Efficient 3D imaging and pathological analysis of the human lymphoma tumor microenvironment using light-sheet immunofluorescence microscopy.

Rationale: The composition and spatial structure of the lymphoma tumor microenvironment (TME) provide key pathological insights for tumor survival and growth, invasion and metastasis, and resistance to immunotherapy. However, the 3D lymphoma TME has not been well studied owing to the limitations of current imaging techniques. In this work, we take full advantage of a series of new techniques to enable the first 3D TME study in intact lymphoma tissue. Methods: Diverse cell subtypes in lymphoma tissues were tagged using a multiplex immunofluorescence labeling technique. To optically clarify the entire tissue, immunolabeling-enabled three-dimensional imaging of solvent-cleared organs (iDISCO+), clear, unobstructed brain imaging cocktails and computational analysis (CUBIC) and stabilization to harsh conditions via intramolecular epoxide linkages to prevent degradation (SHIELD) were comprehensively compared with the ultimate dimensional imaging of solvent-cleared organs (uDISCO) approach selected for clearing lymphoma tissues. A Bessel-beam light-sheet fluorescence microscope (B-LSFM) was developed to three-dimensionally image the clarified tissues at high speed and high resolution. A customized MATLAB program was used to quantify the number and colocalization of the cell subtypes based on the acquired multichannel 3D images. By combining these cutting-edge methods, we successfully carried out high-efficiency 3D visualization and high-content cellular analyses of the lymphoma TME. Results: Several antibodies, including CD3, CD8, CD20, CD68, CD163, CD14, CD15, FOXP3 and Ki67, were screened for labeling the TME in lymphoma tumors. The 3D imaging results of the TME from three types of lymphoma, reactive lymphocytic hyperplasia (RLN), diffuse large B-cell lymphoma (DLBCL), and angioimmunoblastic T-cell lymphoma (AITL), were quantitatively analyzed, and their cell number, localization, and spatial correlation were comprehensively revealed. Conclusion: We present an advanced imaging-based method for efficient 3D visualization and high-content cellular analysis of the lymphoma TME, rendering it a valuable tool for tumor pathological diagnosis and other clinical research.

Impact of building types and CHP plants on air quality (2019-2021) in central-eastern European monocentric agglomeration.

The quality of city air is influenced by many factors, including the density of buildings, the roughness of the terrain, the presence of street canyons, the heat sources in buildings, the types of industry, the topography, and meteorological conditions. Official air quality monitoring systems measure a very limited number of points, making local analysis impossible without the use of mathematical modeling programs. Here, we present an analysis of local air quality in an urban agglomeration. Data were collected over three years (2019, 2020, 2021), using commercial sensors located throughout the area of investigation. Dense downtown buildings equipped with individual heat sources were not found to have any impact on local air quality. The local municipal combined heat and power (CHP) plants contributed <1 ‰ of the measured concentration of particulate matter. Land height and the density of single-family housing were found to significantly affect air quality. We also took into account the influence of weather conditions, wind speed, and wind direction on the concentrations of particulate matter. High concentrations of particulate matter occurred only during heating periods when wind speeds were moderate. Wind direction did not have a direct impact on air quality, despite the expected benefits of ventilation through air corridors.