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

Area of Science:

• Metallomics
• Cellular Biology
• Bioinformatics

Background:

• Metals are crucial in cellular processes but their collective impact on health is poorly understood.
• Existing analytical methods for metallomic data lose information or introduce bias.
• Integrating single-cell metallomic data with health outcomes remains challenging.

Purpose of the Study:

• To develop an analytical framework for accurate multimetal distribution characterization at single-cell resolution.
• To enable comprehensive associations between single-cell metallomic data and health outcomes.
• To overcome limitations of existing pseudobulk and single-cell methods.

Main Methods:

• Utilized quantiles to extract multimetal distribution features from spermatozoa.
• Employed machine learning and factor analysis to create interpretable indices of metal co-occurrence.
• Applied Bayesian Kernel Machine Regression (BKMR) to model metal-health associations.

Main Results:

• Developed a framework enhancing multimetal feature characterization and integration at single-cell resolution.
• Identified a negative association between collective metal features and sperm motility (10% increase in metal features linked to 3.3% decrease in motility).
• Highlighted the biological significance of dynamic metal distribution changes, exemplified by lead and platinum.

Conclusions:

• The novel framework facilitates single-cell metallomic analysis in health effect studies.
• Provides deeper insights into the collective biological roles of metals in organisms.
• Demonstrated generalizability with an additional dataset, validating the analytical approach.