1
|
Horder H, Böhringer D, Endrizzi N, Hildebrand LS, Cianciosi A, Stecher S, Dusi F, Schweinitzer S, Watzling M, Groll J, Jüngst T, Teßmar J, Bauer-Kreisel P, Fabry B, Blunk T. Cancer cell migration depends on adjacent ASC and adipose spheroids in a 3D bioprinted breast cancer model. Biofabrication 2024; 16:035031. [PMID: 38934608 DOI: 10.1088/1758-5090/ad57f7] [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: 01/14/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024]
Abstract
Breast cancer develops in close proximity to mammary adipose tissue and interactions with the local adipose environment have been shown to drive tumor progression. The specific role, however, of this complex tumor microenvironment in cancer cell migration still needs to be elucidated. Therefore, in this study, a 3D bioprinted breast cancer model was developed that allows for a comprehensive analysis of individual tumor cell migration parameters in dependence of adjacent adipose stroma. In this co-culture model, a breast cancer compartment with MDA-MB-231 breast cancer cells embedded in collagen is surrounded by an adipose tissue compartment consisting of adipose-derived stromal cell (ASC) or adipose spheroids in a printable bioink based on thiolated hyaluronic acid. Printing parameters were optimized for adipose spheroids to ensure viability and integrity of the fragile lipid-laden cells. Preservation of the adipogenic phenotype after printing was demonstrated by quantification of lipid content, expression of adipogenic marker genes, the presence of a coherent adipo-specific extracellular matrix, and cytokine secretion. The migration of tumor cells as a function of paracrine signaling of the surrounding adipose compartment was then analyzed using live-cell imaging. The presence of ASC or adipose spheroids substantially increased key migration parameters of MDA-MB-231 cells, namely motile fraction, persistence, invasion distance, and speed. These findings shed new light on the role of adipose tissue in cancer cell migration. They highlight the potential of our 3D printed breast cancer-stroma model to elucidate mechanisms of stroma-induced cancer cell migration and to serve as a screening platform for novel anti-cancer drugs targeting cancer cell dissemination.
Collapse
Affiliation(s)
- Hannes Horder
- Department of Trauma, Hand, Plastic and Reconstructive Surgery, University Hospital Würzburg, Würzburg, Germany
| | - David Böhringer
- Department of Physics, University of Erlangen-Nürnberg, Erlangen, Germany
| | - Nadine Endrizzi
- Department of Physics, University of Erlangen-Nürnberg, Erlangen, Germany
| | - Laura S Hildebrand
- Department of Trauma, Hand, Plastic and Reconstructive Surgery, University Hospital Würzburg, Würzburg, Germany
| | - Alessandro Cianciosi
- Chair for Functional Materials in Medicine and Dentistry at the Institute of Functional Materials and Biofabrication, University of Würzburg and Bavarian Polymer Institute, Würzburg, Germany
| | - Sabrina Stecher
- Department of Trauma, Hand, Plastic and Reconstructive Surgery, University Hospital Würzburg, Würzburg, Germany
| | - Franziska Dusi
- Department of Trauma, Hand, Plastic and Reconstructive Surgery, University Hospital Würzburg, Würzburg, Germany
| | - Sophie Schweinitzer
- Department of Trauma, Hand, Plastic and Reconstructive Surgery, University Hospital Würzburg, Würzburg, Germany
| | - Martin Watzling
- Department of Trauma, Hand, Plastic and Reconstructive Surgery, University Hospital Würzburg, Würzburg, Germany
| | - Jürgen Groll
- Chair for Functional Materials in Medicine and Dentistry at the Institute of Functional Materials and Biofabrication, University of Würzburg and Bavarian Polymer Institute, Würzburg, Germany
| | - Tomasz Jüngst
- Chair for Functional Materials in Medicine and Dentistry at the Institute of Functional Materials and Biofabrication, University of Würzburg and Bavarian Polymer Institute, Würzburg, Germany
| | - Jörg Teßmar
- Chair for Functional Materials in Medicine and Dentistry at the Institute of Functional Materials and Biofabrication, University of Würzburg and Bavarian Polymer Institute, Würzburg, Germany
| | - Petra Bauer-Kreisel
- Department of Trauma, Hand, Plastic and Reconstructive Surgery, University Hospital Würzburg, Würzburg, Germany
| | - Ben Fabry
- Department of Physics, University of Erlangen-Nürnberg, Erlangen, Germany
| | - Torsten Blunk
- Department of Trauma, Hand, Plastic and Reconstructive Surgery, University Hospital Würzburg, Würzburg, Germany
| |
Collapse
|
2
|
Barton DL, Chang YR, Ducker W, Dobnikar J. Data-driven modelling makes quantitative predictions regarding bacteria surface motility. PLoS Comput Biol 2024; 20:e1012063. [PMID: 38743804 PMCID: PMC11125545 DOI: 10.1371/journal.pcbi.1012063] [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: 12/15/2022] [Revised: 05/24/2024] [Accepted: 04/09/2024] [Indexed: 05/16/2024] Open
Abstract
In this work, we quantitatively compare computer simulations and existing cell tracking data of P. aeruginosa surface motility in order to analyse the underlying motility mechanism. We present a three dimensional twitching motility model, that simulates the extension, retraction and surface association of individual Type IV Pili (TFP), and is informed by recent experimental observations of TFP. Sensitivity analysis is implemented to minimise the number of model parameters, and quantitative estimates for the remaining parameters are inferred from tracking data by approximate Bayesian computation. We argue that the motility mechanism is highly sensitive to experimental conditions. We predict a TFP retraction speed for the tracking data we study that is in a good agreement with experimental results obtained under very similar conditions. Furthermore, we examine whether estimates for biologically important parameters, whose direct experimental determination is challenging, can be inferred directly from tracking data. One example is the width of the distribution of TFP on the bacteria body. We predict that the TFP are broadly distributed over the bacteria pole in both walking and crawling motility types. Moreover, we identified specific configurations of TFP that lead to transitions between walking and crawling states.
Collapse
Affiliation(s)
- Daniel L. Barton
- CAS Key Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
| | - Yow-Ren Chang
- National Institute of Standards and Technology (NIST), 100 Bureau Dr, Gaithersburg, Maryland, United States of America
| | - William Ducker
- Department of Chemical Engineering and Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, Virgina, United States of America
| | - Jure Dobnikar
- CAS Key Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- Wenzhou Institute of the University of Chinese Academy of Sciences, Wenzhou, China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
3
|
Brückner DB, Broedersz CP. Learning dynamical models of single and collective cell migration: a review. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2024; 87:056601. [PMID: 38518358 DOI: 10.1088/1361-6633/ad36d2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 03/22/2024] [Indexed: 03/24/2024]
Abstract
Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualising the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasise how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour. Together, these data-driven approaches are a promising avenue for building physical models of cell migration directly from experimental data, and for providing conceptual links between different length-scales of description.
Collapse
Affiliation(s)
- David B Brückner
- Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria
| | - Chase P Broedersz
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Arnold Sommerfeld Center for Theoretical Physics and Center for NanoScience, Department of Physics, Ludwig-Maximilian-University Munich, Theresienstr. 37, D-80333 Munich, Germany
| |
Collapse
|
4
|
Liu Y, Jiao Y, Li X, Li G, Wang W, Liu Z, Qin D, Zhong L, Liu L, Shuai J, Li Z. An entropy-based approach for assessing the directional persistence of cell migration. Biophys J 2024; 123:730-744. [PMID: 38366586 PMCID: PMC10995411 DOI: 10.1016/j.bpj.2024.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 01/23/2024] [Accepted: 02/13/2024] [Indexed: 02/18/2024] Open
Abstract
Cell migration, which is primarily characterized by directional persistence, is essential for the development of normal tissues and organs, as well as for numerous pathological processes. However, there is a lack of simple and efficient tools to analyze the systematic properties of persistence based on cellular trajectory data. Here, we present a novel approach, the entropy of angular distribution , which combines cellular turning dynamics and Shannon entropy to explore the statistical and time-varying properties of persistence that strongly correlate with cellular migration modes. Our results reveal the changes in the persistence of multiple cell lines that are tightly regulated by both intra- and extracellular cues, including Arpin protein, collagen gel/substrate, and physical constraints. Significantly, some previously unreported distinctive details of persistence have also been captured, helping to elucidate how directional persistence is distributed and evolves in different cell populations. The analysis suggests that the entropy of angular distribution-based approach provides a powerful metric for evaluating directional persistence and enables us to better understand the relationships between cellular behaviors and multiscale cues, which also provides some insights into the migration dynamics of cell populations, such as collective cell invasion.
Collapse
Affiliation(s)
- Yanping Liu
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China; Department of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yang Jiao
- Materials Science and Engineering, Arizona State University, Tempe, Arizona; Department of Physics, Arizona State University, Tempe, Arizona
| | - Xinwei Li
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China; Department of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Guoqiang Li
- Chongqing Key Laboratory of Environmental Materials and Remediation Technologies, College of Chemistry and Environmental Engineering, Chongqing University of Arts and Sciences, Chongqing, China
| | - Wei Wang
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China; Department of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Zhichao Liu
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China; Department of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Dui Qin
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China; Department of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Lisha Zhong
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Liyu Liu
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing, China
| | - Jianwei Shuai
- Department of Physics, Xiamen University, Xiamen, China; Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China; Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China.
| | - Zhangyong Li
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China; Department of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China.
| |
Collapse
|
5
|
Liu Y, Jiao Y, Fan Q, Li X, Liu Z, Qin D, Hu J, Liu L, Shuai J, Li Z. Morphological entropy encodes cellular migration strategies on multiple length scales. NPJ Syst Biol Appl 2024; 10:26. [PMID: 38453929 PMCID: PMC10920856 DOI: 10.1038/s41540-024-00353-5] [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: 10/17/2023] [Accepted: 02/26/2024] [Indexed: 03/09/2024] Open
Abstract
Cell migration is crucial for numerous physiological and pathological processes. A cell adapts its morphology, including the overall and nuclear morphology, in response to various cues in complex microenvironments, such as topotaxis and chemotaxis during migration. Thus, the dynamics of cellular morphology can encode migration strategies, from which diverse migration mechanisms can be inferred. However, deciphering the mechanisms behind cell migration encoded in morphology dynamics remains a challenging problem. Here, we present a powerful universal metric, the Cell Morphological Entropy (CME), developed by combining parametric morphological analysis with Shannon entropy. The utility of CME, which accurately quantifies the complex cellular morphology at multiple length scales through the deviation from a perfectly circular shape, is illustrated using a variety of normal and tumor cell lines in different in vitro microenvironments. Our results show how geometric constraints affect the MDA-MB-231 cell nucleus, the emerging interactions of MCF-10A cells migrating on collagen gel, and the critical transition from proliferation to invasion in tumor spheroids. The analysis demonstrates that the CME-based approach provides an effective and physically interpretable tool to measure morphology in real-time across multiple length scales. It provides deeper insight into cell migration and contributes to the understanding of different behavioral modes and collective cell motility in more complex microenvironments.
Collapse
Affiliation(s)
- Yanping Liu
- Department of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China.
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China.
| | - Yang Jiao
- Materials Science and Engineering, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
| | - Qihui Fan
- Beijing National Laboratory for Condensed Matter Physics and CAS Key Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
| | - Xinwei Li
- Department of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Zhichao Liu
- Department of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Dui Qin
- Department of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Jun Hu
- Department of Neurology, Southwest Hospital, Army Medical University, Chongqing, China
| | - Liyu Liu
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing, China
| | - Jianwei Shuai
- Department of Physics, Xiamen University, Xiamen, China.
- Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China.
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China.
| | - Zhangyong Li
- Department of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China.
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China.
| |
Collapse
|
6
|
Choi S, Woo SH, Park I, Lee S, Yeo KI, Lee SH, Lee SY, Yang S, Lee G, Chang WJ, Bashir R, Kim YS, Lee SW. Cellular subpopulations identified using an ensemble average of multiple dielectrophoresis measurements. Comput Biol Med 2024; 170:108011. [PMID: 38271838 DOI: 10.1016/j.compbiomed.2024.108011] [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: 10/08/2023] [Revised: 01/11/2024] [Accepted: 01/16/2024] [Indexed: 01/27/2024]
Abstract
While the average value measurement approach can successfully analyze and predict the general behavior and biophysical properties of an isogenic cell population, it fails when significant differences among individual cells are generated in the population by intracellular changes such as the cell cycle, or different cellular responses to certain stimuli. Detecting such single-cell differences in a cell population has remained elusive. Here, we describe an easy-to-implement and generalizable platform that measures the dielectrophoretic cross-over frequency of individual cells by decreasing measurement noise with a stochastic method and computing ensemble average statistics. This platform enables multiple, real-time, label-free detection of individual cells with significant dielectric variations over time within an isogenic cell population. Using a stochastic method in combination with the platform, we distinguished cell subpopulations from a mixture of drug-untreated and -treated isogenic cells. Furthermore, we demonstrate that our platform can identify drug-treated isogenic cells with different recovery rates.
Collapse
Affiliation(s)
- Seungyeop Choi
- Department of Biomedical Engineering, Yonsei University, Wonju, 26493, Republic of Korea; School of Biomedical Engineering, Korea University, Seoul, 02481, Republic of Korea; BK21 Four Institute of Precision Public Health, Korea University, Seoul, 02841, Republic of Korea
| | - Sung-Hun Woo
- Department of Biomedical Laboratory Science, Yonsei University, Wonju, 26493, Republic of Korea
| | - Insu Park
- Holonyak Micro and Nanotechnology Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA; Department of Biomedical Engineering, Konyang University, Daejeon, 35365, Republic of Korea
| | - Sena Lee
- Department of Precision Medicine, Wonju College of Medicine, Yonsei University, Wonju, 26426, Republic of Korea
| | - Kang In Yeo
- Department of Biomedical Engineering, Yonsei University, Wonju, 26493, Republic of Korea
| | - Sang Hyun Lee
- Department of Biomedical Engineering, Yonsei University, Wonju, 26493, Republic of Korea
| | - Sei Young Lee
- Department of Biomedical Engineering, Yonsei University, Wonju, 26493, Republic of Korea; Department of Medical Informatics and Biostatistics, Graduate School, Yonsei University, Wonju, 26426, Republic of Korea
| | - Sejung Yang
- Department of Precision Medicine, Wonju College of Medicine, Yonsei University, Wonju, 26426, Republic of Korea
| | - Gyudo Lee
- Department of Biotechnology and Bioinformatics, Korea University, Sejong, 30019, Republic of Korea; Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong, 30019, Republic of Korea
| | - Woo-Jin Chang
- Mechanical Engineering Department, University of Wisconsin-Milwaukee, Milwaukee, WI, 53211, USA
| | - Rashid Bashir
- Holonyak Micro and Nanotechnology Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA; Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA; Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA; Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Yoon Suk Kim
- Department of Biomedical Laboratory Science, Yonsei University, Wonju, 26493, Republic of Korea.
| | - Sang Woo Lee
- Department of Biomedical Engineering, Yonsei University, Wonju, 26493, Republic of Korea.
| |
Collapse
|
7
|
Zhang Y, Ge F, Lin X, Xue J, Song Y, Xie H, He Y. Extract latent features of single-particle trajectories with historical experience learning. Biophys J 2023; 122:4451-4466. [PMID: 37885178 PMCID: PMC10698327 DOI: 10.1016/j.bpj.2023.10.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/30/2023] [Accepted: 10/20/2023] [Indexed: 10/28/2023] Open
Abstract
Single-particle tracking has enabled real-time, in situ quantitative studies of complex systems. However, inferring dynamic state changes from noisy and undersampling trajectories encounters challenges. Here, we introduce a data-driven method for extracting features of subtrajectories with historical experience learning (Deep-SEES), where a single-particle tracking analysis pipeline based on a self-supervised architecture automatically searches for the latent space, allowing effective segmentation of the underlying states from noisy trajectories without prior knowledge on the particle dynamics. We validated our method on a variety of noisy simulated and experimental data. Our results showed that the method can faithfully capture both stable states and their dynamic switch. In highly random systems, our method outperformed commonly used unsupervised methods in inferring motion states, which is important for understanding nanoparticles interacting with living cell membranes, active enzymes, and liquid-liquid phase separation. Self-generating latent features of trajectories could potentially improve the understanding, estimation, and prediction of many complex systems.
Collapse
Affiliation(s)
- Yongyu Zhang
- Department of Chemistry, Tsinghua University, Beijing, P.R. China
| | - Feng Ge
- Department of Chemistry, Tsinghua University, Beijing, P.R. China
| | - Xijian Lin
- Department of Chemistry, Tsinghua University, Beijing, P.R. China
| | - Jianfeng Xue
- Department of Chemistry, Tsinghua University, Beijing, P.R. China
| | - Yuxin Song
- Department of Chemistry, Tsinghua University, Beijing, P.R. China
| | - Hao Xie
- Department of Automation, Tsinghua University, Beijing, P.R. China.
| | - Yan He
- Department of Chemistry, Tsinghua University, Beijing, P.R. China.
| |
Collapse
|
8
|
Waigh TA, Korabel N. Heterogeneous anomalous transport in cellular and molecular biology. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2023; 86:126601. [PMID: 37863075 DOI: 10.1088/1361-6633/ad058f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 10/20/2023] [Indexed: 10/22/2023]
Abstract
It is well established that a wide variety of phenomena in cellular and molecular biology involve anomalous transport e.g. the statistics for the motility of cells and molecules are fractional and do not conform to the archetypes of simple diffusion or ballistic transport. Recent research demonstrates that anomalous transport is in many cases heterogeneous in both time and space. Thus single anomalous exponents and single generalised diffusion coefficients are unable to satisfactorily describe many crucial phenomena in cellular and molecular biology. We consider advances in the field ofheterogeneous anomalous transport(HAT) highlighting: experimental techniques (single molecule methods, microscopy, image analysis, fluorescence correlation spectroscopy, inelastic neutron scattering, and nuclear magnetic resonance), theoretical tools for data analysis (robust statistical methods such as first passage probabilities, survival analysis, different varieties of mean square displacements, etc), analytic theory and generative theoretical models based on simulations. Special emphasis is made on high throughput analysis techniques based on machine learning and neural networks. Furthermore, we consider anomalous transport in the context of microrheology and the heterogeneous viscoelasticity of complex fluids. HAT in the wavefronts of reaction-diffusion systems is also considered since it plays an important role in morphogenesis and signalling. In addition, we present specific examples from cellular biology including embryonic cells, leucocytes, cancer cells, bacterial cells, bacterial biofilms, and eukaryotic microorganisms. Case studies from molecular biology include DNA, membranes, endosomal transport, endoplasmic reticula, mucins, globular proteins, and amyloids.
Collapse
Affiliation(s)
- Thomas Andrew Waigh
- Biological Physics, School of Physics and Astronomy, University of Manchester, Manchester M13 9PL, United Kingdom
| | - Nickolay Korabel
- Department of Mathematics, The University of Manchester, Manchester M13 9PL, United Kingdom
| |
Collapse
|
9
|
Belliveau NM, Footer MJ, Akdoǧan E, van Loon AP, Collins SR, Theriot JA. Whole-genome screens reveal regulators of differentiation state and context-dependent migration in human neutrophils. Nat Commun 2023; 14:5770. [PMID: 37723145 PMCID: PMC10507112 DOI: 10.1038/s41467-023-41452-x] [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: 02/15/2023] [Accepted: 08/31/2023] [Indexed: 09/20/2023] Open
Abstract
Neutrophils are the most abundant leukocyte in humans and provide a critical early line of defense as part of our innate immune system. We perform a comprehensive, genome-wide assessment of the molecular factors critical to proliferation, differentiation, and cell migration in a neutrophil-like cell line. Through the development of multiple migration screen strategies, we specifically probe directed (chemotaxis), undirected (chemokinesis), and 3D amoeboid cell migration in these fast-moving cells. We identify a role for mTORC1 signaling in cell differentiation, which influences neutrophil abundance, survival, and migratory behavior. Across our individual migration screens, we identify genes involved in adhesion-dependent and adhesion-independent cell migration, protein trafficking, and regulation of the actomyosin cytoskeleton. This genome-wide screening strategy, therefore, provides an invaluable approach to the study of neutrophils and provides a resource that will inform future studies of cell migration in these and other rapidly migrating cells.
Collapse
Affiliation(s)
- Nathan M Belliveau
- Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, WA, 98195, USA
| | - Matthew J Footer
- Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, WA, 98195, USA
| | - Emel Akdoǧan
- Department of Microbiology and Molecular Genetics, University of California, Davis, Davis, CA, 95616, USA
| | - Aaron P van Loon
- Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, WA, 98195, USA
| | - Sean R Collins
- Department of Microbiology and Molecular Genetics, University of California, Davis, Davis, CA, 95616, USA
| | - Julie A Theriot
- Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, WA, 98195, USA.
| |
Collapse
|
10
|
Torkashvand E. Modeling three-dimensional T-cell motility using clustering and hidden Markov models. Stat Methods Med Res 2023; 32:1318-1337. [PMID: 37303122 DOI: 10.1177/09622802231172041] [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] [Indexed: 06/13/2023]
Abstract
Recent advances in imaging technologies now allow for real-time tracking of fast-moving immune cells as they search for targets such as pathogens and tumor cells through complex three-dimensional tissues. Cytotoxic T cells are specialized immune cells that continually scan tissues for such targets to engage and kill, and have emerged as the principle mediators of breakthrough immunotherapies against cancers. Modeling the way these T cells move is of great value in furthering our understanding of their collective search efficiency. T-cell motility is characterized by heterogeneity at two levels: (a) Individual cells display different distributions of translational speeds and turning angles, and (b) each cell can during a given track, its motility, switch between local search and directional motion. Despite a likely considerable influence on a motile population's search performance, statistical models that accurately capture both such heterogeneities in a distinguishing manner are lacking. Here, we model three-dimensional T-cell trajectories through a spherical representation of their incremental steps and compare model outputs to real-world motility data from primary T cells navigating physiological environments. T cells in a population are clustered based on their directional persistence and characteristic "step lengths" therein capturing between-cell heterogeneity. The motility dynamics of cells within each cluster are individually modeled through hidden Markov model to capture within-cell transitions between local and more extensive search patterns. We explore the importance of explicitly capturing altered motility patterns when cells lie in close proximity to one another, through a non-homogenous hidden Markov model.
Collapse
Affiliation(s)
- Elaheh Torkashvand
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, US
| |
Collapse
|
11
|
Classification at the accuracy limit: facing the problem of data ambiguity. Sci Rep 2022; 12:22121. [PMID: 36543849 PMCID: PMC9772417 DOI: 10.1038/s41598-022-26498-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022] Open
Abstract
Data classification, the process of analyzing data and organizing it into categories or clusters, is a fundamental computing task of natural and artificial information processing systems. Both supervised classification and unsupervised clustering work best when the input vectors are distributed over the data space in a highly non-uniform way. These tasks become however challenging in weakly structured data sets, where a significant fraction of data points is located in between the regions of high point density. We derive the theoretical limit for classification accuracy that arises from this overlap of data categories. By using a surrogate data generation model with adjustable statistical properties, we show that sufficiently powerful classifiers based on completely different principles, such as perceptrons and Bayesian models, all perform at this universal accuracy limit under ideal training conditions. Remarkably, the accuracy limit is not affected by certain non-linear transformations of the data, even if these transformations are non-reversible and drastically reduce the information content of the input data. We further compare the data embeddings that emerge by supervised and unsupervised training, using the MNIST data set and human EEG recordings during sleep. We find for MNIST that categories are significantly separated not only after supervised training with back-propagation, but also after unsupervised dimensionality reduction. A qualitatively similar cluster enhancement by unsupervised compression is observed for the EEG sleep data, but with a very small overall degree of cluster separation. We conclude that the handwritten letters in MNIST can be considered as 'natural kinds', whereas EEG sleep recordings are a relatively weakly structured data set, so that unsupervised clustering will not necessarily re-cover the human-defined sleep stages.
Collapse
|
12
|
Bustos NA, Saad-Roy CM, Cherstvy AG, Wagner CE. Distributed medium viscosity yields quasi-exponential step-size probability distributions in heterogeneous media. SOFT MATTER 2022; 18:8572-8581. [PMID: 36373713 DOI: 10.1039/d2sm00952h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The analysis of the statistics of random walks undertaken by passive particles in complex media has important implications in a number of areas including pathogen transport and drug delivery. In several systems in which heterogeneity is important, the distribution of particle step-sizes has been found to be exponential in nature, as opposed to the Gaussian distribution associated with Brownian motion. Here, we first develop a theoretical framework to study a simplified version of this problem: the motion of passive tracers in a range of sub-environments with different viscosity. We show that in the limit of a large number of equi-distributed sub-environments spanning a broad viscosity range, an exact analytical expression for the underlying particle step-size distribution can be derived, which approaches an exponential distribution when step sizes are small. We then validate this using a simple experimental system of glycerol-water mixtures, in which the volume fraction of glycerol is systematically varied. Overall, the assumption of exponentially distributed step sizes may substantially over-estimate the incidence of large steps in heterogeneous systems, with important implications in the analysis of various biophysical processes.
Collapse
Affiliation(s)
- Nicole A Bustos
- Department of Mechanical Engineering, MIT, Cambridge, MA 02139, USA
| | - Chadi M Saad-Roy
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA
- Miller Institute for Basic Research in Science, University of California, Berkeley, Berkeley, CA 94720, USA
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Andrey G Cherstvy
- Institute for Physics and Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany
| | - Caroline E Wagner
- Department of Bioengineering, McGill University, Montreal, QC H3A 0E9, Canada.
| |
Collapse
|
13
|
Distinct Dynamics of Migratory Response to PD-1 and CTLA-4 Blockade Reveals New Mechanistic Insights for Potential T-Cell Reinvigoration following Immune Checkpoint Blockade. Cells 2022; 11:cells11223534. [PMID: 36428963 PMCID: PMC9688893 DOI: 10.3390/cells11223534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/22/2022] [Accepted: 10/28/2022] [Indexed: 11/10/2022] Open
Abstract
Cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death protein 1 (PD-1), two clinically relevant targets for the immunotherapy of cancer, are negative regulators of T-cell activation and migration. Optimizing the therapeutic response to CTLA-4 and PD-1 blockade calls for a more comprehensive insight into the coordinated function of these immune regulators. Mathematical modeling can be used to elucidate nonlinear tumor-immune interactions and highlight the underlying mechanisms to tackle the problem. Here, we investigated and statistically characterized the dynamics of T-cell migration as a measure of the functional response to these pathways. We used a previously developed three-dimensional organotypic culture of patient-derived tumor spheroids treated with anti-CTLA-4 and anti-PD-1 antibodies for this purpose. Experiment-based dynamical modeling revealed the delayed kinetics of PD-1 activation, which originates from the distinct characteristics of PD-1 and CTLA-4 regulation, and followed through with the modification of their contributions to immune modulation. The simulation results show good agreement with the tumor cell reduction and active immune cell count in each experiment. Our findings demonstrate that while PD-1 activation provokes a more exhaustive intracellular cascade within a mature tumor environment, the time-delayed kinetics of PD-1 activation outweighs its preeminence at the individual cell level and consequently confers a functional dominance to the CTLA-4 checkpoint. The proposed model explains the distinct immunostimulatory pattern of PD-1 and CTLA-4 blockade based on mechanisms involved in the regulation of their expression and may be useful for planning effective treatment schemes targeting PD-1 and CTLA-4 functions.
Collapse
|
14
|
Partridge B, Gonzalez Anton S, Khorshed R, Adams G, Pospori C, Lo Celso C, Lee CF. Heterogeneous run-and-tumble motion accounts for transient non-Gaussian super-diffusion in haematopoietic multi-potent progenitor cells. PLoS One 2022; 17:e0272587. [PMID: 36099240 PMCID: PMC9469981 DOI: 10.1371/journal.pone.0272587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 07/19/2022] [Indexed: 11/23/2022] Open
Abstract
Multi-potent progenitor (MPP) cells act as a key intermediary step between haematopoietic stem cells and the entirety of the mature blood cell system. Their eventual fate determination is thought to be achieved through migration in and out of spatially distinct niches. Here we first analyze statistically MPP cell trajectory data obtained from a series of long time-course 3D in vivo imaging experiments on irradiated mouse calvaria, and report that MPPs display transient super-diffusion with apparent non-Gaussian displacement distributions. Second, we explain these experimental findings using a run-and-tumble model of cell motion which incorporates the observed dynamical heterogeneity of the MPPs. Third, we use our model to extrapolate the dynamics to time-periods currently inaccessible experimentally, which enables us to quantitatively estimate the time and length scales at which super-diffusion transitions to Fickian diffusion. Our work sheds light on the potential importance of motility in early haematopoietic progenitor function.
Collapse
Affiliation(s)
- Benjamin Partridge
- Department of Bioengineering, Imperial College London, South Kensington Campus, London, United Kingdom
| | - Sara Gonzalez Anton
- Department of Life Sciences, Imperial College London, South Kensington Campus, London, United Kingdom
- Sir Francis Crick Institute, London, United Kingdom
| | - Reema Khorshed
- Department of Life Sciences, Imperial College London, South Kensington Campus, London, United Kingdom
| | - George Adams
- Department of Life Sciences, Imperial College London, South Kensington Campus, London, United Kingdom
- Sir Francis Crick Institute, London, United Kingdom
| | - Constandina Pospori
- Department of Life Sciences, Imperial College London, South Kensington Campus, London, United Kingdom
- Sir Francis Crick Institute, London, United Kingdom
| | - Cristina Lo Celso
- Department of Life Sciences, Imperial College London, South Kensington Campus, London, United Kingdom
- Sir Francis Crick Institute, London, United Kingdom
- * E-mail: (CLC); (CFL)
| | - Chiu Fan Lee
- Department of Bioengineering, Imperial College London, South Kensington Campus, London, United Kingdom
- * E-mail: (CLC); (CFL)
| |
Collapse
|
15
|
Cavanagh H, Kempe D, Mazalo JK, Biro M, Endres RG. T cell morphodynamics reveal periodic shape oscillations in three-dimensional migration. J R Soc Interface 2022; 19:20220081. [PMID: 35537475 PMCID: PMC9090490 DOI: 10.1098/rsif.2022.0081] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
T cells use sophisticated shape dynamics (morphodynamics) to migrate towards and neutralize infected and cancerous cells. However, there is limited quantitative understanding of the migration process in three-dimensional extracellular matrices (ECMs) and across timescales. Here, we leveraged recent advances in lattice light-sheet microscopy to quantitatively explore the three-dimensional morphodynamics of migrating T cells at high spatio-temporal resolution. We first developed a new shape descriptor based on spherical harmonics, incorporating key polarization information of the uropod. We found that the shape space of T cells is low-dimensional. At the behavioural level, run-and-stop migration modes emerge at approximately 150 s, and we mapped the morphodynamic composition of each mode using multiscale wavelet analysis, finding 'stereotyped' motifs. Focusing on the run mode, we found morphodynamics oscillating periodically (every approx. 100 s) that can be broken down into a biphasic process: front-widening with retraction of the uropod, followed by a rearward surface motion and forward extension, where intercalation with the ECM in both of these steps likely facilitates forward motion. Further application of these methods may enable the comparison of T cell migration across different conditions (e.g. differentiation, activation, tissues and drug treatments) and improve the precision of immunotherapeutic development.
Collapse
Affiliation(s)
- Henry Cavanagh
- Imperial College London, Centre for Integrative Systems Biology and Bioinformatics, London SW7 2BU, UK
| | - Daryan Kempe
- EMBL Australia, Single Molecule Science Node, School of Medical Sciences, The University of New South Wales, Sydney, NSW 2052, Australia
| | - Jessica K Mazalo
- EMBL Australia, Single Molecule Science Node, School of Medical Sciences, The University of New South Wales, Sydney, NSW 2052, Australia
| | - Maté Biro
- EMBL Australia, Single Molecule Science Node, School of Medical Sciences, The University of New South Wales, Sydney, NSW 2052, Australia
| | - Robert G Endres
- Imperial College London, Centre for Integrative Systems Biology and Bioinformatics, London SW7 2BU, UK
| |
Collapse
|
16
|
LaChance J, Suh K, Clausen J, Cohen DJ. Learning the rules of collective cell migration using deep attention networks. PLoS Comput Biol 2022; 18:e1009293. [PMID: 35476698 PMCID: PMC9106212 DOI: 10.1371/journal.pcbi.1009293] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 05/13/2022] [Accepted: 03/23/2022] [Indexed: 11/18/2022] Open
Abstract
Collective, coordinated cellular motions underpin key processes in all multicellular organisms, yet it has been difficult to simultaneously express the ‘rules’ behind these motions in clear, interpretable forms that effectively capture high-dimensional cell-cell interaction dynamics in a manner that is intuitive to the researcher. Here we apply deep attention networks to analyze several canonical living tissues systems and present the underlying collective migration rules for each tissue type using only cell migration trajectory data. We use these networks to learn the behaviors of key tissue types with distinct collective behaviors—epithelial, endothelial, and metastatic breast cancer cells—and show how the results complement traditional biophysical approaches. In particular, we present attention maps indicating the relative influence of neighboring cells to the learned turning decisions of a ‘focal cell’–the primary cell of interest in a collective setting. Colloquially, we refer to this learned relative influence as ‘attention’, as it serves as a proxy for the physical parameters modifying the focal cell’s future motion as a function of each neighbor cell. These attention networks reveal distinct patterns of influence and attention unique to each model tissue. Endothelial cells exhibit tightly focused attention on their immediate forward-most neighbors, while cells in more expansile epithelial tissues are more broadly influenced by neighbors in a relatively large forward sector. Attention maps of ensembles of more mesenchymal, metastatic cells reveal completely symmetric attention patterns, indicating the lack of any particular coordination or direction of interest. Moreover, we show how attention networks are capable of detecting and learning how these rules change based on biophysical context, such as location within the tissue and cellular crowding. That these results require only cellular trajectories and no modeling assumptions highlights the potential of attention networks for providing further biological insights into complex cellular systems. Collective behaviors are crucial to the function of multicellular life, with large-scale, coordinated cell migration enabling processes spanning organ formation to coordinated skin healing. However, we lack effective tools to discover and cleanly express collective rules at the level of an individual cell. Here, we employ a carefully structured neural network to extract collective information directly from cell trajectory data. The network is trained on data from various systems, including canonical collective cell systems (HUVEC and MDCK cells) which display visually distinct forms of collective motion, and metastatic cancer cells (MDA-MB-231) which are highly uncoordinated. Using these trained networks, we can produce attention maps for each system, which indicate how a cell within a tissue takes in information from its surrounding neighbors, as a function of weights assigned to those neighbors. Thus for a cell type in which cells tend to follow the path of the cell in front, the attention maps will display high weights for cells spatially forward of the focal cell. We present results in terms of additional metrics, such as accuracy plots and number of interacting cells, and encourage future development of improved metrics.
Collapse
Affiliation(s)
- Julienne LaChance
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Kevin Suh
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Jens Clausen
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Daniel J. Cohen
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America
- * E-mail:
| |
Collapse
|
17
|
de Almeida RM, Giardini GS, Vainstein M, Glazier JA, Thomas GL. Exact solution for the Anisotropic Ornstein-Uhlenbeck process. PHYSICA A 2022; 587:126526. [PMID: 36937094 PMCID: PMC10022481 DOI: 10.1016/j.physa.2021.126526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Active-Matter models commonly consider particles with overdamped dynamics subject to a force (speed) with constant modulus and random direction. Some models also include random noise in particle displacement (a Wiener process), resulting in diffusive motion at short time scales. On the other hand, Ornstein-Uhlenbeck processes apply Langevin dynamics to the particles' velocity and predict motion that is not diffusive at short time scales. Experiments show that migrating cells have gradually varying speeds at intermediate and long time scales, with short-time diffusive behavior. While Ornstein-Uhlenbeck processes can describe the moderate-and long-time speed variation, Active-Matter models for over-damped particles can explain the short-time diffusive behavior. Isotropic models cannot explain both regimes, because short-time diffusion renders instantaneous velocity ill-defined, and prevents the use of dynamical equations that require velocity time-derivatives. On the other hand, both models correctly describe some of the different temporal regimes seen in migrating biological cells and must, in the appropriate limit, yield the same observable predictions. Here we propose and solve analytically an Anisotropic Ornstein-Uhlenbeck process for polarized particles, with Langevin dynamics governing the particle's movement in the polarization direction and a Wiener process governing displacement in the orthogonal direction. Our characterization provides a theoretically robust way to compare movement in dimensionless simulations to movement in experiments in which measurements have meaningful space and time units. We also propose an approach to deal with inevitable finite-precision effects in experiments and simulations.
Collapse
Affiliation(s)
- Rita M.C. de Almeida
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Instituto Nacional de Ciência e Tecnologia, Sistemas Complexos, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Program de Pós Graduação em Bioinformática, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil
| | | | - Mendeli Vainstein
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - James A. Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States of America
| | - Gilberto L. Thomas
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| |
Collapse
|
18
|
Disentangling cadherin-mediated cell-cell interactions in collective cancer cell migration. Biophys J 2022; 121:44-60. [PMID: 34890578 PMCID: PMC8758422 DOI: 10.1016/j.bpj.2021.12.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 10/30/2021] [Accepted: 12/06/2021] [Indexed: 01/07/2023] Open
Abstract
Cell dispersion from a confined area is fundamental in a number of biological processes, including cancer metastasis. To date, a quantitative understanding of the interplay of single-cell motility, cell proliferation, and intercellular contacts remains elusive. In particular, the role of E- and N-cadherin junctions, central components of intercellular contacts, is still controversial. Combining theoretical modeling with in vitro observations, we investigate the collective spreading behavior of colonies of human cancer cells (T24). The spreading of these colonies is driven by stochastic single-cell migration with frequent transient cell-cell contacts. We find that inhibition of E- and N-cadherin junctions decreases colony spreading and average spreading velocities, without affecting the strength of correlations in spreading velocities of neighboring cells. Based on a biophysical simulation model for cell migration, we show that the behavioral changes upon disruption of these junctions can be explained by reduced repulsive excluded volume interactions between cells. This suggests that in cancer cell migration, cadherin-based intercellular contacts sharpen cell boundaries leading to repulsive rather than cohesive interactions between cells, thereby promoting efficient cell spreading during collective migration.
Collapse
|
19
|
Wang W, Metzler R, Cherstvy AG. Anomalous diffusion, aging, and nonergodicity of scaled Brownian motion with fractional Gaussian noise: overview of related experimental observations and models. Phys Chem Chem Phys 2022; 24:18482-18504. [DOI: 10.1039/d2cp01741e] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
How does a systematic time-dependence of the diffusion coefficient $D (t)$ affect the ergodic and statistical characteristics of fractional Brownian motion (FBM)? Here, we examine how the behavior of the...
Collapse
|
20
|
Metzner C, Schilling A, Traxdorf M, Schulze H, Krauss P. Sleep as a random walk: a super-statistical analysis of EEG data across sleep stages. Commun Biol 2021; 4:1385. [PMID: 34893700 PMCID: PMC8664947 DOI: 10.1038/s42003-021-02912-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 11/23/2021] [Indexed: 11/15/2022] Open
Abstract
In clinical practice, human sleep is classified into stages, each associated with different levels of muscular activity and marked by characteristic patterns in the EEG signals. It is however unclear whether this subdivision into discrete stages with sharply defined boundaries is truly reflecting the dynamics of human sleep. To address this question, we consider one-channel EEG signals as heterogeneous random walks: stochastic processes controlled by hyper-parameters that are themselves time-dependent. We first demonstrate the heterogeneity of the random process by showing that each sleep stage has a characteristic distribution and temporal correlation function of the raw EEG signals. Next, we perform a super-statistical analysis by computing hyper-parameters, such as the standard deviation, kurtosis, and skewness of the raw signal distributions, within subsequent 30-second epochs. It turns out that also the hyper-parameters have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection. Moreover, we find that the hyper-parameters are not piece-wise constant, as the traditional hypnograms would suggest, but show rising or falling trends within and across sleep stages, pointing to an underlying continuous rather than sub-divided process that controls human sleep. Based on the hyper-parameters, we finally perform a pairwise similarity analysis between the different sleep stages, using a quantitative measure for the separability of data clusters in multi-dimensional spaces. To improve our understand of how EEG activity reflects the dynamics of human sleep, Metzner et al. use human EEG data and superstatistical analysis to demonstrate that each sleep stage has a characteristic distribution and temporal correlation function of raw EEG signals. They also show that the hyper-parameters controlling the EEG signals have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection.
Collapse
Affiliation(s)
- Claus Metzner
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany.
| | - Achim Schilling
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany.,Laboratory of Sensory and Cognitive Neuroscience, Aix-Marseille University, Marseille, France.,Cognitive Computational Neuroscience Group, Friedrich-Alexander University Erlangen-Nuremberg, Nuremberg, Germany
| | - Maximilian Traxdorf
- Department of Otorhinolaryngology, Paracelsus Medical University, Nuremberg, Germany
| | - Holger Schulze
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany
| | - Patrick Krauss
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany.,Cognitive Computational Neuroscience Group, Friedrich-Alexander University Erlangen-Nuremberg, Nuremberg, Germany.,Pattern Recognition Lab, Friedrich-Alexander University Erlangen-Nuremberg, Nuremberg, Germany
| |
Collapse
|
21
|
Metzner C, Hörsch F, Mark C, Czerwinski T, Winterl A, Voskens C, Fabry B. Detecting long-range interactions between migrating cells. Sci Rep 2021; 11:15031. [PMID: 34294808 PMCID: PMC8298713 DOI: 10.1038/s41598-021-94458-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 07/12/2021] [Indexed: 11/26/2022] Open
Abstract
Chemotaxis enables cells to systematically approach distant targets that emit a diffusible guiding substance. However, the visual observation of an encounter between a cell and a target does not necessarily indicate the presence of a chemotactic approach mechanism, as even a blindly migrating cell can come across a target by chance. To distinguish between the chemotactic approach and blind migration, we present an objective method that is based on the analysis of time-lapse recorded cell migration trajectories: For each movement step of a cell relative to the position of a potential target, we compute a p value that quantifies the likelihood of the movement direction under the null-hypothesis of blind migration. The resulting distribution of p values, pooled over all recorded cell trajectories, is then compared to an ensemble of reference distributions in which the positions of targets are randomized. First, we validate our method with simulated data, demonstrating that it reliably detects the presence or absence of remote cell-cell interactions. In a second step, we apply the method to data from three-dimensional collagen gels, interspersed with highly migratory natural killer (NK) cells that were derived from two different human donors. We find for one of the donors an attractive interaction between the NK cells, pointing to a cooperative behavior of these immune cells. When adding nearly stationary K562 tumor cells to the system, we find a repulsive interaction between K562 and NK cells for one of the donors. By contrast, we find attractive interactions between NK cells and an IL-15-secreting variant of K562 tumor cells. We therefore speculate that NK cells find wild-type tumor cells only by chance, but are programmed to leave a target quickly after a close encounter. We provide a freely available Python implementation of our p value method that can serve as a general tool for detecting long-range interactions in collective systems of self-driven agents.
Collapse
Affiliation(s)
- C Metzner
- Biophysics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
| | - F Hörsch
- Biophysics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - C Mark
- Biophysics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - T Czerwinski
- Biophysics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - A Winterl
- Biophysics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - C Voskens
- Department of Dermatology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.,Comprehensive Cancer Center Erlangen - European Metropolitan Area of Nürnberg (CCC-ER-EMN), Erlangen, Germany.,Deutsches Zentrum für Immuntherapie (DZI), Erlangen, Germany
| | - B Fabry
- Biophysics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| |
Collapse
|
22
|
d’Alessandro J, Barbier--Chebbah A, Cellerin V, Benichou O, Mège RM, Voituriez R, Ladoux B. Cell migration guided by long-lived spatial memory. Nat Commun 2021; 12:4118. [PMID: 34226542 PMCID: PMC8257581 DOI: 10.1038/s41467-021-24249-8] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 06/08/2021] [Indexed: 02/06/2023] Open
Abstract
Living cells actively migrate in their environment to perform key biological functions-from unicellular organisms looking for food to single cells such as fibroblasts, leukocytes or cancer cells that can shape, patrol or invade tissues. Cell migration results from complex intracellular processes that enable cell self-propulsion, and has been shown to also integrate various chemical or physical extracellular signals. While it is established that cells can modify their environment by depositing biochemical signals or mechanically remodelling the extracellular matrix, the impact of such self-induced environmental perturbations on cell trajectories at various scales remains unexplored. Here, we show that cells can retrieve their path: by confining motile cells on 1D and 2D micropatterned surfaces, we demonstrate that they leave long-lived physicochemical footprints along their way, which determine their future path. On this basis, we argue that cell trajectories belong to the general class of self-interacting random walks, and show that self-interactions can rule large scale exploration by inducing long-lived ageing, subdiffusion and anomalous first-passage statistics. Altogether, our joint experimental and theoretical approach points to a generic coupling between motile cells and their environment, which endows cells with a spatial memory of their path and can dramatically change their space exploration.
Collapse
Affiliation(s)
- Joseph d’Alessandro
- grid.508487.60000 0004 7885 7602Université de Paris, CNRS, Institut Jacques Monod, Paris, F-75006 France
| | - Alex Barbier--Chebbah
- grid.462844.80000 0001 2308 1657Laboratoire de Physique Théorique de la Matière Condensée, CNRS/Sorbonne Université, Paris, France
| | - Victor Cellerin
- grid.508487.60000 0004 7885 7602Université de Paris, CNRS, Institut Jacques Monod, Paris, F-75006 France
| | - Olivier Benichou
- grid.462844.80000 0001 2308 1657Laboratoire de Physique Théorique de la Matière Condensée, CNRS/Sorbonne Université, Paris, France
| | - René Marc Mège
- grid.508487.60000 0004 7885 7602Université de Paris, CNRS, Institut Jacques Monod, Paris, F-75006 France
| | - Raphaël Voituriez
- grid.462844.80000 0001 2308 1657Laboratoire Jean Perrin and Laboratoire de Physique Théorique de la Matière Condensée, CNRS/Sorbonne Université, Paris, France
| | - Benoît Ladoux
- grid.508487.60000 0004 7885 7602Université de Paris, CNRS, Institut Jacques Monod, Paris, F-75006 France
| |
Collapse
|
23
|
Scott M, Żychaluk K, Bearon RN. A mathematical framework for modelling 3D cell motility: applications to glioblastoma cell migration. MATHEMATICAL MEDICINE AND BIOLOGY-A JOURNAL OF THE IMA 2021; 38:333-354. [PMID: 34189581 DOI: 10.1093/imammb/dqab009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 06/09/2021] [Accepted: 06/10/2021] [Indexed: 11/14/2022]
Abstract
The collection of 3D cell tracking data from live images of micro-tissues is a recent innovation made possible due to advances in imaging techniques. As such there is increased interest in studying cell motility in 3D in vitro model systems but a lack of rigorous methodology for analysing the resulting data sets. One such instance of the use of these in vitro models is in the study of cancerous tumours. Growing multicellular tumour spheroids in vitro allows for modelling of the tumour microenvironment and the study of tumour cell behaviours, such as migration, which improves understanding of these cells and in turn could potentially improve cancer treatments. In this paper, we present a workflow for the rigorous analysis of 3D cell tracking data, based on the persistent random walk model, but adaptable to other biologically informed mathematical models. We use statistical measures to assess the fit of the model to the motility data and to estimate model parameters and provide confidence intervals for those parameters, to allow for parametrization of the model taking correlation in the data into account. We use in silico simulations to validate the workflow in 3D before testing our method on cell tracking data taken from in vitro experiments on glioblastoma tumour cells, a brain cancer with a very poor prognosis. The presented approach is intended to be accessible to both modellers and experimentalists alike in that it provides tools for uncovering features of the data set that may suggest amendments to future experiments or modelling attempts.
Collapse
Affiliation(s)
- M Scott
- Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK
| | - K Żychaluk
- Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK
| | - R N Bearon
- Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK
| |
Collapse
|
24
|
Liu Y, Jiao Y, He D, Fan Q, Zheng Y, Li G, Wang G, Yao J, Chen G, Lou S, Shuai J, Liu L. Deriving time-varying cellular motility parameters via wavelet analysis. Phys Biol 2021; 18. [PMID: 33910180 DOI: 10.1088/1478-3975/abfcad] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 04/28/2021] [Indexed: 11/11/2022]
Abstract
Cell migration, which is regulated by intracellular signaling pathways (ICSP) and extracellular matrix (ECM), plays an indispensable role in many physiological and pathological process such as normal tissue development and cancer metastasis. However, there is a lack of rigorous and quantitative tools for analyzing the time-varying characteristics of cell migration in heterogeneous microenvironment, resulted from, e.g. the time-dependent local stiffness due to microstructural remodeling by migrating cells. Here, we develop a wavelet-analysis approach to derive the time-dependent motility parameters from cell migration trajectories, based on the time-varying persistent random walk model. In particular, the wavelet denoising and wavelet transform are employed to analyze migration velocities and obtain the wavelet power spectrum. Subsequently, the time-dependent motility parameters are derived via Lorentzian power spectrum. Our results based on synthetic data indicate the superiority of the method for estimating the intrinsic transient motility parameters, robust against a variety of stochastic noises. We also carry out a systematic parameter study and elaborate the effects of parameter selection on the performance of the method. Moreover, we demonstrate the utility of our approach via analyzing experimental data ofin vitrocell migration in distinct microenvironments, including the migration of MDA-MB-231 cells in confined micro-channel arrays and correlated migration of MCF-10A cells due to ECM-mediated mechanical coupling. Our analysis shows that our approach can be as a powerful tool to accurately derive the time-dependent motility parameters, and further analyze the time-dependent characteristics of cell migration regulated by complex microenvironment.
Collapse
Affiliation(s)
- Yanping Liu
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing, 401331, People's Republic of China
| | - Yang Jiao
- Materials Science and Engineering, Arizona State University, Tempe, Arizona 85287, United States of America.,Department of Physics, Arizona State University, Tempe, Arizona 85287, United States of America
| | - Da He
- Spine Surgery, Beijing Jishuitan Hospital, Beijing, 100035, People's Republic of China
| | - Qihui Fan
- Beijing National Laboratory for Condensed Matte Physics and CAS Key Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Yu Zheng
- Department of Physics, Arizona State University, Tempe, Arizona 85287, United States of America
| | - Guoqiang Li
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing, 401331, People's Republic of China
| | - Gao Wang
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing, 401331, People's Republic of China
| | - Jingru Yao
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing, 401331, People's Republic of China
| | - Guo Chen
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing, 401331, People's Republic of China
| | - Silong Lou
- Department of Neurosurgery, Chongqing University Cancer Hospital, Chongqing, 400030, People's Republic of China
| | - Jianwei Shuai
- Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, People's Republic of China.,Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325000, People's Republic of China
| | - Liyu Liu
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing, 401331, People's Republic of China
| |
Collapse
|
25
|
Krauss P, Metzner C, Joshi N, Schulze H, Traxdorf M, Maier A, Schilling A. Analysis and visualization of sleep stages based on deep neural networks. Neurobiol Sleep Circadian Rhythms 2021; 10:100064. [PMID: 33763623 PMCID: PMC7973384 DOI: 10.1016/j.nbscr.2021.100064] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 02/27/2021] [Accepted: 03/01/2021] [Indexed: 11/28/2022] Open
Abstract
Automatic sleep stage scoring based on deep neural networks has come into focus of sleep researchers and physicians, as a reliable method able to objectively classify sleep stages would save human resources and simplify clinical routines. Due to novel open-source software libraries for machine learning, in combination with enormous recent progress in hardware development, a paradigm shift in the field of sleep research towards automatic diagnostics might be imminent. We argue that modern machine learning techniques are not just a tool to perform automatic sleep stage classification, but are also a creative approach to find hidden properties of sleep physiology. We have already developed and established algorithms to visualize and cluster EEG data, facilitating first assessments on sleep health in terms of sleep-apnea and consequently reduced daytime vigilance. In the following study, we further analyze cortical activity during sleep by determining the probabilities of momentary sleep stages, represented as hypnodensity graphs and then computing vectorial cross-correlations of different EEG channels. We can show that this measure serves to estimate the period length of sleep cycles and thus can help to find disturbances due to pathological conditions.
Collapse
Affiliation(s)
- Patrick Krauss
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Germany
- Cognitive Computational Neuroscience Group at the Chair of English Philology and Linguistics, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany
- Cognitive Neuroscience Center, University of Groningen, the Netherlands
| | - Claus Metzner
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Germany
- Biophysics, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany
| | - Nidhi Joshi
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Germany
| | - Holger Schulze
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Germany
| | - Maximilian Traxdorf
- Department of Otolaryngology, Head and Neck Surgery, University Hospital Erlangen, Germany
| | - Andreas Maier
- Machine Intelligence, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany
| | - Achim Schilling
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Germany
- Cognitive Computational Neuroscience Group at the Chair of English Philology and Linguistics, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany
- Laboratory of Sensory and Cognitive Neuroscience, Aix-Marseille University, Marseille, France
| |
Collapse
|
26
|
Liu Y, Jiao Y, Fan Q, Zheng Y, Li G, Yao J, Wang G, Lou S, Chen G, Shuai J, Liu L. Shannon entropy for time-varying persistence of cell migration. Biophys J 2021; 120:2552-2565. [PMID: 33940024 DOI: 10.1016/j.bpj.2021.04.026] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 03/10/2021] [Accepted: 04/26/2021] [Indexed: 12/13/2022] Open
Abstract
Cell migration, which can be significantly affected by intracellular signaling pathways and extracellular matrix, plays a crucial role in many physiological and pathological processes. Cell migration is typically modeled as a persistent random walk, which depends on two critical motility parameters, i.e., migration speed and persistence time. It is generally very challenging to efficiently and accurately quantify the migration dynamics from noisy experimental data. Here, we introduce the normalized Shannon entropy (SE) based on the FPS of cellular velocity autocovariance function to quantify migration dynamics. The SE introduced here possesses a similar physical interpretation as the Gibbs entropy for thermal systems in that SE naturally reflects the degree of order or randomness of cellular migration, attaining the maximal value of unity for purely diffusive migration (i.e., SE = 1 for the most "random" dynamics) and the minimal value of 0 for purely ballistic dynamics (i.e., SE = 0 for the most "ordered" dynamics). We also find that SE is strongly correlated with the migration persistence but is less sensitive to the migration speed. Moreover, we introduce the time-varying SE based on the WPS of cellular dynamics and demonstrate its superior utility to characterize the time-dependent persistence of cell migration, which typically results from complex and time-varying intra- or extracellular mechanisms. We employ our approach to analyze experimental data of in vitro cell migration regulated by distinct intracellular and extracellular mechanisms, exhibiting a rich spectrum of dynamic characteristics. Our analysis indicates that the SE and wavelet transform (i.e., SE-based approach) offers a simple and efficient tool to quantify cell migration dynamics in complex microenvironment.
Collapse
Affiliation(s)
- Yanping Liu
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing, China
| | - Yang Jiao
- Materials Science and Engineering, Arizona State University, Tempe, Arizona; Department of Physics, Arizona State University, Tempe, Arizona
| | - Qihui Fan
- Beijing National Laboratory for Condensed Matter Physics and CAS Key Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
| | - Yu Zheng
- Department of Physics, Arizona State University, Tempe, Arizona
| | - Guoqiang Li
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing, China
| | - Jingru Yao
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing, China
| | - Gao Wang
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing, China
| | - Silong Lou
- Department of Neurosurgery, Chongqing University Cancer Hospital, Chongqing, China
| | - Guo Chen
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing, China
| | - Jianwei Shuai
- Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China; Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325000, China.
| | - Liyu Liu
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing, China.
| |
Collapse
|
27
|
Enhanced persistence and collective migration in cooperatively aligning cell clusters. Biophys J 2021; 120:1483-1497. [PMID: 33617837 DOI: 10.1016/j.bpj.2021.02.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/21/2020] [Accepted: 02/03/2021] [Indexed: 12/26/2022] Open
Abstract
Most cells possess the capacity to locomote. Alone or collectively, this allows them to adapt, to rearrange, and to explore their surroundings. The biophysical characterization of such motile processes, in health and in disease, has so far focused mostly on two limiting cases: single-cell motility on the one hand and the dynamics of confluent tissues such as the epithelium on the other. The in-between regime of clusters, composed of relatively few cells moving as a coherent unit, has received less attention. Such small clusters are, however, deeply relevant in development but also in cancer metastasis. In this work, we use cellular Potts models and analytical active matter theory to understand how the motility of small cell clusters changes with N, the number of cells in the cluster. Modeling and theory reveal our two main findings: cluster persistence time increases with N, whereas the intrinsic diffusivity decreases with N. We discuss a number of settings in which the motile properties of more complex clusters can be analytically understood, revealing that the focusing effects of small-scale cooperation and cell-cell alignment can overcome the increased bulkiness and internal disorder of multicellular clusters to enhance overall migrational efficacy. We demonstrate this enhancement for small-cluster collective durotaxis, which is shown to proceed more effectively than for single cells. Our results may provide some novel, to our knowledge, insights into the connection between single-cell and large-scale collective motion and may point the way to the biophysical origins of the enhanced metastatic potential of small tumor cell clusters.
Collapse
|
28
|
Mitterwallner BG, Schreiber C, Daldrop JO, Rädler JO, Netz RR. Non-Markovian data-driven modeling of single-cell motility. Phys Rev E 2021; 101:032408. [PMID: 32289977 DOI: 10.1103/physreve.101.032408] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 01/07/2020] [Indexed: 01/23/2023]
Abstract
Trajectories of human breast cancer cells moving on one-dimensional circular tracks are modeled by the non-Markovian version of the Langevin equation that includes an arbitrary memory function. When averaged over cells, the velocity distribution exhibits spurious non-Gaussian behavior, while single cells are characterized by Gaussian velocity distributions. Accordingly, the data are described by a linear memory model which includes different random walk models that were previously used to account for various aspects of cell motility such as migratory persistence, non-Markovian effects, colored noise, and anomalous diffusion. The memory function is extracted from the trajectory data without restrictions or assumptions, thus making our approach truly data driven, and is used for unbiased single-cell comparison. The cell memory displays time-delayed single-exponential negative friction, which clearly distinguishes cell motion from the simple persistent random walk model and suggests a regulatory feedback mechanism that controls cell migration. Based on the extracted memory function we formulate a generalized exactly solvable cell migration model which indicates that negative friction generates cell persistence over long timescales. The nonequilibrium character of cell motion is investigated by mapping the non-Markovian Langevin equation with memory onto a Markovian model that involves a hidden degree of freedom and is equivalent to the underdamped active Ornstein-Uhlenbeck process.
Collapse
Affiliation(s)
- Bernhard G Mitterwallner
- Fachbereich Physik, Freie Universität Berlin, 14195 Berlin, Germany and Physik Fakultät, Ludwig Maximilians Universität, 80539 München, Germany
| | - Christoph Schreiber
- Fachbereich Physik, Freie Universität Berlin, 14195 Berlin, Germany and Physik Fakultät, Ludwig Maximilians Universität, 80539 München, Germany
| | - Jan O Daldrop
- Fachbereich Physik, Freie Universität Berlin, 14195 Berlin, Germany and Physik Fakultät, Ludwig Maximilians Universität, 80539 München, Germany
| | - Joachim O Rädler
- Fachbereich Physik, Freie Universität Berlin, 14195 Berlin, Germany and Physik Fakultät, Ludwig Maximilians Universität, 80539 München, Germany
| | - Roland R Netz
- Fachbereich Physik, Freie Universität Berlin, 14195 Berlin, Germany and Physik Fakultät, Ludwig Maximilians Universität, 80539 München, Germany
| |
Collapse
|
29
|
Reveal heterogeneous motion states in single nanoparticle trajectory using its own history. Sci China Chem 2020. [DOI: 10.1007/s11426-020-9896-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
30
|
Alteration of cell motility dynamics through collagen fiber density in photopolymerized polyethylene glycol hydrogels. Int J Biol Macromol 2020; 157:414-423. [DOI: 10.1016/j.ijbiomac.2020.04.144] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 04/03/2020] [Accepted: 04/18/2020] [Indexed: 12/17/2022]
|
31
|
Fortuna I, Perrone GC, Krug MS, Susin E, Belmonte JM, Thomas GL, Glazier JA, de Almeida RMC. CompuCell3D Simulations Reproduce Mesenchymal Cell Migration on Flat Substrates. Biophys J 2020; 118:2801-2815. [PMID: 32407685 PMCID: PMC7264849 DOI: 10.1016/j.bpj.2020.04.024] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 04/20/2020] [Accepted: 04/20/2020] [Indexed: 11/16/2022] Open
Abstract
Mesenchymal cell crawling is a critical process in normal development, in tissue function, and in many diseases. Quantitatively predictive numerical simulations of cell crawling thus have multiple scientific, medical, and technological applications. However, we still lack a low-computational-cost approach to simulate mesenchymal three-dimensional (3D) cell crawling. Here, we develop a computationally tractable 3D model (implemented as a simulation in the CompuCell3D simulation environment) of mesenchymal cells crawling on a two-dimensional substrate. The Fürth equation, the usual characterization of mean-squared displacement (MSD) curves for migrating cells, describes a motion in which, for increasing time intervals, cell movement transitions from a ballistic to a diffusive regime. Recent experiments have shown that for very short time intervals, cells exhibit an additional fast diffusive regime. Our simulations' MSD curves reproduce the three experimentally observed temporal regimes, with fast diffusion for short time intervals, slow diffusion for long time intervals, and intermediate time -interval-ballistic motion. The resulting parameterization of the trajectories for both experiments and simulations allows the definition of time- and length scales that translate between computational and laboratory units. Rescaling by these scales allows direct quantitative comparisons among MSD curves and between velocity autocorrelation functions from experiments and simulations. Although our simulations replicate experimentally observed spontaneous symmetry breaking, short-timescale diffusive motion, and spontaneous cell-motion reorientation, their computational cost is low, allowing their use in multiscale virtual-tissue simulations. Comparisons between experimental and simulated cell motion support the hypothesis that short-time actomyosin dynamics affects longer-time cell motility. The success of the base cell-migration simulation model suggests its future application in more complex situations, including chemotaxis, migration through complex 3D matrices, and collective cell motion.
Collapse
Affiliation(s)
- Ismael Fortuna
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Gabriel C Perrone
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Monique S Krug
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Eduarda Susin
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Julio M Belmonte
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana; Department of Physics, North Carolina State University, Raleigh, North Carolina
| | - Gilberto L Thomas
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.
| | - James A Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana
| | - Rita M C de Almeida
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil; Instituto Nacional de Ciência e Tecnologia, Sistemas Complexos, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil; Program de Pós Graduação em Bioinformática, Universidade Federal do Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil.
| |
Collapse
|
32
|
Lo Vecchio S, Thiagarajan R, Caballero D, Vigon V, Navoret L, Voituriez R, Riveline D. Collective Dynamics of Focal Adhesions Regulate Direction of Cell Motion. Cell Syst 2020; 10:535-542.e4. [DOI: 10.1016/j.cels.2020.05.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 02/20/2020] [Accepted: 05/19/2020] [Indexed: 01/14/2023]
|
33
|
Garner RM, Skariah G, Hadjitheodorou A, Belliveau NM, Savinov A, Footer MJ, Theriot JA. Neutrophil-like HL-60 cells expressing only GFP-tagged β-actin exhibit nearly normal motility. Cytoskeleton (Hoboken) 2020; 77:181-196. [PMID: 32072765 PMCID: PMC7383899 DOI: 10.1002/cm.21603] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 12/21/2019] [Accepted: 01/27/2020] [Indexed: 12/30/2022]
Abstract
Observations of actin dynamics in living cells using fluorescence microscopy have been foundational in the exploration of the mechanisms underlying cell migration. We used CRISPR/Cas9 gene editing to generate neutrophil‐like HL‐60 cell lines expressing GFP‐β‐actin from the endogenous locus (ACTB). In light of many previous reports outlining functional deficiencies of labeled actin, we anticipated that HL‐60 cells would only tolerate a monoallelic edit, as biallelic edited cells would produce no normal β‐actin. Surprisingly, we recovered viable monoallelic GFP‐β‐actin cells as well as biallelic edited GFP‐β‐actin cells, in which one copy of the ACTB gene is silenced and the other contains the GFP tag. Furthermore, the edited cells migrate with similar speeds and persistence as unmodified cells in a variety of motility assays, and have nearly normal cell shapes. These results might partially be explained by our observation that GFP‐β‐actin incorporates into the F‐actin network in biallelic edited cells at similar efficiencies as normal β‐actin in unedited cells. Additionally, the edited cells significantly upregulate γ‐actin, perhaps helping to compensate for the loss of normal β‐actin. Interestingly, biallelic edited cells have only modest changes in global gene expression relative to the monoallelic line, as measured by RNA sequencing. While monoallelic edited cells downregulate expression of the tagged allele and are thus only weakly fluorescent, biallelic edited cells are quite bright and well‐suited for live cell microscopy. The nondisruptive phenotype and direct interpretability of this fluorescent tagging approach make it a promising tool for studying actin dynamics in these rapidly migrating and highly phagocytic cells.
Collapse
Affiliation(s)
- Rikki M Garner
- Biophysics Program, Stanford University School of Medicine, Stanford, CA.,Department of Biology, Howard Hughes Medical Institute, University of Washington, Seattle, WA
| | - Gemini Skariah
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA
| | - Amalia Hadjitheodorou
- Department of Bioengineering, Stanford University Schools of Medicine and Engineering, Stanford, CA
| | - Nathan M Belliveau
- Department of Biology, Howard Hughes Medical Institute, University of Washington, Seattle, WA
| | - Andrew Savinov
- Department of Genome Sciences, University of Washington, Seattle, WA
| | - Matthew J Footer
- Department of Biology, Howard Hughes Medical Institute, University of Washington, Seattle, WA
| | - Julie A Theriot
- Department of Biology, Howard Hughes Medical Institute, University of Washington, Seattle, WA
| |
Collapse
|
34
|
Brückner DB, Fink A, Rädler JO, Broedersz CP. Disentangling the behavioural variability of confined cell migration. J R Soc Interface 2020. [DOI: 10.1098/rsif.2019.0689] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Cell-to-cell variability is inherent to numerous biological processes, including cell migration. Quantifying and characterizing the variability of migrating cells is challenging, as it requires monitoring many cells for long time windows under identical conditions. Here, we observe the migration of single human breast cancer cells (MDA-MB-231) in confining two-state micropatterns. To describe the stochastic dynamics of this confined migration, we employ a dynamical systems approach. We identify statistics to measure the behavioural variance of the migration, which significantly exceeds that predicted by a population-averaged stochastic model. This additional variance can be explained by the combination of an ‘ageing’ process and population heterogeneity. To quantify population heterogeneity, we decompose the cells into subpopulations of slow and fast cells, revealing the presence of distinct classes of dynamical systems describing the migration, ranging from bistable to limit cycle behaviour. Our findings highlight the breadth of migration behaviours present in cell populations.
Collapse
Affiliation(s)
- David B. Brückner
- Arnold-Sommerfeld-Center for Theoretical Physics and Center for NanoScience, Ludwig-Maximilians-Universität, München, Bayern, Germany
| | - Alexandra Fink
- Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, München, Bayern, Germany
| | - Joachim O. Rädler
- Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, München, Bayern, Germany
| | - Chase P. Broedersz
- Arnold-Sommerfeld-Center for Theoretical Physics and Center for NanoScience, Ludwig-Maximilians-Universität, München, Bayern, Germany
| |
Collapse
|
35
|
Czajkowski M, Sussman DM, Marchetti MC, Manning ML. Glassy dynamics in models of confluent tissue with mitosis and apoptosis. SOFT MATTER 2019; 15:9133-9149. [PMID: 31674622 DOI: 10.1039/c9sm00916g] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Recent work on particle-based models of tissues has suggested that any finite rate of cell division and cell death is sufficient to fluidize an epithelial tissue. At the same time, experimental evidence has indicated the existence of glassy dynamics in some epithelial layers despite continued cell cycling. To address this discrepancy, we quantify the role of cell birth and death on glassy states in confluent tissues using simulations of an active vertex model that includes cell motility, cell division, and cell death. Our simulation data is consistent with a simple ansatz in which the rate of cell-life cycling and the rate of relaxation of the tissue in the absence of cell cycling contribute independently and additively to the overall rate of cell motion. Specifically, we find that a glass-like regime with caging behavior indicated by subdiffusive cell displacements can be achieved in systems with sufficiently low rates of cell cycling.
Collapse
Affiliation(s)
- Michael Czajkowski
- Physics Department, Georgia Institute of Technology, Atlanta, GA 30332, USA.
| | - Daniel M Sussman
- Physics Department and BioInspired Institute, Syracuse University, Syracuse, NY 13244, USA
| | - M Cristina Marchetti
- Department of Physics, University of California at Santa Barbara, Santa Barbara, CA 93106, USA
| | - M Lisa Manning
- Physics Department and BioInspired Institute, Syracuse University, Syracuse, NY 13244, USA
| |
Collapse
|
36
|
Kwon T, Kwon OS, Cha HJ, Sung BJ. Stochastic and Heterogeneous Cancer Cell Migration: Experiment and Theory. Sci Rep 2019; 9:16297. [PMID: 31704971 PMCID: PMC6841739 DOI: 10.1038/s41598-019-52480-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 10/16/2019] [Indexed: 12/14/2022] Open
Abstract
Cell migration, an essential process for normal cell development and cancer metastasis, differs from a simple random walk: the mean-square displacement (〈(Δr)2(t)〉) of cells sometimes shows non-Fickian behavior, and the spatiotemporal correlation function (G(r, t)) of cells is often non-Gaussian. We find that this intriguing cell migration should be attributed to heterogeneity in a cell population, even one with a homogeneous genetic background. There are two limiting types of heterogeneity in a cell population: cellular heterogeneity and temporal heterogeneity. Cellular heterogeneity accounts for the cell-to-cell variation in migration capacity, while temporal heterogeneity arises from the temporal noise in the migration capacity of single cells. We illustrate that both cellular and temporal heterogeneity need to be taken into account simultaneously to elucidate cell migration. We investigate the two-dimensional migration of A549 lung cancer cells using time-lapse microscopy and find that the migration of A549 cells is Fickian but has a non-Gaussian spatiotemporal correlation. We find that when a theoretical model considers both cellular and temporal heterogeneity, the model reproduces all of the anomalous behaviors of cancer cell migration.
Collapse
Affiliation(s)
- Taejin Kwon
- Department of Chemistry and Research Institute for Basic Science, Sogang University, Seoul, 04107, Republic of Korea
| | - Ok-Seon Kwon
- Department of Life Sciences, Sogang University, Seoul, 04107, Republic of Korea
| | - Hyuk-Jin Cha
- College of Pharmacy, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Bong June Sung
- Department of Chemistry and Research Institute for Basic Science, Sogang University, Seoul, 04107, Republic of Korea.
| |
Collapse
|
37
|
Jana A, Nookaew I, Singh J, Behkam B, Franco AT, Nain AS. Crosshatch nanofiber networks of tunable interfiber spacing induce plasticity in cell migration and cytoskeletal response. FASEB J 2019; 33:10618-10632. [PMID: 31225977 PMCID: PMC6766658 DOI: 10.1096/fj.201900131r] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 05/30/2019] [Indexed: 01/14/2023]
Abstract
Biomechanical cues within tissue microenvironments are critical for maintaining homeostasis, and their disruption can contribute to malignant transformation and metastasis. Once transformed, metastatic cancer cells can migrate persistently by adapting (plasticity) to changes in the local fibrous extracellular matrix, and current strategies to recapitulate persistent migration rely exclusively on the use of aligned geometries. Here, the controlled interfiber spacing in suspended crosshatch networks of nanofibers induces cells to exhibit plasticity in migratory behavior (persistent and random) and the associated cytoskeletal arrangement. At dense spacing (3 and 6 µm), unexpectedly, elongated cells migrate persistently (in 1 dimension) at high speeds in 3-dimensional shapes with thick nuclei, and short focal adhesion cluster (FAC) lengths. With increased spacing (18 and 36 µm), cells attain 2-dimensional morphologies, have flattened nuclei and longer FACs, and migrate randomly by rapidly detaching their trailing edges that strain the nuclei by ∼35%. At 54-µm spacing, kite-shaped cells become near stationary. Poorly developed filamentous actin stress fibers are found only in cells on 3-µm networks. Gene-expression profiling shows a decrease in transcriptional potential and a differential up-regulation of metabolic pathways. The consistency in observed phenotypes across cell lines supports using this platform to dissect hallmarks of plasticity in migration in vitro.-Jana, A., Nookaew, I., Singh, J., Behkam, B., Franco, A. T., Nain, A. S. Crosshatch nanofiber networks of tunable interfiber spacing induce plasticity in cell migration and cytoskeletal response.
Collapse
Affiliation(s)
- Aniket Jana
- Department of Mechanical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | - Intawat Nookaew
- Department of Physiology and Biophysics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Jugroop Singh
- Department of Biomedical Engineering and Mechanics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | - Bahareh Behkam
- Department of Mechanical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
- Department of Biomedical Engineering and Mechanics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | - Aime T. Franco
- Department of Physiology and Biophysics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Amrinder S. Nain
- Department of Mechanical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
- Department of Biomedical Engineering and Mechanics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| |
Collapse
|
38
|
Metzner C. On the efficiency of chemotactic pursuit - Comparing blind search with temporal and spatial gradient sensing. Sci Rep 2019; 9:14091. [PMID: 31575917 PMCID: PMC6773759 DOI: 10.1038/s41598-019-50514-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 09/13/2019] [Indexed: 02/04/2023] Open
Abstract
In chemotaxis, cells are modulating their migration patterns in response to concentration gradients of a guiding substance. Immune cells are believed to use such chemotactic sensing for remotely detecting and homing in on pathogens. Considering that immune cells may encounter a multitude of targets with vastly different migration properties, ranging from immobile to highly mobile, it is not clear which strategies of chemotactic pursuit are simultaneously efficient and versatile. We tackle this problem theoretically and define a tunable response function that maps temporal or spatial concentration gradients to migration behavior. The seven free parameters of this response function are optimized numerically with the objective of maximizing search efficiency against a wide spectrum of target cell properties. Finally, we reverse-engineer the best-performing parameter sets to uncover strategies of chemotactic pursuit that are efficient under different biologically realistic boundary conditions. Although strategies based on the temporal or spatial sensing of chemotactic gradients are significantly more efficient than unguided migration, such ‘blind search’ turns out to work surprisingly well, in particular if the immune cells are fast and directionally persistent. The resulting simulated data can be used for the design of chemotaxis experiments and for the development of algorithms that automatically detect and quantify goal oriented behavior in measured immune cell trajectories.
Collapse
Affiliation(s)
- Claus Metzner
- Biophysics Group, Department of Physics, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany.
| |
Collapse
|
39
|
d'Alessandro J, Mas L, Aubry L, Rieu JP, Rivière C, Anjard C. Collective regulation of cell motility using an accurate density-sensing system. J R Soc Interface 2019; 15:rsif.2018.0006. [PMID: 29563247 DOI: 10.1098/rsif.2018.0006] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Accepted: 02/22/2018] [Indexed: 01/31/2023] Open
Abstract
The capacity of living cells to sense their population density and to migrate accordingly is essential for the regulation of many physiological processes. However, the mechanisms used to achieve such functions are poorly known. Here, based on the analysis of multiple trajectories of vegetative Dictyostelium discoideum cells, we investigate such a system extensively. We show that the cells secrete a high-molecular-weight quorum-sensing factor (QSF) in their medium. This extracellular signal induces, in turn, a reduction of the cell movements, in particular, through the downregulation of a mode of motility with high persistence time. This response appears independent of cAMP and involves a G-protein-dependent pathway. Using a mathematical analysis of the cells' response function, we evidence a negative feedback on the QSF secretion, which unveils a powerful generic mechanism for the cells to detect when they exceed a density threshold. Altogether, our results provide a comprehensive and dynamical view of this system enabling cells in a scattered population to adapt their motion to their neighbours without physical contact.
Collapse
Affiliation(s)
- Joseph d'Alessandro
- University Lyon, Université Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, 69622, Villeurbanne, France
| | - Lauriane Mas
- University Grenoble Alpes, CEA, Inserm, BIG-BGE, 38000 Grenoble, France
| | - Laurence Aubry
- University Grenoble Alpes, CEA, Inserm, BIG-BGE, 38000 Grenoble, France
| | - Jean-Paul Rieu
- University Lyon, Université Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, 69622, Villeurbanne, France
| | - Charlotte Rivière
- University Lyon, Université Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, 69622, Villeurbanne, France
| | - Christophe Anjard
- University Lyon, Université Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, 69622, Villeurbanne, France
| |
Collapse
|
40
|
Thapa S, Lukat N, Selhuber-Unkel C, Cherstvy AG, Metzler R. Transient superdiffusion of polydisperse vacuoles in highly motile amoeboid cells. J Chem Phys 2019; 150:144901. [PMID: 30981236 DOI: 10.1063/1.5086269] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Affiliation(s)
- Samudrajit Thapa
- Institute for Physics and Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany
| | - Nils Lukat
- Institute of Materials Science, Christian-Albrechts-Universität zu Kiel, 24143 Kiel, Germany
| | | | - Andrey G. Cherstvy
- Institute for Physics and Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany
| | - Ralf Metzler
- Institute for Physics and Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany
| |
Collapse
|
41
|
Cherstvy AG, Thapa S, Wagner CE, Metzler R. Non-Gaussian, non-ergodic, and non-Fickian diffusion of tracers in mucin hydrogels. SOFT MATTER 2019; 15:2526-2551. [PMID: 30734041 DOI: 10.1039/c8sm02096e] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Native mucus is polymer-based soft-matter material of paramount biological importance. How non-Gaussian and non-ergodic is the diffusive spreading of pathogens in mucus? We study the passive, thermally driven motion of micron-sized tracers in hydrogels of mucins, the main polymeric component of mucus. We report the results of the Bayesian analysis for ranking several diffusion models for a set of tracer trajectories [C. E. Wagner et al., Biomacromolecules, 2017, 18, 3654]. The models with "diffusing diffusivity", fractional and standard Brownian motion are used. The likelihood functions and evidences of each model are computed, ranking the significance of each model for individual traces. We find that viscoelastic anomalous diffusion is often most probable, followed by Brownian motion, while the model with a diffusing diffusion coefficient is only realised rarely. Our analysis also clarifies the distribution of time-averaged displacements, correlations of scaling exponents and diffusion coefficients, and the degree of non-Gaussianity of displacements at varying pH levels. Weak ergodicity breaking is also quantified. We conclude that-consistent with the original study-diffusion of tracers in the mucin gels is most non-Gaussian and non-ergodic at low pH that corresponds to the most heterogeneous networks. Using the Bayesian approach with the nested-sampling algorithm, together with the quantitative analysis of multiple statistical measures, we report new insights into possible physical mechanisms of diffusion in mucin gels.
Collapse
Affiliation(s)
- Andrey G Cherstvy
- Institute for Physics & Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany.
| | | | | | | |
Collapse
|
42
|
Passucci G, Brasch ME, Henderson JH, Zaburdaev V, Manning ML. Identifying the mechanism for superdiffusivity in mouse fibroblast motility. PLoS Comput Biol 2019; 15:e1006732. [PMID: 30763309 PMCID: PMC6392322 DOI: 10.1371/journal.pcbi.1006732] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 02/27/2019] [Accepted: 12/20/2018] [Indexed: 12/02/2022] Open
Abstract
We seek to characterize the motility of mouse fibroblasts on 2D substrates. Utilizing automated tracking techniques, we find that cell trajectories are super-diffusive, where displacements scale faster than t1/2 in all directions. Two mechanisms have been proposed to explain such statistics in other cell types: run and tumble behavior with Lévy-distributed run times, and ensembles of cells with heterogeneous speed and rotational noise. We develop an automated toolkit that directly compares cell trajectories to the predictions of each model and demonstrate that ensemble-averaged quantities such as the mean-squared displacements and velocity autocorrelation functions are equally well-fit by either model. However, neither model correctly captures the short-timescale behavior quantified by the displacement probability distribution or the turning angle distribution. We develop a hybrid model that includes both run and tumble behavior and heterogeneous noise during the runs, which correctly matches the short-timescale behaviors and indicates that the run times are not Lévy distributed. The analysis tools developed here should be broadly useful for distinguishing between mechanisms for superdiffusivity in other cells types and environments.
Collapse
Affiliation(s)
- Giuseppe Passucci
- Physics Department, Syracuse University, Syracuse, New York, United States of America
| | - Megan E. Brasch
- Biomedical and Chemical Engineering Department, Syracuse University, Syracuse, New York, United States of America
| | - James H. Henderson
- Biomedical and Chemical Engineering Department, Syracuse University, Syracuse, New York, United States of America
- Syracuse Biomaterials Institute, Syracuse University, Syracuse, New York, United States of America
| | - Vasily Zaburdaev
- Institute of Supercomputing Technologies, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Department of Biology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - M. Lisa Manning
- Physics Department, Syracuse University, Syracuse, New York, United States of America
- Syracuse Biomaterials Institute, Syracuse University, Syracuse, New York, United States of America
| |
Collapse
|
43
|
Thapa S, Lomholt MA, Krog J, Cherstvy AG, Metzler R. Bayesian analysis of single-particle tracking data using the nested-sampling algorithm: maximum-likelihood model selection applied to stochastic-diffusivity data. Phys Chem Chem Phys 2018; 20:29018-29037. [PMID: 30255886 DOI: 10.1039/c8cp04043e] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We employ Bayesian statistics using the nested-sampling algorithm to compare and rank multiple models of ergodic diffusion (including anomalous diffusion) as well as to assess their optimal parameters for in silico-generated and real time-series. We focus on the recently-introduced model of Brownian motion with "diffusing diffusivity"-giving rise to widely-observed non-Gaussian displacement statistics-and its comparison to Brownian and fractional Brownian motion, also for the time-series with some measurement noise. We conduct this model-assessment analysis using Bayesian statistics and the nested-sampling algorithm on the level of individual particle trajectories. We evaluate relative model probabilities and compute best-parameter sets for each diffusion model, comparing the estimated parameters to the true ones. We test the performance of the nested-sampling algorithm and its predictive power both for computer-generated (idealised) trajectories as well as for real single-particle-tracking trajectories. Our approach delivers new important insight into the objective selection of the most suitable stochastic model for a given time-series. We also present first model-ranking results in application to experimental data of tracer diffusion in polymer-based hydrogels.
Collapse
Affiliation(s)
- Samudrajit Thapa
- Institute for Physics & Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany
| | | | | | | | | |
Collapse
|
44
|
Matsuda Y, Hanasaki I, Iwao R, Yamaguchi H, Niimi T. Estimation of diffusive states from single-particle trajectory in heterogeneous medium using machine-learning methods. Phys Chem Chem Phys 2018; 20:24099-24108. [PMID: 30204178 DOI: 10.1039/c8cp02566e] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
We propose a novel approach to analyze random walks in heterogeneous medium using a hybrid machine-learning method based on a gamma mixture and a hidden Markov model. A gamma mixture and a hidden Markov model respectively provide the number and the most probable sequence of diffusive states from the time series position data of particles/molecules obtained by single-particle/molecule tracking (SPT/SMT) method. We evaluate the performance of our proposed method for numerically generated trajectories. It is shown that our proposed method can correctly extract the number of diffusive states when each trajectory is long enough to be frame averaged. We also indicate that our method can provide an indicator whether the assumption of a medium consisting of discrete diffusive states is appropriate or not based on the available amount of trajectory data. Then, we demonstrate an application of our method to the analysis of experimentally obtained SPT data.
Collapse
Affiliation(s)
- Yu Matsuda
- Department of Modern Mechanical Engineering, Waseda University, 3-4-1 Ookubo, Shinjuku-ku, Tokyo 169-8555, Japan.
| | | | | | | | | |
Collapse
|
45
|
Abstract
Three-dimensional (3D) cell culture systems have gained increasing interest not only for 3D migration studies but also for their use in drug screening, tissue engineering, and ex vivo modeling of metastatic behavior in the field of cancer biology and morphogenesis in the field of developmental biology. The goal of studying cells in a 3D context is to attempt to more faithfully recapitulate the physiological microenvironment of tissues, including mechanical and structural parameters that we envision will reveal more predictive data for development programs and disease states. In this review, we discuss the pros and cons of several well-characterized 3D cell culture systems for performing 3D migration studies. We discuss the intracellular and extracellular signaling mechanisms that govern cell migration. We also describe the mathematical models and relevant assumptions that can be used to describe 3D cell movement.
Collapse
Affiliation(s)
- Pei-Hsun Wu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences in Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland 21218, USA;, ,
| | - Daniele M. Gilkes
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences in Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland 21218, USA;, ,
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Denis Wirtz
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences in Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland 21218, USA;, ,
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| |
Collapse
|
46
|
Abstract
Time series generated by complex systems like financial markets and the earth’s atmosphere often represent superstatistical random walks: on short time scales, the data follow a simple low-level model, but the model parameters are not constant and can fluctuate on longer time scales according to a high-level model. While the low-level model is often dictated by the type of the data, the high-level model, which describes how the parameters change, is unknown in most cases. Here we present a computationally efficient method to infer the time course of the parameter variations from time-series with short-range correlations. Importantly, this method evaluates the model evidence to objectively select between competing high-level models. We apply this method to detect anomalous price movements in financial markets, characterize cancer cell invasiveness, identify historical policies relevant for working safety in coal mines, and compare different climate change scenarios to forecast global warming. Systematic changes in stock market prices or in the migration behaviour of cancer cells may be hidden behind random fluctuations. Here, Mark et al. describe an empirical approach to identify when and how such real-world systems undergo systematic changes.
Collapse
|
47
|
Ebata H, Yamamoto A, Tsuji Y, Sasaki S, Moriyama K, Kuboki T, Kidoaki S. Persistent random deformation model of cells crawling on a gel surface. Sci Rep 2018; 8:5153. [PMID: 29581462 PMCID: PMC5980085 DOI: 10.1038/s41598-018-23540-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 03/15/2018] [Indexed: 12/19/2022] Open
Abstract
In general, cells move on a substrate through extension and contraction of the cell body. Though cell movement should be explained by taking into account the effect of such shape fluctuations, past approaches to formulate cell-crawling have not sufficiently quantified the relationship between cell movement (velocity and trajectory) and shape fluctuations based on experimental data regarding actual shaping dynamics. To clarify this relationship, we experimentally characterized cell-crawling in terms of shape fluctuations, especially extension and contraction, by using an elasticity-tunable gel substrate to modulate cell shape. As a result, an amoeboid swimmer-like relation was found to arise between the cell velocity and cell-shape dynamics. To formulate this experimentally-obtained relationship between cell movement and shaping dynamics, we established a persistent random deformation (PRD) model based on equations of a deformable self-propelled particle adopting an amoeboid swimmer-like velocity-shape relationship. The PRD model successfully explains the statistical properties of velocity, trajectory and shaping dynamics of the cells including back-and-forth motion, because the velocity equation exhibits time-reverse symmetry, which is essentially different from previous models. We discuss the possible application of this model to classify the phenotype of cell migration based on the characteristic relation between movement and shaping dynamics.
Collapse
Affiliation(s)
- Hiroyuki Ebata
- Laboratory of Biomedical and Biophysical Chemistry, Institute for Materials Chemistry and Engineering, Kyushu University, CE41-204, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan.
| | - Aki Yamamoto
- Laboratory of Biomedical and Biophysical Chemistry, Institute for Materials Chemistry and Engineering, Kyushu University, CE41-204, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
| | - Yukie Tsuji
- Laboratory of Biomedical and Biophysical Chemistry, Institute for Materials Chemistry and Engineering, Kyushu University, CE41-204, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
| | - Saori Sasaki
- Laboratory of Biomedical and Biophysical Chemistry, Institute for Materials Chemistry and Engineering, Kyushu University, CE41-204, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
| | - Kousuke Moriyama
- Laboratory of Biomedical and Biophysical Chemistry, Institute for Materials Chemistry and Engineering, Kyushu University, CE41-204, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
| | - Thasaneeya Kuboki
- Laboratory of Biomedical and Biophysical Chemistry, Institute for Materials Chemistry and Engineering, Kyushu University, CE41-204, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
| | - Satoru Kidoaki
- Laboratory of Biomedical and Biophysical Chemistry, Institute for Materials Chemistry and Engineering, Kyushu University, CE41-204, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan.
| |
Collapse
|
48
|
Budini AA, Cáceres MO. First-passage time for superstatistical Fokker-Planck models. Phys Rev E 2018; 97:012137. [PMID: 29448367 DOI: 10.1103/physreve.97.012137] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Indexed: 06/08/2023]
Abstract
The first-passage-time (FPT) problem is studied for superstatistical models assuming that the mesoscopic system dynamics is described by a Fokker-Planck equation. We show that all moments of the random intensive parameter associated to the superstatistical approach can be put in one-to-one correspondence with the moments of the FPT. For systems subjected to an additional uncorrelated external force, the same statistical information is obtained from the dependence of the FPT moments on the external force. These results provide an alternative technique for checking the validity of superstatistical models. As an example, we characterize the mean FPT for a forced Brownian particle.
Collapse
Affiliation(s)
- Adrián A Budini
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Centro Atómico Bariloche, Avenida E. Bustillo Km 9.5, 8400 Bariloche, Argentina and Universidad Tecnológica Nacional (UTN-FRBA), Fanny Newbery 111, 8400 Bariloche, Argentina
| | - Manuel O Cáceres
- Centro Atómico Bariloche, CNEA, Instituto Balseiro and CONICET, 8400 Bariloche, Argentina
| |
Collapse
|
49
|
Cherstvy AG, Nagel O, Beta C, Metzler R. Non-Gaussianity, population heterogeneity, and transient superdiffusion in the spreading dynamics of amoeboid cells. Phys Chem Chem Phys 2018; 20:23034-23054. [DOI: 10.1039/c8cp04254c] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
What is the underlying diffusion process governing the spreading dynamics and search strategies employed by amoeboid cells?
Collapse
Affiliation(s)
- Andrey G. Cherstvy
- Institute for Physics & Astronomy
- University of Potsdam
- 14476 Potsdam-Golm
- Germany
| | - Oliver Nagel
- Institute for Physics & Astronomy
- University of Potsdam
- 14476 Potsdam-Golm
- Germany
| | - Carsten Beta
- Institute for Physics & Astronomy
- University of Potsdam
- 14476 Potsdam-Golm
- Germany
| | - Ralf Metzler
- Institute for Physics & Astronomy
- University of Potsdam
- 14476 Potsdam-Golm
- Germany
| |
Collapse
|
50
|
Tamada A, Igarashi M. Revealing chiral cell motility by 3D Riesz transform-differential interference contrast microscopy and computational kinematic analysis. Nat Commun 2017; 8:2194. [PMID: 29259161 PMCID: PMC5736583 DOI: 10.1038/s41467-017-02193-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 11/10/2017] [Indexed: 11/22/2022] Open
Abstract
Left–right asymmetry is a fundamental feature of body plans, but its formation mechanisms and roles in functional lateralization remain unclear. Accumulating evidence suggests that left–right asymmetry originates in the cellular chirality. However, cell chirality has not yet been quantitatively investigated, mainly due to the absence of appropriate methods. Here we combine 3D Riesz transform-differential interference contrast (RT-DIC) microscopy and computational kinematic analysis to characterize chiral cellular morphology and motility. We reveal that filopodia of neuronal growth cones exhibit 3D left-helical motion with retraction and right-screw rotation. We next apply the methods to amoeba Dictyostelium discoideum and discover right-handed clockwise cell migration on a 2D substrate and right-screw rotation of subcellular protrusions along the radial axis in a 3D substrate. Thus, RT-DIC microscopy and the computational kinematic analysis are useful and versatile tools to reveal the mechanisms of left–right asymmetry formation and the emergence of lateralized functions. The lack of an appropriate method has hampered quantitative measurements of cell chirality. Here, the authors combine Riesz transform-differential interference contrast microscopy and computational kinematic analysis to reveal chiral cell motility of neuronal growth cone filopodia and cellular slime mold.
Collapse
Affiliation(s)
- Atsushi Tamada
- Center for Transdisciplinary Research, Institute for Research Promotion, Niigata University, Niigata, 951-8510, Japan. .,Department of Neurochemistry and Molecular Cell Biology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, 951-8510, Japan. .,Decoding and Controlling Brain Information, Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, Kawaguchi, Saitama, 332-0012, Japan.
| | - Michihiro Igarashi
- Center for Transdisciplinary Research, Institute for Research Promotion, Niigata University, Niigata, 951-8510, Japan.,Department of Neurochemistry and Molecular Cell Biology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, 951-8510, Japan
| |
Collapse
|