Discovering Macrophage Functions Using In Vivo Optical Imaging Techniques

Abstract

Macrophages are an important component of host defense and inflammation and play a pivotal role in immune regulation, tissue remodeling, and metabolic regulation. Since macrophages are ubiquitous in human bodies and have versatile physiological functions, they are involved in virtually every disease, including cancer, diabetes, multiple sclerosis, and atherosclerosis. Molecular biological and histological methods have provided critical information on macrophage biology. However, many in vivo dynamic behaviors of macrophages are poorly understood and yet to be discovered. A better understanding of macrophage functions and dynamics in pathogenesis will open new opportunities for better diagnosis, prognostic assessment, and therapeutic intervention. In this article, we will review the advances in macrophage tracking and analysis with in vivo optical imaging in the context of different diseases. Moreover, this review will cover the challenges and solutions for optical imaging techniques during macrophage intravital imaging.

Autofluorescence imaging of 3D tumor-macrophage microscale cultures resolves spatial and temporal dynamics of macrophage metabolism

“…environmental conditions that characterize in vivo tumors (e.g., hypoxia, acidosis, nutrient starvation) and are powerful to study primary human cells in an in vivo-like environment [81][82][83][84][85][86] . Previous studies have shown that metabolic autofluorescence can identify macrophages in 2D cultures and in vivo but have not explored spatiotemporal heterogeneity in macrophage metabolism within the TME 34,41,42,53 . This is the first study to monitor cell-level macrophage metabolism in microfluidic models of intact TME.…”

“…Lifetime measurements can distinguish between free and protein-bound forms of NAD(P)H and FAD, characterized by distinct molecular conformations that affect fluorescence quenching 49 . Previous studies have shown that metabolic autofluorescence imaging detects spatial and temporal changes in stromal cells across in vivo and 3D in vitro models 31,38,39,42,[50][51][52][53][54][55] . Thus, metabolic autofluorescence imaging of microscale 3D models was demonstrated to quantify metabolic activity and visualize macrophage heterogeneity within the 3D TME using primary human cancer, human cell lines, and mouse cell lines…”

Molecular model linking Th2 polarized M2 tumour‐associated macrophages with deaminase‐mediated cancer progression mutation signatures

“…This hypothesized deaminase‐mediated cancer progression model thus predicts the presence of an elevated expression of M2 polarized macrophages that is accompanied by the identification of new functionally active APOBEC/ADAR family heterodimers. Future directions include establishing the deaminase content of EVs extruded from M2 TAMS and establishing the dynamic role of active APOBEC (and ADAR) heterodimer genetic markers as common and dominant features predicting cancer progression. Current rapid protein isolation and identification technologies, and genomic sequencing analyses will facilitate future investigation of these hypothesized mechanisms.…”

Autofluorescence Imaging of 3D Tumor–Macrophage Microscale Cultures Resolves Spatial and Temporal Dynamics of Macrophage Metabolism

“…Z-scores for each metabolic autofluorescence variable were calculated for passively-and actively-migrating mouse macrophages in co-culture by subtracting the variable mean of the monoculture condition from the variable mean per condition, then dividing by the monoculture standard deviation at each time point (Fig 5F). Multi-class random forest models were generated from all 11 autofluorescence variables to classify passive and active migration in 3D mouse co-cultures across all timepoints (24,48, and 72 hours). High classification accuracy was observed for test data predictions for passive vs. active migration, with an accuracy of at least 85% for all test data predictions ( Fig 5G, Supplementary Fig 3Q).…”

“…Lifetime measurements can distinguish between free and protein-bound forms of NAD(P)H and FAD, characterized by distinct molecular conformations that affect fluorescence quenching (19). Previous studies have shown that metabolic autofluorescence imaging detects spatial and temporal changes in stromal cells across in vivo and 3D in vitro models (17,(22)(23)(24). This study aims to validate whether autofluorescence imaging of NAD(P)H and FAD can monitor changes in macrophage metabolism with polarization and migration.…”