Dataset Information

High-resolution label-free mapping of murine kidney vasculature by raster-scanning optoacoustic mesoscopy: an ex vivo study.

ABSTRACT:

Background

Chronic kidney disease (CKD) is a global burden affecting both children and adults. Novel imaging modalities hold great promise to visualize and quantify structural, functional, and molecular organ damage. The aim of the study was to visualize and quantify murine renal vasculature using label-free raster scanning optoacoustic mesoscopy (RSOM) in explanted organs from mice with renal injury.

Material and methods

For the experiments, freshly bisected kidneys of alpha 8 integrin knock-out (KO) and wildtype mice (WT) were used. A total of n=7 female (n=4 KO, n=3 WT) and n=6 male animals (n=2 KO, n=4 WT) aged 6 weeks were examined with RSOM optoacoustic imaging systems (RSOM Explorer P50 at SWL 532nm and/or ms-P50 imaging system at 532 nm, 555 nm, 579 nm, and 606 nm). Images were reconstructed using a dedicated software, analyzed for size and vascular area and compared to standard histologic sections.

Results

RSOM enabled mapping of murine kidney size and vascular area, revealing differences between kidney sizes of male (m) and female (f) mice (merged frequencies (MF) f vs. m: 52.42±6.24 mm² vs. 69.18±15.96 mm², p=0.0156) and absolute vascular area (MF f vs. m: 35.67±4.22 mm² vs. 49.07±13.48 mm², p=0.0036). Without respect to sex, the absolute kidney area was found to be smaller in knock-out (KO) than in wildtype (WT) mice (WT vs. KO: MF: p=0.0255) and showed a similar trend for the relative vessel area (WT vs. KO: MF p=0.0031). Also the absolute vessel areas of KO compared to WT were found significantly different (MF p=0.0089). A significant decrease in absolute vessel area was found in KO compared to WT male mice (MF WT vs. KO: 54.37±9.35 mm² vs. 34.93±13.82 mm², p=0.0232). In addition, multispectral RSOM allowed visualization of oxygenated and deoxygenated parenchymal regions by spectral unmixing.

Conclusion

This study demonstrates the capability of RSOM for label-free visualization of differences in vascular morphology in ex vivo murine renal tissue at high resolution. Due to its scalability optoacoustic imaging provides an emerging modality with potential for further preclinical and clinical imaging applications.

SUBMITTER: Goebel CA

PROVIDER: S-EPMC9253231 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The wild boar is an important crop raider as well as a reservoir and agent of spread of swine diseases. Due to increasing densities and expanding ranges worldwide, the related economic losses in livestock and agricultural sectors are significant and on the rise. Its management and control would strongly benefit from accurate and detailed spatial information on species distribution and abundance, which are often available only for small areas. Data are commonly available at aggregated administrative units with little or no information about the distribution of the species within the unit. In this paper, a four-step geostatistical downscaling approach is presented and used to disaggregate wild boar population density statistics from administrative units of different shape and size (polygons) to 5 km resolution raster maps by incorporating auxiliary fine scale environmental variables. 1) First a stratification method was used to define homogeneous bioclimatic regions for the analysis; 2) Under a geostatistical framework, the wild boar densities at administrative units, i.e. subnational areas, were decomposed into trend and residual components for each bioclimatic region. Quantitative relationships between wild boar data and environmental variables were estimated through multiple regression and used to derive trend components at 5 km spatial resolution. Next, the residual components (i.e., the differences between the trend components and the original wild boar data at administrative units) were downscaled at 5 km resolution using area-to-point kriging. The trend and residual components obtained at 5 km resolution were finally added to generate fine scale wild boar estimates for each bioclimatic region. 3) These maps were then mosaicked to produce a final output map of predicted wild boar densities across most of Eurasia. 4) Model accuracy was assessed at each different step using input as well as independent data. We discuss advantages and limits of the method and its potential application in animal health.