Integrated intracellular organization and its variations in human iPS cells.

Understanding how a subset of expressed genes dictates cellular phenotype is a considerable challenge owing to the large numbers of molecules involved, their combinatorics and the plethora of cellular behaviours that they determine1,2. Here we reduced this complexity by focusing on cellular organization-a key readout and driver of cell behaviour3,4-at the level of major cellular structures that represent distinct organelles and functional machines, and generated the WTC-11 hiPSC Single-Cell Image Dataset v1, which contains more than 200,000 live cells in 3D, spanning 25 key cellular structures. The scale and quality of this dataset permitted the creation of a generalizable analysis framework to convert raw image data of cells and their structures into dimensionally reduced, quantitative measurements that can be interpreted by humans, and to facilitate data exploration. This framework embraces the vast cell-to-cell variability that is observed within a normal population, facilitates the integration of cell-by-cell structural data and allows quantitative analyses of distinct, separable aspects of organization within and across different cell populations. We found that the integrated intracellular organization of interphase cells was robust to the wide range of variation in cell shape in the population; that the average locations of some structures became polarized in cells at the edges of colonies while maintaining the 'wiring' of their interactions with other structures; and that, by contrast, changes in the location of structures during early mitotic reorganization were accompanied by changes in their wiring.

Building the next generation of virtual cells to understand cellular biology.

Cell science has made significant progress by focusing on understanding individual cellular processes through reductionist approaches. However, the sheer volume of knowledge collected presents challenges in integrating this information across different scales of space and time to comprehend cellular behaviors, as well as making the data and methods more accessible for the community to tackle complex biological questions. This perspective proposes the creation of next-generation virtual cells, which are dynamic 3D models that integrate information from diverse sources, including simulations, biophysical models, image-based models, and evidence-based knowledge graphs. These virtual cells would provide statistically accurate and holistic views of real cells, bridging the gap between theoretical concepts and experimental data, and facilitating productive new collaborations among researchers across related fields.

PhysiCell Studio: a graphical tool to make agent-based modeling more accessible.

Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. In the best case, the model will be defined using some format specification - a markup language - that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open-source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a GUI that allows editing the XML model definition, including the creation and deletion of fundamental objects: cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years, which has led to many improvements. There is both a desktop and cloud version. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open-source software and contributions from the community are encouraged.

The Arabidopsis thaliana Myo-inositol 1-phosphate synthase1 gene is required for Myo-inositol synthesis and suppression of cell death.

l-myo-inositol 1-phosphate synthase (MIPS; EC 5.5.1.4) catalyzes the rate-limiting step in the synthesis of myo-inositol, a critical compound in the cell. Plants contain multiple MIPS genes, which encode highly similar enzymes. We characterized the expression patterns of the three MIPS genes in Arabidopsis thaliana and found that MIPS1 is expressed in most cell types and developmental stages, while MIPS2 and MIPS3 are mainly restricted to vascular or related tissues. MIPS1, but not MIPS2 or MIPS3, is required for seed development, for physiological responses to salt and abscisic acid, and to suppress cell death. Specifically, a loss in MIPS1 resulted in smaller plants with curly leaves and spontaneous production of lesions. The mips1 mutants have lower myo-inositol, ascorbic acid, and phosphatidylinositol levels, while basal levels of inositol (1,4,5)P(3) are not altered in mips1 mutants. Furthermore, mips1 mutants exhibited elevated levels of ceramides, sphingolipid precursors associated with cell death, and were complemented by a MIPS1-green fluorescent protein (GFP) fusion construct. MIPS1-, MIPS2-, and MIPS3-GFP each localized to the cytoplasm. Thus, MIPS1 has a significant impact on myo-inositol levels that is critical for maintaining levels of ascorbic acid, phosphatidylinositol, and ceramides that regulate growth, development, and cell death.

PhysiCell Studio: a graphical tool to make agent-based modeling more accessible.

Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. Hopefully the model will be defined using some format specification, e.g., a markup language, that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a graphical user interface that allows editing the XML model definition, including the creation and deletion of fundamental objects, e.g., cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years which has led to many improvements. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open source software and contributions from the community are encouraged.

The dawn of interoperating spatial models in cell biology.

Spatial simulations are becoming an increasingly ubiquitous component in the cycle of discovery, experimentation, and communication across the sciences. In cell biology, many researchers share a vision of developing multiscale models that recapitulate observable behaviors spanning from atoms to cells to tissues. For this dream to become a reality, however, simulation technologies must provide a means for integration and interoperability as they advance. Already, the field has developed numerous methods that span scales of length, time, and complexity to create an extensive body of effective simulation approaches, and although these approaches rarely interoperate, they collectively cover a large spectrum of knowledge that future models may handle in a more unified manner. Here, we discuss the importance of making the data, workflows, and outputs of spatial simulations shareable and interoperable; and how democratization could encourage diverse biologists to participate more easily in developing models to advance our understanding of biological systems.

Visualization Multi-Pipeline for Communicating Biology.

We propose a system to facilitate biology communication by developing a pipeline to support the instructional visualization of heterogeneous biological data on heterogeneous user-devices. Discoveries and concepts in biology are typically summarized with illustrations assembled manually from the interpretation and application of heterogenous data. The creation of such illustrations is time consuming, which makes it incompatible with frequent updates to the measured data as new discoveries are made. Illustrations are typically non-interactive, and when an illustration is updated, it still has to reach the user. Our system is designed to overcome these three obstacles. It supports the integration of heterogeneous datasets, reflecting the knowledge that is gained from different data sources in biology. After pre-processing the datasets, the system transforms them into visual representations as inspired by scientific illustrations. As opposed to traditional scientific illustration these representations are generated in real-time - they are interactive. The code generating the visualizations can be embedded in various software environments. To demonstrate this, we implemented both a desktop application and a remote-rendering server in which the pipeline is embedded. The remote-rendering server supports multi-threaded rendering and it is able to handle multiple users simultaneously. This scalability to different hardware environments, including multi-GPU setups, makes our system useful for efficient public dissemination of biological discoveries.

Targeted development of registries of biological parts.

BACKGROUND: The design and construction of novel biological systems by combining basic building blocks represents a dominant paradigm in synthetic biology. Creating and maintaining a database of these building blocks is a way to streamline the fabrication of complex constructs. The Registry of Standard Biological Parts (Registry) is the most advanced implementation of this idea. METHODS/PRINCIPAL FINDINGS: By analyzing inclusion relationships between the sequences of the Registry entries, we build a network that can be related to the Registry abstraction hierarchy. The distribution of entry reuse and complexity was extracted from this network. The collection of clones associated with the database entries was also analyzed. The plasmid inserts were amplified and sequenced. The sequences of 162 inserts could be confirmed experimentally but unexpected discrepancies have also been identified. CONCLUSIONS/SIGNIFICANCE: Organizational guidelines are proposed to help design and manage this new type of scientific resources. In particular, it appears necessary to compare the cost of ensuring the integrity of database entries and associated biological samples with their value to the users. The initial strategy that permits including any combination of parts irrespective of its potential value leads to an exponential and economically unsustainable growth that may be detrimental to the quality and long-term value of the resource to its users.