Machine learning identifies signatures of macrophage reactivity and tolerance that predict disease outcomes.

ABSTRACT:

Background

Single-cell transcriptomic studies have greatly improved organ-specific insights into macrophage polarization states are essential for the initiation and resolution of inflammation in all tissues; however, such insights are yet to translate into therapies that can predictably alter macrophage fate.

Method

Using machine learning algorithms on human macrophages, here we reveal the continuum of polarization states that is shared across diverse contexts. A path, comprised of 338 genes accurately identified both physiologic and pathologic spectra of "reactivity" and "tolerance", and remained relevant across tissues, organs, species, and immune cells (>12,500 diverse datasets).

Findings

This 338-gene signature identified macrophage polarization states at single-c

SUBMITTER: Ghosh P 

PROVIDER: S-EPMC10388732 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature