Generative modeling of biological shapes and images using a probabilistic α-shape sampler.

Understanding morphological variation is an important task in many areas of computational biology. Recent studies have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best describe the variation between classes of shapes. A major part in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes for these data are often small due to them being expensive to collect. Meanwhile, the current landscape of generative models for shapes has been mostly limited to approaches that use black-box inference-making it difficult to systematically assess the power and calibration of sub-image models. In this paper, we introduce the α-shape sampler: a probabilistic framework for generating realistic 2D and 3D shapes based on probability distributions which can be learned from real data. We demonstrate our framework using proof-of-concept examples and in two real applications in biology where we generate (i) 2D images of healthy and septic neutrophils and (ii) 3D computed tomography (CT) scans of primate mandibular molars. The α-shape sampler R package is open-source and can be downloaded at https://github.com/lcrawlab/ashapesampler.

Topological data analysis of spatial patterning in heterogeneous cell populations: clustering and sorting with varying cell-cell adhesion.

Different cell types aggregate and sort into hierarchical architectures during the formation of animal tissues. The resulting spatial organization depends (in part) on the strength of adhesion of one cell type to itself relative to other cell types. However, automated and unsupervised classification of these multicellular spatial patterns remains challenging, particularly given their structural diversity and biological variability. Recent developments based on topological data analysis are intriguing to reveal similarities in tissue architecture, but these methods remain computationally expensive. In this article, we show that multicellular patterns organized from two interacting cell types can be efficiently represented through persistence images. Our optimized combination of dimensionality reduction via autoencoders, combined with hierarchical clustering, achieved high classification accuracy for simulations with constant cell numbers. We further demonstrate that persistence images can be normalized to improve classification for simulations with varying cell numbers due to proliferation. Finally, we systematically consider the importance of incorporating different topological features as well as information about each cell type to improve classification accuracy. We envision that topological machine learning based on persistence images will enable versatile and robust classification of complex tissue architectures that occur in development and disease.

Multiscale geometric and topological analyses for characterizing and predicting immune responses from single cell data.

Single cell genomics has revolutionized our ability to map immune heterogeneity and responses. With the influx of large-scale data sets from diverse modalities, the resolution achieved has supported the long-held notion that immune cells are naturally organized into hierarchical relationships, characterized at multiple levels. Such a multigranular structure corresponds to key geometric and topological features. Given that differences between an effective and ineffective immunological response may not be found at one level, there is vested interest in characterizing and predicting outcomes from such features. In this review, we highlight single cell methods and principles for learning geometric and topological properties of data at multiple scales, discussing their contributions to immunology. Ultimately, multiscale approaches go beyond classical clustering, revealing a more comprehensive picture of cellular heterogeneity.

Cell cycle controls long-range calcium signaling in the regenerating epidermis.

Skin homeostasis is maintained by stem cells, which must communicate to balance their regenerative behaviors. Yet, how adult stem cells signal across regenerative tissue remains unknown due to challenges in studying signaling dynamics in live mice. We combined live imaging in the mouse basal stem cell layer with machine learning tools to analyze patterns of Ca2+ signaling. We show that basal cells display dynamic intercellular Ca2+ signaling among local neighborhoods. We find that these Ca2+ signals are coordinated across thousands of cells and that this coordination is an emergent property of the stem cell layer. We demonstrate that G2 cells are required to initiate normal levels of Ca2+ signaling, while connexin43 connects basal cells to orchestrate tissue-wide coordination of Ca2+ signaling. Lastly, we find that Ca2+ signaling drives cell cycle progression, revealing a communication feedback loop. This work provides resolution into how stem cells at different cell cycle stages coordinate tissue-wide signaling during epidermal regeneration.

Current trends in artificial intelligence in reproductive endocrinology.

PURPOSE OF REVIEW: Artificial Intelligence, a tool that integrates computer science and machine learning to mimic human decision-making processes, is transforming the world and changing the way we live. Recently, the healthcare industry has gradually adopted artificial intelligence in many applications and obtained some degree of success. In this review, we summarize the current applications of artificial intelligence in Reproductive Endocrinology, in both laboratory and clinical settings. RECENT FINDINGS: Artificial Intelligence has been used to select the embryos with high implantation potential, proper ploidy status, to predict later embryo development, and to increase pregnancy and live birth rates. Some studies also suggested that artificial intelligence can help improve infertility diagnosis and patient management. Recently, it has been demonstrated that artificial intelligence also plays a role in effective laboratory quality control and performance. SUMMARY: In this review, we discuss various applications of artificial intelligence in different areas of reproductive medicine. We summarize the current findings with their potentials and limitations, and also discuss the future direction for research and clinical applications.

The need for speed: Migratory cells in tight spaces boost their molecular clock.

Cells migrating in spatial confinement exhibit higher intracellular calcium levels, which increases the oscillation frequency of a "molecular clock" that synchronizes guanine nucleotide exchange factor GEF-H1 and microtubule polymerization for more frequent bursts of speed.

Topological data analysis of collective and individual epithelial cells using persistent homology of loops.

Interacting, self-propelled particles such as epithelial cells can dynamically self-organize into complex multicellular patterns, which are challenging to classify without a priori information. Classically, different phases and phase transitions have been described based on local ordering, which may not capture structural features at larger length scales. Instead, topological data analysis (TDA) determines the stability of spatial connectivity at varying length scales (i.e. persistent homology), and can compare different particle configurations based on the "cost" of reorganizing one configuration into another. Here, we demonstrate a topology-based machine learning approach for unsupervised profiling of individual and collective phases based on large-scale loops. We show that these topological loops (i.e. dimension 1 homology) are robust to variations in particle number and density, particularly in comparison to connected components (i.e. dimension 0 homology). We use TDA to map out phase diagrams for simulated particles with varying adhesion and propulsion, at constant population size as well as when proliferation is permitted. Next, we use this approach to profile our recent experiments on the clustering of epithelial cells in varying growth factor conditions, which are compared to our simulations. Finally, we characterize the robustness of this approach at varying length scales, with sparse sampling, and over time. Overall, we envision TDA will be broadly applicable as a model-agnostic approach to analyze active systems with varying population size, from cytoskeletal motors to motile cells to flocking or swarming animals.

Breast Cancer Cells Transition from Mesenchymal to Amoeboid Migration in Tunable Three-Dimensional Silk-Collagen Hydrogels.

Invading cancer cells adapt their migration phenotype in response to mechanical and biochemical cues from the extracellular matrix. For instance, mesenchymal migration is associated with strong cell-matrix adhesions and an elongated morphology, while amoeboid migration is associated with minimal cell-matrix adhesions and a rounded morphology. However, it remains challenging to elucidate the role of matrix mechan-ics and biochemistry, since these are both dependent on ECM protein concentration. Here, we demonstrate a composite silk fibroin and collagen I hydrogel where stiffness and microstructure can be systematically tuned over a wide range. Using an overlay assay geometry, we show that the invasion of metastatic breast cancer cells exhibits a biphasic dependence on silk fibroin concentration at fixed collagen I concentration, first increasing as the hydrogel stiffness increases, then decreasing as the pore size of silk fibroin decreases. Indeed, mesenchymal morphology exhibits a similar biphasic depen-dence on silk fibroin concentration, while amoeboid morphologies were favored when cell-matrix adhesions were less effective. We used exogenous biochemical treatment to perturb cells towards increased contractility and a mesenchymal morphology, as well as to disrupt cytoskeletal function and promote an amoeboid morphology. Overall, we envision that this tunable biomaterial platform in a 96-well plate format will be widely applicable to screen cancer cell migration against combinations of designer biomaterials and targeted inhibitors.

Motility-limited aggregation of mammary epithelial cells into fractal-like clusters.

Migratory cells transition between dispersed individuals and multicellular collectives during development, wound healing, and cancer. These transitions are associated with coordinated behaviors as well as arrested motility at high cell densities, but remain poorly understood at lower cell densities. Here, we show that dispersed mammary epithelial cells organize into arrested, fractal-like clusters at low density in reduced epidermal growth factor (EGF). These clusters exhibit a branched architecture with a fractal dimension of [Formula: see text], reminiscent of diffusion-limited aggregation of nonliving colloidal particles. First, cells display diminished motility in reduced EGF, which permits irreversible adhesion upon cell-cell contact. Subsequently, leader cells emerge that guide collectively migrating strands and connect clusters into space-filling networks. Thus, this living system exhibits gelation-like arrest at low cell densities, analogous to the glass-like arrest of epithelial monolayers at high cell densities. We quantitatively capture these behaviors with a jamming-like phase diagram based on local cell density and EGF. These individual to collective transitions represent an intriguing link between living and nonliving systems, with potential relevance for epithelial morphogenesis into branched architectures.

Analyzing collective motion with machine learning and topology.

We use topological data analysis and machine learning to study a seminal model of collective motion in biology [M. R. D'Orsogna et al., Phys. Rev. Lett. 96, 104302 (2006)]. This model describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as flocking and milling. To classify the emergent collective motion in a large library of numerical simulations and to recover model parameters from the simulation data, we apply machine learning techniques to two different types of input. First, we input time series of order parameters traditionally used in studies of collective motion. Second, we input measures based on topology that summarize the time-varying persistent homology of simulation data over multiple scales. This topological approach does not require prior knowledge of the expected patterns. For both unsupervised and supervised machine learning methods, the topological approach outperforms the one that is based on traditional order parameters.

Coupling mechanical tension and GTPase signaling to generate cell and tissue dynamics.

Regulators of the actin cytoskeleton such Rho GTPases can modulate forces developed in cells by promoting actomyosin contraction. At the same time, through mechanosensing, tension is known to affect the activity of Rho GTPases. What happens when these effects act in concert? Using a minimal model (1 GTPase coupled to a Kelvin-Voigt element), we show that two-way feedback between signaling ('RhoA') and mechanical tension (stretching) leads to a spectrum of cell behaviors, including contracted or relaxed cells, and cells that oscillate between these extremes. When such 'model cells' are connected to one another in a row or in a 2D sheet ('epithelium'), we observe waves of contraction/relaxation and GTPase activity sweeping through the tissue. The minimal model lends itself to full bifurcation analysis, and suggests a mechanism that explains behavior observed in the context of development and collective cell behavior.

Polarization and migration in the zebrafish posterior lateral line system.

Collective cell migration plays an important role in development. Here, we study the posterior lateral line primordium (PLLP) a group of about 100 cells, destined to form sensory structures, that migrates from head to tail in the zebrafish embryo. We model mutually inhibitory FGF-Wnt signalling network in the PLLP and link tissue subdivision (Wnt receptor and FGF receptor activity domains) to receptor-ligand parameters. We then use a 3D cell-based simulation with realistic cell-cell adhesion, interaction forces, and chemotaxis. Our model is able to reproduce experimentally observed motility with leading cells migrating up a gradient of CXCL12a, and trailing (FGF receptor active) cells moving actively by chemotaxis towards FGF ligand secreted by the leading cells. The 3D simulation framework, combined with experiments, allows an investigation of the role of cell division, chemotaxis, adhesion, and other parameters on the shape and speed of the PLLP. The 3D model demonstrates reasonable behaviour of control as well as mutant phenotypes.