Predictive framework of macrophage activation
Sanin D.E. et al. (BioRxiv) DOI: 10.1101/2021.08.02.454825
Since the advent of single cell RNA sequencing (scRNAseq), immunologists use this powerful tool to identify transcriptional diversity among a variety of immune cells, discovering new subsets in different inflammatory conditions. The transcriptional and functional diversity of macrophages remains a constant discussion point. More recent advances point towards several factors being major determinants of macrophage identity and transcriptional make-up, including their origin, tissue environment and exposure to inflammatory stimuli. Nowadays, macrophages exist most likely in tissues as highly plastic cells, which can determine different cellular functions (see: Mosser and Edwards, 2008, Nature Reviews Immunology).
In this preprint, Sanin et al. aimed to identify a common reference framework across different tissues and biological conditions that define macrophage activation states in tissue, focusing on the fate and function of incoming monocytes engrafting in to the tissue. Using a reference scRNAseq dataset originating from a type 1 (L. mono.) and type 2 (H. poly.) infection, the authors isolated stromal and immune cells from adipose tissues. Focussing on macrophages, the authors identified distinct clusters which after trajectory analysis revealed four main differentiation/activation paths that monocytes undergo when entering the tissue. These paths include: “Phagocytic”, “Oxidative stress”, “Inflammatory” and “Remodelling” based on the enriched pathway at the end point clusters of each lineage. The exploration of the scRNA-seq data also highlighted the expression of RELMɑ encoding gene Retnla as an early signature of monocyte tissue engraftment and differentiation into macrophages, which was confirmed experimentally using adoptive transfer of isolated monocytes into peritoneal cavity and monitoring expression of RELMa during macrophage differentiation.
In a next step, using these labels, the authors used data transfer implementation methods to label tissue macrophages according to the reference dataset and found that these activation paths can indeed be identified/labelled across 12 different tissues and 25 biological conditions. Even when only using the labels, but maintaining the original dataset, common transcriptional features and networks can be identified. This data points towards specific transcriptional paths that macrophages adapt in inflammatory conditions across tissues and in this way identifies the smallest denominators of macrophages in tissues on a single cell level. This paper provides an important clarification on the finite number of major transcriptional profiles acquired by monocytes differentiating into macrophages in tissue and will serve as a valuable resource for identifying and classifying macrophages along this path and the development of interventions targeting specific macrophage paths in a broad set of inflammatory conditions.
Although the study captures the fate of monocyte-derived macrophages in tissues during inflammation well, it misses to label up to 50% of the macrophages. This may be due to the stated reasons such as identity of fetal-liver derived macrophages, intermediary stages and their thresholding levels. Nevertheless, like this the paper unfortunately misses some of the transcriptional states and functional identities of macrophages in the tissue.
Therefore, it would be great to get a closer understanding of these unclassified cell types and provide beside the labelled schemes also the unbiased analysis of the dataset which should then also identify the missing clusters.
While the paper surely shows interesting classification of transcriptional profiles, it would improve the manuscript to isolate late and final stage macrophages from each path and show their functionality in in vivo or in vitro settings to confirm the functions beyond the transcriptional level.
The findings in this preprint are broadly applicable to many fields of immunology since it established monocyte-derived macrophages along predefined paths. It further helps to understand the plasticity and groupings that can be used when talking about macrophages across tissues and introduces four major differentiation pathways and phenotypes.This study can help to homogenise the language which is used when talking about macrophage states and identities, beyond the M1-M2 gradient but limited by four main function.
The novelty of this preprint lies in the broad applicability of the findings across inflammatory conditions, highlighting on the way the powerful use of their generated datasets for future research.
Lastly, their aim to make this analysis and its underlying dataset available in a public database for researchers to interactively explore will proof a useful tool.
Reviewed by Felix Clemens Richter as part of the cross-institutional journal club of the Immunology Institute of the Icahn School of Medicine, Mount Sinai and the Kennedy Institute of Rheumatology, University of Oxford. Follow him on Twitter.