid evaporative ionization mass spectrometry profiles of human breast cancer cells using a 3D in vitro assay.
Article in Analytical and bioanalytical chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Rapid evaporative ionization mass spectrometry (REIMS) enables near real-time sampling and classification of tissues on the basis of lipid and small molecule profiles. Because samples are ablated during REIMS, validation of mass spectra by assignment of class labels using gold-standard methods remains challenging. Further, determining the number of abnormal cells within a mixture of normal cells by REIMS remains an elusive but critical parameter when evaluating the utility of REIMS for use in clinical settings. To address this, we developed a three-dimensional assay based on fluorescently labelled human breast cell lines, MCF-10A (pseudo-normal) and MDA-MB-231 (malignant), embedded in matrigel cubes that enables calculation of cell numbers ablated by REIMS by measuring fluorescence before and after REIMS sampling. We observed a reasonably uniform distribution of cells throughout the matrigel matrix, which yielded REIMS spectra rich in complex glycerophospholipids resembling solid tissues and cell pellets. A non-linear relationship between signal intensity and the number of cells ablated was observed. We trained multivariate models using 10% increments of MDA-MB-231 cells mixed with MCF-10A, and blindly tested the models on separate cubes containing 1-100% MDA-MB-231s. Percent difference from target MDA-MB-231 cell composition was within 34% for cubes containing as little as 5% MDA-MB-231 cells. Overall, our study highlights how a simple 3D in vitro approach may help reveal the quantitative potential of mass spectral profiling methods such as REIMS to assist with validating REIMS spectra containing mixtures of different cell lines. This assay platform may help calibrate REIMS-based methods to recognize specific cell type mixtures encountered at surgical resection margins.
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