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Svandova E, Vesela B, Kratochvilova A, Holomkova K, Oralova V, Dadakova K, Burger T, Sharpe P, Lesot H, Matalova E. Markers of dental pulp stem cells in in vivo developmental context. Ann Anat 2023; 250:152149. [PMID: 37574172 DOI: 10.1016/j.aanat.2023.152149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/10/2023] [Accepted: 07/24/2023] [Indexed: 08/15/2023]
Abstract
Teeth and their associated tissues contain several populations of mesenchymal stem cells, one of which is represented by dental pulp stem cells (DPSCs). These cells have mainly been characterised in vitro and numerous positive and negati ve markers for these cells have been suggested. To investigate the presence and localization of these molecules during development, forming dental pulp was examined using the mouse first mandibular molar as a model. The stages corresponding to postnatal (P) days 0, 7, 14, and 21 were investigated. The expression was monitored using customised PCR Arrays. Additionally, in situ localization of the key trio of markers (Cd73, Cd90, Cd105 coded by genes Nt5e, Thy1, Eng) was performed at prenatal and postnatal stages using immunohistochemistry. The expression panel of 24 genes assigned as in vitro markers of DPSCs or mesenchymal stem cells (MSCs) revealed their developmental dynamics during formation of dental pulp mesenchyme. Among the positive markers, Vcam1, Fgf2, Nes were identified as increasing and Cd44, Cd59b, Mcam, Alcam as decreasing between perinatal vs. postnatal stages towards adulthood. Within the panel of negative DPSC markers, Cd14, Itgb2, Ptprc displayed increased and Cd24a decreased levels at later stages of pulp formation. Within the key trio of markers, Nt5e did not show any significant expression difference within the investigated period. Thy1 displayed a strong decrease between P0 and P7 while Eng increased between these stages. In situ localization of Cd73, Cd90 and Cd105 showed them overlap in differentiated odontoblasts and in the sub-odontoblastic layer that is speculated to host odontoblast progenitors. The highly prevalent expression of particularly Cd73 and Cd90 opens the question of potential multiple functions of these molecules. The results from this study add to the in vitro based knowledge by showing dynamics in the expression of DPSC/MSC markers during dental pulp formation in an in vivo context and thus with respect to the natural environment important for commitment of stem cells.
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Affiliation(s)
- Eva Svandova
- Institute of Animal Physiology and Genetics, Brno, Czech Republic; Masaryk University, Brno, Czech Republic
| | - Barbora Vesela
- Institute of Animal Physiology and Genetics, Brno, Czech Republic; Veterinary University, Brno, Czech Republic
| | | | | | - Veronika Oralova
- Institute of Animal Physiology and Genetics, Brno, Czech Republic
| | | | - Tom Burger
- Veterinary University, Brno, Czech Republic
| | - Paul Sharpe
- Institute of Animal Physiology and Genetics, Brno, Czech Republic; King's College London, London, United Kingdom.
| | - Herve Lesot
- Institute of Animal Physiology and Genetics, Brno, Czech Republic
| | - Eva Matalova
- Institute of Animal Physiology and Genetics, Brno, Czech Republic; Veterinary University, Brno, Czech Republic
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2
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Zargari A, Lodewijk GA, Mashhadi N, Cook N, Neudorf CW, Araghbidikashani K, Hays R, Kozuki S, Rubio S, Hrabeta-Robinson E, Brooks A, Hinck L, Shariati SA. DeepSea is an efficient deep-learning model for single-cell segmentation and tracking in time-lapse microscopy. CELL REPORTS METHODS 2023; 3:100500. [PMID: 37426758 PMCID: PMC10326378 DOI: 10.1016/j.crmeth.2023.100500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 02/01/2023] [Accepted: 05/17/2023] [Indexed: 07/11/2023]
Abstract
Time-lapse microscopy is the only method that can directly capture the dynamics and heterogeneity of fundamental cellular processes at the single-cell level with high temporal resolution. Successful application of single-cell time-lapse microscopy requires automated segmentation and tracking of hundreds of individual cells over several time points. However, segmentation and tracking of single cells remain challenging for the analysis of time-lapse microscopy images, in particular for widely available and non-toxic imaging modalities such as phase-contrast imaging. This work presents a versatile and trainable deep-learning model, termed DeepSea, that allows for both segmentation and tracking of single cells in sequences of phase-contrast live microscopy images with higher precision than existing models. We showcase the application of DeepSea by analyzing cell size regulation in embryonic stem cells.
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Affiliation(s)
- Abolfazl Zargari
- Department of Electrical and Computer Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Gerrald A. Lodewijk
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Najmeh Mashhadi
- Department of Computer Science and Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Nathan Cook
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Celine W. Neudorf
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | | | - Robert Hays
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Sayaka Kozuki
- Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA, USA
- Institute for the Biology of Stem Cells, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Stefany Rubio
- Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA, USA
- Institute for the Biology of Stem Cells, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Eva Hrabeta-Robinson
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Angela Brooks
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Lindsay Hinck
- Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA, USA
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
- Institute for the Biology of Stem Cells, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - S. Ali Shariati
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
- Institute for the Biology of Stem Cells, University of California, Santa Cruz, Santa Cruz, CA, USA
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3
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Danciu DP, Hooli J, Martin-Villalba A, Marciniak-Czochra A. Mathematics of neural stem cells: Linking data and processes. Cells Dev 2023; 174:203849. [PMID: 37179018 DOI: 10.1016/j.cdev.2023.203849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/29/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023]
Abstract
Adult stem cells are described as a discrete population of cells that stand at the top of a hierarchy of progressively differentiating cells. Through their unique ability to self-renew and differentiate, they regulate the number of end-differentiated cells that contribute to tissue physiology. The question of how discrete, continuous, or reversible the transitions through these hierarchies are and the precise parameters that determine the ultimate performance of stem cells in adulthood are the subject of intense research. In this review, we explain how mathematical modelling has improved the mechanistic understanding of stem cell dynamics in the adult brain. We also discuss how single-cell sequencing has influenced the understanding of cell states or cell types. Finally, we discuss how the combination of single-cell sequencing technologies and mathematical modelling provides a unique opportunity to answer some burning questions in the field of stem cell biology.
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Affiliation(s)
- Diana-Patricia Danciu
- Heidelberg University, Institute of Mathematics (IMA), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Jooa Hooli
- Heidelberg University, Institute of Mathematics (IMA), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany; Heidelberg University, Faculty of Biosciences, Im Neuenheimer Feld 234, 69120 Heidelberg, Germany; German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Ana Martin-Villalba
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Anna Marciniak-Czochra
- Heidelberg University, Institute of Mathematics (IMA), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany.
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4
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Yin C, Udrescu M, Gupta G, Cheng M, Lihu A, Udrescu L, Bogdan P, Mannino DM, Mihaicuta S. Fractional Dynamics Foster Deep Learning of COPD Stage Prediction. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2203485. [PMID: 36808826 PMCID: PMC10131808 DOI: 10.1002/advs.202203485] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 01/03/2023] [Indexed: 05/28/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. Current COPD diagnosis (i.e., spirometry) could be unreliable because the test depends on an adequate effort from the tester and testee. Moreover, the early diagnosis of COPD is challenging. The authors address COPD detection by constructing two novel physiological signals datasets (4432 records from 54 patients in the WestRo COPD dataset and 13824 medical records from 534 patients in the WestRo Porti COPD dataset). The authors demonstrate their complex coupled fractal dynamical characteristics and perform a fractional-order dynamics deep learning analysis to diagnose COPD. The authors found that the fractional-order dynamical modeling can extract distinguishing signatures from the physiological signals across patients with all COPD stages-from stage 0 (healthy) to stage 4 (very severe). They use the fractional signatures to develop and train a deep neural network that predicts COPD stages based on the input features (such as thorax breathing effort, respiratory rate, or oxygen saturation). The authors show that the fractional dynamic deep learning model (FDDLM) achieves a COPD prediction accuracy of 98.66% and can serve as a robust alternative to spirometry. The FDDLM also has high accuracy when validated on a dataset with different physiological signals.
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Affiliation(s)
- Chenzhong Yin
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Mihai Udrescu
- Department of Computer and Information TechnologyPolitehnica University of Timisoara2 Vasile Parvan Blvd.Timişoara300223Romania
| | - Gaurav Gupta
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Mingxi Cheng
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Andrei Lihu
- Department of Computer and Information TechnologyPolitehnica University of Timisoara2 Vasile Parvan Blvd.Timişoara300223Romania
| | - Lucretia Udrescu
- Department I – Drug Analysis“Victor Babeş”University of Medicine and Pharmacy Timişoara2 Eftimie Murgu Sq.Timişoara300041Romania
| | - Paul Bogdan
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
| | | | - Stefan Mihaicuta
- Department of PulmonologyCenter for Research and Innovation in Precision Medicine of Respiratory Diseases, “Victor Babes” University of Medicine and Pharmacy2 Eftimie Murgu Sq.Timişoara300041Romania
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5
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Kukolj T, Lazarević J, Borojević A, Ralević U, Vujić D, Jauković A, Lazarević N, Bugarski D. A Single-Cell Raman Spectroscopy Analysis of Bone Marrow Mesenchymal Stem/Stromal Cells to Identify Inter-Individual Diversity. Int J Mol Sci 2022; 23:4915. [PMID: 35563306 PMCID: PMC9103070 DOI: 10.3390/ijms23094915] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/21/2022] [Accepted: 04/25/2022] [Indexed: 12/15/2022] Open
Abstract
The heterogeneity of stem cells represents the main challenge in regenerative medicine development. This issue is particularly pronounced when it comes to the use of primary mesenchymal stem/stromal cells (MSCs) due to a lack of identification markers. Considering the need for additional approaches in MSCs characterization, we applied Raman spectroscopy to investigate inter-individual differences between bone marrow MSCs (BM-MSCs). Based on standard biological tests, BM-MSCs of analyzed donors fulfill all conditions for their characterization, while no donor-related specifics were observed in terms of BM-MSCs morphology, phenotype, multilineage differentiation potential, colony-forming capacity, expression of pluripotency-associated markers or proliferative capacity. However, examination of BM-MSCs at a single-cell level by Raman spectroscopy revealed that despite similar biochemical background, fine differences in the Raman spectra of BM-MSCs of each donor can be detected. After extensive principal component analysis (PCA) of Raman spectra, our study revealed the possibility of this method to diversify BM-MSCs populations, whereby the grouping of cell populations was most prominent when cell populations were analyzed in pairs. These results indicate that Raman spectroscopy, as a label-free assay, could have a huge potential in understanding stem cell heterogeneity and sorting cell populations with a similar biochemical background that can be significant for the development of personalized therapy approaches.
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Affiliation(s)
- Tamara Kukolj
- Group for Hematology and Stem Cells, Institute for Medical Research, National Institute of Republic of Serbia, University of Belgrade, 11129 Belgrade, Serbia; (A.J.); (D.B.)
| | - Jasmina Lazarević
- Center for Solid State Physics and New Materials, Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia; (J.L.); (U.R.); (N.L.)
| | - Ana Borojević
- Mother and Child Health Care Institute of Serbia ‘’Dr Vukan Čupić’’, 11000 Belgrade, Serbia; (A.B.); (D.V.)
| | - Uroš Ralević
- Center for Solid State Physics and New Materials, Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia; (J.L.); (U.R.); (N.L.)
| | - Dragana Vujić
- Mother and Child Health Care Institute of Serbia ‘’Dr Vukan Čupić’’, 11000 Belgrade, Serbia; (A.B.); (D.V.)
- School of Medicine, University of Belgrade, 11000 Belgrade, Serbia
| | - Aleksandra Jauković
- Group for Hematology and Stem Cells, Institute for Medical Research, National Institute of Republic of Serbia, University of Belgrade, 11129 Belgrade, Serbia; (A.J.); (D.B.)
| | - Nenad Lazarević
- Center for Solid State Physics and New Materials, Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia; (J.L.); (U.R.); (N.L.)
| | - Diana Bugarski
- Group for Hematology and Stem Cells, Institute for Medical Research, National Institute of Republic of Serbia, University of Belgrade, 11129 Belgrade, Serbia; (A.J.); (D.B.)
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6
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Danciu DP, Stolper J, Centanin L, Marciniak-Czochra A. Identifying stem cell numbers and functional heterogeneities during postembryonic organ growth. iScience 2022; 25:103819. [PMID: 35198882 PMCID: PMC8844824 DOI: 10.1016/j.isci.2022.103819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 10/31/2021] [Accepted: 01/21/2022] [Indexed: 10/28/2022] Open
Abstract
Uncovering the number of stem cells necessary for organ growth has been challenging in vertebrate systems. Here, we developed a mathematical model characterizing stem cells in the fish gill, an organ displaying non-exhaustive growth. We employ a Markov model, stochastically simulated via an adapted Gillespie algorithm, and further improved through probability theory. The stochastic algorithm produces a simulated dataset for comparison with experimental clonal data by inspecting quantifiable properties. The analytical approach skips the step of artificial data generation and goes directly to the quantification, being more abstract and efficient. We report that a reduced number of stem cells actively contribute to growing and maintaining the gills. The model also highlights a functional heterogeneity among the stem cells involved, where activation and quiescence phases determine their relative growth contribution. Overall, our work presents a method for inferring the number and properties of stem cells required in a lifelong growing system.
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Affiliation(s)
- Diana-Patricia Danciu
- Institute of Applied Mathematics, Heidelberg University, 69120 Heidelberg, Baden-Württemberg, Germany.,Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, 69120 Heidelberg, Baden-Württemberg, Germany
| | - Julian Stolper
- Centre for Organismal Studies (COS), Heidelberg University, 69120 Heidelberg, Baden-Württemberg, Germany.,Murdoch Children's Research Institute, University of Melbourne, 3052 Parkville, VIC, Australia
| | - Lázaro Centanin
- Centre for Organismal Studies (COS), Heidelberg University, 69120 Heidelberg, Baden-Württemberg, Germany
| | - Anna Marciniak-Czochra
- Institute of Applied Mathematics, Heidelberg University, 69120 Heidelberg, Baden-Württemberg, Germany.,Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, 69120 Heidelberg, Baden-Württemberg, Germany
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7
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Single-Cell Transcriptome Analysis of Human Adipose-Derived Stromal Cells Identifies a Contractile Cell Subpopulation. Stem Cells Int 2021; 2021:5595172. [PMID: 34007285 PMCID: PMC8102097 DOI: 10.1155/2021/5595172] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/15/2021] [Accepted: 03/27/2021] [Indexed: 12/02/2022] Open
Abstract
The potential for human adipose-derived stromal cells (hASCs) to be used as a therapeutic product is being assessed in multiple clinical trials. However, much is still to be learned about these cells before they can be used with confidence in the clinical setting. An inherent characteristic of hASCs that is not well understood is their heterogeneity. The aim of this exploratory study was to characterize the heterogeneity of freshly isolated hASCs after two population doublings (P2) using single-cell transcriptome analysis. A minimum of two subpopulations were identified at P2. A major subpopulation was identified as contractile cells which, based on gene expression patterns, are likely to be pericytes and/or vascular smooth muscle cells (vSMCs).
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8
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Wadkin LE, Orozco-Fuentes S, Neganova I, Lako M, Barrio RA, Baggaley AW, Parker NG, Shukurov A. OCT4 expression in human embryonic stem cells: spatio-temporal dynamics and fate transitions. Phys Biol 2021; 18:026003. [PMID: 33296887 DOI: 10.1088/1478-3975/abd22b] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The improved in vitro regulation of human embryonic stem cell (hESC) pluripotency and differentiation trajectories is required for their promising clinical applications. The temporal and spatial quantification of the molecular interactions controlling pluripotency is also necessary for the development of successful mathematical and computational models. Here we use time-lapse experimental data of OCT4-mCherry fluorescence intensity to quantify the temporal and spatial dynamics of the pluripotency transcription factor OCT4 in a growing hESC colony in the presence and absence of BMP4. We characterise the internal self-regulation of OCT4 using the Hurst exponent and autocorrelation analysis, quantify the intra-cellular fluctuations and consider the diffusive nature of OCT4 evolution for individual cells and pairs of their descendants. We find that OCT4 abundance in the daughter cells fluctuates sub-diffusively, showing anti-persistent self-regulation. We obtain the stationary probability distributions governing hESC transitions amongst the different cell states and establish the times at which pro-fate cells (which later give rise to pluripotent or differentiated cells) cluster in the colony. By quantifying the similarities between the OCT4 expression amongst neighbouring cells, we show that hESCs express similar OCT4 to cells within their local neighbourhood within the first two days of the experiment and before BMP4 treatment. Our framework allows us to quantify the relevant properties of proliferating hESC colonies and the procedure is widely applicable to other transcription factors and cell populations.
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Affiliation(s)
- L E Wadkin
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, United Kingdom
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9
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Korolj A, Wu HT, Radisic M. A healthy dose of chaos: Using fractal frameworks for engineering higher-fidelity biomedical systems. Biomaterials 2019; 219:119363. [PMID: 31376747 PMCID: PMC6759375 DOI: 10.1016/j.biomaterials.2019.119363] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Revised: 07/09/2019] [Accepted: 07/14/2019] [Indexed: 12/18/2022]
Abstract
Optimal levels of chaos and fractality are distinctly associated with physiological health and function in natural systems. Chaos is a type of nonlinear dynamics that tends to exhibit seemingly random structures, whereas fractality is a measure of the extent of organization underlying such structures. Growing bodies of work are demonstrating both the importance of chaotic dynamics for proper function of natural systems, as well as the suitability of fractal mathematics for characterizing these systems. Here, we review how measures of fractality that quantify the dose of chaos may reflect the state of health across various biological systems, including: brain, skeletal muscle, eyes and vision, lungs, kidneys, tumours, cell regulation, skin and wound repair, bone, vasculature, and the heart. We compare how reports of either too little or too much chaos and fractal complexity can be damaging to normal biological function, and suggest that aiming for the healthy dose of chaos may be an effective strategy for various biomedical applications. We also discuss rising examples of the implementation of fractal theory in designing novel materials, biomedical devices, diagnostics, and clinical therapies. Finally, we explain important mathematical concepts of fractals and chaos, such as fractal dimension, criticality, bifurcation, and iteration, and how they are related to biology. Overall, we promote the effectiveness of fractals in characterizing natural systems, and suggest moving towards using fractal frameworks as a basis for the research and development of better tools for the future of biomedical engineering.
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Affiliation(s)
- Anastasia Korolj
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Canada
| | - Hau-Tieng Wu
- Department of Statistical Science, Duke University, Durham, NC, USA; Department of Mathematics, Duke University, Durham, NC, USA; Mathematics Division, National Center for Theoretical Sciences, Taipei, Taiwan
| | - Milica Radisic
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Canada; Toronto General Research Institute, University Health Network, Toronto, Canada; The Heart and Stroke/Richard Lewar Center of Excellence, Toronto, Canada.
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10
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Hiscock TW, Miesfeld JB, Mosaliganti KR, Link BA, Megason SG. Feedback between tissue packing and neurogenesis in the zebrafish neural tube. Development 2018; 145:dev.157040. [PMID: 29678815 DOI: 10.1242/dev.157040] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 04/03/2018] [Indexed: 01/22/2023]
Abstract
Balancing the rate of differentiation and proliferation in developing tissues is essential to produce organs of robust size and composition. Although many molecular regulators have been established, how these connect to physical and geometrical aspects of tissue architecture is poorly understood. Here, using high-resolution timelapse imaging, we find that changes to cell geometry associated with dense tissue packing play a significant role in regulating differentiation rate in the zebrafish neural tube. Specifically, progenitors that are displaced away from the apical surface due to crowding, tend to differentiate in a Notch-dependent manner. Using simulations we show that interplay between progenitor density, cell shape and changes in differentiation rate could naturally result in negative-feedback control on progenitor cell number. Given these results, we suggest a model whereby differentiation rate is regulated by density dependent effects on cell geometry to: (1) correct variability in cell number; and (2) balance the rates of proliferation and differentiation over development to 'fill' the available space.
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Affiliation(s)
- Tom W Hiscock
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Joel B Miesfeld
- Department of Cell Biology, Neurobiology and Anatomy, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | | | - Brian A Link
- Department of Cell Biology, Neurobiology and Anatomy, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Sean G Megason
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
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11
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Campi G, Cristofaro F, Pani G, Fratini M, Pascucci B, Corsetto PA, Weinhausen B, Cedola A, Rizzo AM, Visai L, Rea G. Heterogeneous and self-organizing mineralization of bone matrix promoted by hydroxyapatite nanoparticles. NANOSCALE 2017; 9:17274-17283. [PMID: 29090300 DOI: 10.1039/c7nr05013e] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The mineralization process is crucial to the load-bearing characteristics of the bone extracellular matrix. In this work, we have studied the spatiotemporal dynamics of mineral deposition by human bone marrow mesenchymal stem cells differentiating toward osteoblasts promoted by the presence of exogenous hydroxyapatite nanoparticles. At the molecular level, the added nanoparticles positively modulated the expression of bone-specific markers and enhanced calcified matrix deposition during osteogenic differentiation. The nucleation, growth and spatial arrangement of newly deposited hydroxyapatite nanocrystals have been evaluated using scanning micro X-ray diffraction and scanning micro X-ray fluorescence. As leading results, we have found the emergence of a complex scenario where the spatial organization and temporal evolution of the process exhibit heterogeneous and self-organizing dynamics. At the same time the possibility of controlling the differentiation kinetics, through the addition of synthetic nanoparticles, paves the way to empower the generation of more structured bone scaffolds in tissue engineering and to design new drugs in regenerative medicine.
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Affiliation(s)
- G Campi
- Institute of Crystallography - CNR, via Salaria Km 29.300, 00015, Monterotondo Roma, Italy.
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12
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Koorehdavoudi H, Bogdan P, Wei G, Marculescu R, Zhuang J, Carlsen RW, Sitti M. Multi-fractal characterization of bacterial swimming dynamics: a case study on real and simulated Serratia marcescens. Proc Math Phys Eng Sci 2017; 473:20170154. [PMID: 28804259 PMCID: PMC5549567 DOI: 10.1098/rspa.2017.0154] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 06/15/2017] [Indexed: 12/19/2022] Open
Abstract
To add to the current state of knowledge about bacterial swimming dynamics, in this paper, we study the fractal swimming dynamics of populations of Serratia marcescens bacteria both in vitro and in silico, while accounting for realistic conditions like volume exclusion, chemical interactions, obstacles and distribution of chemoattractant in the environment. While previous research has shown that bacterial motion is non-ergodic, we demonstrate that, besides the non-ergodicity, the bacterial swimming dynamics is multi-fractal in nature. Finally, we demonstrate that the multi-fractal characteristic of bacterial dynamics is strongly affected by bacterial density and chemoattractant concentration.
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Affiliation(s)
- Hana Koorehdavoudi
- Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA 90089-1453, USA
| | - Paul Bogdan
- Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089-2560, USA
| | - Guopeng Wei
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Radu Marculescu
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Jiang Zhuang
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Rika Wright Carlsen
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.,Department of Engineering, Robert Morris University, Pittsburgh, PA 15108, USA
| | - Metin Sitti
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.,Physical Intelligence Department, Max-Planck Institute for Intelligent Systems, 70569 Stuttgart, Germany
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13
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Alsehli H, Gari M, Abuzinadah M, Abuzenadah A. The emerging importance of high content screening for future therapeutics. J Microsc Ultrastruct 2017. [DOI: 10.1016/j.jmau.2017.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Sun M, Zaman MH. Modeling, signaling and cytoskeleton dynamics: integrated modeling-experimental frameworks in cell migration. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2017; 9:10.1002/wsbm.1365. [PMID: 27863122 PMCID: PMC5338640 DOI: 10.1002/wsbm.1365] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 08/29/2016] [Accepted: 09/14/2016] [Indexed: 12/20/2022]
Abstract
Cell migration is a complex and multistep process involved in homeostasis maintenance, morphogenesis, and disease development, such as cancer metastasis. Modeling cell migration and the relevant cytoskeleton dynamics have profound implications for studying fundamental development and disease diagnosis. This review focuses on some recent models of both cell migration and migration-related cytoskeleton dynamics, addressing issues such as the difference between amoeboid and mesenchymal migration modes, and between single-cell migration and collective cell migration. The review also highlights the computational integration among variable external cues, especially the biochemical and mechanical signaling that affects cell migration. Finally, we aim to identify the gaps in our current knowledge and potential strategies to develop integrated modeling-experimental frameworks for multiscale behavior integrating gene expression, cell signaling, mechanics, and multicellular dynamics. WIREs Syst Biol Med 2017, 9:e1365. doi: 10.1002/wsbm.1365 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Meng Sun
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Muhammad H. Zaman
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Howard Hughes Medical Institute
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Rady Raz N, Akbarzadeh-T. MR, Tafaghodi M. Bioinspired Nanonetworks for Targeted Cancer Drug Delivery. IEEE Trans Nanobioscience 2015; 14:894-906. [DOI: 10.1109/tnb.2015.2489761] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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16
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Smith Q, Stukalin E, Kusuma S, Gerecht S, Sun SX. Stochasticity and Spatial Interaction Govern Stem Cell Differentiation Dynamics. Sci Rep 2015; 5:12617. [PMID: 26227093 PMCID: PMC4521170 DOI: 10.1038/srep12617] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2014] [Accepted: 07/06/2015] [Indexed: 11/30/2022] Open
Abstract
Stem cell differentiation underlies many fundamental processes such as development, tissue growth and regeneration, as well as disease progression. Understanding how stem cell differentiation is controlled in mixed cell populations is an important step in developing quantitative models of cell population dynamics. Here we focus on quantifying the role of cell-cell interactions in determining stem cell fate. Toward this, we monitor stem cell differentiation in adherent cultures on micropatterns and collect statistical cell fate data. Results show high cell fate variability and a bimodal probability distribution of stem cell fraction on small (80–140 μm diameter) micropatterns. On larger (225–500 μm diameter) micropatterns, the variability is also high but the distribution of the stem cell fraction becomes unimodal. Using a stochastic model, we analyze the differentiation dynamics and quantitatively determine the differentiation probability as a function of stem cell fraction. Results indicate that stem cells can interact and sense cellular composition in their immediate neighborhood and adjust their differentiation probability accordingly. Blocking epithelial cadherin (E-cadherin) can diminish this cell-cell contact mediated sensing. For larger micropatterns, cell motility adds a spatial dimension to the picture. Taken together, we find stochasticity and cell-cell interactions are important factors in determining cell fate in mixed cell populations.
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Affiliation(s)
- Quinton Smith
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Evgeny Stukalin
- 1] Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218. [2] Johns Hopkins Physical Sciences-Oncology Center, Johns Hopkins University, Baltimore, MD 21218
| | - Sravanti Kusuma
- 1] Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218. [2] Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Sharon Gerecht
- 1] Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218. [2] Johns Hopkins Physical Sciences-Oncology Center, Johns Hopkins University, Baltimore, MD 21218. [3] Department of Material Science and Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Sean X Sun
- 1] Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218. [2] Johns Hopkins Physical Sciences-Oncology Center, Johns Hopkins University, Baltimore, MD 21218. [3] Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218
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Rabajante JF, Babierra AL. Branching and oscillations in the epigenetic landscape of cell-fate determination. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2015; 117:240-249. [DOI: 10.1016/j.pbiomolbio.2015.01.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2014] [Revised: 01/05/2015] [Accepted: 01/18/2015] [Indexed: 12/15/2022]
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