1
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Dirk R, Fischer JL, Schardt S, Ankenbrand MJ, Fischer SC. Recognition and reconstruction of cell differentiation patterns with deep learning. PLoS Comput Biol 2023; 19:e1011582. [PMID: 37889897 PMCID: PMC10631711 DOI: 10.1371/journal.pcbi.1011582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 11/08/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023] Open
Abstract
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.
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Affiliation(s)
- Robin Dirk
- Julius-Maximilians-Universität Würzburg, Fakultät für Biologie, Center for Computational and Theoretical Biology, Klara-Oppenheimer-Weg 32, Campus Hubland Nord, Germany
| | - Jonas L. Fischer
- Julius-Maximilians-Universität Würzburg, Fakultät für Biologie, Center for Computational and Theoretical Biology, Klara-Oppenheimer-Weg 32, Campus Hubland Nord, Germany
| | - Simon Schardt
- Julius-Maximilians-Universität Würzburg, Fakultät für Biologie, Center for Computational and Theoretical Biology, Klara-Oppenheimer-Weg 32, Campus Hubland Nord, Germany
| | - Markus J. Ankenbrand
- Julius-Maximilians-Universität Würzburg, Fakultät für Biologie, Center for Computational and Theoretical Biology, Klara-Oppenheimer-Weg 32, Campus Hubland Nord, Germany
| | - Sabine C. Fischer
- Julius-Maximilians-Universität Würzburg, Fakultät für Biologie, Center for Computational and Theoretical Biology, Klara-Oppenheimer-Weg 32, Campus Hubland Nord, Germany
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2
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Schardt S, Fischer SC. Adjusting the range of cell-cell communication enables fine-tuning of cell fate patterns from checkerboard to engulfing. J Math Biol 2023; 87:54. [PMID: 37679573 PMCID: PMC10485129 DOI: 10.1007/s00285-023-01959-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 09/09/2023]
Abstract
During development, spatio-temporal patterns ranging from checkerboard to engulfing occur with precise proportions of the respective cell fates. Key developmental regulators are intracellular transcriptional interactions and intercellular signaling. We present an analytically tractable mathematical model based on signaling that reliably generates different cell type patterns with specified proportions. Employing statistical mechanics, We derived a cell fate decision model for two cell types. A detailed steady state analysis on the resulting dynamical system yielded necessary conditions to generate spatially heterogeneous patterns. This allows the cell type proportions to be controlled by a single model parameter. Cell-cell communication is realized by local and global signaling mechanisms. These result in different cell type patterns. A nearest neighbor signal yields checkerboard patterns. Increasing the signal dispersion, cell fate clusters and an engulfing pattern can be generated. Altogether, the presented model allows us to reliably generate heterogeneous cell type patterns of different kinds as well as desired proportions.
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Affiliation(s)
- Simon Schardt
- Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany
| | - Sabine C. Fischer
- Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany
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3
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Cable J, Arlotta P, Parker KK, Hughes AJ, Goodwin K, Mummery CL, Kamm RD, Engle SJ, Tagle DA, Boj SF, Stanton AE, Morishita Y, Kemp ML, Norfleet DA, May EE, Lu A, Bashir R, Feinberg AW, Hull SM, Gonzalez AL, Blatchley MR, Montserrat Pulido N, Morizane R, McDevitt TC, Mishra D, Mulero-Russe A. Engineering multicellular living systems-a Keystone Symposia report. Ann N Y Acad Sci 2022; 1518:183-195. [PMID: 36177947 PMCID: PMC9771928 DOI: 10.1111/nyas.14896] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The ability to engineer complex multicellular systems has enormous potential to inform our understanding of biological processes and disease and alter the drug development process. Engineering living systems to emulate natural processes or to incorporate new functions relies on a detailed understanding of the biochemical, mechanical, and other cues between cells and between cells and their environment that result in the coordinated action of multicellular systems. On April 3-6, 2022, experts in the field met at the Keystone symposium "Engineering Multicellular Living Systems" to discuss recent advances in understanding how cells cooperate within a multicellular system, as well as recent efforts to engineer systems like organ-on-a-chip models, biological robots, and organoids. Given the similarities and common themes, this meeting was held in conjunction with the symposium "Organoids as Tools for Fundamental Discovery and Translation".
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Affiliation(s)
| | - Paola Arlotta
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Kevin Kit Parker
- Wyss Institute for Biologically Inspired Engineering and John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
- Harvard Stem Cell Institute, Harvard University, Cambridge, Massachusetts, USA
| | - Alex J Hughes
- Department of Bioengineering, School of Engineering and Applied Science and Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Katharine Goodwin
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA
| | - Christine L Mummery
- Department of Anatomy and Embryology and LUMC hiPSC Hotel, Leiden University Medical Center, Leiden, the Netherlands
| | - Roger D Kamm
- Department of Mechanical Engineering and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Sandra J Engle
- Translational Biology, Biogen, Cambridge, Massachusetts, USA
| | - Danilo A Tagle
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA
| | - Sylvia F Boj
- Hubrecht Organoid Technology (HUB), Utrecht, the Netherlands
| | - Alice E Stanton
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Yoshihiro Morishita
- Laboratory for Developmental Morphogeometry, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
- Precursory Research for Embryonic Science and Technology (PRESTO) Program, Japan Science and Technology Agency, Kawaguchi, Japan
| | - Melissa L Kemp
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
| | - Dennis A Norfleet
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
| | - Elebeoba E May
- Department of Biomedical Engineering and HEALTH Research Institute, University of Houston, Houston, Texas, USA
- Wisconsin Institute of Discovery and Department of Medical Microbiology & Immunology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Aric Lu
- Wyss Institute for Biologically Inspired Engineering and John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
- Draper Laboratory, Biological Engineering Division, Cambridge, Massachusetts, USA
| | - Rashid Bashir
- Beckman Institute for Advanced Science and Technology, Urbana, Illinois, USA
- Holonyak Micro & Nanotechnology Laboratory, Department of Electrical and Computer Engineering and Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Adam W Feinberg
- Department of Biomedical Engineering and Department of Materials Science & Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Sarah M Hull
- Department of Chemical Engineering, Stanford University, Stanford, California, USA
| | - Anjelica L Gonzalez
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
| | - Michael R Blatchley
- BioFrontiers Institute and Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado, USA
| | | | - Ryuji Morizane
- Nephrology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Todd C McDevitt
- The Gladstone Institutes and Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA
| | - Deepak Mishra
- Department of Biological Engineering, Synthetic Biology Center, Cambridge, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Adriana Mulero-Russe
- Parker H. Petit Institute for Bioengineering and Bioscience and School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
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4
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Cervera J, Ramirez P, Levin M, Mafe S. Community effects allow bioelectrical reprogramming of cell membrane potentials in multicellular aggregates: Model simulations. Phys Rev E 2021; 102:052412. [PMID: 33327213 DOI: 10.1103/physreve.102.052412] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 11/04/2020] [Indexed: 12/11/2022]
Abstract
Bioelectrical patterns are established by spatiotemporal correlations of cell membrane potentials at the multicellular level, being crucial to development, regeneration, and tumorigenesis. We have conducted multicellular simulations on bioelectrical community effects and intercellular coupling in multicellular aggregates. The simulations aim at establishing under which conditions a local heterogeneity consisting of a small patch of cells can be stabilized against a large aggregate of surrounding identical cells which are in a different bioelectrical state. In this way, instructive bioelectrical information can be persistently encoded in spatiotemporal patterns of separated domains with different cell polarization states. The multicellular community effects obtained are regulated both at the single-cell and intercellular levels, and emerge from a delicate balance between the degrees of intercellular coupling in: (i) the small patch, (ii) the surrounding bulk, and (iii) the interface that separates these two regions. The model is experimentally motivated and consists of two generic voltage-gated ion channels that attempt to establish the depolarized and polarized cell states together with coupling conductances whose individual and intercellular different states permit a dynamic multicellular connectivity. The simulations suggest that community effects may allow the reprogramming of single-cell bioelectrical states, in agreement with recent experimental data. A better understanding of the resulting electrical regionalization can assist the electroceutical correction of abnormally depolarized regions initiated in the bulk of normal tissues as well as suggest new biophysical mechanisms for the establishment of target patterns in multicellular engineering.
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Affiliation(s)
- Javier Cervera
- Departamento Termodinàmica, Universitat de València, E-46100 Burjassot, Spain
| | - Patricio Ramirez
- Departamento Física Aplicada, Universidad Politécnica de Valencia, E-46022 Valencia, Spain
| | - Michael Levin
- Department of Biology and Allen Discovery Center at Tufts University, Medford, Massachusetts 02155-4243, USA
| | - Salvador Mafe
- Departamento Termodinàmica, Universitat de València, E-46100 Burjassot, Spain
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5
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Joy DA, Libby ARG, McDevitt TC. Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis. Stem Cell Reports 2021; 16:1317-1330. [PMID: 33979602 PMCID: PMC8185472 DOI: 10.1016/j.stemcr.2021.04.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 04/14/2021] [Accepted: 04/14/2021] [Indexed: 01/09/2023] Open
Abstract
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.
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Affiliation(s)
- David A Joy
- UC Berkeley-UC San Francisco Graduate Program in Bioengineering, San Francisco, CA, USA; Gladstone Institutes, San Francisco, CA, USA
| | - Ashley R G Libby
- Gladstone Institutes, San Francisco, CA, USA; Developmental and Stem Cell Biology PhD Program, University of California, San Francisco, San Francisco, CA, USA
| | - Todd C McDevitt
- Gladstone Institutes, San Francisco, CA, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
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6
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Norfleet DA, Park E, Kemp ML. Computational modeling of organoid development. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2020. [DOI: 10.1016/j.cobme.2019.12.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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7
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Libby ARG, Briers D, Haghighi I, Joy DA, Conklin BR, Belta C, McDevitt TC. Automated Design of Pluripotent Stem Cell Self-Organization. Cell Syst 2019; 9:483-495.e10. [PMID: 31759947 PMCID: PMC7089762 DOI: 10.1016/j.cels.2019.10.008] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 07/17/2019] [Accepted: 10/23/2019] [Indexed: 11/20/2022]
Abstract
Human pluripotent stem cells (hPSCs) have the intrinsic ability to self-organize into complex multicellular organoids that recapitulate many aspects of tissue development. However, robustly directing morphogenesis of hPSC-derived organoids requires novel approaches to accurately control self-directed pattern formation. Here, we combined genetic engineering with computational modeling, machine learning, and mathematical pattern optimization to create a data-driven approach to control hPSC self-organization by knock down of genes previously shown to affect stem cell colony organization, CDH1 and ROCK1. Computational replication of the in vitro system in silico using an extended cellular Potts model enabled machine learning-driven optimization of parameters that yielded emergence of desired patterns. Furthermore, in vitro the predicted experimental parameters quantitatively recapitulated the in silico patterns. These results demonstrate that morphogenic dynamics can be accurately predicted through model-driven exploration of hPSC behaviors via machine learning, thereby enabling spatial control of multicellular patterning to engineer human organoids and tissues. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.
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Affiliation(s)
- Ashley R G Libby
- Developmental and Stem Cell Biology PhD Program, University of California, San Francisco, San Francisco, CA, USA; Gladstone Institute of Cardiovascular Disease, Gladstone Institutes, San Francisco, CA, USA
| | | | - Iman Haghighi
- Systems Engineering Department at Boston University, Boston, MA, USA
| | - David A Joy
- Gladstone Institute of Cardiovascular Disease, Gladstone Institutes, San Francisco, CA, USA; UC Berkeley-UC San Francisco Bioengineering Graduate Program, San Francisco, CA, USA
| | - Bruce R Conklin
- Gladstone Institute of Cardiovascular Disease, Gladstone Institutes, San Francisco, CA, USA; Departments of Medicine, Pharmacology, and Ophthalmology, University of California, San Francisco, San Francisco, CA, USA
| | - Calin Belta
- Systems Engineering Department at Boston University, Boston, MA, USA.
| | - Todd C McDevitt
- Gladstone Institute of Cardiovascular Disease, Gladstone Institutes, San Francisco, CA, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
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8
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Abstract
The complexity of morphogenesis poses a fundamental challenge to understanding the mechanisms governing the formation of biological patterns and structures. Over the past century, numerous processes have been identified as critically contributing to morphogenetic events, but the interplay between the various components and aspects of pattern formation have been much harder to grasp. The combination of traditional biology with mathematical and computational methods has had a profound effect on our current understanding of morphogenesis and led to significant insights and advancements in the field. In particular, the theoretical concepts of reaction–diffusion systems and positional information, proposed by Alan Turing and Lewis Wolpert, respectively, dramatically influenced our general view of morphogenesis, although typically in isolation from one another. In recent years, agent-based modeling has been emerging as a consolidation and implementation of the two theories within a single framework. Agent-based models (ABMs) are unique in their ability to integrate combinations of heterogeneous processes and investigate their respective dynamics, especially in the context of spatial phenomena. In this review, we highlight the benefits and technical challenges associated with ABMs as tools for examining morphogenetic events. These models display unparalleled flexibility for studying various morphogenetic phenomena at multiple levels and have the important advantage of informing future experimental work, including the targeted engineering of tissues and organs.
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Mathew B, Muñoz-Descalzo S, Corujo-Simon E, Schröter C, Stelzer EHK, Fischer SC. Mouse ICM Organoids Reveal Three-Dimensional Cell Fate Clustering. Biophys J 2018; 116:127-141. [PMID: 30514631 PMCID: PMC6341222 DOI: 10.1016/j.bpj.2018.11.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 10/26/2018] [Accepted: 11/09/2018] [Indexed: 02/05/2023] Open
Abstract
During mammalian preimplantation, cells of the inner cell mass (ICM) adopt either an embryonic or an extraembryonic fate. This process is tightly regulated in space and time and has been studied previously in mouse embryos and embryonic stem cell models. Current research suggests that cell fates are arranged in a salt-and-pepper pattern of random cell positioning or a spatially alternating pattern. However, the details of the three-dimensional patterns of cell fate specification have not been investigated in the embryo nor in in vitro systems. We developed ICM organoids as a, to our knowledge, novel three-dimensional in vitro stem cell system to model mechanisms of fate decisions that occur in the ICM. ICM organoids show similarities to the in vivo system that arise regardless of the differences in geometry and total cell number. Inspecting ICM organoids and mouse embryos, we describe a so far unknown local clustering of cells with identical fates in both systems. These findings are based on the three-dimensional quantitative analysis of spatiotemporal patterns of NANOG and GATA6 expression in combination with computational rule-based modeling. The pattern identified by our analysis is distinct from the current view of a salt-and-pepper pattern. Our investigation of the spatial distributions both in vivo and in vitro dissects the contributions of the different parts of the embryo to cell fate specifications. In perspective, our combination of quantitative in vivo and in vitro analyses can be extended to other mammalian organisms and thus creates a powerful approach to study embryogenesis.
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Affiliation(s)
- Biena Mathew
- Physikalische Biologie, Fachbereich Biowissenschaften, Buchmann Institute for Molecular Life Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Silvia Muñoz-Descalzo
- Department of Biology and Biochemistry, University of Bath, Bath, United Kingdom; Instituto Universitario de Investigaciones Biomédicas y Sanitarias, Universidad Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Elena Corujo-Simon
- Instituto Universitario de Investigaciones Biomédicas y Sanitarias, Universidad Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Christian Schröter
- Department of Systemic Cell Biology, Max-Planck-Institute of Molecular Physiology, Dortmund, Germany
| | - Ernst H K Stelzer
- Physikalische Biologie, Fachbereich Biowissenschaften, Buchmann Institute for Molecular Life Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Sabine C Fischer
- Physikalische Biologie, Fachbereich Biowissenschaften, Buchmann Institute for Molecular Life Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany.
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10
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Dynamic intercellular transport modulates the spatial patterning of differentiation during early neural commitment. Nat Commun 2018; 9:4111. [PMID: 30291250 PMCID: PMC6173785 DOI: 10.1038/s41467-018-06693-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 09/17/2018] [Indexed: 01/01/2023] Open
Abstract
The initiation of heterogeneity within a population of phenotypically identical progenitors is a critical event for the onset of morphogenesis and differentiation patterning. Gap junction communication within multicellular systems produces complex networks of intercellular connectivity that result in heterogeneous distributions of intracellular signaling molecules. In this study, we investigate emergent systems-level behavior of the intercellular network within embryonic stem cell (ESC) populations and corresponding spatial organization during early neural differentiation. An agent-based model incorporates experimentally-determined parameters to yield complex transport networks for delivery of pro-differentiation cues between neighboring cells, reproducing the morphogenic trajectories during retinoic acid-accelerated mouse ESC differentiation. Furthermore, the model correctly predicts the delayed differentiation and preserved spatial features of the morphogenic trajectory that occurs in response to intercellular perturbation. These findings suggest an integral role of gap junction communication in the temporal coordination of emergent patterning during early differentiation and neural commitment of pluripotent stem cells.
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11
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Kawade K, Tsukaya H. Probing the stochastic property of endoreduplication in cell size determination of Arabidopsis thaliana leaf epidermal tissue. PLoS One 2017; 12:e0185050. [PMID: 28926847 PMCID: PMC5605191 DOI: 10.1371/journal.pone.0185050] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 09/04/2017] [Indexed: 12/21/2022] Open
Abstract
Cell size distribution is highly reproducible, whereas the size of individual cells often varies greatly within a tissue. This is obvious in a population of Arabidopsis thaliana leaf epidermal cells, which ranged from 1,000 to 10,000 μm2 in size. Endoreduplication is a specialized cell cycle in which nuclear genome size (ploidy) is doubled in the absence of cell division. Although epidermal cells require endoreduplication to enhance cellular expansion, the issue of whether this mechanism is sufficient for explaining cell size distribution remains unclear due to a lack of quantitative understanding linking the occurrence of endoreduplication with cell size diversity. Here, we addressed this question by quantitatively summarizing ploidy profile and cell size distribution using a simple theoretical framework. We first found that endoreduplication dynamics is a Poisson process through cellular maturation. This finding allowed us to construct a mathematical model to predict the time evolution of a ploidy profile with a single rate constant for endoreduplication occurrence in a given time. We reproduced experimentally measured ploidy profile in both wild-type leaf tissue and endoreduplication-related mutants with this analytical solution, further demonstrating the probabilistic property of endoreduplication. We next extended the mathematical model by incorporating the element that cell size is determined according to ploidy level to examine cell size distribution. This analysis revealed that cell size is exponentially enlarged 1.5 times every endoreduplication round. Because this theoretical simulation successfully recapitulated experimentally observed cell size distributions, we concluded that Poissonian endoreduplication dynamics and exponential size-boosting are the sources of the broad cell size distribution in epidermal tissue. More generally, this study contributes to a quantitative understanding whereby stochastic dynamics generate steady-state biological heterogeneity.
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Affiliation(s)
- Kensuke Kawade
- Okazaki Institute for Integrative Bioscience, Okazaki, Aichi, Japan
- National Institute for Basic Biology, Okazaki, Aichi, Japan
- Department of Basic Biology, School of Life Science, Graduate University for Advanced Studies (SOKENDAI), Okazaki, Aichi, Japan
- * E-mail:
| | - Hirokazu Tsukaya
- Okazaki Institute for Integrative Bioscience, Okazaki, Aichi, Japan
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo, Japan
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12
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Xie L, Smith GR, Schwartz R. Derivative-Free Optimization of Rate Parameters of Capsid Assembly Models from Bulk in Vitro Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:844-855. [PMID: 27168601 PMCID: PMC5581941 DOI: 10.1109/tcbb.2016.2563421] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The assembly of virus capsids proceeds by a complicated cascade of association and dissociation steps, the great majority of which cannot be directly experimentally observed. This has made capsid assembly a rich field for computational models, but there are substantial obstacles to model inference for such systems. Here, we describe progress on fitting kinetic rate constants defining capsid assembly models to experimental data, a difficult data-fitting problem because of the high computational cost of simulating assembly trajectories, the stochastic noise inherent to the models, and the limited and noisy data available for fitting. We evaluate the merits of data-fitting methods based on derivative-free optimization (DFO) relative to gradient-based methods used in prior work. We further explore the advantages of alternative data sources through simulation of a model of time-resolved mass spectrometry data, a technology for monitoring bulk capsid assembly that can be expected to provide much richer data than previously used static light scattering approaches. The results show that advances in both the data and the algorithms can improve model inference. More informative data sources lead to high-quality fits for all methods, but DFO methods show substantial advantages on less informative data sources that better represent current experimental practice.
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Affiliation(s)
- Lu Xie
- Joint Carnegie Mellon/University of Pittsburgh Ph.D. Program in Computational Biology, Pittsburgh, PA USA and Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA USA 15213
| | - Gregory R. Smith
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA USA 15213
| | - Russell Schwartz
- Department of Biological Sciences and Computational Biology Department, Pittsburgh, PA USA 15213.
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13
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Affiliation(s)
- Alexander A. Spector
- Department
of Biomedical Engineering and ‡Translational Tissue Engineering
Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- Institute for Nanobiotechnology (INBT) and ∥Department of Material Sciences & Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore 21218, Maryland, United States
| | - Warren L. Grayson
- Department
of Biomedical Engineering and ‡Translational Tissue Engineering
Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- Institute for Nanobiotechnology (INBT) and ∥Department of Material Sciences & Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore 21218, Maryland, United States
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14
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Galvanauskas V, Grincas V, Simutis R, Kagawa Y, Kino-oka M. Current state and perspectives in modeling and control of human pluripotent stem cell expansion processes in stirred-tank bioreactors. Biotechnol Prog 2017; 33:355-364. [DOI: 10.1002/btpr.2431] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Revised: 12/10/2016] [Indexed: 01/02/2023]
Affiliation(s)
| | - Vykantas Grincas
- Department of Automation; Kaunas University of Technology; Kaunas Lithuania
| | - Rimvydas Simutis
- Department of Automation; Kaunas University of Technology; Kaunas Lithuania
| | - Yuki Kagawa
- Department of Biotechnology; Osaka University; Osaka Japan
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15
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Pir P, Le Novère N. Mathematical Models of Pluripotent Stem Cells: At the Dawn of Predictive Regenerative Medicine. Methods Mol Biol 2016; 1386:331-50. [PMID: 26677190 DOI: 10.1007/978-1-4939-3283-2_15] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Regenerative medicine, ranging from stem cell therapy to organ regeneration, is promising to revolutionize treatments of diseases and aging. These approaches require a perfect understanding of cell reprogramming and differentiation. Predictive modeling of cellular systems has the potential to provide insights about the dynamics of cellular processes, and guide their control. Moreover in many cases, it provides alternative to experimental tests, difficult to perform for practical or ethical reasons. The variety and accuracy of biological processes represented in mathematical models grew in-line with the discovery of underlying molecular mechanisms. High-throughput data generation led to the development of models based on data analysis, as an alternative to more established modeling based on prior mechanistic knowledge. In this chapter, we give an overview of existing mathematical models of pluripotency and cell fate, to illustrate the variety of methods and questions. We conclude that current approaches are yet to overcome a number of limitations: Most of the computational models have so far focused solely on understanding the regulation of pluripotency, and the differentiation of selected cell lineages. In addition, models generally interrogate only a few biological processes. However, a better understanding of the reprogramming process leading to ESCs and iPSCs is required to improve stem-cell therapies. One also needs to understand the links between signaling, metabolism, regulation of gene expression, and the epigenetics machinery.
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Affiliation(s)
- Pınar Pir
- Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK.
| | - Nicolas Le Novère
- Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK.
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16
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White DE, Sylvester JB, Levario TJ, Lu H, Streelman JT, McDevitt TC, Kemp ML. Quantitative multivariate analysis of dynamic multicellular morphogenic trajectories. Integr Biol (Camb) 2016; 7:825-33. [PMID: 26095427 DOI: 10.1039/c5ib00072f] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Interrogating fundamental cell biology principles that govern tissue morphogenesis is critical to better understanding of developmental biology and engineering novel multicellular systems. Recently, functional micro-tissues derived from pluripotent embryonic stem cell (ESC) aggregates have provided novel platforms for experimental investigation; however elucidating the factors directing emergent spatial phenotypic patterns remains a significant challenge. Computational modelling techniques offer a unique complementary approach to probe mechanisms regulating morphogenic processes and provide a wealth of spatio-temporal data, but quantitative analysis of simulations and comparison to experimental data is extremely difficult. Quantitative descriptions of spatial phenomena across multiple systems and scales would enable unprecedented comparisons of computational simulations with experimental systems, thereby leveraging the inherent power of computational methods to interrogate the mechanisms governing emergent properties of multicellular biology. To address these challenges, we developed a portable pattern recognition pipeline consisting of: the conversion of cellular images into networks, extraction of novel features via network analysis, and generation of morphogenic trajectories. This novel methodology enabled the quantitative description of morphogenic pattern trajectories that could be compared across diverse systems: computational modelling of multicellular structures, differentiation of stem cell aggregates, and gastrulation of cichlid fish. Moreover, this method identified novel spatio-temporal features associated with different stages of embryo gastrulation, and elucidated a complex paracrine mechanism capable of explaining spatiotemporal pattern kinetic differences in ESC aggregates of different sizes.
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Affiliation(s)
- Douglas E White
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
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17
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Rostami MR, Wu J, Tzanakakis ES. Inverse problem analysis of pluripotent stem cell aggregation dynamics in stirred-suspension cultures. J Biotechnol 2015; 208:70-9. [PMID: 26036699 DOI: 10.1016/j.jbiotec.2015.05.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2015] [Revised: 05/12/2015] [Accepted: 05/25/2015] [Indexed: 10/23/2022]
Abstract
The cultivation of stem cells as aggregates in scalable bioreactor cultures is an appealing modality for the large-scale manufacturing of stem cell products. Aggregation phenomena are central to such bioprocesses affecting the viability, proliferation and differentiation trajectory of stem cells but a quantitative framework is currently lacking. A population balance equation (PBE) model was used to describe the temporal evolution of the embryonic stem cell (ESC) cluster size distribution by considering collision-induced aggregation and cell proliferation in a stirred-suspension vessel. For ESC cultures at different agitation rates, the aggregation kernel representing the aggregation dynamics was successfully recovered as a solution of the inverse problem. The rate of change of the average aggregate size was greater at the intermediate rate tested suggesting a trade-off between increased collisions and agitation-induced shear. Results from forward simulation with obtained aggregation kernels were in agreement with transient aggregate size data from experiments. We conclude that the framework presented here can complement mechanistic studies offering insights into relevant stem cell clustering processes. More importantly from a process development standpoint, this strategy can be employed in the design and control of bioreactors for the generation of stem cell derivatives for drug screening, tissue engineering and regenerative medicine.
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Affiliation(s)
| | - Jincheng Wu
- Department of Chemical and Biological Engineering, Tufts University, Medford, MA 02155, USA.
| | - Emmanuel S Tzanakakis
- Department of Chemical and Biological Engineering, Tufts University, Medford, MA 02155, USA; Tufts Clinical and Translational Science Institute, Tufts Medical Center, Boston, MA 02111, USA.
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18
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Herberg M, Roeder I. Computational modelling of embryonic stem-cell fate control. Development 2015; 142:2250-60. [DOI: 10.1242/dev.116343] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The maintenance of pluripotency in embryonic stem cells (ESCs), its loss during lineage specification or its re-induction to generate induced pluripotent stem cells are central topics in stem cell biology. To uncover the molecular basis and the design principles of pluripotency control, a multitude of experimental, but also an increasing number of computational, studies have been published. Here, we consider recent reports that apply computational or mathematical modelling approaches to describe the regulatory processes that underlie cell fate decisions in mouse ESCs. We summarise the principles, the strengths and potentials but also the limitations of different computational strategies.
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Affiliation(s)
- Maria Herberg
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden D-01307, Germany
| | - Ingo Roeder
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden D-01307, Germany
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19
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Norton KA, Popel AS. An agent-based model of cancer stem cell initiated avascular tumour growth and metastasis: the effect of seeding frequency and location. J R Soc Interface 2015; 11:20140640. [PMID: 25185580 DOI: 10.1098/rsif.2014.0640] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
It is very important to understand the onset and growth pattern of breast primary tumours as well as their metastatic dissemination. In most cases, it is the metastatic disease that ultimately kills the patient. There is increasing evidence that cancer stem cells are closely linked to the progression of the metastatic tumour. Here, we investigate stem cell seeding to an avascular tumour site using an agent-based stochastic model of breast cancer metastatic seeding. The model includes several important cellular features such as stem cell symmetric and asymmetric division, migration, cellular quiescence, senescence, apoptosis and cell division cycles. It also includes external features such as stem cell seeding frequency and location. Using this model, we find that cell seeding rate and location are important features for tumour growth. We also define conditions in which the tumour growth exhibits decremented and exponential growth patterns. Overall, we find that seeding, senescence and division limit affect not only the number of stem cells, but also their spatial and temporal distribution.
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Affiliation(s)
- Kerri-Ann Norton
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA Department of Oncology and Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD 21205, USA
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20
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Herberg M, Zerjatke T, de Back W, Glauche I, Roeder I. Image-based quantification and mathematical modeling of spatial heterogeneity in ESC colonies. Cytometry A 2015; 87:481-90. [DOI: 10.1002/cyto.a.22598] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Revised: 09/11/2014] [Accepted: 11/06/2014] [Indexed: 01/29/2023]
Affiliation(s)
- Maria Herberg
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden; Dresden Germany
| | - Thomas Zerjatke
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden; Dresden Germany
| | - Walter de Back
- Center for Information Services and High Performance Computing, Technische Universität Dresden; Dresden Germany
| | - Ingmar Glauche
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden; Dresden Germany
| | - Ingo Roeder
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden; Dresden Germany
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21
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Qi H, Huang G, Han YL, Lin W, Li X, Wang S, Lu TJ, Xu F. In vitro spatially organizing the differentiation in individual multicellular stem cell aggregates. Crit Rev Biotechnol 2014; 36:20-31. [PMID: 25025275 DOI: 10.3109/07388551.2014.922917] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
With significant potential as a robust source to produce specific somatic cells for regenerative medicine, stem cells have attracted increasing attention from both academia and government. In vivo, stem cell differentiation is a process under complicated regulations to precisely build tissue with unique spatial structures. Since multicellular spheroidal aggregates of stem cells, commonly called as embryoid bodies (EBs), are considered to be capable of recapitulating the events in early stage of embryonic development, a variety of methods have been developed to form EBs in vitro for studying differentiation of embryonic stem cells. The regulation of stem cell differentiation is crucial in directing stem cells to build tissue with the correct spatial architecture for specific functions. However, stem cells within the three-dimensional multicellular aggregates undergo differentiation in a less unpredictable and spatially controlled manner in vitro than in vivo. Recently, various microengineering technologies have been developed to manipulate stem cells in vitro in a spatially controlled manner. Herein, we take the spotlight on these technologies and researches that bring us the new potential for manipulation of stem cells for specific purposes.
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Affiliation(s)
- Hao Qi
- a MOE Key laboratory of Biomedical Information Engineering , School of Life Science and Technology, Xi'an Jiaotong University , Xi'an , People's Republic of China .,b Bioinspired Engineering and Biomechanics Center, Xi'an Jiaotong University , Xi'an , People's Republic of China .,c Department of Medical Genome Sciences , Graduate School of Frontier Sciences, University of Tokyo , Kashiwa , Chiba , Japan
| | - Guoyou Huang
- a MOE Key laboratory of Biomedical Information Engineering , School of Life Science and Technology, Xi'an Jiaotong University , Xi'an , People's Republic of China .,b Bioinspired Engineering and Biomechanics Center, Xi'an Jiaotong University , Xi'an , People's Republic of China
| | - Yu Long Han
- a MOE Key laboratory of Biomedical Information Engineering , School of Life Science and Technology, Xi'an Jiaotong University , Xi'an , People's Republic of China .,b Bioinspired Engineering and Biomechanics Center, Xi'an Jiaotong University , Xi'an , People's Republic of China
| | - Wang Lin
- a MOE Key laboratory of Biomedical Information Engineering , School of Life Science and Technology, Xi'an Jiaotong University , Xi'an , People's Republic of China .,b Bioinspired Engineering and Biomechanics Center, Xi'an Jiaotong University , Xi'an , People's Republic of China
| | - Xiujun Li
- d Department of Chemistry , University of Texas at EI Paso , EI Paso , TX , USA , and
| | - Shuqi Wang
- e Brigham Women's Hospital, Harvard Medical School , Boston , MA , USA
| | - Tian Jian Lu
- b Bioinspired Engineering and Biomechanics Center, Xi'an Jiaotong University , Xi'an , People's Republic of China
| | - Feng Xu
- a MOE Key laboratory of Biomedical Information Engineering , School of Life Science and Technology, Xi'an Jiaotong University , Xi'an , People's Republic of China .,b Bioinspired Engineering and Biomechanics Center, Xi'an Jiaotong University , Xi'an , People's Republic of China
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22
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Meng X, Leslie P, Zhang Y, Dong J. Stem cells in a three-dimensional scaffold environment. SPRINGERPLUS 2014; 3:80. [PMID: 24570851 PMCID: PMC3931863 DOI: 10.1186/2193-1801-3-80] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Accepted: 01/31/2014] [Indexed: 02/08/2023]
Abstract
Stem cells have emerged as important players in the generation and maintenance of many tissues. However, the accurate in vitro simulation of the native stem cell niche remains difficult due at least in part to the lack of a comprehensive definition of the critical factors of the stem cell niche based on in vivo models. Three-dimensional (3D) cell culture systems have allowed the development of useful models for investigating stem cell physiology particularly with respect to their ability to sense and generate mechanical force in response to their surrounding environment. We review the use of 3D culture systems for stem cell culture and discuss the relationship between stem cells and 3D growth matrices including the roles of the extracellular matrix, scaffolds, soluble factors, cell-cell interactions and shear stress effects within this environment. We also discuss the potential for novel methods that mimic the native stem cell niche in vitro as well as the current associated challenges.
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Affiliation(s)
- Xuan Meng
- Hospital & Institute of Hepatobiliary Surgery, Chinese PLA General Hospital, Fuxing Road 28, Haidian District, Beijing, 100853 China ; Department of Pharmacology, School of Medicine, University of North Carolina, Chapel Hill, NC 27599-7512 USA ; Hospital & Institute of Hepatobiliary Surgery, Chinese PLA General Hospital, Fuxing Road 28, Haidian District, Beijing, 100853 China
| | - Patrick Leslie
- Hospital & Institute of Hepatobiliary Surgery, Chinese PLA General Hospital, Fuxing Road 28, Haidian District, Beijing, 100853 China ; Department of Pharmacology, School of Medicine, University of North Carolina, Chapel Hill, NC 27599-7512 USA ; Hospital & Institute of Hepatobiliary Surgery, Chinese PLA General Hospital, Fuxing Road 28, Haidian District, Beijing, 100853 China
| | - Yanping Zhang
- Hospital & Institute of Hepatobiliary Surgery, Chinese PLA General Hospital, Fuxing Road 28, Haidian District, Beijing, 100853 China ; Department of Pharmacology, School of Medicine, University of North Carolina, Chapel Hill, NC 27599-7512 USA ; Department of Pharmacology, School of Medicine, University of North Carolina, Chapel Hill, NC 27599-7512 USA
| | - Jiahong Dong
- Hospital & Institute of Hepatobiliary Surgery, Chinese PLA General Hospital, Fuxing Road 28, Haidian District, Beijing, 100853 China
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23
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Wilson JL, Suri S, Singh A, Rivet CA, Lu H, McDevitt TC. Single-cell analysis of embryoid body heterogeneity using microfluidic trapping array. Biomed Microdevices 2014; 16:79-90. [PMID: 24085533 PMCID: PMC3945678 DOI: 10.1007/s10544-013-9807-3] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The differentiation of pluripotent stem cells as embryoid bodies (EBs) remains a common method for inducing differentiation toward many lineages. However, differentiation via EBs typically yields a significant amount of heterogeneity in the cell population, as most cells differentiate simultaneously toward different lineages, while others remain undifferentiated. Moreover, physical parameters, such as the size of EBs, can modulate the heterogeneity of differentiated phenotypes due to the establishment of nutrient and oxygen gradients. One of the challenges in examining the cellular composition of EBs is the lack of analytical methods that are capable of determining the phenotype of all of the individual cells that comprise a single EB. Therefore, the objective of this work was to examine the ability of a microfluidic cell trapping array to analyze the heterogeneity of cells comprising EBs during the course of early differentiation. The heterogeneity of single cell phenotype on the basis of protein expression of the pluripotent transcription factor OCT-4 was examined for populations of EBs and single EBs of different sizes at distinct stages of differentiation. Results from the cell trap device were compared with flow cytometry and whole mount immunostaining. Additionally, single cells from dissociated pooled EBs or individual EBs were examined separately to discern potential differences in the value or variance of expression between the different methods of analysis. Overall, the analytical method described represents a novel approach for evaluating how heterogeneity is manifested in EB cultures and may be used in the future to assess the kinetics and patterns of differentiation in addition to the loss of pluripotency.
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Affiliation(s)
- Jenna L. Wilson
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Shalu Suri
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Ankur Singh
- The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Catherine A. Rivet
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Hang Lu
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- The Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA
| | - Todd C. McDevitt
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- The Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA
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24
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Kinney MA, Hookway TA, Wang Y, McDevitt TC. Engineering three-dimensional stem cell morphogenesis for the development of tissue models and scalable regenerative therapeutics. Ann Biomed Eng 2014; 42:352-67. [PMID: 24297495 PMCID: PMC3939035 DOI: 10.1007/s10439-013-0953-9] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2013] [Accepted: 11/21/2013] [Indexed: 12/11/2022]
Abstract
The physiochemical stem cell microenvironment regulates the delicate balance between self-renewal and differentiation. The three-dimensional assembly of stem cells facilitates cellular interactions that promote morphogenesis, analogous to the multicellular, heterotypic tissue organization that accompanies embryogenesis. Therefore, expansion and differentiation of stem cells as multicellular aggregates provides a controlled platform for studying the biological and engineering principles underlying spatiotemporal morphogenesis and tissue patterning. Moreover, three-dimensional stem cell cultures are amenable to translational screening applications and therapies, which underscores the broad utility of scalable suspension cultures across laboratory and clinical scales. In this review, we discuss stem cell morphogenesis in the context of fundamental biophysical principles, including the three-dimensional modulation of adhesions, mechanics, and molecular transport and highlight the opportunities to employ stem cell spheroids for tissue modeling, bioprocessing, and regenerative therapies.
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Affiliation(s)
- Melissa A. Kinney
- The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology/Emory University, Atlanta, GA, USA
| | - Tracy A. Hookway
- The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology/Emory University, Atlanta, GA, USA
| | - Yun Wang
- The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology/Emory University, Atlanta, GA, USA
| | - Todd C. McDevitt
- The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology/Emory University, Atlanta, GA, USA
- The Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA
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25
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Addington CP, Pauken CM, Caplan MR, Stabenfeldt SE. The role of SDF-1α-ECM crosstalk in determining neural stem cell fate. Biomaterials 2014; 35:3263-72. [PMID: 24438907 DOI: 10.1016/j.biomaterials.2013.12.102] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2013] [Accepted: 12/28/2013] [Indexed: 02/04/2023]
Abstract
The consequences of central nervous system injury are far-reaching and debilitating and, while an endogenous repair response to neural injury has been observed in recent years, the mechanisms behind this response remain unclear. Neural progenitor/stem cell (NPSC) migration to the site of injury from the neural stem cell niches (e.g. subventricular zone and hippocampus) has been observed to be vasophilic in nature. While the chemotactic stimuli directing NPSC homing to injury is not well established, it is thought to be due in part to an increasing gradient of chemotactic cytokines, such as stromal cell-derived factor 1α (SDF-1α). Based on these recent findings, we hypothesize that critical crosstalk between SDF-1α and the extracellular matrix (ECM) drives injury-induced NPSC behavior. In this study, we investigated the effect of SDF-1α and ECM substrates (Matrigel, laminin, and vitronectin) on the migration, differentiation, and proliferation of NPSCs in vitro using standard assays. The results demonstrated that SDF-1α and laminin-based ECM (Matrigel and laminin) significantly and synergistically enhanced NPSC migration and acute neuronal differentiation. These effects were significantly attenuated with the addition of AMD3100 (an antagonist against the SDF-1α receptor, CXCR4). SDF-1α alone significantly increased NPSC proliferation regardless of ECM substrate, however no synergy was observed between SDF-1α and the ECM. These results serve to elucidate the relationship between adhesive and soluble signaling factors of interest and their effect on NPSC behavior following neural injury. Furthermore, these results better inform the next generation of biomaterials aimed at stimulating endogenous neural regeneration for neural injury and neurodegenerative diseases.
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Affiliation(s)
- Caroline P Addington
- School of Biological and Health Systems Engineering, Arizona State University, P.O. Box 879709, Tempe, AZ 85287-9709, USA
| | - Christine M Pauken
- School of Biological and Health Systems Engineering, Arizona State University, P.O. Box 879709, Tempe, AZ 85287-9709, USA
| | - Michael R Caplan
- School of Biological and Health Systems Engineering, Arizona State University, P.O. Box 879709, Tempe, AZ 85287-9709, USA
| | - Sarah E Stabenfeldt
- School of Biological and Health Systems Engineering, Arizona State University, P.O. Box 879709, Tempe, AZ 85287-9709, USA.
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26
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Sasai M, Kawabata Y, Makishi K, Itoh K, Terada TP. Time scales in epigenetic dynamics and phenotypic heterogeneity of embryonic stem cells. PLoS Comput Biol 2013; 9:e1003380. [PMID: 24348228 PMCID: PMC3861442 DOI: 10.1371/journal.pcbi.1003380] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2013] [Accepted: 10/11/2013] [Indexed: 11/28/2022] Open
Abstract
A remarkable feature of the self-renewing population of embryonic stem cells (ESCs) is their phenotypic heterogeneity: Nanog and other marker proteins of ESCs show large cell-to-cell variation in their expression level, which should significantly influence the differentiation process of individual cells. The molecular mechanism and biological implication of this heterogeneity, however, still remain elusive. We address this problem by constructing a model of the core gene-network of mouse ESCs. The model takes account of processes of binding/unbinding of transcription factors, formation/dissolution of transcription apparatus, and modification of histone code at each locus of genes in the network. These processes are hierarchically interrelated to each other forming the dynamical feedback loops. By simulating stochastic dynamics of this model, we show that the phenotypic heterogeneity of ESCs can be explained when the chromatin at the Nanog locus undergoes the large scale reorganization in formation/dissolution of transcription apparatus, which should have the timescale similar to the cell cycle period. With this slow transcriptional switching of Nanog, the simulated ESCs fluctuate among multiple transient states, which can trigger the differentiation into the lineage-specific cell states. From the simulated transitions among cell states, the epigenetic landscape underlying transitions is calculated. The slow Nanog switching gives rise to the wide basin of ESC states in the landscape. The bimodal Nanog distribution arising from the kinetic flow running through this ESC basin prevents transdifferentiation and promotes the definite decision of the cell fate. These results show that the distribution of timescales of the regulatory processes is decisively important to characterize the fluctuation of cells and their differentiation process. The analyses through the epigenetic landscape and the kinetic flow on the landscape should provide a guideline to engineer cell differentiation. Embryonic stem cells (ESCs) can proliferate indefinitely by keeping pluripotency, i.e., the ability to differentiate into any cell-lineage. ESCs, therefore, have been the focus of intense biological and medical interests. A remarkable feature of ESCs is their phenotypic heterogeneity: ESCs show large cell-to-cell fluctuation in the expression level of Nanog, which is a key factor to maintain pluripotency. Since Nanog regulates many genes in ESCs, this fluctuation should seriously affect individual cells when they start differentiation. In this paper we analyze this phenotypic fluctuation by simulating the stochastic dynamics of gene network in ESCs. The model takes account of the mutually interrelated processes of gene regulation such as binding/unbinding of transcription factors, formation/dissolution of transcription apparatus, and histone-code modification. We show the distribution of timescales of these processes is decisively important to characterize the dynamical behavior of the gene network, and that the slow formation/dissolution of transcription apparatus at the Nanog locus explains the observed large fluctuation of ESCs. The epigenetic landscapes are calculated based on the stochastic simulation, and the role of the phenotypic fluctuation in the differentiation process is analyzed through the landscape picture.
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Affiliation(s)
- Masaki Sasai
- Department of Computational Science and Engineering, Nagoya University, Nagoya, Japan ; Department of Applied Physics, Nagoya University, Nagoya, Japan ; School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea ; Okazaki Institute for Integrative Bioscience, Okazaki, Japan
| | - Yudai Kawabata
- Department of Applied Physics, Nagoya University, Nagoya, Japan
| | - Koh Makishi
- Department of Computational Science and Engineering, Nagoya University, Nagoya, Japan
| | - Kazuhito Itoh
- Department of Computational Science and Engineering, Nagoya University, Nagoya, Japan
| | - Tomoki P Terada
- Department of Computational Science and Engineering, Nagoya University, Nagoya, Japan ; Department of Applied Physics, Nagoya University, Nagoya, Japan
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27
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Wu J, Tzanakakis ES. Deconstructing stem cell population heterogeneity: single-cell analysis and modeling approaches. Biotechnol Adv 2013; 31:1047-62. [PMID: 24035899 DOI: 10.1016/j.biotechadv.2013.09.001] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2013] [Revised: 09/02/2013] [Accepted: 09/03/2013] [Indexed: 12/26/2022]
Abstract
Isogenic stem cell populations display cell-to-cell variations in a multitude of attributes including gene or protein expression, epigenetic state, morphology, proliferation and proclivity for differentiation. The origins of the observed heterogeneity and its roles in the maintenance of pluripotency and the lineage specification of stem cells remain unclear. Addressing pertinent questions will require the employment of single-cell analysis methods as traditional cell biochemical and biomolecular assays yield mostly population-average data. In addition to time-lapse microscopy and flow cytometry, recent advances in single-cell genomic, transcriptomic and proteomic profiling are reviewed. The application of multiple displacement amplification, next generation sequencing, mass cytometry and spectrometry to stem cell systems is expected to provide a wealth of information affording unprecedented levels of multiparametric characterization of cell ensembles under defined conditions promoting pluripotency or commitment. Establishing connections between single-cell analysis information and the observed phenotypes will also require suitable mathematical models. Stem cell self-renewal and differentiation are orchestrated by the coordinated regulation of subcellular, intercellular and niche-wide processes spanning multiple time scales. Here, we discuss different modeling approaches and challenges arising from their application to stem cell populations. Integrating single-cell analysis with computational methods will fill gaps in our knowledge about the functions of heterogeneity in stem cell physiology. This combination will also aid the rational design of efficient differentiation and reprogramming strategies as well as bioprocesses for the production of clinically valuable stem cell derivatives.
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Affiliation(s)
- Jincheng Wu
- Department of Chemical and Biological Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA.
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