Digital Image Analyses on Whole-Lung Slides in Mouse Models of Acute Pneumonia

Descriptive histopathology of mouse models of pneumonia is essential in assessing the outcome of infections, molecular manipulations, or therapies in the context of whole lungs. Quantitative comparisons between experimental groups, however, have been limited to laborious stereology or ill-defined scoring systems that depend on the subjectivity of a more or less experienced observer. Here, we introduce self-learning digital image analyses that allow us to transform optical information from whole mouse lung sections into statistically testable data. A pattern-recognition-based software and a nuclear count algorithm were adopted to quantify user-defined pathologies from whole slide scans of lungs infected with Streptococcus pneumoniae or influenza A virus compared with PBS-challenged lungs. The readout parameters "relative area affected" and "nuclear counts per area" are proposed as relevant criteria for the quantification of lesions from hematoxylin and eosin-stained sections, also allowing for the generation of a heat map of, for example, immune cell infiltrates with anatomical assignments across entire lung sections. Moreover, when combined with immunohistochemical labeling of marker proteins, both approaches are useful for the identification and counting of, for example, immune cell populations, as validated here by direct comparisons with flow cytometry data. The solutions can easily and flexibly be adjusted to specificities of different models or pathogens. Automated digital analyses of whole mouse lung sections may set a new standard for the user-defined, high-throughput comparative quantification of histological and immunohistochemical images. Still, our algorithms established here are only a start, and need to be tested in additional studies and other applications in the future.

IL-37 Causes Excessive Inflammation and Tissue Damage in Murine Pneumococcal Pneumonia

Streptococcus pneumoniae infections can lead to severe complications with excessive immune activation and tissue damage. Interleukin-37 (IL-37) has gained importance as a suppressor of innate and acquired immunity, and its effects have been therapeutic as they prevent tissue damage in autoimmune and inflammatory diseases. By using RAW macrophages, stably transfected with human IL-37, we showed a 70% decrease in the cytokine levels of IL-6, TNF-α, and IL-1β, and a 2.2-fold reduction of the intracellular killing capacity of internalized pneumococci in response to pneumococcal infection. In a murine model of infection with S. pneumoniae, using mice transgenic for human IL-37b (IL-37tg), we observed an initial decrease in cytokine expression of IL-6, TNF-α, and IL-1β in the lungs, followed by a late-phase enhancement of pneumococcal burden and subsequent increase of proinflammatory cytokine levels. Additionally, a marked increase in recruitment of alveolar macrophages and neutrophils was noted, while TRAIL mRNA was reduced 3-fold in lungs of IL-37tg mice, resulting in necrotizing pneumonia with augmented death of infiltrating neutrophils, enhanced bacteremic spread, and increased mortality. In conclusion, we have identified that IL-37 modulates several core components of a successful inflammatory response to pneumococcal pneumonia, which lead to increased inflammation, tissue damage, and mortality.