Polymeric Infrared and Fluorescent Probes to Assess Macrophage Diversity in Bronchoalveolar Lavage Fluid of Asthma and Other Pulmonary Disease Patients

Bronchial asthma remains a serious medical problem, as approximately 10% of patients fail to achieve adequate symptom control with available treatment options. Macrophages play a pivotal role in the pathophysiology of asthma, as well as in some other respiratory disorders. Typically, they are classified into two major classes, M1 and M2; however, recent findings have indicated that in fact there is a whole range of macrophage polarization and functional diversity beyond this bimodal division. The isolation of individual cell sub-populations and the identification of their role and diagnostic/therapeutic significance is still a challenge. Here, we have attempted to assess the differences between patient-derived macrophage populations from bronchoalveolar lavage fluid (BALF) samples in different pulmonary disease conditions, based on their capability to interact with a range of specific and relatively non-specific carbohydrate-based ligands (containing galactose (linear or cyclic form), mannose, trimannose, etc.). Obviously, the main target of these ligands was CD206; however, other minor receptors, able to bind carbohydrates, have also been reported for macrophages. Trimannose binds most specifically to CD206 macrophage receptors, while monomannose has intermediate affinity, and galactose has low affinity and may involve binding to other receptors. This clearly indicates the ligands were chosen based on their predicted binding strength and specificity for CD206, providing the rationale for the study. In some cases, the activated macrophage affinity to galactose base ligands was higher than that to mannose, indicating that complexes of CD206 or other carbohydrate-binding receptors may contribute substantially to macrophage functional features. In addition, variations in receptor clustering and distribution may substantially affect affinity to the same ligand. Interestingly, with a panel of 6-10 different carbohydrate-based ligands with FTIR or fluorescent marker, we were able not only to distinguish between healthy and disease states but also between closely related diseases such as purulent endobronchitis, obstructive bronchitis, pneumonia, and bronchial asthma. For further investigation, specific sub-populations of macrophages, seen as hallmarks to specific diseases, can be isolated and studied separately, likely giving new insights with diagnostic and therapeutic significance for hard-to-treat patients. The group of patients with resistant disease can also be identified with this approach as a fingerprint method to find a more targeted therapeutic strategy, improving their clinical outcomes. As expected, this will provide a large additional array of data for analysis, compared to the work going on in the world. The dataset used by other researchers mainly for known "antibody" ligands is semi-quantitative and insufficient for the purposes of typing as yet unknown and uncomplicated sub-populations. The analysis of the presented data in combination with personalized information from patients' medical records will be carried out using both traditional methods and machine learning methods.