A Framework Integrating Single-Cell Metallomic Data in Health Effect Analysis via Quantile Features and Machine Learning

Heavy and essential metals coexist in a cell, collectively participate in cellular biological processes, and lead to overall health consequences. However, current analytical strategies cause information loss when using aggregated features and are unable to establish comprehensive associations between single-cell metallomic data and health outcomes. We developed an analytical framework with more accurate characterization of multimetal distribution features, enabling the integration of single-cell metallomic data into health-related studies. We used quantiles to comprehensively extract the features of multimetals in spermatozoa population. Then, machine learning and factor analysis further consolidated key features into interpretable indices of multimetal co-occurrence characteristics. This framework addressed the conservation of the pseudobulk method and bias of the single-cell method, due to a more detailed characterization and appropriate integration of multimetal features at single-cell resolution. We showcase a negative association between multiple metals and sperm motility in the Bayesian Kernel Machine Regression (BKMR) model with a 10% increase in the collective features of 34 metals corresponding to a 3.3% reduction in sperm motility. Notably, the contribution of the multifaceted effects of lead and platinum suggests that dynamic changes in metal distribution may hold biological significance comparable to their total content. Finally, the generalizability of this analytical framework was validated with an additional single-cell metallomic data set. This method facilitates the application of single-cell metallomic measurement techniques in health effect studies, providing deeper insights for understanding the collective biological roles of metals in multicell organisms.