Yao et al. developed MetaPhenotype, a meta-learning-based model designed to address limitations in adaptability and transferability often encountered in machine learning models for SCMS data analysis. SCMS is a powerful tool for investigating cellular heterogeneity, such as phenotypes, through the variation of molecular species in individual cells. However, its application to rare cell populations is often constrained by the limited availability of cell samples. To overcome these challenges, two pairs of isogenic melanoma cancer cell lines (each has primary and metastatic phenotypes) were analyzed using the Single-probe SCMS technique. Both control and drug treated cells were analyzed. The SCMS metabolomics data of one cell pair (no drug treatment) served as the training and evaluation datasets for MetaPhenotype, which was subsequently applied to classify the remaining data. The MetaPhenotype model demonstrated rapid adaptation and exceptional transferability, achieving high prediction accuracy of over 90% with minimal new training samples. Moreover, it enabled the identification of a small subset of critical molecular species essential for phenotype classification. This work highlights the potential of MetaPhenotype to lower the demand for extensive sample acquisition, facilitating accurate cell phenotype classification even with limited SCMS datasets. The applicability of MetaPhenotype extends beyond melanoma cell lines and the specific SCMS platform employed in this study, offering potential for broader use in metabolomics studies across diverse SCMS platforms and cell systems.