Automated Design of Pluripotent Stem Cell Self-Organization

Topological data analysis of pattern formation of human induced pluripotent stem cell colonies

Understanding the multicellular organization of stem cells is vital for determining the mechanisms that coordinate cell fate decision-making during differentiation; these mechanisms range from neighbor-to-neighbor communication to tissue-level biochemical gradients. Current methods for quantifying multicellular patterning tend to capture the spatial properties of cell colonies at a fixed scale and typically rely on human annotation. We present a computational pipeline that utilizes topological data analysis to generate quantitative, multiscale descriptors which capture the shape of data extracted from 2D multichannel microscopy images. By applying our pipeline to certain stem cell colonies, we detected subtle differences in patterning that reflect distinct spatial organization associated with loss of pluripotency. These results yield insight into putative directed cellular organization and morphogen-mediated, neighbor-to-neighbor signaling. Because of its broad applicability to immunofluorescence microscopy images, our pipeline is well-positioned to serve as a general-purpose tool for the quantitative study of multicellular pattern formation.

AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks

Many applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. The emerging field of diverse intelligence investigates the problem-solving capacities of unconventional agents. However, few quantitative tools exist for exploring the competencies of non-conventional systems. Here, we view gene regulatory networks (GRNs) as agents navigating a problem space and develop automated tools to map the robust goal states GRNs can reach despite perturbations. Our contributions include: (1) Adapting curiosity-driven exploration algorithms from AI to discover the range of reachable goal states of GRNs, and (2) Proposing empirical tests inspired by behaviorist approaches to assess their navigation competencies. Our data shows that models inferred from biological data can reach a wide spectrum of steady states, exhibiting various competencies in physiological network dynamics without requiring structural changes in network properties or connectivity. We also explore the applicability of these 'behavioral catalogs' for comparing evolved competencies across biological networks, for designing drug interventions in biomedical contexts and synthetic gene networks for bioengineering. These tools and the emphasis on behavior-shaping open new paths for efficiently exploring the complex behavior of biological networks. For the interactive version of this paper, please visit https://developmentalsystems.org/curious-exploration-of-grn-competencies.

Improving 3D deep learning segmentation with biophysically motivated cell synthesis

Biomedical research increasingly relies on three-dimensional (3D) cell culture models and artificial-intelligence-based analysis can potentially facilitate a detailed and accurate feature extraction on a single-cell level. However, this requires for a precise segmentation of 3D cell datasets, which in turn demands high-quality ground truth for training. Manual annotation, the gold standard for ground truth data, is too time-consuming and thus not feasible for the generation of large 3D training datasets. To address this, we present a framework for generating 3D training data, which integrates biophysical modeling for realistic cell shape and alignment. Our approach allows the in silico generation of coherent membrane and nuclei signals, that enable the training of segmentation models utilizing both channels for improved performance. Furthermore, we present a generative adversarial network (GAN) training scheme that generates not only image data but also matching labels. Quantitative evaluation shows superior performance of biophysical motivated synthetic training data, even outperforming manual annotation and pretrained models. This underscores the potential of incorporating biophysical modeling for enhancing synthetic training data quality.

Artificial intelligence and machine learning applications for cultured meat

Cultured meat has the potential to provide a complementary meat industry with reduced environmental, ethical, and health impacts. However, major technological challenges remain which require time-and resource-intensive research and development efforts. Machine learning has the potential to accelerate cultured meat technology by streamlining experiments, predicting optimal results, and reducing experimentation time and resources. However, the use of machine learning in cultured meat is in its infancy. This review covers the work available to date on the use of machine learning in cultured meat and explores future possibilities. We address four major areas of cultured meat research and development: establishing cell lines, cell culture media design, microscopy and image analysis, and bioprocessing and food processing optimization. In addition, we have included a survey of datasets relevant to CM research. This review aims to provide the foundation necessary for both cultured meat and machine learning scientists to identify research opportunities at the intersection between cultured meat and machine learning.

Algorithms for Autonomous Formation of Multicellular Shapes from Single Cells

Multicellular organisms originate from a single cell, ultimately giving rise to mature organisms of heterogeneous cell type composition in complex structures. Recent work in the areas of stem cell biology and tissue engineering has laid major groundwork in the ability to convert certain types of cells into other types, but there has been limited progress in the ability to control the morphology of cellular masses as they grow. Contemporary approaches to this problem have included the use of artificial scaffolds, 3D bioprinting, and complex media formulations; however, there are no existing approaches to controlling this process purely through genetics and from a single-cell starting point. Here we describe a computer-aided design approach, called CellArchitect, for designing recombinase-based genetic circuits for controlling the formation of multicellular masses into arbitrary shapes in human cells.

Identification of Distinct, Quantitative Pattern Classes from Emergent Tissue-Scale hiPSC Bioelectric Properties

Bioelectric signals possess the ability to robustly control and manipulate patterning during embryogenesis and tissue-level regeneration. Endogenous local and global electric fields function as a spatial 'pre-pattern', controlling cell fates and tissue-scale anatomical boundaries; however, the mechanisms facilitating these robust multiscale outcomes are poorly characterized. Computational modeling addresses the need to predict in vitro patterning behavior and further elucidate the roles of cellular bioelectric signaling components in patterning outcomes. Here, we modified a previously designed image pattern recognition algorithm to distinguish unique spatial features of simulated non-excitable bioelectric patterns under distinct cell culture conditions. This algorithm was applied to comparisons between simulated patterns and experimental microscopy images of membrane potential (Vmem) across cultured human iPSC colonies. Furthermore, we extended the prediction to a novel co-culture condition in which cell sub-populations possessing different ionic fluxes were simulated; the defining spatial features were recapitulated in vitro with genetically modified colonies. These results collectively inform strategies for modeling multiscale spatial characteristics that emerge in multicellular systems, characterizing the molecular contributions to heterogeneity of membrane potential in non-excitable cells, and enabling downstream engineered bioelectrical tissue design.

TBXT dose sensitivity and the decoupling of nascent mesoderm specification from EMT progression in 2D human gastruloids

In the nascent mesoderm, TBXT expression must be precisely regulated to ensure that cells exit the primitive streak and pattern the anterior-posterior axis, but how varying dosage informs morphogenesis is not well understood. In this study, we define the transcriptional consequences of TBXT dosage reduction during early human gastrulation using human induced pluripotent stem cell models of gastrulation and mesoderm differentiation. Multi-omic single-nucleus RNA and single-nucleus ATAC sequencing of 2D gastruloids comprising wild-type, TBXT heterozygous or TBXT null human induced pluripotent stem cells reveal that varying TBXT dosage does not compromise the ability of a cell to differentiate into nascent mesoderm, but instead directly influences the temporal progression of the epithelial-to-mesenchymal transition with wild type transitioning first, followed by TBXT heterozygous and then TBXT null. By differentiating cells into nascent mesoderm in a monolayer format, we further illustrate that TBXT dosage directly impacts the persistence of junctional proteins and cell-cell adhesions. These results demonstrate that epithelial-to-mesenchymal transition progression can be decoupled from the acquisition of mesodermal identity in the early gastrula and shed light on the mechanisms underlying human embryogenesis.

Decellularized extracellular matrix for organoid and engineered organ culture

The repair and regeneration of tissues and organs using engineered biomaterials has attracted great interest in tissue engineering and regenerative medicine. Recent advances in organoids and engineered organs technologies have enabled scientists to generate 3D tissue that recapitulate the structural and functional characteristics of native organs, opening up new avenues in regenerative medicine. The matrix is one of the most important aspects for improving organoids and engineered organs construction. However, the clinical application of these techniques remained a big challenge because current commercial matrix does not represent the complexity of native microenvironment, thereby limiting the optimal regenerative capacity. Decellularized extracellular matrix (dECM) is expected to maintain key native matrix biomolecules and is believed to hold enormous potential for regenerative medicine applications. Thus, it is worth investigating whether the dECM can be used as matrix for improving organoid and engineered organs construction. In this review, the characteristics of dECM and its preparation method were summarized. In addition, the present review highlights the applications of dECM in the fabrication of organoids and engineered organs.

Human Stem Cells for Ophthalmology: Recent Advances in Diagnostic Image Analysis and Computational Modelling

Purpose of Review

To explore the advances and future research directions in image analysis and computational modelling of human stem cells (hSCs) for ophthalmological applications.

Recent Findings

hSCs hold great potential in ocular regenerative medicine due to their application in cell-based therapies and in disease modelling and drug discovery using state-of-the-art 2D and 3D organoid models. However, a deeper characterisation of their complex, multi-scale properties is required to optimise their translation to clinical practice. Image analysis combined with computational modelling is a powerful tool to explore mechanisms of hSC behaviour and aid clinical diagnosis and therapy.

Summary

Many computational models draw on a variety of techniques, often blending continuum and discrete approaches, and have been used to describe cell differentiation and self-organisation. Machine learning tools are having a significant impact in model development and improving image classification processes for clinical diagnosis and treatment and will be the focus of much future research.

TBXT dose sensitivity and the decoupling of nascent mesoderm specification from EMT progression in 2D human gastruloids

In the nascent mesoderm, levels of Brachyury (TBXT) expression must be precisely regulated to ensure cells exit the primitive streak and pattern the anterior-posterior axis, but how this varying dosage informs morphogenesis is not well understood. In this study, we define the transcriptional consequences of TBXT dose reduction during early human gastrulation using human induced pluripotent stem cell (hiPSC)-based models of gastrulation and mesoderm differentiation. Multiomic single-nucleus RNA and single-nucleus ATAC sequencing of 2D gastruloids comprised of WT, TBXT heterozygous (TBXT-Het), or TBXT null (TBXT-KO) hiPSCs reveal that varying TBXT dosage does not compromise a cell's ability to differentiate into nascent mesoderm, but that the loss of TBXT significantly delays the temporal progression of the epithelial to mesenchymal transition (EMT). This delay is dependent on TBXT dose, as cells heterozygous for TBXT proceed with EMT at an intermediate pace relative to WT or TBXT-KO. By differentiating iPSCs of the allelic series into nascent mesoderm in a monolayer format, we further illustrate that TBXT dose directly impacts the persistence of junctional proteins and cell-cell adhesions. These results demonstrate that EMT progression can be decoupled from the acquisition of mesodermal identity in the early gastrula and shed light on the mechanisms underlying human embryogenesis.

Recognition and reconstruction of cell differentiation patterns with deep learning

Cell lineage decisions occur in three-dimensional spatial patterns that are difficult to identify by eye. There is an ongoing effort to replicate such patterns using mathematical modeling. One approach uses long ranging cell-cell communication to replicate common spatial arrangements like checkerboard and engulfing patterns. In this model, the cell-cell communication has been implemented as a signal that disperses throughout the tissue. On the other hand, machine learning models have been developed for pattern recognition and pattern reconstruction tasks. We combined synthetic data generated by the mathematical model with spatial summary statistics and deep learning algorithms to recognize and reconstruct cell fate patterns in organoids of mouse embryonic stem cells. Application of Moran's index and pair correlation functions for in vitro and synthetic data from the model showed local clustering and radial segregation. To assess the patterns as a whole, a graph neural network was developed and trained on synthetic data from the model. Application to in vitro data predicted a low signal dispersion value. To test this result, we implemented a multilayer perceptron for the prediction of a given cell fate based on the fates of the neighboring cells. The results show a 70% accuracy of cell fate imputation based on the nine nearest neighbors of a cell. Overall, our approach combines deep learning with mathematical modeling to link cell fate patterns with potential underlying mechanisms.

In-silico and in-vitro morphometric analysis of intestinal organoids

Organoids offer a powerful model to study cellular self-organisation, the growth of specific tissue morphologies in-vitro, and to assess potential medical therapies. However, the intrinsic mechanisms of these systems are not entirely understood yet, which can result in variability of organoids due to differences in culture conditions and basement membrane extracts used. Improving the standardisation of organoid cultures is essential for their implementation in clinical protocols. Developing tools to assess and predict the behaviour of these systems may produce a more robust and standardised biological model to perform accurate clinical studies. Here, we developed an algorithm to automate crypt-like structure counting on intestinal organoids in both in-vitro and in-silico images. In addition, we modified an existing two-dimensional agent-based mathematical model of intestinal organoids to better describe the system physiology, and evaluated its ability to replicate budding structures compared to new experimental data we generated. The crypt-counting algorithm proved useful in approximating the average number of budding structures found in our in-vitro intestinal organoid culture images on days 3 and 7 after seeding. Our changes to the in-silico model maintain the potential to produce simulations that replicate the number of budding structures found on days 5 and 7 of in-vitro data. The present study aims to aid in quantifying key morphological structures and provide a method to compare both in-vitro and in-silico experiments. Our results could be extended later to 3D in-silico models.

Systems bioengineering approaches for developmental toxicology

Developmental toxicology is the field of study that examines the effects of chemical and physical agents on developing organisms. By using principles of systems biology and bioengineering, a systems bioengineering approach could be applied to study the complex interactions between developing organisms, the environment, and toxic agents. This approach would result in a holistic understanding of the effects of toxic agents on organisms, by considering the interactions between different biological systems and the impacts of toxicants on those interactions. It would be useful in identifying key biological pathways and mechanisms affected by toxic agents, as well as in the development of predictive models to assess potential risks of exposure to toxicants during development. In this review, we discuss the relevance of systems bioengineering to the field of developmental toxicity and provide up-to-date examples that illustrate the use of engineering principles for this application.

Self-organized collective cell behaviors as design principles for synthetic developmental biology

Over the past two decades, molecular cell biology has graduated from a mostly analytic science to one with substantial synthetic capability. This success is built on a deep understanding of the structure and function of biomolecules and molecular mechanisms. For synthetic biology to achieve similar success at the scale of tissues and organs, an equally deep understanding of the principles of development is required. Here, we review some of the central concepts and recent progress in tissue patterning, morphogenesis and collective cell migration and discuss their value for synthetic developmental biology, emphasizing in particular the power of (guided) self-organization and the role of theoretical advances in making developmental insights applicable in synthesis.

Genetically modified cell spheroids for tissue engineering and regenerative medicine

Cell spheroids offer cell-to-cell interactions and show advantages in survival rate and paracrine effect to solve clinical and biomedical inquiries ranging from tissue engineering and regenerative medicine to disease pathophysiology. Therefore, cell spheroids are ideal vehicles for gene delivery. Genetically modified spheroids can enhance specific gene expression to promote tissue regeneration. Gene deliveries to cell spheroids are via viral vectors or non-viral vectors. Some new technologies like CRISPR/Cas9 also have been used in genetically modified methods to deliver exogenous gene to the host chromosome. It has been shown that genetically modified cell spheroids had the potential to differentiate into bone, cartilage, vascular, nerve, cardiomyocytes, skin, and skeletal muscle as well as organs like the liver to replace the diseased organ in the animal and pre-clinical trials. This article reviews the recent articles about genetically modified spheroid cells and explains the fabrication, applications, development timeline, limitations, and future directions of genetically modified cell spheroid.

Deep learning models will shape the future of stem cell research

Our ability to understand and control stem cell biology is being augmented by developments on two fronts, our ability to collect more data describing cell state and our capability to comprehend these data using deep learning models. Here we consider the impact deep learning will have in the future of stem cell research. We explore the importance of generating data suitable for these methods, the requirement for close collaboration between experimental and computational researchers, and the challenges we face to do this fairly and effectively. Achieving this will ensure that the resulting deep learning models are biologically meaningful and computationally tractable.

Machine Learning in Tissue Engineering

Machine learning (ML) and artificial intelligence have accelerated scientific discovery, augmented clinical practice, and deepened fundamental understanding of many biological phenomena. ML technologies have now been applied to diverse areas of tissue engineering research, including biomaterial design, scaffold fabrication, and cell/tissue modeling. Emerging ML-empowered strategies include machine-optimized polymer synthesis, predictive modeling of scaffold fabrication processes, complex analyses of structure-function relationships, and deep learning of spatialized cell phenotypes and tissue composition. The emergence of ML in tissue engineering, while relatively recent, has already enabled increasingly complex and multivariate analyses of the relationships between biological, chemical, and physical factors in driving tissue regenerative outcomes. This review highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research. Impact statement Machine learning (ML) has accelerated scientific discovery and augmented clinical practice across multiple fields. Now, ML has driven exciting new paradigms in tissue engineering research, including machine-optimized biomaterial design, predictive modeling of scaffold fabrication, and spatiotemporal analysis of cell and tissue systems. The emergence of ML in tissue engineering, while relatively recent, has already enabled increasingly complex analyses of the relationships between biological, chemical, and physical factors in driving tissue regenerative outcomes. This review highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research.

Stem cells for organoids

Organoids are three-dimensional (3D) cell culture systems that simulate the structures and functions of organs, involving applications in disease modeling, drug screening, and cellular developmental biology. The material matrix in organoids can provide a 3D environment for stem cells to differentiate into different cell types and continuously self-renew, thereby realizing the in vitro culture of organs, which has received extensive attention in recent years. However, some challenges still exist in organoids, including low maturity, high heterogeneity, and lack of spatiotemporal regulation. Therefore, in this review, we summarized the culturing protocols and various applications of stem cell-derived organoids and proposed insightful thoughts for engineering stem cells into organoids in view of the current shortcomings, to achieve the further application and clinical translation of stem cells and engineered stem cells in organoid research.

Diseased, differentiated and difficult: Strategies for improved engineering of in vitro neurological systems

The rapidly growing field of cellular engineering is enabling scientists to more effectively create in vitro models of disease and develop specific cell types that can be used to repair damaged tissue. In particular, the engineering of neurons and other components of the nervous system is at the forefront of this field. The methods used to engineer neural cells can be largely divided into systems that undergo directed differentiation through exogenous stimulation (i.e., via small molecules, arguably following developmental pathways) and those that undergo induced differentiation via protein overexpression (i.e., genetically induced and activated; arguably bypassing developmental pathways). Here, we highlight the differences between directed differentiation and induced differentiation strategies, how they can complement one another to generate specific cell phenotypes, and impacts of each strategy on downstream applications. Continued research in this nascent field will lead to the development of improved models of neurological circuits and novel treatments for those living with neurological injury and disease.

Pluripotent stem cells for skeletal tissue engineering

Here, we review the use of human pluripotent stem cells for skeletal tissue engineering. A number of approaches have been used for generating cartilage and bone from both human embryonic stem cells and induced pluripotent stem cells. These range from protocols relying on intrinsic cell interactions and signals from co-cultured cells to those attempting to recapitulate the series of steps occurring during mammalian skeletal development. The importance of generating authentic tissues rather than just differentiated cells is emphasized and enabling technologies for doing this are reported. We also review the different methods for characterization of skeletal cells and constructs at the tissue and single-cell level, and indicate newer resources not yet fully utilized in this field. There have been many challenges in this research area but the technologies to overcome these are beginning to appear, often adopted from related fields. This makes it more likely that cost-effective and efficacious human pluripotent stem cell-engineered constructs may become available for skeletal repair in the near future.

Parameterized Computational Framework for the Description and Design of Genetic Circuits of Morphogenesis Based on Contact-Dependent Signaling and Changes in Cell-Cell Adhesion

Synthetic development is a nascent field of research that uses the tools of synthetic biology to design genetic programs directing cellular patterning and morphogenesis in higher eukaryotic cells, such as mammalian cells. One specific example of such synthetic genetic programs was based on cell-cell contact-dependent signaling using synthetic Notch pathways and was shown to drive the formation of multilayered spheroids by modulating cell-cell adhesion via differential expression of cadherin family proteins in a mouse fibroblast cell line (L929). The design method for these genetic programs relied on trial and error, which limited the number of possible circuits and parameter ranges that could be explored. Here, we build a parameterized computational framework that, given a cell-cell communication network driving changes in cell adhesion and initial conditions as inputs, predicts developmental trajectories. We first built a general computational framework where contact-dependent cell-cell signaling networks and changes in cell-cell adhesion could be designed in a modular fashion. We then used a set of available in vitro results (that we call the "training set" in analogy to similar pipelines in the machine learning field) to parameterize the computational model with values for adhesion and signaling. We then show that this parameterized model can qualitatively predict experimental results from a "testing set" of available in vitro data that varied the genetic network in terms of adhesion combinations, initial number of cells, and even changes to the network architecture. Finally, this parameterized model is used to recommend novel network implementation for the formation of a four-layered structure that has not been reported previously. The framework that we develop here could function as a testing ground to identify the reachable space of morphologies that can be obtained by controlling contact-dependent cell-cell communications and adhesion with these molecular tools and in this cellular system. Additionally, we discuss how the model could be expanded to include other forms of communication or effectors for the computational design of the next generation of synthetic developmental trajectories.

Bioengineering Approaches for the Advanced Organoid Research

Recent advances in 3D cell culture technology have enabled scientists to generate stem cell derived organoids that recapitulate the structural and functional characteristics of native organs. Current organoid technologies have been striding toward identifying the essential factors for controlling the processes involved in organoid development, including physical cues and biochemical signaling. There is a growing demand for engineering dynamic niches characterized by conditions that resemble in vivo organogenesis to generate reproducible and reliable organoids for various applications. Innovative biomaterial-based and advanced engineering-based approaches have been incorporated into conventional organoid culture methods to facilitate the development of organoid research. The recent advances in organoid engineering, including extracellular matrices and genetic modulation, are comprehensively summarized to pinpoint the parameters critical for organ-specific patterning. Moreover, perspective trends in developing tunable organoids in response to exogenous and endogenous cues are discussed for next-generation developmental studies, disease modeling, and therapeutics.

Brain Organoids: Filling the Need for a Human Model of Neurological Disorder

Neurological disorders are among the leading causes of death worldwide, accounting for almost all onsets of dementia in the elderly, and are known to negatively affect motor ability, mental and cognitive performance, as well as overall wellbeing and happiness. Currently, most neurological disorders go untreated due to a lack of viable treatment options. The reason for this lack of options is s poor understanding of the disorders, primarily due to research models that do not translate well into the human in vivo system. Current models for researching neurological disorders, neurodevelopment, and drug interactions in the central nervous system include in vitro monolayer cell cultures, and in vivo animal models. These models have shortcomings when it comes to translating research about disorder pathology, development, and treatment to humans. Brain organoids are three-dimensional (3D) cultures of stem cell-derived neural cells that mimic the development of the in vivo human brain with high degrees of accuracy. Researchers have started developing these miniature brains to model neurodevelopment, and neuropathology. Brain organoids have been used to model a wide range of neurological disorders, including the complex and poorly understood neurodevelopmental and neurodegenerative disorders. In this review, we discuss the brain organoid technology, placing special focus on the different brain organoid models that have been developed, discussing their strengths, weaknesses, and uses in neurological disease modeling.

A spatiotemporal microenvironment model to improve design of a 3D bioreactor for red cell production

Cellular microenvironments provide stimuli including paracrine and autocrine growth factors and physico-chemical cues, which support efficient in vivo cell production unmatched by current in vitro biomanufacturing platforms. While three-dimensional (3D) culture systems aim to recapitulate niche architecture and function of the target tissue/organ, they are limited in accessing spatiotemporal information to evaluate and optimize in situ cell/tissue process development. Herein, a mathematical modelling framework is parameterized by single-cell phenotypic imaging and multiplexed biochemical assays to simulate the non-uniform tissue distribution of nutrients/metabolites and growth factors in cell niche environments. This model is applied to a bone marrow mimicry 3D perfusion bioreactor containing dense stromal and hematopoietic tissue with limited red blood cell (RBC) egress. The model characterized an imbalance between endogenous cytokine production and nutrient starvation within the microenvironmental niches, and recommended increased cell inoculum density and enhanced medium exchange, guiding the development of a miniaturized prototype bioreactor. The second-generation prototype improved the distribution of nutrients and growth factors and supported a 50-fold increase in RBC production efficiency. This image-informed bioprocess modelling framework leverages spatiotemporal niche information to enhance biochemical factor utilization and improve cell manufacturing in 3D systems.

An integrated pipeline for high-throughput screening and profiling of spheroids using simple live image analysis of frame to frame variations

High-throughput imaging methods can be applied to relevant cell culture models, fostering their use in research and translational applications. Improvements in microscopy, computational capabilities and data analysis have enabled high-throughput, high-content approaches from endpoint 2D microscopy images. Nonetheless, trade-offs in acquisition, computation and storage between content and throughput remain, in particular when cells and cell structures are imaged in 3D. Moreover, live 3D phase contrast microscopy images are not often amenable to analysis because of the high level of background noise. Cultures of Human induced pluripotent stem cells (hiPSC) offer unprecedented scope to profile and screen conditions affecting cell fate decisions, self-organisation and early embryonic development. However, quantifying changes in the morphology or function of cell structures derived from hiPSCs over time presents significant challenges. Here, we report a novel method based on the analysis of live phase contrast microscopy images of hiPSC spheroids. We compare self-renewing versus differentiating media conditions, which give rise to spheroids with distinct morphologies; round versus branched, respectively. These cell structures are segmented from 2D projections and analysed based on frame-to-frame variations. Importantly, a tailored convolutional neural network is trained and applied to predict culture conditions from time-frame images. We compare our results with more classic and involved endpoint 3D confocal microscopy and propose that such approaches can complement spheroid-based assays developed for the purpose of screening and profiling. This workflow can be realistically implemented in laboratories using imaging-based high-throughput methods for regenerative medicine and drug discovery.

Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis

Lineage tracing is a powerful tool in developmental biology to interrogate the evolution of tissue formation, but the dense, three-dimensional nature of tissue limits the assembly of individual cell trajectories into complete reconstructions of development. Human induced pluripotent stem cells (hiPSCs) can recapitulate aspects of developmental processes, providing an in vitro platform to assess the dynamic collective behaviors directing tissue morphogenesis. Here, we trained an ensemble of neural networks to track individual hiPSCs in time-lapse microscopy, generating longitudinal measures of cell and cellular neighborhood properties on timescales from minutes to days. Our analysis reveals that, while individual cell parameters are not strongly affected by pluripotency maintenance conditions or morphogenic cues, regional changes in cell behavior predict cell fate and colony organization. By generating complete multicellular reconstructions of hiPSC behavior, our tracking pipeline enables fine-grained understanding of morphogenesis by elucidating the role of regional behavior in early tissue formation.

Opportunities and challenges with stem cell-based embryo models

Stem cell-based embryo models open an unprecedented avenue for modeling embryogenesis, cell lineage differentiation, tissue morphogenesis, and organogenesis in mammalian development. Experimentation on these embryo models can lead to a better understanding of the mechanisms of development and offers opportunities for functional genomic studies of disease-causing mechanisms, identification of therapeutic targets, and preclinical modeling of advanced therapeutics for precision medicine. An immediate challenge is to create embryo models of high fidelity to embryogenesis and organogenesis in vivo, to ensure that the knowledge gleaned is biologically meaningful and clinically relevant.

Development of a kinetic model expressing anomalous phenomena in human induced pluripotent stem cell culture

During culture with feeder cells, deviation from the undifferentiated state of human induced pluripotent stem cells (hiPSCs) occurs at a very low frequency. Anomalous cell migration in central and peripheral regions of hiPSC colonies has been suggested to be the trigger for this phenomenon. To confirm this hypothesis, sequential cell migration prior to deviation must be demonstrated. This has been difficult using in vitro methods. We therefore developed a kinetic model with a proposed definition of anomalous cell migration as continuous relatively fast or slow cell migration. The developed model was validated via in silico reproduction of deviation phenomenon observed in vitro, such as the positions of deviated cells in a colony and the frequency of deviation in culture. This model suggests that anomalous cell migration-driven hiPSC deviation can be explained by two factors: a mechanical stimulus, represented by cell migration, and duration of the mechanical stimulus. The factor "duration of mechanical stimulus" sets our model apart from others, and helps to realize the ultra-rare trigger (approximately 10^-5) of deviation from the undifferentiated state in hiPSC culture.

Predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model

The mechanisms of pattern formation during embryonic development remain poorly understood. Embryonic stem cells in culture self-organise to form spatial patterns of gene expression upon geometrical confinement indicating that patterning is an emergent phenomenon that results from the many interactions between the cells. Here, we applied an agent-based modelling approach in order to identify plausible biological rules acting at the meso-scale within stem cell collectives that may explain spontaneous patterning. We tested different models involving differential motile behaviours with or without biases due to neighbour interactions. We introduced a new metric, termed stem cell aggregate pattern distance (SCAPD) to probabilistically assess the fitness of our models with empirical data. The best of our models improves fitness by 70% and 77% over the random models for a discoidal or an ellipsoidal stem cell confinement respectively. Collectively, our findings show that a parsimonious mechanism that involves differential motility is sufficient to explain the spontaneous patterning of the cells upon confinement. Our work also defines a region of the parameter space that is compatible with patterning. We hope that our approach will be applicable to many biological systems and will contribute towards facilitating progress by reducing the need for extensive and costly experiments.

The Importance of Computational Modeling in Stem Cell Research

The generation of large amounts of omics data is increasingly enabling not only the processing and analysis of large data sets but also the development of computational models in the field of stem cell research. Although computational models have been proposed in recent decades, we believe that the stem cell community is not fully aware of the potentiality of computational modeling in guiding their experimental research. In this regard, we discuss how single-cell technologies provide the right framework for computational modeling at different scales of biological organization in order to address challenges in the stem cell field and to guide experimentalists in the design of new strategies for stem cell therapies and treatment of congenital disorders.

Exploiting CRISPR Cas9 in Three-Dimensional Stem Cell Cultures to Model Disease

Three-dimensional (3D) cell culture methods have been widely used on a range of cell types, including stem cells to modulate precisely the cellular biophysical and biochemical microenvironment and control various cell signaling cues. As a result, more in vivo-like microenvironments are recapitulated, particularly through the formation of multicellular spheroids and organoids, which may yield more valid mechanisms of disease. Recently, genome-engineering tools such as CRISPR Cas9 have expanded the repertoire of techniques to control gene expression, which complements external signaling cues with intracellular control elements. As a result, the combination of CRISPR Cas9 and 3D cell culture methods enhance our understanding of the molecular mechanisms underpinning several disease phenotypes and may lead to developing new therapeutics that may advance more quickly and effectively into clinical candidates. In addition, using CRISPR Cas9 tools to rescue genes brings us one step closer to its use as a gene therapy tool for various degenerative diseases. Herein, we provide an overview of bridging of CRISPR Cas9 genome editing with 3D spheroid and organoid cell culture to better understand disease progression in both patient and non-patient derived cells, and we address potential remaining gaps that must be overcome to gain widespread use.