Measuring Aggregation of Events about a Mass Using Spatial Point Pattern Methods

Modeling the effect of acquired resistance on cancer therapy outcomes

Adaptive therapy (AT) is an evolution-based treatment strategy that exploits cell-cell competition. Acquired resistance can change the competitive nature of cancer cells in a tumor, impacting AT outcomes. We aimed to determine if adaptive therapy can still be effective with cell's acquiring resistance. We developed an agent-based model for spatial tumor growth considering three different types of acquired resistance: random genetic mutations during cell division, drug-induced reversible (plastic) phenotypic changes, and drug-induced irreversible phenotypic changes. These three resistance mechanisms lead to different spatial distributions of resistant cells. To quantify the spatial distribution, we propose an extension of Ripley's K-function, Sampled Ripley's K-function (SRKF), which calculates the non-randomness of the resistance distribution over the tumor domain. Our model predicts that the emergent spatial distribution of resistance can determine the time to progression under both adaptive and continuous therapy (CT). Notably, a high rate of random genetic mutations leads to quicker progression under AT than CT due to the emergence of many small clumps of resistant cells. Drug-induced phenotypic changes accelerate tumor progression irrespective of the treatment strategy. Low-rate switching to a sensitive state reduces the benefits of AT compared to CT. Furthermore, we also demonstrated that drug-induced resistance necessitates aggressive treatment under CT, regardless of the presence of cancer-associated fibroblasts. However, there is an optimal dose that can most effectively delay tumor relapse under AT by suppressing resistance. In conclusion, this study demonstrates that diverse resistance mechanisms can shape the distribution of resistance and thus determine the efficacy of adaptive therapy.

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.

Possible frequent multiple mitochondrial DNA copies in a single nucleoid in HeLa cells

Previously, a number of ~ 1.4 of mitochondrial DNA (mtDNA) molecules in a single nucleoid was reported, which would reflect a minimum nucleoid division. We applied 3D-double-color direct stochastic optical reconstruction microscopy (dSTORM), i.e. nanoscopy with ~ 25-40 nm x,y-resolution, together with our novel method of Delaunay segmentation of 3D data to identify unbiased 3D-overlaps. Noncoding D-loops were recognized in HeLa cells by mtDNA fluorescence in situ hybridization (mtFISH) 7S-DNA 250-bp probe, containing biotin, visualized by anti-biotin/Cy3B-conjugated antibodies. Other mtFISH probes with biotin or Alexa Fluor 647 (A647) against ATP6-COX3 gene overlaps (1,100 bp) were also used. Nucleoids were imaged by anti-DNA/(A647-)-Cy3B-conjugated antibodies. Resulting histograms counting mtFISH-loci/nucleoid overlaps demonstrated that 45% to 70% of visualized nucleoids contained two or more D-loops or ATP6-COX3-loci, indicating two or more mtDNA molecules per nucleoid. With increasing number of mtDNA per nucleoid, diameters were larger and their distribution histograms peaked at ~ 300 nm. A wide nucleoid diameter distribution was obtained also using 2D-STED for their imaging by anti-DNA/A647. At unchanged mtDNA copy number in osteosarcoma 143B cells, TFAM expression increased nucleoid spatial density 1.67-fold, indicating expansion of existing mtDNA and its redistribution into more nucleoids upon the higher TFAM/mtDNA stoichiometry. Validation of nucleoid imaging was also done with two TFAM mutants unable to bend or dimerize, respectively, which reduced both copy number and nucleoid spatial density by 80%. We conclude that frequently more than one mtDNA molecule exists within a single nucleoid in HeLa cells and that mitochondrial nucleoids do exist in a non-uniform size range.

3D super-resolution microscopy reflects mitochondrial cristae alternations and mtDNA nucleoid size and distribution

3D super-resolution microscopy based on the direct stochastic optical reconstruction microscopy (dSTORM) with primary Alexa-Fluor-647-conjugated antibodies is a powerful method for accessing changes of objects that could be normally resolved only by electron microscopy. Despite the fact that mitochondrial cristae yet to become resolved, we have indicated changes in cristae width and/or morphology by dSTORM of ATP-synthase F₁ subunit α (F₁α). Obtained 3D images were analyzed with the help of Ripley's K-function modeling spatial patterns or transferring them into distance distribution function. Resulting histograms of distances frequency distribution provide most frequent distances (MFD) between the localized single antibody molecules. In fasting state of model pancreatic β-cells, INS-1E, MFD between F₁α were ~80 nm at 0 and 3 mM glucose, whereas decreased to 61 nm and 57 nm upon glucose-stimulated insulin secretion (GSIS) at 11 mM and 20 mM glucose, respectively. Shorter F₁α interdistances reflected cristae width decrease upon GSIS, since such repositioning of F₁α correlated to average 20 nm and 15 nm cristae width at 0 and 3 mM glucose, and 9 nm or 8 nm after higher glucose simulating GSIS (11, 20 mM glucose, respectively). Also, submitochondrial entities such as nucleoids of mtDNA were resolved e.g. after bromo-deoxyuridine (BrDU) pretreatment using anti-BrDU dSTORM. MFD in distances distribution histograms reflected an average nucleoid diameter (<100 nm) and average distances between nucleoids (~1000 nm). Double channel PALM/dSTORM with Eos-lactamase-β plus anti-TFAM dSTORM confirmed the latter average inter-nucleoid distance. In conclusion, 3D single molecule (dSTORM) microscopy is a reasonable tool for studying mitochondrion.