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Discrimination of cancer subtypes at the single-cell level is critical for the early diagnosis and accurate treatment of cancer. However, the discrimination of breast cancer subtypes based on their metabolite information, which could provide a new perspective of the cellular metabolomics, is still in its infancy. Herein, a high-coverage single cell metabolic analysis was carried out for the discrimination of breast cancer subtypes by combining multiple microextraction with mass spectrometry (MS). About 4300 ion signals were extracted from each cell and assigned to lipids, energy metabolites, and so on. Based on the multivariate analysis of the metabolite information, four subtypes of breast cancer were successfully discriminated. Characteristic components of each subtype were also identified as potential biomarkers such as phosphatidylcholine (PC; PC (32:1), PC (34:1)), UDP/UDP-HexNAc, and Hex-bis-P/Hex-P). Moreover, metabolomics correlation analysis at the single-cell level further revealed the coregulation clusters of the identified components, which provided more metabolites data for bioinformatics studies. Overall, our results on single cell metabolic analysis could give new insights to precision medicine, early diagnosis, and cancer treatments.
Wang et al. (Thu,) studied this question.