Monocyte Differentiation and Heterogeneity: Inter-Subset and Interindividual Differences

Abstract

The three subsets of human monocytes, classical, intermediate, and nonclassical, show phenotypic heterogeneity, particularly in their expression of CD14 and CD16. This has enabled researchers to delve into the functions of each subset in the steady state as well as in disease. Studies have revealed that monocyte heterogeneity is multi-dimensional. In addition, that their phenotype and function differ between subsets is well established. However, it is becoming evident that heterogeneity also exists within each subset, between health and disease (current or past) states, and even between individuals. This realisation casts long shadows, impacting how we identify and classify the subsets, the functions we assign to them, and how they are examined for alterations in disease. Perhaps the most fascinating is evidence that, even in relative health, interindividual differences in monocyte subsets exist. It is proposed that the individual’s microenvironment could cause long-lasting or irreversible changes to monocyte precursors that echo to monocytes and through to their derived macrophages. Here, we will discuss the types of heterogeneity recognised in monocytes, the implications of these for monocyte research, and most importantly, the relevance of this heterogeneity for health and disease.

Assessment of inter‐operator variability in peripheral monocyte subset gating strategy using flow cytometry in patients with suspected acute stroke

“…As expected, the separation between Mon2 and Mon3 was more complex, especially due to the continuum of the events [12]. Nevertheless, although we evidenced slight inter‐operator variabilities, the CVs of Mon2 and Mon3 counts were below the CLSI‐recommended 20% threshold in most patients.…”

“…While FCM gating of peripheral Mon1, Mon2, and Mon3 is complex in inflammatory states such as infections and chronic or acute cardiovascular diseases [1–6], no previous study has evaluated the contribution of gating operator variability within FCM Mon data analysis in these settings. Part of the difficulty in gating arises from the fact that monocytes differentiate from the classical through the intermediate to the non‐classical subsets in a continuous spectrum [12]: monocytes undergo functional changes modifying their surface marker expression. Whatever the developed gating strategy, the key issue was to apply it consistently between operators.…”

“…We thus sought to quantify inter‐operator variability in Mon subset results using the same pre‐defined gating strategy that we developed. One of the originalities of our gating method lies on the use of CD91, strengthened with the HLA‐DR in addition to the commonly used markers, namely CD45, CD14, and CD16 [9, 12, 13]: this combination provides a strong backbone for accurate total monocyte identification. Indeed, CD91 is specifically expressed on monocytes and its expression does not vary with age [9, 10].…”

“…Consequently, gating strategies that encompass a pan‐monocytic marker such as CD91 has been encouraged [9, 10]. Finally, the main difficulty lies in the phenotypic heterogeneity in the expression of CD14 and CD16 during monocyte differentiation: the three monocyte subsets overlap when they are solely characterized by CD14 and CD16 expression levels, leading to inaccurate classification [11, 12]; therefore, the placement of gates discriminating among the monocyte subpopulations is a subjective exercise. The European Society of Cardiology (ESC) working groups on “Atherosclerosis and vascular biology” and “Thrombosis” has recommended using additional cell surface markers expression to improve the monocyte subset gating and overcome the issue of inter‐subset contamination [13].…”

“…Especially, the discrimination between Mon2 and Mon3 subsets might be improved by analyzing CCR2 (CC chemokine receptor type 2), CD11b (CR3‐alpha‐chain), or HLA‐DR (human‐leukocyte‐antigen‐DR), more expressed on Mon2 than Mon3 [13]. Incorporating CX3CR1 (chemokine C‐X3‐C motif receptor), CD62L (L‐selectin), or CD86 (costimulatory protein) in the FCM analysis have also been shown to better discriminate the subsets [8, 11, 12].…”