Mass spectrometry (MS) has become an indispensable tool for metabolomics studies. However, due to the lack of applicable experimental platforms, suitable algorithm, software, and quantitative analyses of cell heterogeneity and subpopulations, investigating global metabolomics profiling at the single cell level remains challenging. We combined the Single-probe single cell MS (SCMS) experimental technique with a bioinformatics software package, SinCHet-MS (Single Cell Heterogeneity for Mass Spectrometry), to characterize changes of tumor heterogeneity, quantify cell subpopulations, and prioritize the metabolite biomarkers of each subpopulation. As proof of principle studies, two melanoma cancer cell lines, the primary (WM115; with a lower drug resistance) and the metastatic (WM266-4; with a higher drug resistance), were used as models. Our results indicate that after the treatment of the anticancer drug vemurafenib, a new subpopulation emerged in WM115 cells, while the proportion of the existing subpopulations was changed in the WM266-4 cells. In addition, metabolites for each subpopulation can be prioritized. Combining the SCMS experimental technique with a bioinformatics tool, our label-free approach can be applied to quantitatively study cell heterogeneity, prioritize markers for further investigation, and improve the understanding of cell metabolism in human diseases and response to therapy.