scholarly journals A Review of Cell-Based Computational Modeling in Cancer Biology

2019 ◽  
pp. 1-13 ◽  
Author(s):  
John Metzcar ◽  
Yafei Wang ◽  
Randy Heiland ◽  
Paul Macklin
Keyword(s):  
Stem Cells ◽  
Computational Modeling ◽  
Single Cell ◽  
Stromal Cells ◽  
Cancer Biology ◽  
Large Scale ◽  
Computational Models ◽  
Three Dimensional ◽  
Discrete Models ◽  
Simulation Studies

Cancer biology involves complex, dynamic interactions between cancer cells and their tissue microenvironments. Single-cell effects are critical drivers of clinical progression. Chemical and mechanical communication between tumor and stromal cells can co-opt normal physiologic processes to promote growth and invasion. Cancer cell heterogeneity increases cancer’s ability to test strategies to adapt to microenvironmental stresses. Hypoxia and treatment can select for cancer stem cells and drive invasion and resistance. Cell-based computational models (also known as discrete models, agent-based models, or individual-based models) simulate individual cells as they interact in virtual tissues, which allows us to explore how single-cell behaviors lead to the dynamics we observe and work to control in cancer systems. In this review, we introduce the broad range of techniques available for cell-based computational modeling. The approaches can range from highly detailed models of just a few cells and their morphologies to millions of simpler cells in three-dimensional tissues. Modeling individual cells allows us to directly translate biologic observations into simulation rules. In many cases, individual cell agents include molecular-scale models. Most models also simulate the transport of oxygen, drugs, and growth factors, which allow us to link cancer development to microenvironmental conditions. We illustrate these methods with examples drawn from cancer hypoxia, angiogenesis, invasion, stem cells, and immunosurveillance. An ecosystem of interoperable cell-based simulation tools is emerging at a time when cloud computing resources make software easier to access and supercomputing resources make large-scale simulation studies possible. As the field develops, we anticipate that high-throughput simulation studies will allow us to rapidly explore the space of biologic possibilities, prescreen new therapeutic strategies, and even re-engineer tumor and stromal cells to bring cancer systems under control.

In vitro Three Dimensional Scaffold-free Construct of Human Adipose-derived Stem Cells in Coculture with Endothelial Cells and Fibroblasts

2017 ◽  
Vol 68 (6) ◽  
pp. 1341-1344
Author(s):  
Grigore Berea ◽  
Gheorghe Gh. Balan ◽  
Vasile Sandru ◽  
Paul Dan Sirbu
Keyword(s):  
Tissue Engineering ◽  
Stem Cells ◽  
Endothelial Cells ◽  
Single Cell ◽  
Cell Line ◽  
Bone Repair ◽  
Umbilical Vein ◽  
Human Fibroblasts ◽  
Three Dimensional ◽  
Adipose Derived Stem Cells

Complex interactions between stem cells, vascular cells and fibroblasts represent the substrate of building microenvironment-embedded 3D structures that can be grafted or added to bone substitute scaffolds in tissue engineering or clinical bone repair. Human Adipose-derived Stem Cells (hASCs), human umbilical vein endothelial cells (HUVECs) and normal dermal human fibroblasts (NDHF) can be mixed together in three dimensional scaffold free constructs and their behaviour will emphasize their potential use as seeding points in bone tissue engineering. Various combinations of the aforementioned cell lines were compared to single cell line culture in terms of size, viability and cell proliferation. At 5 weeks, viability dropped for single cell line spheroids while addition of NDHF to hASC maintained the viability at the same level at 5 weeks Fibroblasts addition to the 3D construct of stem cells and endothelial cells improves viability and reduces proliferation as a marker of cell differentiation toward osteogenic line.

scholarly journals A single-cell Raman-based platform to identify developmental stages of human pluripotent stem cell-derived neurons

2020 ◽  
Vol 117 (31) ◽  
pp. 18412-18423 ◽  
Author(s):  
Chia-Chen Hsu ◽  
Jiabao Xu ◽  
Bas Brinkhof ◽  
Hui Wang ◽  
Zhanfeng Cui ◽  
...  
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Single Cell ◽  
Raman Spectra ◽  
Large Scale ◽  
Developmental Stages ◽  
Single Cells ◽  
Classification Model ◽  
Label Free ◽  
Machine Learning Classification

Stem cells with the capability to self-renew and differentiate into multiple cell derivatives provide platforms for drug screening and promising treatment options for a wide variety of neural diseases. Nevertheless, clinical applications of stem cells have been hindered partly owing to a lack of standardized techniques to characterize cell molecular profiles noninvasively and comprehensively. Here, we demonstrate that a label-free and noninvasive single-cell Raman microspectroscopy (SCRM) platform was able to identify neural cell lineages derived from clinically relevant human induced pluripotent stem cells (hiPSCs). By analyzing the intrinsic biochemical profiles of single cells at a large scale (8,774 Raman spectra in total), iPSCs and iPSC-derived neural cells can be distinguished by their intrinsic phenotypic Raman spectra. We identified a Raman biomarker from glycogen to distinguish iPSCs from their neural derivatives, and the result was verified by the conventional glycogen detection assays. Further analysis with a machine learning classification model, utilizing t-distributed stochastic neighbor embedding (t-SNE)-enhanced ensemble stacking, clearly categorized hiPSCs in different developmental stages with 97.5% accuracy. The present study demonstrates the capability of the SCRM-based platform to monitor cell development using high content screening with a noninvasive and label-free approach. This platform as well as our identified biomarker could be extensible to other cell types and can potentially have a high impact on neural stem cell therapy.

scholarly journals Organoid Models for Cancer Research

2019 ◽  
Vol 3 (1) ◽  
pp. 223-234 ◽  
Author(s):  
Hans Clevers ◽  
David A. Tuveson
Keyword(s):  
Cancer Research ◽  
Tumor Microenvironment ◽  
Personalized Medicine ◽  
High Throughput ◽  
Stromal Cells ◽  
Transcriptome Sequencing ◽  
Cancer Biology ◽  
Three Dimensional ◽  
Model Systems ◽  
Liver Cancers

Organoid cultures have emerged as powerful model systems accelerating discoveries in cellular and cancer biology. These three-dimensional cultures are amenable to diverse techniques, including high-throughput genome and transcriptome sequencing, as well as genetic and biochemical perturbation, making these models well suited to answer a variety of questions. Recently, organoids have been generated from diverse human cancers, including breast, colon, pancreas, prostate, bladder, and liver cancers, and studies involving these models are expanding our knowledge of the etiology and characteristics of these malignancies. Co-cultures of cancer organoids with non-neoplastic stromal cells enable investigation of the tumor microenvironment. In addition, recent studies have established that organoids have a place in personalized medicine approaches. Here, we describe the application of organoid technology to cancer discovery and treatment.

Kinetics of hematopoietic stem cells and supportive activities of stromal cells in a three-dimensional bone marrow culture system

2015 ◽  
Vol 33 (5-6) ◽  
pp. 347-355 ◽  
Author(s):  
Tomonori Harada ◽  
Yukio Hirabayashi ◽  
Yoshihiro Hatta ◽  
Isao Tsuboi ◽  
Wilhelm Robert Glomm ◽  
...  
Keyword(s):  
Stem Cells ◽  
Bone Marrow ◽  
Hematopoietic Stem Cells ◽  
Stromal Cells ◽  
Culture System ◽  
Three Dimensional ◽  
Bone Marrow Culture ◽  
Hematopoietic Stem ◽  
Kinetics Of

scholarly journals C-STEM: ENGINEERING NICHE-LIKE MICRO-COMPARTMENTS FOR OPTIMAL AND SCALE-INDEPENDENT EXPANSION OF HUMAN PLURIPOTENT STEM CELLS IN BIOREACTORS

2021 ◽  
Author(s):  
Philippe J.R. Cohen ◽  
Elisa Luquet ◽  
Justine Pletenka ◽  
Andrea Leonard ◽  
Elise Warter ◽  
...  
Keyword(s):  
Stem Cells ◽  
Pluripotent Stem Cells ◽  
Large Scale ◽  
Scale Up ◽  
Three Dimensional ◽  
Culture Conditions ◽  
Human Pluripotent Stem Cells ◽  
Cell Therapies ◽  
Cellular Source

Human pluripotent stem cells (hPSCs) have emerged as the most promising cellular source for cell therapies. To overcome scale up limitations of classical 2D culture systems, suspension cultures have been developed to meet the need of large-scale culture in regenerative medicine. Despite constant improvements, current protocols relying on the generation of micro-carriers or cell aggregates only achieve moderate amplification performance. Here, guided by reports showing that hPSCs can self-organize in vitro into cysts reminiscent of the epiblast stage in embryo development, we developed a physio-mimetic approach for hPSC culture. We engineered stem cell niche microenvironments inside microfluidics-assisted core-shell microcapsules. We demonstrate that lumenized three-dimensional colonies maximize viability and expansion rates while maintaining pluripotency. By optimizing capsule size and culture conditions, we scale-up this method to industrial scale stirred tank bioreactors and achieve an unprecedented hPSC amplification rate of 282-fold in 6.5 days.

Three-Dimensional Modeling of Reduced Material Properties in Ceramics With Added Porosity

2019 ◽  
Author(s):  
Stephanie A. Wimmer ◽  
Virginia G. DeGiorgi ◽  
Edward P. Gorzkowski ◽  
Heonjune Ryou
Keyword(s):  
Mechanical Properties ◽  
Thermal Conductivity ◽  
Finite Element ◽  
Computational Modeling ◽  
Computational Models ◽  
Thermal Protection ◽  
Turbine Blades ◽  
Three Dimensional ◽  
Ceramic Coatings ◽  
Grain Structure

Abstract Manufacturing methods to create ceramic coatings with tailored thermal conductivity are crucial to the development of thermal protection systems for many components including turbine blades in high temperature engines. A designed microstructure of grains, pores, and other defects can reduce the thermal conductivity of the ceramic. However, the same microstructure characteristics can reduce mechanical properties to the point of failure. This work is part of a larger program with the goal of optimizing ceramic coating microstructure for thermal protection while retaining sufficient mechanical strength for the intended application. Processing parameters have been examined to identify methods designed to maintain a nano-sized grain structure of yttria-stabilized zirconia while controlling the added porosity with a specific shape and size. In this paper computational modeling is used to evaluate the effects of porosity on coating performance, both thermal and structural. Coating porosity is incorporated in the computational models by randomly placing empty spaces or defects in the shape of spherical voids, oblate pores, or penny cracks. In addition to computational modeling, prototype coatings are developed in the laboratory with specific porosity. The size and orientation of defects in the computational modeling effort are statistically generated to match experiments. The locations of the defects are totally random. Finite element models are created which include various levels of porosity to calculate effective thermal and mechanical properties. Comparisons are made between three-dimensional finite-element simulations and measured data. The influences of pore size as well as three dimensional computational modeling artifacts are examined.

Computational Modeling to Predict Mechanical Function of Joints: Application to the Lower Leg With Simulation of Two Cadaver Studies

2007 ◽  
Vol 129 (6) ◽  
pp. 811-817 ◽  
Author(s):  
Peter C. Liacouras ◽  
Jennifer S. Wayne
Keyword(s):  
Experimental Data ◽  
Computational Modeling ◽  
Computational Models ◽  
Three Dimensional ◽  
Lower Leg ◽  
Joint Kinematics ◽  
Syndesmotic Injury ◽  
Joint Function ◽  
Calcaneofibular Ligament ◽  
Cadaver Studies

Computational models of musculoskeletal joints and limbs can provide useful information about joint mechanics. Validated models can be used as predictive devices for understanding joint function and serve as clinical tools for predicting the outcome of surgical procedures. A new computational modeling approach was developed for simulating joint kinematics that are dictated by bone/joint anatomy, ligamentous constraints, and applied loading. Three-dimensional computational models of the lower leg were created to illustrate the application of this new approach. Model development began with generating three-dimensional surfaces of each bone from CT images and then importing into the three-dimensional solid modeling software SOLIDWORKS and motion simulation package COSMOSMOTION. Through SOLIDWORKS and COSMOSMOTION, each bone surface file was filled to create a solid object and positioned necessary components added, and simulations executed. Three-dimensional contacts were added to inhibit intersection of the bones during motion. Ligaments were represented as linear springs. Model predictions were then validated by comparison to two different cadaver studies, syndesmotic injury and repair and ankle inversion following ligament transection. The syndesmotic injury model was able to predict tibial rotation, fibular rotation, and anterior/posterior displacement. In the inversion simulation, calcaneofibular ligament extension and angles of inversion compared well. Some experimental data proved harder to simulate accurately, due to certain software limitations and lack of complete experimental data. Other parameters that could not be easily obtained experimentally can be predicted and analyzed by the computational simulations. In the syndesmotic injury study, the force generated in the tibionavicular and calcaneofibular ligaments reduced with the insertion of the staple, indicating how this repair technique changes joint function. After transection of the calcaneofibular ligament in the inversion stability study, a major increase in force was seen in several of the ligaments on the lateral aspect of the foot and ankle, indicating the recruitment of other structures to permit function after injury. Overall, the computational models were able to predict joint kinematics of the lower leg with particular focus on the ankle complex. This same approach can be taken to create models of other limb segments such as the elbow and wrist. Additional parameters can be calculated in the models that are not easily obtained experimentally such as ligament forces, force transmission across joints, and three-dimensional movement of all bones. Muscle activation can be incorporated in the model through the action of applied forces within the software for future studies.

scholarly journals Importance of the origin of mesenchymal (stem) stromal cells in cancer biology: “alliance” or “war” in intercellular signals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Noemi Eiro ◽  
Maria Fraile ◽  
Silvia Fernández-Francos ◽  
Rosario Sánchez ◽  
Luis A. Costa ◽  
...  
Keyword(s):  
Stem Cells ◽  
Stromal Cells ◽  
Cancer Biology ◽  
Mononuclear Cells ◽  
Specific Effect ◽  
Cancer Associated Fibroblasts ◽  
Intercellular Signaling ◽  
Production Safety ◽  
Tumor Tissues ◽  
New Research

AbstractMesenchymal stem cells (MSCs) play a central role in the intercellular signaling within the tumor microenvironment (TME), exchanging signals with cancer cells and tumor stromal cells, such as cancer-associated fibroblasts and inflammatory mononuclear cells. Research attributes both pro-tumor and anti-tumor actions to MSCs; however, evidence indicates that MSCs specific effect on the tumor depends on the source of the MSCs and the type of tumor. There are consistent data proving that MSCs from reproductive tissues, such as the uterus, umbilical cord or placenta, have potent anti-tumor effects and tropism towards tumor tissues. More interestingly, products derived from MSCs, such as secretome or extracellular vesicles, seem to reproduce the effects of their parental cells, showing a potential advantage for clinical treatments by avoiding the drawbacks associated with cell therapy. Given these perspectives, it appears necessary new research to optimize the production, safety and antitumor potency of the products derived from the MSCs suitable for oncological therapies.

scholarly journals Single-Cell Transcriptomic Attributes and Unbiased Computational Modeling for the Prediction of Immunomodulatory Potency of Mesenchymal Stromal Cells

2020 ◽  
Author(s):  
Pallab Pradhan ◽  
Paramita Chatterjee ◽  
Hazel Y. Stevens ◽  
Chad Glen ◽  
Camila Medrano-Trochez ◽  
...  
Keyword(s):  
Single Cell ◽  
Mesenchymal Stromal Cells ◽  
Stromal Cells ◽  
Computational Models ◽  
Functional Characterization ◽  
Symbolic Regression ◽  
Phenotypic Characterization ◽  
Cell Therapies

ABSTRACTMesenchymal stromal cells (MSCs) are currently being tested in numerous clinical trials as potential cell therapies for the treatment of various diseases and due to their potential immunomodulatory, pro-angiogenic, and regenerative properties. However, variabilities in tissue sources, donors, and manufacturing processes and the lack of defined critical quality attributes (CQAs) and clinically relevant mechanism of action (MoA) pose significant challenges to identify MSC cell therapy products with a predictable therapeutic outcome. This also hinders regulatory considerations and broad clinical translation of MSCs. MSC products are often administered to the patient immediately after thawing from cryopreserved vials (out-of-thaw). However, the qualifying quality-control assays are either performed before cryopreservation, or after culturing the post-thaw cells for 24-48 hours (culture-rescued), none of which represent the out-of-thaw product administered to patients. In this study, we performed a broad functional characterization of out-of-thaw and culture-rescue MSCs from bone marrow (BM-MSCs) and cord tissue (CT-MSCs) using macrophage activation and T cell proliferation-based in vitro potency assays and deep phenotypic characterization using single-cell RNA-sequencing. Using this data, we developed unbiased computational models, specifically symbolic regression (SR) and canonical correlation analysis (CCA) models to predict the immunomodulatory potency of MSCs. Overall, our results suggest that manufacturing conditions (OOT vs. CR) have a strong effect on MSC-function on MSC interactions with macrophages and T cells. Furthermore, single-cell RNA-seq analyses of out-of-thaw BM and CT-MSCs indicate a tissue of origin-dependent variability and heterogeneity in the transcriptome profile. Using symbolic regression modeling we identified specific single-cell transcriptomic attributes of MSCs that predict their immunomodulatory potency. In addition, CCA modeling predicted MSC donors with high or low immunomodulatory potency from their transcriptome profiles. Taken together, our results provide a broad framework for identifying predictive CQAs of MSCs that could ultimately help in better understanding of their MOAs and improved reproducibility and manufacturing control of MSCs.

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