Digital cell quantification identifies global immune cell dynamics during influenza infection

Area of Science:

• Immunology
• Computational Biology
• Infectious Disease

Background:

• Immune cells coordinate tissue homeostasis, but their dynamic changes during infection are complex.
• Current technologies limit the ability to track numerous immune cell types simultaneously in vivo.
• Understanding global immune cell dynamics is crucial for comprehending disease processes.

Purpose of the Study:

• To develop a computational method for inferring in vivo immune cell population dynamics.
• To analyze global immune cell changes in mouse lungs during influenza infection.
• To identify specific roles of immune cell subpopulations, particularly dendritic cells, during infection.

Main Methods:

• Developed digital cell quantification (DCQ), a computational method integrating genome-wide gene expression data.
• Utilized an immune cell compendium to infer quantities of 213 immune cell subpopulations.
• Applied DCQ to mouse lung samples across ten time points during a 7-day flu infection model.

Main Results:

• Identified significant quantitative changes in 70 immune cell types, including innate, adaptive, and progenitor cells.
• Revealed previously unreported dynamics of four immune dendritic cell subtypes.
• Suggested distinct roles for CD103(+) CD11b(-) DCs in early infection and CD8(+) pDCs in late flu infection stages.

Conclusions:

• DCQ provides a powerful tool to study complex in vivo immune cell dynamics.
• Influenza infection induces widespread, dynamic alterations across diverse immune cell populations.
• Specific dendritic cell subsets play critical, time-dependent roles in the host's response to flu infection.