The Accuracy of Determining Cluster Size by Analyzing Ripley’s K Function in Single Molecule Localization Microscopy

Different patterns of mast cell distribution in B-cell non-Hodgkin lymphomas

Tumor growth, progression, and metastatic capability in non-Hodgkin lymphomas (NHLs) are influenced by different component of tumor microenvironment, including inflammatory cells. Among these latter, mast cells play a crucial role. The spatial distribution of mast cells inside the tumor stroma of different types of B-cell NHLs has not yet been investigated. The aim of this study is to analyze the pattern of distribution of mast cells in biopsy samples obtained from three different types of B-cell NHLs by utilizing an image analysis system and a mathematical model to allow a quantitative estimation to characterize their spatial distribution. As concerns the spatial distributions exhibited by mast cells in diffuse large B cell lymphoma (DLBCL), some clustering was detected in both activated B-like (ABC) and germinal center B-like (GBC) groups. In follicular lymphoma (FL), mast cell spatial distribution tends to uniformly fill the tissue space as far as the grade of the pathology increases. Finally, in marginal lymphoma tissue (MALT) lymphoma, mast cells maintain a significantly clustered spatial distribution, suggesting a lower tendency of the cells to fill the tissue space in this pathological condition. Overall, the data of this study confirm that the analysis of the spatial distribution of the tumor cells is of particular significance for the knowledge of the biological processes occurring in tumor stroma and for the development of parameters to characterize the morphologic organization of the cellular patterns in different types of tumors.

Generation of synthetic microstructures containing casting defects: a machine learning approach

This paper presents a new strategy to generate synthetic samples containing casting defects. Four samples of Inconel 100 containing casting defects such as shrinkages and pores have been characterized using X-ray tomography and are used as reference for this application. Shrinkages are known to be tortuous in shape and more detrimental for the mechanical properties of materials, especially metal fatigue, whereas pores can be of two types: broken shrinkage pores with arbitrary shape and gaseous pores of spherical shape. For the generation of synthetic samples, an integrated module of Spatial Point Pattern (SPP) analysis and deep learning techniques such as Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) are used. The SPP analysis describes the spatial distributions of casting defects in material space, whereas GANs and CNNs generate a defect of arbitrary morphology very close to real defects. SPP analysis reveals the existence of two different void nucleation mechanisms during metal solidification associated to shrinkages and pores. Our deep learning model successfully generates casting defects with defect size ranging from 100 µm to 1.5 mm and of very realistic shapes. The entire synthetic microstructure generation process respects the global defect statistics of reference samples and the generated samples are validated by statistically comparing with real samples.

Insight into the Different Channel Proteins of Human Red Blood Cell Membranes Revealed by Combined dSTORM and AFM Techniques

Membrane proteins tend to interact with each other in the cell membranes to form protein clusters and perform the corresponding physiological functions. However, because channel proteins are involved in many biological functions, their distribution and nano-organization in these protein clusters are unclear. To study the distribution patterns and relationships between the different channel proteins, we identified the locations of glucose transporter 1 (Glut1) and Band3 (anion transporter 1) precisely in the topography of the cytoplasmic side of the human red blood cell (hRBC) membranes using combined atomic force microscopy (AFM) and single-molecule localization microscopy (SMLM). The AFM results revealed that membrane proteins interacted with each other and aggregated into protein islands. The SMLM results showed that Glut1 and Band3 tended to form protein clusters in the hRBC membranes, and there was a strong colocalization between the two proteins. The results of the combined AFM and SMLM method indicated that the protein clusters of Glut1 and Band3 were mainly located in the protein islands of topography, and the protein islands in topography also interacted with each other to assemble into larger protein clusters or functional microdomains.