Understanding vascular structures and dysfunction is a fundamental challenge. This task has been approached by using traditional methodologies such as microscopic computed tomography and magnetic resonance imaging. Both techniques are not only expensive but also time-consuming. Here, we present a new method for visualizing vascular structures in different organs in an efficient manner. A cationic near infrared (NIR) fluorescent dye was developed with attractive features to specifically stain blood vessels. Furthermore, we refined the process of organ staining and harvesting by retrograde perfusion and optimized the subsequent dehydration and clearing process by the use of an automatic tissue processor and a non-toxic substance, ethyl-cinnamate. Using this approach, the time interval between organ harvesting and microscopic analysis can be reduced from day(s) or weeks to 4 hours. Finally, we have demonstrated that the new NIR fluorescent agent in combination with confocal or light-sheet microscopy can be efficiently used for visualization of vascular structures, such as the blood vessels in different organs e.g. glomeruli in kidneys, with an extremely high resolution. Our approach facilitates the development of automatic image processing and the quantitative analysis to study vascular and kidney diseases.
In biological assays, automated cell/colony segmentation and counting is imperative owing to huge image sets. Problems occurring due to drifting image acquisition conditions, background noise and high variation in colony features in experiments demand a user-friendly, adaptive and robust image processing/analysis method. We present AutoCellSeg (based on MATLAB) that implements a supervised automatic and robust image segmentation method. AutoCellSeg utilizes multi-thresholding aided by a feedback-based watershed algorithm taking segmentation plausibility criteria into account. It is usable in different operation modes and intuitively enables the user to select object features interactively for supervised image segmentation method. It allows the user to correct results with a graphical interface. This publicly available tool outperforms tools like OpenCFU and CellProfiler in terms of accuracy and provides many additional useful features for end-users.
OPEN ACCESS Citation: Gamez C, Schneider-Wald B, Schuette A, Mack M, Hauk L, Khan AuM, et al. (2020) Bioreactor for mobilization of mesenchymal stem/ stromal cells into scaffolds under mechanical stimulation: Preliminary results. PLoS ONE 15(1): e0227553. https://doi.org/10.1371/journal. ResultsThe bioreactor was able to stimulate the scaffolds and the cells for 24.4 (±1.7) hours, exerting compression with vertical displacements of 185.8 (±17.8) μm and a force-amplitude of 1.87 (±1.37; min 0.31, max 4.42) N. Our results suggest that continuous mechanical stimulation hampered the viability of the cells located at the cell reservoir when comparing to intermittent mechanical stimulation (34.4 ± 2.0% vs. 66.8 ± 5.9%, respectively).Functionalizing alginate scaffolds with laminin-521 (LN521) seemed to enhance the mobilization of cells from 48 (±21) to 194 (±39) cells/mm 3 after applying intermittent mechanical loading. ConclusionThe bioreactor presented here was able to provide mechanical stimulation that seemed to induce the mobilization of MSCs into LN521-alginate scaffolds under an intermittent loading regime.Bioreactor for mobilization of MSCs under mechanical stimulation PLOS ONE | https://doi.
Developers of image processing routines rely on benchmark data sets to give qualitative comparisons of new image analysis algorithms and pipelines. Such data sets need to include artifacts in order to occlude and distort the required information to be extracted from an image. Robustness, the quality of an algorithm related to the amount of distortion is often important. However, using available benchmark data sets an evaluation of illumination robustness is difficult or even not possible due to missing ground truth data about object margins and classes and missing information about the distortion. We present a new framework for robustness evaluation. The key aspect is an image benchmark containing 9 object classes and the required ground truth for segmentation and classification. Varying levels of shading and background noise are integrated to distort the data set. To quantify the illumination robustness, we provide measures for image quality, segmentation and classification success and robustness. We set a high value on giving users easy access to the new benchmark, therefore, all routines are provided within a software package, but can as well easily be replaced to emphasize other aspects.
3D imaging is becoming more and more popular, as it allows us to identify interactions between structures in organs. Furthermore, it gives the possibility to quantify and size these structures. To allow 3D imaging, the tissue sample has to be transparent. This is usually achieved by using optical tissue clearing protocols. Although using optical tissue clearing often results in perfect 3D images, these protocols have some pitfalls, like long duration of sample preparation (up to several weeks), use of toxic substances, damage to antibody staining, fluorescent proteins or dyes, high refractive indices, and high costs of sample processing.Recently we described [Huang et al., Scientific Reports 9(1): 521 (2019)] a fast, safe, and inexpensive ethyl cinnamate (ECi) based optical tissue clearing protocol. Here, we present extensions of our protocol with respect to the deparaffinization of old paraffin‐embedded samples allowing 3D imaging of the blocks. In addition, we learned to remove ECi from the samples allowing the use of routine immunolabeling protocols. Furthermore, we demonstrate new pictures of lungs after expansion microscopy and adaptation of already existing protocols. The aim of our work is, in summary, to describe the advances in these methodologies, focusing on the morphological imaging of kidneys and lungs.
Cystic kidney disease (CKD) is a heterogeneous group of genetic disorders and one of the most common causes of end-stage renal disease. Here, we investigate the potential effects of long-term human stem cell treatment on kidney function and the gene expression profile of PKD/Mhm (Cy/+) rats. Human adipose-derived stromal cells (ASC) and human skin-derived ABCB5+ stromal cells (2 × 106) were infused intravenously or intraperitoneally monthly, over 6 months. Additionally, ASC and ABCB5+-derived conditioned media were administrated intraperitoneally. The gene expression profile results showed a significant reprogramming of metabolism-related pathways along with downregulation of the cAMP, NF-kB and apoptosis pathways. During the experimental period, we measured the principal renal parameters as well as renal function using an innovative non-invasive transcutaneous device. All together, these analyses show a moderate amelioration of renal function in the ABCB5+ and ASC-treated groups. Additionally, ABCB5+ and ASC-derived conditioned media treatments lead to milder but still promising improvements. Even though further analyses have to be performed, the preliminary results obtained in this study can lay the foundations for a novel therapeutic approach with the application of cell-based therapy in CKD.
The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts.
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