A mathematical framework for human neutrophil state transitions inferred from single-cell RNA sequence data.

Area of Science:

• Immunology
• Computational Biology
• Systems Biology

Background:

• Neutrophils are key innate immune cells, but their population dynamics and heterogeneity are not fully understood.
• Modeling neutrophil subset dynamics is crucial for understanding immune responses and disease states.

Purpose of the Study:

• To develop a mathematical model describing human neutrophil maturation states and transitions.
• To utilize single-cell gene expression data to define neutrophil subsets and inform model dynamics.

Main Methods:

• Mathematical modeling of biological systems.
• Single-cell RNA sequencing data analysis.
• Pseudo-time analysis to infer developmental trajectories.
• Bayesian inference for parameter estimation and inter-individual variation.

Main Results:

• Identified five distinct neutrophil clusters from healthy human single-cell gene expression data.
• Developed a model where precursor neutrophils mature through immature states, with options for an interferon-responsive path or further standard maturation.
• Quantified transition rates, indicating rapid precursor to immature neutrophil transition (mean < 1 hour) versus slower subsequent transitions (mean > 12 hours).
• Estimated that approximately 25% of neutrophils enter the interferon-responsive pathway.

Conclusions:

• The developed mathematical model provides a framework for understanding human neutrophil population dynamics.
• Neutrophil maturation involves distinct branching pathways, including a significant interferon-responsive subset.
• The model captures inter-individual variability in neutrophil subset proportions using Bayesian inference.