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Artículos de revistas sobre el tema "Cell interaction"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 29 de julio de 2024

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1

Brückner, David B., Nicolas Arlt, Alexandra Fink, Pierre Ronceray, Joachim O. Rädler y Chase P. Broedersz. "Learning the dynamics of cell–cell interactions in confined cell migration". Proceedings of the National Academy of Sciences 118, n.º 7 (12 de febrero de 2021): e2016602118. http://dx.doi.org/10.1073/pnas.2016602118.

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The migratory dynamics of cells in physiological processes, ranging from wound healing to cancer metastasis, rely on contact-mediated cell–cell interactions. These interactions play a key role in shaping the stochastic trajectories of migrating cells. While data-driven physical formalisms for the stochastic migration dynamics of single cells have been developed, such a framework for the behavioral dynamics of interacting cells still remains elusive. Here, we monitor stochastic cell trajectories in a minimal experimental cell collider: a dumbbell-shaped micropattern on which pairs of cells perform repeated cellular collisions. We observe different characteristic behaviors, including cells reversing, following, and sliding past each other upon collision. Capitalizing on this large experimental dataset of coupled cell trajectories, we infer an interacting stochastic equation of motion that accurately predicts the observed interaction behaviors. Our approach reveals that interacting noncancerous MCF10A cells can be described by repulsion and friction interactions. In contrast, cancerous MDA-MB-231 cells exhibit attraction and antifriction interactions, promoting the predominant relative sliding behavior observed for these cells. Based on these experimentally inferred interactions, we show how this framework may generalize to provide a unifying theoretical description of the diverse cellular interaction behaviors of distinct cell types.

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2

Ogushi, Fumiko y Hiroshi Kori. "3P277 Dependence of cell differentiation ratio on cell-cell interaction and noise(24. Mathematical biology,Poster)". Seibutsu Butsuri 53, supplement1-2 (2013): S257. http://dx.doi.org/10.2142/biophys.53.s257_6.

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3

Chesterton, C. J. "Cell to cell interaction". FEBS Letters 293, n.º 1-2 (1 de noviembre de 1991): 229. http://dx.doi.org/10.1016/0014-5793(91)81201-i.

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4

Manimaran, K., Arun Kumar, AvinashGandi D y S. Sankaranarayanan. "Interaction of Human Dental Pulp Stem Cells and Ameloblastic Cell In-vitro- A Preclinical Analysis". Annals of Oral Health and Dental Research 2, n.º 1 (17 de enero de 2018): A1–5. http://dx.doi.org/10.21276/aohdr.1831.

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5

Oropesa-Nuñez, Reinier, Andrea Mescola, Massimo Vassalli y Claudio Canale. "Impact of Experimental Parameters on Cell–Cell Force Spectroscopy Signature". Sensors 21, n.º 4 (4 de febrero de 2021): 1069. http://dx.doi.org/10.3390/s21041069.

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Atomic force microscopy is an extremely versatile technique, featuring atomic-scale imaging resolution, and also offering the possibility to probe interaction forces down to few pN. Recently, this technique has been specialized to study the interaction between single living cells, one on the substrate, and a second being adhered on the cantilever. Cell–cell force spectroscopy offers a unique tool to investigate in fine detail intra-cellular interactions, and it holds great promise to elucidate elusive phenomena in physiology and pathology. Here we present a systematic study of the effect of the main measurement parameters on cell–cell curves, showing the importance of controlling the experimental conditions. Moreover, a simple theoretical interpretation is proposed, based on the number of contacts formed between the two interacting cells. The results show that single cell–cell force spectroscopy experiments carry a wealth of information that can be exploited to understand the inner dynamics of the interaction of living cells at the molecular level.

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6

Hwang, Inkyu, Jing-Feng Huang, Hidehiro Kishimoto, Anders Brunmark, Per A. Peterson, Michael R. Jackson, Charles D. Surh, Zeling Cai y Jonathan Sprent. "T Cells Can Use Either T Cell Receptor or Cd28 Receptors to Absorb and Internalize Cell Surface Molecules Derived from Antigen-Presenting Cells". Journal of Experimental Medicine 191, n.º 7 (27 de marzo de 2000): 1137–48. http://dx.doi.org/10.1084/jem.191.7.1137.

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At the site of contact between T cells and antigen-presenting cells (APCs), T cell receptor (TCR)–peptide–major histocompatibility complex (MHC) interaction is intensified by interactions between other molecules, notably by CD28 and lymphocyte function-associated antigen 1 (LFA-1) on T cells interacting with B7 (B7-1 and B7-2), and intracellular adhesion molecule 1 (ICAM-1), respectively, on APCs. Here, we show that during T cell–APC interaction, T cells rapidly absorb various molecules from APCs onto the cell membrane and then internalize these molecules. This process is dictated by at least two receptors on T cells, namely CD28 and TCR molecules. The biological significance of T cell uptake of molecules from APCs is unclear. One possibility is that this process may allow activated T cells to move freely from one APC to another and eventually gain entry into the circulation.

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7

Yuan, Ye, Carlos Cosme, Taylor Sterling Adams, Jonas Schupp, Koji Sakamoto, Nikos Xylourgidis, Matthew Ruffalo, Jiachen Li, Naftali Kaminski y Ziv Bar-Joseph. "CINS: Cell Interaction Network inference from Single cell expression data". PLOS Computational Biology 18, n.º 9 (12 de septiembre de 2022): e1010468. http://dx.doi.org/10.1371/journal.pcbi.1010468.

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Studies comparing single cell RNA-Seq (scRNA-Seq) data between conditions mainly focus on differences in the proportion of cell types or on differentially expressed genes. In many cases these differences are driven by changes in cell interactions which are challenging to infer without spatial information. To determine cell-cell interactions that differ between conditions we developed the Cell Interaction Network Inference (CINS) pipeline. CINS combines Bayesian network analysis with regression-based modeling to identify differential cell type interactions and the proteins that underlie them. We tested CINS on a disease case control and on an aging mouse dataset. In both cases CINS correctly identifies cell type interactions and the ligands involved in these interactions improving on prior methods suggested for cell interaction predictions. We performed additional mouse aging scRNA-Seq experiments which further support the interactions identified by CINS.

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8

Okuyama, Akihiko. "INTRATESTICULAR CELL TO CELL INTERACTION". Japanese Journal of Urology 83, n.º 7 (1992): 1027–35. http://dx.doi.org/10.5980/jpnjurol1989.83.1027.

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9

Yamasaki, Hiroshi. "Cell-Cell Interaction and Carcinogenesis". Toxicologic Pathology 14, n.º 3 (abril de 1986): 363–69. http://dx.doi.org/10.1177/019262338601400313.

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10

Lin, Yingxin, Lipin Loo, Andy Tran, David M. Lin, Cesar Moreno, Daniel Hesselson, G. Gregory Neely y Jean Y. H. Yang. "Scalable workflow for characterization of cell-cell communication in COVID-19 patients". PLOS Computational Biology 18, n.º 10 (5 de octubre de 2022): e1010495. http://dx.doi.org/10.1371/journal.pcbi.1010495.

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COVID-19 patients display a wide range of disease severity, ranging from asymptomatic to critical symptoms with high mortality risk. Our ability to understand the interaction of SARS-CoV-2 infected cells within the lung, and of protective or dysfunctional immune responses to the virus, is critical to effectively treat these patients. Currently, our understanding of cell-cell interactions across different disease states, and how such interactions may drive pathogenic outcomes, is incomplete. Here, we developed a generalizable and scalable workflow for identifying cells that are differentially interacting across COVID-19 patients with distinct disease outcomes and use this to examine eight public single-cell RNA-seq datasets (six from peripheral blood mononuclear cells, one from bronchoalveolar lavage and one from nasopharyngeal), with a total of 211 individual samples. By characterizing the cell-cell interaction patterns across epithelial and immune cells in lung tissues for patients with varying disease severity, we illustrate diverse communication patterns across individuals, and discover heterogeneous communication patterns among moderate and severe patients. We further illustrate patterns derived from cell-cell interactions are potential signatures for discriminating between moderate and severe patients. Overall, this workflow can be generalized and scaled to combine multiple scRNA-seq datasets to uncover cell-cell interactions.

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11

Guo, Chutian. "Cell Type Specific Gene Interaction between Microbiota and Antidepressant Drugs". International Journal of Bioscience, Biochemistry and Bioinformatics 11, n.º 2 (abril de 2021): 14–21. http://dx.doi.org/10.17706/ijbbb.2021.11.2.14-21.

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12

Pasqual, Giulia. "Characterising cell-cell interactions". EU Research 32, Autumn 2022 (2022): 24–25. http://dx.doi.org/10.56181/qvsz2211.

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T-cells are an important part of the immune system, but they need to interact with dendritic cells before they can be activated and acquire specific functions. We spoke to Dr Giulia Pasqual about her research into the interactions between dendritic- and T-cells, and how this interaction influences the subsequent fate of T-cells.

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13

Van De Water, Thomas R. "Tissue interactions and cell differentiation: neurone–sensory cell interaction during otic development". Development 103, Supplement (1 de septiembre de 1988): 185–93. http://dx.doi.org/10.1242/dev.103.supplement.185.

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Statoacoustic ganglion neurones (SAG) are produced by the same group of cells (otic placode) that produce all of the receptor cells that populate the sensory areas of the inner ear. The observation that ingrowth of SAG neurites to presumptive sensory areas of the inner ear preceded cytodifferentiation of those receptor cells suggested a causal relationship. Results from in vivo, in ovo and in vitro studies do not support a causal relationship. These studies support the hypothesis that the programme for labyrinthine sensory cell differentiation is intrinsic and does not require the extrinsic stimulus of neuronal interaction to trigger its expression. In contrast, developing statoacoustic ganglion neurones appear to require a trophic influence that is supplied by either their peripheral or central target tissues for their survival and maturation in vitro. A mechanism for the ingrowth of SAG dendrites to their appropriate target sites within the inner ear proposes that attractant fields produced by areas of differentiating sensory cells act to guide the nerve growth cones of ingrowing SAG neurites to the appropriate tissues. Preliminary results from a hcterochronic series of SAG implants to common age otocysts suggest that these SAG neurones are capable of responding to the attractant fields which are produced by presumptive labyrinthine sensory epithelium over an extended period of otic development. Both in ovo and in vitro studies suggest that spatiotemporal patterns of extracellular matrix molecules may be important components of the attractant fields which are produced by the sensory areas of the developing inner ear and may ultimately result in the specificity of their neuronal connections.

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14

van Vliet, Simon, Christoph Hauert, Kyle Fridberg, Martin Ackermann y Alma Dal Co. "Global dynamics of microbial communities emerge from local interaction rules". PLOS Computational Biology 18, n.º 3 (4 de marzo de 2022): e1009877. http://dx.doi.org/10.1371/journal.pcbi.1009877.

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Most microbes live in spatially structured communities (e.g., biofilms) in which they interact with their neighbors through the local exchange of diffusible molecules. To understand the functioning of these communities, it is essential to uncover how these local interactions shape community-level properties, such as the community composition, spatial arrangement, and growth rate. Here, we present a mathematical framework to derive community-level properties from the molecular mechanisms underlying the cell-cell interactions for systems consisting of two cell types. Our framework consists of two parts: a biophysical model to derive the local interaction rules (i.e. interaction range and strength) from the molecular parameters underlying the cell-cell interactions and a graph based model to derive the equilibrium properties of the community (i.e. composition, spatial arrangement, and growth rate) from these local interaction rules. Our framework shows that key molecular parameters underlying the cell-cell interactions (e.g., the uptake and leakage rates of molecules) determine community-level properties. We apply our model to mutualistic cross-feeding communities and show that spatial structure can be detrimental for these communities. Moreover, our model can qualitatively recapitulate the properties of an experimental microbial community. Our framework can be extended to a variety of systems of two interacting cell types, within and beyond the microbial world, and contributes to our understanding of how community-level properties emerge from microscopic interactions between cells.

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15

Parekkadan, Biju, Yevgeny Berdichevsky, Daniel Irimia, Avrum Leeder, Gabriel Yarmush, Mehmet Toner, John B. Levine y Martin L. Yarmush. "Cell–cell interaction modulates neuroectodermal specification of embryonic stem cells". Neuroscience Letters 438, n.º 2 (junio de 2008): 190–95. http://dx.doi.org/10.1016/j.neulet.2008.03.094.

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16

Nadel, Jay A. "Cell-to-Cell Interaction in Airways". Chest 93, n.º 6 (junio de 1988): 1281–82. http://dx.doi.org/10.1378/chest.93.6.1281.

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17

Canipari, Rita. "Cell–cell interaction and oocyte growth". Zygote 2, n.º 4 (noviembre de 1994): 343–45. http://dx.doi.org/10.1017/s0967199400002173.

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In most mammals, oocytes initiate meiosis in late fetal life; by the time of birthe they have already entered the diplotene stage of prophase I of meiosis and becaome arrested thereafter at the dictyate state(Baker, 1972). At this stage they became surrounded by a few nonproliferating flat follicle cells forming a unit called the resting or primordial follicle.

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18

Hoshino, Takashi, Kazuya Shimizu, Tomoyuki Honda, Tomomi Kawakatsu, Taihei Fukuyama, Takeshi Nakamura, Michiyuki Matsuda y Yoshimi Takai. "A Novel Role of Nectins in Inhibition of the E-Cadherin–induced Activation of Rac and Formation of Cell-Cell Adherens Junctions". Molecular Biology of the Cell 15, n.º 3 (marzo de 2004): 1077–88. http://dx.doi.org/10.1091/mbc.e03-05-0321.

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Nectins are Ca2+-independent immunoglobulin (Ig)-like cell-cell adhesion molecules. The trans-interactions of nectins recruit cadherins to the nectin-based cell-cell adhesion, resulting in formation of cell-cell adherens junctions (AJs) in epithelial cells and fibroblasts. The trans-interaction of E-cadherin induces activation of Rac small G protein, whereas the trans-interactions of nectins induce activation of not only Rac but also Cdc42 small G protein. We showed by the fluorescent resonance energy transfer (FRET) imaging that the trans-interaction of E-cadherin induced dynamic activation and inactivation of Rac, which led to dynamic formation and retraction of lamellipodia. Moreover, we found here that the nectins, which did not trans-interact with other nectins (non–trans-interacting nectins), inhibited the E-cadherin–induced activation of Rac and reduced the velocity of the formation of the E-cadherin-based cell-cell AJs. The inhibitory effect of non–trans-interacting nectins was suppressed by the activation of Cdc42 induced by the trans-interactions of nectins. These results indicate a novel role of nectins in regulation of the E-cadherin–induced activation of Rac and formation of cell-cell AJs.

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19

Pancheva, Alexandrina, Helen Wheadon, Simon Rogers y Thomas D. Otto. "Using topic modeling to detect cellular crosstalk in scRNA-seq". PLOS Computational Biology 18, n.º 4 (8 de abril de 2022): e1009975. http://dx.doi.org/10.1371/journal.pcbi.1009975.

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Cell-cell interactions are vital for numerous biological processes including development, differentiation, and response to inflammation. Currently, most methods for studying interactions on scRNA-seq level are based on curated databases of ligands and receptors. While those methods are useful, they are limited to our current biological knowledge. Recent advances in single cell protocols have allowed for physically interacting cells to be captured, and as such we have the potential to study interactions in a complimentary way without relying on prior knowledge. We introduce a new method based on Latent Dirichlet Allocation (LDA) for detecting genes that change as a result of interaction. We apply our method to synthetic datasets to demonstrate its ability to detect genes that change in an interacting population compared to a reference population. Next, we apply our approach to two datasets of physically interacting cells to identify the genes that change as a result of interaction, examples include adhesion and co-stimulatory molecules which confirm physical interaction between cells. For each dataset we produce a ranking of genes that are changing in subpopulations of the interacting cells. In addition to the genes discussed in the original publications, we highlight further candidates for interaction in the top 100 and 300 ranked genes. Lastly, we apply our method to a dataset generated by a standard droplet-based protocol not designed to capture interacting cells, and discuss its suitability for analysing interactions. We present a method that streamlines detection of interactions and does not require prior clustering and generation of synthetic reference profiles to detect changes in expression.

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20

Tang, Xiaoqing, Xiaoming Liu, Pengyun Li, Yuqing Lin, Kojima Masaru, Qiang Huang y Tatsuo Arai. "Efficient Cell Trapping for Cell-Cell Interaction Analysis". Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2019 (2019): 2A2—S07. http://dx.doi.org/10.1299/jsmermd.2019.2a2-s07.

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21

Hara, Kodai, Masayuki Uchida, Risa Tagata, Hideshi Yokoyama, Yoshinobu Ishikawa, Asami Hishiki y Hiroshi Hashimoto. "Structure of proliferating cell nuclear antigen (PCNA) bound to an APIM peptide reveals the universality of PCNA interaction". Acta Crystallographica Section F Structural Biology Communications 74, n.º 4 (22 de marzo de 2018): 214–21. http://dx.doi.org/10.1107/s2053230x18003242.

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Proliferating cell nuclear antigen (PCNA) provides a molecular platform for numerous protein–protein interactions in DNA metabolism. A large number of proteins associated with PCNA have a well characterized sequence termed the PCNA-interacting protein box motif (PIPM). Another PCNA-interacting sequence termed the AlkB homologue 2 PCNA-interacting motif (APIM), comprising the five consensus residues (K/R)-(F/Y/W)-(L/I/V/A)-(L/I/V/A)-(K/R), has also been identified in various proteins. In contrast to that with PIPM, the PCNA–APIM interaction is less well understood. Here, the crystal structure of PCNA bound to a peptide carrying an APIM consensus sequence, RFLVK, was determined and structure-based interaction analysis was performed. The APIM peptide binds to the PIPM-binding pocket on PCNA in a similar way to PIPM. The phenylalanine and leucine residues within the APIM consensus sequence and a hydrophobic residue that precedes the APIM consensus sequence are crucially involved in interactions with the hydrophobic pocket of PCNA. This interaction is essential for overall binding. These results provide a structural basis for regulation of the PCNA interaction and might aid in the development of specific inhibitors of this interaction.

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22

Chatterjee, Sharmila y Ulhas P. Naik. "Pericyte-endothelial cell interaction". Cell Adhesion & Migration 6, n.º 3 (mayo de 2012): 157–59. http://dx.doi.org/10.4161/cam.20252.

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23

LANGUINO, L., S. COLELLA, N. POLENTARUTTI, L. MAES, A. MANTOVANI, G. MARGUERIE y E. DEJANA. "Fibrinogen-endothelial cell interaction". Cell Biology International Reports 10, n.º 6 (junio de 1986): 491. http://dx.doi.org/10.1016/0309-1651(86)90066-4.

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24

Owens, Trevor y Rana Zeine. "The cell biology of T-dependent B cell activation". Biochemistry and Cell Biology 67, n.º 9 (1 de septiembre de 1989): 481–89. http://dx.doi.org/10.1139/o89-078.

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The requirement that CD4+ helper T cells recognize antigen in association with class II Major Histocompatibility Complex (MHC) encoded molecules constrains T cells to activation through intercellular interaction. The cell biology of the interactions between CD4+ T cells and antigen-presenting cells includes multipoint intermolecular interactions that probably involve aggregation of both polymorphic and monomorphic T cell surface molecules. Such aggregations have been shown in vitro to markedly enhance and, in some cases, induce T cell activation. The production of T-derived lymphokines that have been implicated in B cell activation is dependent on ligation of the T cell receptor for antigen and its associated CD3 signalling complex. T-dependent help for B cell activation is therefore similarly MHC-restricted and involves T–B intercellular interaction. Recent reports that describe antigen-independent B cell activation through coculture with T cells activated by anti-T-cell receptor or anti-CD3 antibodies suggest that cellular interaction with T cells, independent of antigen presentation or lymphokine secretion, induces or triggers B cells to become responsive to T-derived lymphokines, and that this may be an integral component of the physiological, antigen- and MHC-restricted T-dependent B cell activation that leads to antibody production.Key words: T helper, B cell, activation, contact, lymphokines.

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25

Doyle, Andrew D., Shayan S. Nazari y Kenneth M. Yamada. "Cell–extracellular matrix dynamics". Physical Biology 19, n.º 2 (12 de enero de 2022): 021002. http://dx.doi.org/10.1088/1478-3975/ac4390.

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Abstract The sites of interaction between a cell and its surrounding microenvironment serve as dynamic signaling hubs that regulate cellular adaptations during developmental processes, immune functions, wound healing, cell migration, cancer invasion and metastasis, as well as in many other disease states. For most cell types, these interactions are established by integrin receptors binding directly to extracellular matrix proteins, such as the numerous collagens or fibronectin. For the cell, these points of contact provide vital cues by sampling environmental conditions, both chemical and physical. The overall regulation of this dynamic interaction involves both extracellular and intracellular components and can be highly variable. In this review, we highlight recent advances and hypotheses about the mechanisms and regulation of cell–ECM interactions, from the molecular to the tissue level, with a particular focus on cell migration. We then explore how cancer cell invasion and metastasis are deeply rooted in altered regulation of this vital interaction.

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26

Santoso, Sentot, Valeria V. Orlova, Kaimei Song, Ulrich J. Sachs, Cornelia L. Andrei-Selmer y Triantafyllos Chavakis. "The Homophilic Binding of Junctional Adhesion Molecule-C Mediates Tumor Cell-Endothelial Cell Interactions". Journal of Biological Chemistry 280, n.º 43 (23 de agosto de 2005): 36326–33. http://dx.doi.org/10.1074/jbc.m505059200.

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The junctional adhesion molecule C (JAM-C) was recently shown to undergo a heterophilic interaction with the leukocyte β2 integrin Mac-1, thereby mediating interactions between vascular cells in inflammatory cell recruitment. Here, the homophilic interaction of JAM-C is presented and functionally characterized to mediate tumor cell-endothelial cell interactions. Recombinant soluble JAM-C in fluid phase bound to immobilized JAM-C as assessed in a purified system; moreover, JAM-C-transfected Chinese hamster ovary (CHO) cells adhered to immobilized JAM-C. The homophilic interaction of JAM-C was mediated by the isolated amino-terminal Ig domain (D1), but not the carboxyl-terminal Ig domain (D2), of the molecule. Dimerization of JAM-A is dependent on the sequence RVE in the amino-terminal Ig domain. This motif is conserved in JAM-C (Arg64-Ile65-Glu66), and a single amino acid mutation in this motif (E66R) abolished the homophilic interaction of JAM-C. The lung carcinoma cell line NCI-H522 was found to express JAM-C. NCI-H522 cells adhered to immobilized JAM-C, as well as to JAM-C-transfected CHO cells, but not to mock-transfected CHO cells or to CHO cells transfected with the JAM-C mutant (E66R). Adhesion of NCI-H522 cells to JAM-C protein or JAM-C-transfected CHO cells was abolished in the presence of soluble JAM-C or the isolated D1. Furthermore, the adhesion of NCI-H522 cells to endothelial cells was significantly blocked by soluble JAM-C or the isolated D1. Thus, JAM-C undergoes a homophilic interaction via the Arg64-Ile65-Glu66 motif on the membrane-distal Ig domain of the molecule. The homophilic interaction of JAM-C can mediate tumor cell-endothelial cell interactions and may thereby be involved in the process of tumor cell metastasis.

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27

Kneitz, C., M. Goller, M. Wilhelm, C. Mehringer, G. Wohlleben, A. Schimpl y H.-P. Tony. "Inhibition of T cell/B cell interaction by B-CLL cells". Leukemia 13, n.º 1 (enero de 1999): 98–104. http://dx.doi.org/10.1038/sj.leu.2401235.

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28

Onoda, Makoto. "Cell-to-cell interaction in the testis." Newsletter of Japan Society for Comparative Endocrinology, n.º 65 (1992): 8–12. http://dx.doi.org/10.5983/nl2001jsce.18.8.

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29

IMURA, Katsuaki, Katsuko FURUKAWA, Takashi USHIDA y Tetsuya TATEISHI. "Induction of chondrogenesis by cell-cell interaction". Proceedings of the Bioengineering Conference Annual Meeting of BED/JSME 2003.15 (2003): 289–90. http://dx.doi.org/10.1299/jsmebio.2003.15.289.

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30

Lamponi, Stefania, Clara Dl Canio y Rolando Barbucci. "Heterotypic Cell-Cell Interaction on Micropatterned Surfaces". International Journal of Artificial Organs 32, n.º 8 (agosto de 2009): 507–16. http://dx.doi.org/10.1177/039139880903200805.

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Purpose The aim of this paper was to study the influence of chemical and topographical signals on cell behavior and to obtain a heterotypic cell-cell interaction on microstructured domains. Methods The polysaccharide hyaluronic acid (Hyal) was photoimmobilized on glass surfaces in order to obtain a pattern with squares and rectangles of different dimensions and chemistry. The microstructured surfaces were characterized by Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM). The behavior of Human Coronary Artery Endothelial Cells (HCAEC) and human tumoral dermal fibroblasts (C54) was investigated on these micropatterned surfaces by adhesion studies. Moreover, heterotypic interaction among C54 and HCAEC adherent on patterned surfaces was evaluated by time-lapse video microscopy. Results Surface analysis revealed the presence of a pattern consisting of alternating glass and Hyal microstructures whose dimensions decreased from the center to the edge of the sample. Neither HCAEC nor C54 adhered to the immobilized Hyal but both adapted their shape to the different sizes of the glass squares and rectangles. The number of adherent cells depended on the dimensions of both the glass domains and the nuclei of the cells. Co-cultured C54 on HCAEC patterned surfaces showed a heterotypic cell-cell interaction in the same chemical and topographic domain. Conclusions A heterotypic cell-cell interaction occurred in the same chemical and topographic micro-domains but in narrow areas only. Moreover, the number of cells adhering to the glass domains and cell morphology depended on the dimensions of both adhesive areas and cell nuclei.

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31

Häussinger, D. y F. Lang. "Interaction of Cell Volume and Cell Function". Kidney and Blood Pressure Research 13, n.º 3 (1990): 162–79. http://dx.doi.org/10.1159/000173362.

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32

Danese, Silvio y Claudio Fiocchi. "Endothelial Cell-Immune Cell Interaction in IBD". Digestive Diseases 34, n.º 1-2 (2016): 43–50. http://dx.doi.org/10.1159/000442925.

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The proper delivery of immune cells throughout the host's various tissues and organs is essential to health, and abnormalities in the type and quantity of leukocyte distribution is usually associated with disease. Because of its size and presence of a very large amount of immunocytes in the mucosa and mesenteric lymph nodes, the gut is the recipient of a constant influx of leukocytes, a process tightly regulated by multiple factors. These include cell adhesion molecules on the leukocytes and their counter-receptors on the microvascular endothelial cells in the bowel wall, a number of chemokines and cytokines that help attracting immune cells, platelets, bacterial products, danger signals, the size of the vascular and lymphatic beds and the process of leukocyte exit and circulation in the blood and lymphatic fluid. The disruption of any of the above regulatory mechanism can lead to inflammation, as is the case for inflammatory bowel disease. Learning how leukocyte and endothelial cells mutually function in health and what goes wrong in inflammation offers the opportunity to intervene therapeutically and re-establish the normal crosstalk between leukocytes and endothelial cells.

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33

Chappell, Dale B. y Nicholas P. Restifo. "T cell-tumor cell: a fatal interaction?" Cancer Immunology, Immunotherapy 47, n.º 2 (15 de octubre de 1998): 65–71. http://dx.doi.org/10.1007/s002620050505.

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Inooka, Shoshi y Tatsushi Toyokuni. "Sphingosine Transfer in Cell-to-Cell Interaction". Biochemical and Biophysical Research Communications 218, n.º 3 (enero de 1996): 872–76. http://dx.doi.org/10.1006/bbrc.1996.0155.

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35

Murillo, Gonzalo, Andreu Blanquer, Carolina Vargas-Estevez, Lleonard Barrios, Elena Ibáñez, Carme Nogués y Jaume Esteve. "Electromechanical Nanogenerator-Cell Interaction Modulates Cell Activity". Advanced Materials 29, n.º 24 (24 de abril de 2017): 1605048. http://dx.doi.org/10.1002/adma.201605048.

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36

Septiadi, Dedy, Federica Crippa, Thomas Lee Moore, Barbara Rothen-Rutishauser y Alke Petri-Fink. "Nanoparticle-Cell Interaction: A Cell Mechanics Perspective". Advanced Materials 30, n.º 19 (9 de enero de 2018): 1704463. http://dx.doi.org/10.1002/adma.201704463.

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37

Van Der Donk, J. A., A. L. De Ruiter-Bootsma, A. M. Ultee-Van Gessel y P. J. J. Wauben-Penris. "Cell-cell interaction between rat sertoli cells and mouse germ cells in vitro". Experimental Cell Research 164, n.º 1 (mayo de 1986): 191–98. http://dx.doi.org/10.1016/0014-4827(86)90466-0.

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38

Miang-Lon Ng, Patricia y Thomas Lufkin. "Embryonic stem cells: protein interaction networks". BioMolecular Concepts 2, n.º 1-2 (1 de abril de 2011): 13–25. http://dx.doi.org/10.1515/bmc.2011.008.

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AbstractEmbryonic stem cells have the ability to differentiate into nearly all cell types. However, the molecular mechanism of its pluripotency is still unclear. Oct3/4, Sox2 and Nanog are important factors of pluripotency. Oct3/4 (hereafter referred to as Oct4), in particular, has been an irreplaceable factor in the induction of pluripotency in adult cells. Proteins interacting with Oct4 and Nanog have been identified via affinity purification and mass spectrometry. These data, together with iterative purifications of interacting proteins allowed a protein interaction network to be constructed. The network currently includes 77 transcription factors, all of which are interconnected in one network. In-depth studies of some of these transcription factors show that they all recruit the NuRD complex. Hence, transcription factor clustering and chromosomal remodeling are key mechanism used by embryonic stem cells. Studies using RNA interference suggest that more pluripotency genes are yet to be discovered via protein-protein interactions. More work is required to complete and curate the embryonic stem cell protein interaction network. Analysis of a saturated protein interaction network by system biology tools can greatly aid in the understanding of the embryonic stem cell pluripotency network.

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39

Noe, Remi, Sothea Touch, Christine Gaboriaud, Jitka Fučíková, Veronique Fremeaux-Bacchi, Guido Kroemer, Oliver Kepp y Lubka T. Roumenina. "Complement interaction with cells undergoing immunogenic cell death". Molecular Immunology 89 (septiembre de 2017): 122. http://dx.doi.org/10.1016/j.molimm.2017.06.054.

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40

van Wamel, Annemieke, Ayache Bouakaz, Michel Versluis y Nico de Jong. "Micromanipulation of endothelial cells: Ultrasound-microbubble-cell interaction". Ultrasound in Medicine & Biology 30, n.º 9 (septiembre de 2004): 1255–58. http://dx.doi.org/10.1016/j.ultrasmedbio.2004.07.015.

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41

Levine, J. F. y F. E. Stockdale. "Cell-cell interactions promote mammary epithelial cell differentiation." Journal of Cell Biology 100, n.º 5 (1 de mayo de 1985): 1415–22. http://dx.doi.org/10.1083/jcb.100.5.1415.

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Mammary epithelium differentiates in a stromal milieu of adipocytes and fibroblasts. To investigate cell-cell interactions that may influence mammary epithelial cell differentiation, we developed a co-culture system of murine mammary epithelium and adipocytes and other fibroblasts. Insofar as caseins are specific molecular markers of mammary epithelial differentiation, rat anti-mouse casein monoclonal antibodies were raised against the three major mouse casein components to study this interaction. Mammary epithelium from mid-pregnant mice was plated on confluent irradiated monolayers of 3T3-L1 cells, a subclone of the Swiss 3T3 cell line that differentiates into adipocytes in monolayer culture and other cell monolayers (3T3-C2 cells, Swiss 3T3 cells, and human foreskin fibroblasts). Casein was synthesized by mammary epithelium only in the presence of co-cultured cells and the lactogenic hormone combination of insulin, hydrocortisone, and prolactin. Synthesis and accumulation of alpha-, beta-, and gamma-mouse casein within the epithelium was shown by immunohistochemical staining of cultured cells with anti-casein monoclonal antibodies, and the specificity of the immunohistochemical reaction was demonstrated using immunoblots. A competitive immunoassay was used to measure the amount of casein secreted into the culture medium. In a 24-h period, mammary epithelium co-cultured with 3T3-L1 cells secreted 12-20 micrograms beta-casein per culture dish. There was evidence of specificity in the cell-cell interaction that mediates hormone-dependent casein synthesis. Swiss 3T3 cells, newborn foreskin fibroblasts, substrate-attached material ("extracellular matrix"), and tissue culture plastic did not support casein synthesis, whereas monolayers of 3T3-L1 and 3T3-C2 cells, a subclone of Swiss 3T3 cells that does not undergo adipocyte differentiation, did. We conclude that interaction between mammary epithelium and other specific nonepithelial cells markedly influences the acquisition of hormone sensitivity of the epithelium and hormone-dependent differentiation.

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42

Kulharia, Mahesh. "Geometrical and electro-static determinants of protein-protein interactions". Bioinformation 17, n.º 10 (31 de octubre de 2021): 851–60. http://dx.doi.org/10.6026/97320630017851.

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Protein-protein interactions (PPI) are pivotal to the numerous processes in the cell. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the of forces interactions. The interaction interface of obligatory protein-protein complexes differs from that of the transient interactions. We have created a large database of protein-protein interactions containing over100 thousand interfaces. The structural redundancy was eliminated to obtain a non-redundant database of over 2,265 interaction interfaces. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the offorces interactions. The residue interaction propensity and all of the rest of the parametric scores converged to a statistical indistinguishable common sub-range and followed the similar distribution trends for all three classes of sequence-based classifications PPInS. This indicates that the principles of molecular recognition are dependent on the preciseness of the fit in the interaction interfaces. Thus, it reinforces the importance of geometrical and electrostatic complementarity as the main determinants for PPIs.

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Purushothaman, Anurag, Shyam Kumar Bandari, Jian Liu, James A. Mobley, Elizabeth E. Brown y Ralph D. Sanderson. "Fibronectin on the Surface of Myeloma Cell-derived Exosomes Mediates Exosome-Cell Interactions". Journal of Biological Chemistry 291, n.º 4 (24 de noviembre de 2015): 1652–63. http://dx.doi.org/10.1074/jbc.m115.686295.

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Exosomes regulate cell behavior by binding to and delivering their cargo to target cells; however, the mechanisms mediating exosome-cell interactions are poorly understood. Heparan sulfates on target cell surfaces can act as receptors for exosome uptake, but the ligand for heparan sulfate on exosomes has not been identified. Using exosomes isolated from myeloma cell lines and from myeloma patients, we identify exosomal fibronectin as a key heparan sulfate-binding ligand and mediator of exosome-cell interactions. We discovered that heparan sulfate plays a dual role in exosome-cell interaction; heparan sulfate on exosomes captures fibronectin, and on target cells it acts as a receptor for fibronectin. Removal of heparan sulfate from the exosome surface releases fibronectin and dramatically inhibits exosome-target cell interaction. Antibody specific for the Hep-II heparin-binding domain of fibronectin blocks exosome interaction with tumor cells or with marrow stromal cells. Regarding exosome function, fibronectin-mediated binding of exosomes to myeloma cells activated p38 and pERK signaling and expression of downstream target genes DKK1 and MMP-9, two molecules that promote myeloma progression. Antibody against fibronectin inhibited the ability of myeloma-derived exosomes to stimulate endothelial cell invasion. Heparin or heparin mimetics including Roneparstat, a modified heparin in phase I trials in myeloma patients, significantly inhibited exosome-cell interactions. These studies provide the first evidence that fibronectin binding to heparan sulfate mediates exosome-cell interactions, revealing a fundamental mechanism important for exosome-mediated cross-talk within tumor microenvironments. Moreover, these results imply that therapeutic disruption of fibronectin-heparan sulfate interactions will negatively impact myeloma tumor growth and progression.

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Jacobs, Evan S., Thaddeus C. George, Sukhwinder Singh, Richard Wnek, Paul Fischer, Shila K. Nordone, Gregg A. Dean y Patricia Fitzgerald-Bocarsly. "Plasmacytoid dendritic cell interaction with HIV-infected T cells: Live cell nibbling vs cell–cell fusion". Cytokine 48, n.º 1-2 (octubre de 2009): 66–67. http://dx.doi.org/10.1016/j.cyto.2009.07.214.

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Milicevic, Katarina D., Danijela B. Bataveljic, Jelena J. Bogdanovic Pristov, Pavle R. Andjus y Ljiljana M. Nikolic. "Astroglial Cell-to-Cell Interaction with Autoreactive Immune Cells in Experimental Autoimmune Encephalomyelitis Involves P2X7 Receptor, β3-Integrin, and Connexin-43". Cells 12, n.º 13 (5 de julio de 2023): 1786. http://dx.doi.org/10.3390/cells12131786.

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In multiple sclerosis (MS), glial cells astrocytes interact with the autoreactive immune cells that attack the central nervous system (CNS), which causes and sustains neuroinflammation. However, little is known about the direct interaction between these cells when they are in close proximity in the inflamed CNS. By using an experimental autoimmune encephalomyelitis (EAE) model of MS, we previously found that in the proximity of autoreactive CNS-infiltrated immune cells (CNS-IICs), astrocytes respond with a rapid calcium increase that is mediated by the autocrine P2X7 receptor (P2X7R) activation. We now reveal that the mechanisms regulating this direct interaction of astrocytes and CNS-IICs involve the coupling between P2X7R, connexin-43, and β3-integrin. We found that P2X7R and astroglial connexin-43 interact and concentrate in the immediate proximity of the CNS-IICs in EAE. P2X7R also interacts with β3-integrin, and the block of astroglial αvβ3-integrin reduces the P2X7R-dependent calcium response of astrocytes upon encountering CNS-IICs. This interaction was dependent on astroglial mitochondrial activity, which regulated the ATP-driven P2X7R activation and facilitated the termination of the astrocytic calcium response evoked by CNS-IICs. By further defining the interactions between the CNS and the immune system, our findings provide a novel perspective toward expanding integrin-targeting therapeutic approaches for MS treatment by controlling the cell–cell interactions between astrocytes and CNS-IICs.

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46

Fathima, Samreen, Swati Sinha y Sainitin Donakonda. "Network Analysis Identifies Drug Targets and Small Molecules to Modulate Apoptosis Resistant Cancers". Cancers 13, n.º 4 (18 de febrero de 2021): 851. http://dx.doi.org/10.3390/cancers13040851.

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Programed cell death or apoptosis fails to induce cell death in many recalcitrant cancers. Thus, there is an emerging need to activate the alternate cell death pathways in such cancers. In this study, we analyzed the apoptosis-resistant colon adenocarcinoma, glioblastoma multiforme, and small cell lung cancers transcriptome profiles. We extracted clusters of non-apoptotic cell death genes from each cancer to understand functional networks affected by these genes and their role in the induction of cell death when apoptosis fails. We identified transcription factors regulating cell death genes and protein–protein interaction networks to understand their role in regulating cell death mechanisms. Topological analysis of networks yielded FANCD2 (ferroptosis, negative regulator, down), NCOA4 (ferroptosis, up), IKBKB (alkaliptosis, down), and RHOA (entotic cell death, down) as potential drug targets in colon adenocarcinoma, glioblastoma multiforme, small cell lung cancer phenotypes respectively. We also assessed the miRNA association with the drug targets. We identified tumor growth-related interacting partners based on the pathway information of drug-target interaction networks. The protein–protein interaction binding site between the drug targets and their interacting proteins provided an opportunity to identify small molecules that can modulate the activity of functional cell death interactions in each cancer. Overall, our systematic screening of non-apoptotic cell death-related genes uncovered targets helpful for cancer therapy.

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47

Wang, Jun, Douglas Tham, Wei Wei, Young Shik Shin, Chao Ma, Habib Ahmad, Qihui Shi, Jenkan Yu, Raphael D. Levine y James R. Heath. "Quantitating Cell–Cell Interaction Functions with Applications to Glioblastoma Multiforme Cancer Cells". Nano Letters 12, n.º 12 (7 de noviembre de 2012): 6101–6. http://dx.doi.org/10.1021/nl302748q.

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48

Tay, Neil Q., Tiffany Juan, Joseph J. Muldoon, Justin Eyquem y Michael T. McManus. "Abstract 6633: Tracking cell-cell interactions using intercellular barcode transfer". Cancer Research 84, n.º 6_Supplement (22 de marzo de 2024): 6633. http://dx.doi.org/10.1158/1538-7445.am2024-6633.

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Abstract Cell-cell interaction is one of the fundamental biological mechanisms by which cells communicate and is a key modality by which cancer cells interact with other cells in their microenvironment. Current methods, such as enzymatic labeling and proximity-based tagging, allow the experimental tracking of these interactions but are not compatible with sequencing-based readouts, which are essential for high-throughput analyses. We have developed a highly scalable intercellular barcode transfer technology that bridges this gap. Our novel “Relay” technology offers a scalable solution to monitor cell-cell interactions with unprecedented single-cell resolution, seamlessly integrating with existing high-throughput and sequencing platforms. Relay employs RNA barcodes to mark and track cell interaction events, providing definitive quantitative data on cell interaction partners. We used Relay to track interactions between cancer cells and T cells, capturing both potent antigen-specific engagements and subtler contacts involved in antigen sampling. Additionally, by integrating CRISPR libraries with Relay, we pioneered a novel approach for pooled CRISPR screens, focusing on intercellular interactions across different cell types. In this context, the cell harboring genetic perturbation may differ from the cell under analysis. To demonstrate this, we are conducting genome-wide perturbations in cancer cells and interrogating cancer cell factors that affect antigen sampling and trogocytosis by T cells and macrophages. Beyond its application in CRISPR screening, Relay can also be used with single-cell RNA sequencing to directly track and measure immune-cancer and immune-immune cell interactions at the single cell level. We applied single-cell Relay to a dendritic cell-T cell system to link dendritic cell signatures to T cell signatures based on direct cell contact and regulatory signaling. The design of Relay as a highly modular system extends its utility beyond cancer research, making it broadly applicable across various biological models and systems where cell-cell communication is a fundamental property in complex multicellular settings. This tool promises to accelerate the transition from theoretical understanding to practical exploration of cell-cell interactions, with profound implications for immunology, oncology, and beyond. Citation Format: Neil Q. Tay, Tiffany Juan, Joseph J. Muldoon, Justin Eyquem, Michael T. McManus. Tracking cell-cell interactions using intercellular barcode transfer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6633.

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Inagaki, Naoki, Hirokazu Kawasaki y Hiroichi Nagai. "Regulation of mast cell activation through cell to cell interaction". Japanese Journal of Pharmacology 67 (1995): 72. http://dx.doi.org/10.1016/s0021-5198(19)46258-6.

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50

Bowerman, B., F. E. Tax, J. H. Thomas y J. R. Priess. "Cell interactions involved in development of the bilaterally symmetrical intestinal valve cells during embryogenesis in Caenorhabditis elegans". Development 116, n.º 4 (1 de diciembre de 1992): 1113–22. http://dx.doi.org/10.1242/dev.116.4.1113.

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We describe two different cell interactions that appear to be required for the proper development of a pair of bilaterally symmetrical cells in Caenorhabditis elegans called the intestinal valve cells. Previous experiments have shown that at the beginning of the 4-cell stage of embryogenesis, two sister blastomeres called ABa and ABp are equivalent in development potential. We show that cell interactions between ABp and a neighboring 4-cell-stage blastomere called P2 distinguish the fates of ABa and ABp by inducing descendants of ABp to produce the intestinal valve cells, a cell type not made by ABa. A second cell interaction appears to occur later in embryogenesis when two bilaterally symmetrical descendants of ABp, which both have the potential to produce valve cells, contact each other; production of the valve cells subsequently becomes limited to only one of the two descendants. This second interaction does not occur properly if the two symmetrical descendants of ABp are prevented from contacting each other. Thus the development of the intestinal valve cells appears to require both an early cell interaction that establishes a bilaterally symmetrical pattern of cell fate and a later interaction that breaks the symmetrical cell fate pattern by restricting to only one of two cells the ability to produce a pair of valve cells.

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