biostudies-literatureYao SNational Institute of Allergy and Infectious DiseasesDivision of ChemistryOffice of the Vice President for Research and PartnershipsNIAID NIH HHSNational Institute of General Medical SciencesNIGMS NIH HHS19238-19247https://www.ebi.ac.uk/biostudies/studies/S-EPMC11673283biostudies-literatureUnknown96(49)Single-cell mass spectrometry (SCMS) is an emerging tool for studying cell heterogeneity according to variation of molecular species in single cells. Although it has become increasingly common to employ machine learning models in SCMS data analysis, such as the classification of cell phenotypes, the existing machine learning models often suffer from low adaptability and transferability. In addition, SCMS studies of rare cells can be restricted by limited number of cell samples. To overcome these limitations, we performed SCMS analyses of melanoma cancer cell lines with two phenotypes (i.e., primary and metastatic cells). We then developed a meta-learning-based model, MetaPhenotype, that can be trained using a small amount of SCMS data to accurately classify cells into primary or metastaticAnalytical chemistryMetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells.PMC11673283R01AI177469R01 GM135392R01GM135392R01 AI1774692305182Yang WNguyen TDYang ZShao YYao SLan YChen DfalseMetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells.Single-cell mass spectrometry (SCMS) is an emerging tool for studying cell heterogeneity according to variation of molecular species in single cells. Although it has become increasingly common to employ machine learning models in SCMS data analysis, such as the classification of cell phenotypes, the existing machine learning models often suffer from low adaptability and transferability. In addition, SCMS studies of rare cells can be restricted by limited number of cell samples. To overcome these limitations, we performed SCMS analyses of melanoma cancer cell lines with two phenotypes (i.e., primary and metastatic cells). We then developed a meta-learning-based model, MetaPhenotype, that can be trained using a small amount of SCMS data to accurately classify cells into primary or metastatic2024-01-01T00:00:00Z2024 Dec2025-04-04T01:33:15.919Z2025-04-04T01:33:15.919ZS-EPMC116732833957011910.1021/acs.analchem.4c02038