Spironolactone Inhibits Cardiomyocyte Hypertrophy by Regulating the Ca2+/Calcineurin/p-NFATc3 Pathway

This study aimed to investigate the protective effect and molecular mechanism of spironolactone in isoproterenol-induced cardiomyocyte hypertrophy. In this study, primary cardiomyocytes were extracted from the heart of neonatal rats. After stable culture, they were processed with isoproterenol alone or isoproterenol (10 μM) combined with different doses (low dose of 10 μM and high dose of 50 μM), and the cellular activity was determined by MTT experiment. The volume of cells was measured with an inverted microscope and CIAS-1000 cell image analysis system. The mRNA expression levels of ANP and BNP in cells were explored by RT-qPCR. The levels of ANP and BNP proteins and NFATc3 phosphorylation in the nucleus were detected by western blot. The extracellular Ca2+ concentration and CaN activity were measured by colorimetry with the kit. Isoproterenol significantly enlarged the volume of cardiomyocytes ( p < 0.001 ), upregulated mRNA and expression levels of ANP and BNP proteins ( p < 0.001 ), increased extracellular Ca2+ concentration and CaN activity ( p < 0.001 ), and upregulated NFATc3 phosphorylation in the nucleus ( p < 0.001 ). The volume of cells treated with isoproterenol combined with different doses of spironolactone significantly decreased compared with those treated with isoproterenol alone ( p < 0.001 ). mRNA and expression levels of ANP and BNP proteins downregulated significantly ( p < 0.001 ). The extracellular Ca2+ ( p < 0.01 ) concentration and CaN activity ( p < 0.001 ) decreased significantly, and NFATc3 phosphorylation in the nucleus downregulated significantly ( p < 0.001 ). There was no significant difference in cell volume ( p = 0.999 ), ANP and BNP mRNA ( p = 0.695 ), expression levels of proteins, CaN activity (0.154), and NFATc3 phosphorylation in the nucleus between the cells treated with isoproterenol combined with high-dose spironolactone and those in the control group. In conclusion, spironolactone can reverse isoproterenol-induced cardiomyocyte hypertrophy by inhibiting the Ca2+/CaN/NFATc3 pathway.

Single-cell image analysis reveals a protective role for microglia in glioblastoma

Background Microglia and tumour associated macrophages (TAMs) constitute up to half of the total tumour mass of glioblastomas. Despite these myeloid populations being ontogenetically distinct, they have been largely conflated. Recent single-cell transcriptomic studies have identified genes that distinguish microglia from TAMs. Here we investigated whether the transcribed proteins of genes enriched in microglial or TAM populations can be used to differentiate these myeloid cells in immunohistochemically stained human glioblastoma tissue. Methods Tissue sections from resected low-grade, meningioma, and glioblastoma (grade IV) tumours and epilepsy tissues were immunofluorescently triple-labelled for Iba1 (pan-myeloid marker), CD14 or CD163 (preferential TAM-markers) and either P2RY12 or TMEM119 (microglial-specific markers). Using a single cell-based image analysis pipeline, we quantified the abundance of each marker within single myeloid cells, allowing the identification and analysis of myeloid populations. Results P2RY12 and TMEM119 successfully discriminated microglia from TAMs in glioblastoma. In contrast, CD14 and CD163 expression were not restricted to invading TAMs, and were upregulated by tumour microglia. Notably, a higher ratio of microglia-to-TAMs significantly correlated with increased patient survival. Conclusions We demonstrate the validity of previously defined microglial-specific genes P2RY12 and TMEM119 as robust discriminators of microglia and TAMs at the protein level in human tissue. Moreover, our data suggests that a higher proportion of microglia may be beneficial for patient survival in glioblastoma. Accordingly, this tissue-based method for myeloid population differentiation could serve as a useful prognostic tool.

ME-VAE: Multi-Encoder Variational AutoEncoder for Controlling Multiple Transformational Features in Single Cell Image Analysis

Image-based cell phenotyping relies on quantitative measurements as encoded representations of cells; however, defining suitable representations that capture complex imaging features is challenging since there are many obstacles, including segmentation and identifying subcellular compartments for feature extraction. Variational autoencoder (VAE) approaches produce encouraging results by mapping from an image to a representative descriptor, and outperform classical hand-crafted features for morphology, intensity, and texture at differentiating data. Although VAEs show promising results for capturing morphological and organizational features in tissue, single cell image analyses based on VAEs often fail to identify biologically informative features due to the intrinsic amount of uninformative variability. Herein, we propose a multi-encoder VAE (ME-VAE) in single cell image analysis using transformed images as a self-supervised signal to extract transform-invariant biologically meaningful features. We show that the proposed architecture improves analysis by making distinct populations more separable compared to traditional VAEs and intensity measurements by enhancing phenotypic differences between cells and by improving correlations to other modalities.

White Blood Cell Detection Depending on Binary Analysis Based on Red, Blue and Hue Components

The automatic detection of white blood cells remains an unresolved problem in medical imaging. white blood cell image analysis has included researchers from the fields of medicine and computer vision alike. In this paper, a new algorithm for white blood cell detection is proposed. This algorithm is based on the binary conversion of red and blue identity compounds and gradient after defining the values of specific areas while deleting unwanted small areas depending on the median filter. In the experimental results from white blood cell images, the proposed algorithm was compared with several other algorithms for detection. A quality scale was used that compares manual cell counts with the automatic detection of algorithms where the proposed algorithm obtained a high distinction accuracy reaching 98 percent, as compared to other methods.