Role of cellular effectors in the emergence of ventilation defects during allergic bronchoconstriction

Abstract

It is not known whether local factors within the airway wall or parenchyma may influence the emergence and spatial distribution of ventilation defects (VDs), thereby modulating the dynamic system behavior of the lung during bronchoconstriction. We assessed the relationship between the distribution of cellular effectors and the emergence of defects in regional ventilation distribution following allergen challenge. We performed high-resolution K-edge subtraction (KES) synchrotron imaging during xenon inhalation and measured the forced oscillatory input impedance in ovalbumin (OVA)-sensitized Brown-Norway rats (n = 12) at baseline and repeatedly following OVA challenge. Histological slices with best anatomic matching to the computed tomographic images were stained with a modified May-Grunwald Giemsa and immunohistochemical staining with monoclonal anti-rat CD68, in six rats. Slides were digitized and total cells and eosinophils were counted in the walls of bronchi and vessels randomly selected within and outside of VDs on the basis of xenon-KES images. Ventilated alveolar area decreased and ventilation heterogeneity, Newtonian resistance, tissue damping, and elastance increased following OVA challenge. Eosinophil, total cell, and CD68+ counts were significantly higher in the bronchial and vascular walls within vs. outside of the VDs. The minimal central airway diameters during OVA-induced bronchoconstriction were correlated with eosinophil (R = -0.85; P = 0.031) and total cell densities (R = -0.82; P = 0.046) in the airway walls within the poorly ventilated zones. Our findings suggest that allergic airway inflammation is locally heterogeneous and is topographically associated with the local emergence of VDs following allergen challenge.

Imaging Regional Lung Structure and Function in Small Animals Using Synchrotron Radiation Phase-Contrast and K-Edge Subtraction Computed Tomography

“…Using the dual-energy KES synchrotron imaging method, X-ray attenuation by tissue density and Xe concentration is computed separately, using a custom material decomposition algorithm as described previously (Bayat et al, 2001). At this resolution, Layachi et al (2013) found higher eosinophil, monocyte and total cell densities within vs. outside FIGURE 2 | 4D microscopy of rat lung using a synchrotron X-ray source. High-intensity coherent X-rays generated from electrons orbiting in a storage ring, are rendered monochromatic using bent silicon crystal optics, and detected by a PCO Edge 5.5 camera coupled to a Cerium-doped Lutetium Aluminum Garnet (LuAG:Ce) scintillator and optics yielding an isotropic pixel size of 6 µm 3 .…”

“…Note the transient emergence of regional lung ventilation defects after ovalbumin challenge. Reproduced with permission from Layachi et al (2013) , Bayat et al (2021) .…”

“…KES-CT imaging is performed in parallel-beam geometry. Two CT images are simultaneously acquired during the Xe inhalation maneuver, using a solid state ( Bayat et al, 2021 ) or a charge-coupled device (CCD) detector ( Layachi et al, 2013 ; Figure 1A ). The size of the field of view is determined by that of the radiation beam and the detector resolution.…”

Spatial pattern formation in the lung

“…Left panel, voxel fractional ventilation map from hyperpolarized 3he MRI, colored from red (high) to black (low) reproduced with permission from [3]; right panel, xenon density from K-edge subtraction synchrotron imaging, colored from red (high, 4.0 mg/ml) to black (low, 0.0 mg/ml) reproduced with permission from [4]. …”

Systems‐level airway models of bronchoconstriction

“…On top of this, many lung/airway diseases, such as asthma, are highly heterogeneous in terms of presentation and consist of distinct subtypes (phenotypes or endotypes) . Finally, add in the fact that many observed behaviors, such as airway smooth muscle (ASM) fluidization or clustered ventilation defects, are rich and complex, and the end result is that it is difficult to stitch together the available data into a coherent understanding of airway behavior, especially in disease.…”