Learning Morphological, Spatial, and Dynamic Models of Cellular Components

In this chapter, we describe protocols for using the CellOrganizer software on the Jupyter Notebook platform to analyze and model cell and organelle shape and spatial arrangement. CellOrganizer is an open-source system for using microscope images to learn statistical models of the structure of cell components and how those components are organized relative to each other. Such models capture the statistical variation in the organization of cellular components by jointly modeling the distributions of their number, shape, and spatial distributions. These models can be created for different cell types or conditions and compared to reflect differences in their spatial organizations. The models are also generative, in that they can be used to synthesize new cell instances reflecting what a model learned and to provide well-structured cell geometries that can be used for biochemical simulations.

Proof-of-concept for the detection of early osteoarthritis pathology by clinically applicable endomicroscopy and quantitative AI-supported optical biopsy

OBJECTIVE

Clinical trials for osteoarthritis (OA), the leading cause of global disability, are unable to pinpoint the early, potentially reversible disease with clinical technology. Hence, disease-modifying drug candidates cannot be tested early in the disease. To overcome this obstacle, we asked whether early OA-pathology detection is possible with current clinical technology.

METHODS

We determined the relationship between two sensitive early OA markers, atomic force microscopy (AFM)-measured human articular cartilage (AC) surface stiffness, and location-matched superficial zone chondrocyte spatial organizations (SCSOs), asking whether a significant loss of surface stiffness can be detected in early OA SCSO stages. We then tested whether current clinical technology can visualize and accurately diagnose the SCSOs using an approved probe-based confocal laser-endomicroscope and a random forest (RF) model.

RESULTS

We demonstrated a correlation between AC surface stiffness and the SCSO (r_rm = -0.91; 95%CI: -0.97, -0.73), and an extensive loss of surface stiffness specifically in those ACs with early OA-typical SCSO (95%CIs: string SCSO: 269-173 kPa, double string SCSO: 77-46 kPa). This established the SCSO as a visualizable, functionally relevant surrogate marker of early OA AC surface pathology. Moreover, SCSO-based stiffness discrimination worked well in each patient's AC. We then demonstrated feasibility of visualizing the SCSO by clinical laser-endomicroscopy and, importantly, accurate SCSO diagnosis using RF.

CONCLUSION

We present the proof-of-concept of early OA-pathology detection with available clinical technology, introducing a future-oriented, AI-supported, non-destructive quantitative optical biopsy for early disease detection. Operationalizing SCSO recognition, this approach allows testing for correlations between local tissue architectures with other experimental and clinical read-outs, but needs clinical validation and a larger sample size for defining diagnostic thresholds.

Elastin-PLGA hybrid electrospun nanofiber scaffolds for salivary epithelial cell self-organization and polarization

Development of electrospun nanofibers that mimic the structural, mechanical and biochemical properties of natural extracellular matrices (ECMs) is a promising approach for tissue regeneration. Electrospun fibers of synthetic polymers partially mimic the topography of the ECM, however, their high stiffness, poor hydrophilicity and lack of in vivo-like biochemical cues is not optimal for epithelial cell self-organization and function. In search of a biomimetic scaffold for salivary gland tissue regeneration, we investigated the potential of elastin, an ECM protein, to generate elastin hybrid nanofibers that have favorable physical and biochemical properties for regeneration of the salivary glands. Elastin was introduced to our previously developed poly-lactic-co-glycolic acid (PLGA) nanofiber scaffolds by two methods, blend electrospinning (EP-blend) and covalent conjugation (EP-covalent). Both methods for elastin incorporation into the nanofibers improved the wettability of the scaffolds while only blend electrospinning of elastin-PLGA nanofibers and not surface conjugation of elastin to PLGA fibers, conferred increased elasticity to the nanofibers measured by Young's modulus. After two days, only the blend electrospun nanofiber scaffolds facilitated epithelial cell self-organization into cell clusters, assessed with nuclear area and nearest neighbor distance measurements, leading to the apicobasal polarization of salivary gland epithelial cells after six days, which is vital for cell function. This study suggests that elastin electrospun nanofiber scaffolds have potential application in regenerative therapies for salivary glands and other epithelial organs.

STATEMENT OF SIGNIFICANCE

Regenerating the salivary glands by mimicking the extracellular matrix (ECM) is a promising approach for long term treatment of salivary gland damage. Despite their topographic similarity to the ECM, electrospun fibers of synthetic polymers lack the biochemical complexity, elasticity and hydrophilicity of the ECM. Elastin is an ECM protein abundant in the salivary glands and responsible for tissue elasticity. Although it's widely used for tissue regeneration of other organs, little is known about its utility in regenerating the salivary tissue. This study describes the use of elastin to improve the elasticity, hydrophilicity and biochemical complexity of synthetic nanofibers and its potential in directing in vivo-like organization of epithelial salivary cells which helps the design of efficient salivary gland regeneration scaffolds.

Building cell models and simulations from microscope images

The use of fluorescence microscopy has undergone a major revolution over the past twenty years, both with the development of dramatic new technologies and with the widespread adoption of image analysis and machine learning methods. Many open source software tools provide the ability to use these methods in a wide range of studies, and many molecular and cellular phenotypes can now be automatically distinguished. This article presents the next major challenge in microscopy automation, the creation of accurate models of cell organization directly from images, and reviews the progress that has been made towards this challenge.

Dynamic multiprotein assemblies shape the spatial structure of cell signaling

Cell signaling underlies critical cellular decisions. Coordination, efficiency as well as fail-safe mechanisms are key elements. How the cell ensures that these hallmarks are at play are important questions. Cell signaling is often viewed as taking place through discrete and cross-talking pathways; oftentimes these are modularized to emphasize distinct functions. While simple, convenient and clear, such models largely neglect the spatial structure of cell signaling; they also convey inter-modular (or inter-protein) spatial separation that may not exist. Here our thesis is that cell signaling is shaped by a network of multiprotein assemblies. While pre-organized, the assemblies and network are loose and dynamic. They contain transiently-associated multiprotein complexes which are often mediated by scaffolding proteins. They are also typically anchored in the membrane, and their continuum may span the cell. IQGAP1 scaffolding protein which binds proteins including Raf, calmodulin, Mek, Erk, actin, and tens more, with actin shaping B-cell (and likely other) membrane-anchored nanoclusters and allosterically polymerizing in dynamic cytoskeleton formation, and Raf anchoring in the membrane along with Ras, provides a striking example. The multivalent network of dynamic proteins and lipids, with specific interactions forming and breaking, can be viewed as endowing gel-like properties. Collectively, this reasons that efficient, productive and reliable cell signaling takes place primarily through transient, preorganized and cooperative protein-protein interactions spanning the cell rather than stochastic, diffusion-controlled processes.