@article{bellingham-johnstun_thorn_belmonte_laplante_2023, title={Microtubule competition and cell growth recenter the nucleus after anaphase in fission yeast}, url={https://doi.org/10.1101/2023.01.31.526443}, DOI={10.1101/2023.01.31.526443}, abstractNote={ABSTRACT Cells actively position their nucleus based on their activity. In fission yeast, microtubule-dependent nuclear centering is critical for symmetrical cell division. After spindle disassembly at the end of anaphase, the nucleus recenters over a ~90 min period, approximately half of the duration of the cell cycle. Live cell and simulation experiments support the cooperation of two distinct mechanisms in the slow recentering of the nucleus. First, a push-push mechanism acts from spindle disassembly to septation and involves the opposing actions of the mitotic Spindle Pole Body microtubules that push the nucleus away from the ends of the cell while post-anaphase array of microtubules basket the nucleus and limit its migration toward the division plane. Second, a slow-and-grow mechanism finalizes nuclear centering in the newborn cell. In this mechanism, microtubule competition stalls the nucleus while asymmetric cell growth slowly centers it. Our work underlines how intrinsic properties of microtubules differently impact nuclear positioning according to microtubule network organization and cell size.}, author={Bellingham-Johnstun, Kimberly and Thorn, Annelise and Belmonte, Julio and Laplante, Caroline}, year={2023}, month={Feb} }
 @article{silva_chan_norman_sobral_zanin_gassmann_belmonte_carvalho_2023, title={β-heavy-spectrin stabilizes the constricting contractile ring during cytokinesis}, url={https://doi.org/10.1083/jcb.202202024}, DOI={10.1083/jcb.202202024}, abstractNote={Cytokinesis requires the constriction of an actomyosin-based contractile ring and involves multiple F-actin crosslinkers. We show that partial depletion of the C. elegans cytokinetic formin generates contractile rings with low F-actin levels that constrict but are structurally fragile, and we use this background to investigate the roles of the crosslinkers plastin/PLST-1 and β-heavy-spectrin/SMA-1 during ring constriction. We show that the removal of PLST-1 or SMA-1 has opposite effects on the structural integrity of fragile rings. PLST-1 loss reduces cortical tension that resists ring constriction and makes fragile rings less prone to ruptures and regressions, whereas SMA-1 loss exacerbates structural defects, leading to frequent ruptures and cytokinesis failure. Fragile rings without SMA-1 or containing a shorter SMA-1, repeatedly rupture at the same site, and SMA-1::GFP accumulates at repair sites in fragile rings and in rings cut by laser microsurgery. These results establish that β-heavy-spectrin stabilizes the constricting ring and reveals the importance of β-heavy-spectrin size for network connectivity at low F-actin density.}, journal={Journal of Cell Biology}, author={Silva, Ana Marta and Chan, Fung-Yi and Norman, Michael J. and Sobral, Ana Filipa and Zanin, Esther and Gassmann, Reto and Belmonte, Julio Monti and Carvalho, Ana Xavier}, year={2023}, month={Jan} }
 @article{adhyapok_piatkowska_norman_clendenon_stern_glazier_belmonte_2021, title={A mechanical model of early somite segmentation}, volume={24}, ISSN={["2589-0042"]}, url={https://doi.org/10.1016/j.isci.2021.102317}, DOI={10.1016/j.isci.2021.102317}, abstractNote={<h2>Summary</h2> Somitogenesis is often described using the clock-and-wavefront (CW) model, which does not explain how molecular signaling rearranges the pre-somitic mesoderm (PSM) cells into somites. Our scanning electron microscopy analysis of chicken embryos reveals a caudally-progressing epithelialization front in the dorsal PSM that precedes somite formation. Signs of apical constriction and tissue segmentation appear in this layer 3-4 somite lengths caudal to the last-formed somite. We propose a mechanical instability model in which a steady increase of apical contractility leads to periodic failure of adhesion junctions within the dorsal PSM and positions the future inter-somite boundaries. This model produces spatially periodic segments whose size depends on the speed of the activation front of contraction (<i>F</i>), and the buildup rate of contractility (Λ). The Λ/<i>F</i> ratio determines whether this mechanism produces spatially and temporally regular or irregular segments, and whether segment size increases with the front speed.}, number={4}, journal={ISCIENCE}, publisher={Elsevier BV}, author={Adhyapok, Priyom and Piatkowska, Agnieszka M. and Norman, Michael J. and Clendenon, Sherry G. and Stern, Claudio D. and Glazier, James A. and Belmonte, Julio M.}, year={2021}, month={Apr} }
 @article{bhide_gombalova_moenke_stegmaier_zinchenko_kreshuk_belmonte_leptin_2021, title={Mechanical competition alters the cellular interpretation of an endogenous genetic program}, volume={220}, ISSN={["1540-8140"]}, url={https://doi.org/10.1083/jcb.202104107}, DOI={10.1083/jcb.202104107}, abstractNote={The intrinsic genetic program of a cell is not sufficient to explain all of the cell’s activities. External mechanical stimuli are increasingly recognized as determinants of cell behavior. In the epithelial folding event that constitutes the beginning of gastrulation in Drosophila, the genetic program of the future mesoderm leads to the establishment of a contractile actomyosin network that triggers apical constriction of cells and thereby tissue folding. However, some cells do not constrict but instead stretch, even though they share the same genetic program as their constricting neighbors. We show here that tissue-wide interactions force these cells to expand even when an otherwise sufficient amount of apical, active actomyosin is present. Models based on contractile forces and linear stress–strain responses do not reproduce experimental observations, but simulations in which cells behave as ductile materials with nonlinear mechanical properties do. Our models show that this behavior is a general emergent property of actomyosin networks in a supracellular context, in accordance with our experimental observations of actin reorganization within stretching cells.}, number={11}, journal={JOURNAL OF CELL BIOLOGY}, author={Bhide, Sourabh and Gombalova, Denisa and Moenke, Gregor and Stegmaier, Johannes and Zinchenko, Valentyna and Kreshuk, Anna and Belmonte, Julio M. and Leptin, Maria}, year={2021}, month={Nov} }
 @article{sobral_chan_norman_osorio_dias_ferreira_barbosa_cheerambathur_gassmann_belmonte_et al._2021, title={Plastin and spectrin cooperate to stabilize the actomyosin cortex during cytokinesis}, volume={31}, ISSN={["1879-0445"]}, url={https://doi.org/10.1016/j.cub.2021.09.055}, DOI={10.1016/j.cub.2021.09.055}, abstractNote={Cytokinesis, the process that partitions the mother cell into two daughter cells, requires the assembly and constriction of an equatorial actomyosin network. Different types of non-motor F-actin crosslinkers localize to the network, but their functional contribution remains poorly understood. Here, we describe a synergy between the small rigid crosslinker plastin and the large flexible crosslinker spectrin in the C. elegans one-cell embryo. In contrast to single inhibitions, co-inhibition of plastin and the βH-spectrin (SMA-1) results in cytokinesis failure due to progressive disorganization and eventual collapse of the equatorial actomyosin network. Cortical localization dynamics of non-muscle myosin II in co-inhibited embryos mimic those observed after drug-induced F-actin depolymerization, suggesting that the combined action of plastin and spectrin stabilizes F-actin in the contractile ring. An in silico model predicts that spectrin is more efficient than plastin at stabilizing the ring and that ring formation is relatively insensitive to βH-spectrin length, which is confirmed in vivo with a sma-1 mutant that lacks 11 of its 29 spectrin repeats. Our findings provide the first evidence that spectrin contributes to cytokinesis and highlight the importance of crosslinker interplay for actomyosin network integrity.}, number={24}, journal={CURRENT BIOLOGY}, publisher={Elsevier BV}, author={Sobral, Ana Filipa and Chan, Fung-Yi and Norman, Michael J. and Osorio, Daniel S. and Dias, Ana Beatriz and Ferreira, Vanessa and Barbosa, Daniel J. and Cheerambathur, Dhanya and Gassmann, Reto and Belmonte, Julio Monti and et al.}, year={2021}, month={Dec}, pages={5415-+} }
 @article{lesnicar-pucko_belmonte_musy_glazier_sharpe_2020, title={Cellular mechanisms of chick limb bud morphogenesis}, volume={9}, url={https://doi.org/10.1101/2020.09.10.292359}, DOI={10.1101/2020.09.10.292359}, abstractNote={Summary Although some of the molecular pathways involved in limb bud morphogenesis have been identified, the cellular basis of the process is not yet understood. Proposed cell behaviours include active cell migration and oriented cell division, but ultimately, these questions can only be resolved by watching individual mesenchymal cells within a completely normal developmental context. We developed a minimally-invasive in ovo two-photon technique, to capture high quality time-lapse sequences up to 100 microns deep in the unperturbed growing chick limb bud. Using this technique, we characterized cell shapes and other oriented behaviours throughout the limb bud, and found that cell intercalation drives tissue movements, rather than oriented cell divisions or migration. We then developed a 3D cell-based computer simulation of morphogenesis, in which cellular extensions physically pull cells towards each other, with directional bias controlled by molecular gradients from the ectoderm (Wnts) and the Apical Ectodermal Ridge (FGFs). We defined the initial and target shapes of the chick limb bud in 3D by OPT scanning, and explored which orientations of mesenchymal intercalation correctly explain limb morphogenesis. The model made a couple of predictions: Firstly, that elongation can only be explained when cells intercalate along the direction towards the nearest ectoderm. This produces a general convergence of tissue towards the central proximo-distal (PD) axis of the limb, and a resultant extension of the tissue along the PD axis. Secondly, the correct in silico morphology can only be achieved if the contractile forces of mesenchymal cells in the very distal region (under the Apical Ectodermal Ridge) have shorter life times than in the rest of the limb bud, effectively making the tissue more fluid by augmenting the rate of cell rearrangement. We argue that this less-organised region of mesenchyme is necessary to prevent PD-oriented intercalation events in the distal tip that would otherwise inhibit outgrowth.}, publisher={Cold Spring Harbor Laboratory}, author={Lesnicar-Pucko, Gaja and Belmonte, Julio M and Musy, Marco and Glazier, James A. and Sharpe, James}, year={2020}, month={Sep} }
 @article{fortuna_perrone_krug_susin_belmonte_thomas_glazier_almeida_2020, title={CompuCell3D Simulations Reproduce Mesenchymal Cell Migration on Flat Substrates}, volume={118}, ISSN={["1542-0086"]}, url={http://dx.doi.org/10.1016/j.bpj.2020.04.024}, DOI={10.1016/j.bpj.2020.04.024}, abstractNote={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.}, number={11}, journal={BIOPHYSICAL JOURNAL}, publisher={Elsevier BV}, author={Fortuna, Ismael and Perrone, Gabriel C. and Krug, Monique S. and Susin, Eduarda and Belmonte, Julio M. and Thomas, Gilberto L. and Glazier, James A. and Almeida, Rita M. C.}, year={2020}, month={Jun}, pages={2801–2815} }
 @article{bhide_gombalova_mönke_stegmaier_zinchenko_kreshuk_belmonte_leptin_2020, title={Mechanical competition alters the cellular interpretation of an endogenous genetic programme}, volume={10}, url={https://doi.org/10.1101/2020.10.15.333963}, DOI={10.1101/2020.10.15.333963}, abstractNote={Abstract The intrinsic genetic programme of a cell is not sufficient to explain all of the cell’s activities. External mechanical stimuli are increasingly recognized as determinants of cell behaviour. In the epithelial folding event that constitutes the beginning of gastrulation in Drosophila , the genetic programme of the future mesoderm leads to the establishment of a contractile actomyosin network that triggers apical constriction of cells, and thereby, tissue folding. However, some cells do not constrict but instead stretch, even though they share the same genetic programme as their constricting neighbours. We show here that tissue-wide interactions force these cells to expand even when an otherwise sufficient amount of apical, active actomyosin is present. Models based on contractile forces and linear stress-strain responses do not reproduce experimental observations, but simulations in which cells behave as ductile materials with non-linear mechanical properties do. Our models show that this behaviour is a general emergent property of actomyosin networks [in a supracellular context, in accordance with our experimental observations of actin reorganisation within stretching cells.}, publisher={Cold Spring Harbor Laboratory}, author={Bhide, Sourabh and Gombalova, Denisa and Mönke, Gregor and Stegmaier, Johannes and Zinchenko, Valentyna and Kreshuk, Anna and Belmonte, Julio M and Leptin, Maria}, year={2020}, month={Oct} }
 @article{parameterizing cell movement when the instantaneous cell migration velocity is ill-defined_2020, url={http://dx.doi.org/10.1016/j.physa.2020.124493}, DOI={10.1016/j.physa.2020.124493}, abstractNote={Cell crawling has usually been characterized by a diffusion constant D and instantaneous velocity 〈|v→|2〉. However, experimentally 〈|v→|2〉 diverges. A three regime (diffusive-ballistic-diffusive) modified Fürth equation parameterized by D, the dimensionless excess diffusion coefficient S and the persistence time P is compatible with experiment. S allows comparison of trajectories across experiments and sets limits on the intervals and duration of experiments required to assess cell movement. Cell trajectories in a variety of published experiments are consistent with longitudinal Langevin dynamics and a transverse Wiener process with S∼1+constant∗D−1.}, journal={Physica A: Statistical Mechanics and its Applications}, year={2020}, month={Jul} }
 @article{a mechanical model of early somite segmentation_2019, url={https://doi.org/10.1101/804203}, DOI={10.1101/804203}, abstractNote={Abstract The clock-and-wavefront model (CW) hypothesizes that the formation of somites in vertebrate embryos results from the interplay of molecular oscillations with a wave traveling along the body axis. This model however does not explain how molecular information is interpreted by cells to modulate their rearrangement into somites. Here we performed Scanning Electron Microscopy (SEM) on the pre-somitic mesoderm (PSM) of chicken embryos at stages 11-12 to describe in detail the cell shape changes occurring along the axis of the PSM. This reveals a wave of epithelialization of the dorsal PSM that precedes somite segmentation. Signs of spatially periodic apical constriction appear in this layer starting at least 3-4 somite lengths caudal to the most recently formed somite. The sizes of these clusters correspond to the typical diameter of chicken somites. We propose that a mechanical instability process leads to the separation of cells into these structures and positions the future inter-somite boundaries. We present a model in which a wave of apical constriction leads to increasing tension and periodic failure of adhesion junctions within the dorsal epithelial layer of the PSM, thus positioning somite boundaries. This model can produce spatially periodic segments whose size depends on the speed of the contraction wave ( W ) and the rate of increase of apical contractility (Λ). The Λ/W ratio determines whether this mechanism produces spatially and temporally regular or irregular segments, and whether segment sizes increase with the wave speed (scaling) as in the CW model. We discuss the limitations of a purely mechanical model of somite segmentation and the role of biomechanics along with CW during somitogenesis.}, year={2019}, month={Oct} }
 @article{wollrab_belmonte_baldauf_leptin_nédeléc_koenderink_2019, title={Polarity sorting drives remodeling of actin-myosin networks}, url={https://doi.org/10.1242/jcs.219717}, DOI={10.1242/jcs.219717}, abstractNote={Cytoskeletal networks of actin filaments and myosin motors drive many dynamic cell processes. A key characteristic of these networks is their contractility. Despite intense experimental and theoretical efforts, it is not clear what mechanism favors network contraction over expansion. Recent work points to a dominant role for the nonlinear mechanical response of actin filaments, which can withstand stretching but buckle upon compression. Here we present an alternative mechanism. We study how interactions between actin and myosin-2 at the single filament level translate into contraction at the network scale by performing time-lapse imaging on reconstituted quasi-2D-networks mimicking the cell cortex. We observe myosin end-dwelling after it runs processively along actin filaments. This leads to transport and clustering of actin filament ends and the formation of transiently stable bipolar structures. Further we show that myosin-driven polarity sorting produces polar actin asters, which act as contractile nodes that drive contraction in crosslinked networks. Computer simulations comparing the roles of the end-dwelling mechanism and a buckling-dependent mechanism show that the relative contribution of end-dwelling contraction increases as the network mesh-size decreases.}, journal={Journal of Cell Science}, author={Wollrab, Viktoria and Belmonte, Julio M. and Baldauf, Lucia and Leptin, Maria and Nédeléc, François and Koenderink, Gijsje H.}, year={2019}, month={Feb} }
 @article{bun_dmitrieff_belmonte_nédélec_lénárt_2018, title={A disassembly-driven mechanism explains F-actin-mediated chromosome transport in starfish oocytes}, volume={7}, url={https://doi.org/10.7554/eLife.31469}, DOI={10.7554/eLife.31469}, abstractNote={While contraction of sarcomeric actomyosin assemblies is well understood, this is not the case for disordered networks of actin filaments (F-actin) driving diverse essential processes in animal cells. For example, at the onset of meiosis in starfish oocytes a contractile F-actin network forms in the nuclear region transporting embedded chromosomes to the assembling microtubule spindle. Here, we addressed the mechanism driving contraction of this 3D disordered F-actin network by comparing quantitative observations to computational models. We analyzed 3D chromosome trajectories and imaged filament dynamics to monitor network behavior under various physical and chemical perturbations. We found no evidence of myosin activity driving network contractility. Instead, our observations are well explained by models based on a disassembly-driven contractile mechanism. We reconstitute this disassembly-based contractile system in silico revealing a simple architecture that robustly drives chromosome transport to prevent aneuploidy in the large oocyte, a prerequisite for normal embryonic development.}, journal={eLife}, publisher={eLife Sciences Organisation, Ltd.}, author={Bun, Philippe and Dmitrieff, Serge and Belmonte, Julio M and Nédélec, François J and Lénárt, Péter}, year={2018}, month={Jan} }
 @article{rognoni_pisco_hiratsuka_sipilä_belmonte_mobasseri_philippeos_dilão_watt_2018, title={Fibroblast state switching orchestrates dermal maturation and wound healing}, volume={14}, url={http://dx.doi.org/10.15252/msb.20178174}, DOI={10.15252/msb.20178174}, abstractNote={Article29 August 2018Open Access Transparent process Fibroblast state switching orchestrates dermal maturation and wound healing Emanuel Rognoni Emanuel Rognoni orcid.org/0000-0001-6050-2860 Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK Search for more papers by this author Angela Oliveira Pisco Angela Oliveira Pisco orcid.org/0000-0003-0142-2355 Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK Search for more papers by this author Toru Hiratsuka Toru Hiratsuka orcid.org/0000-0002-5359-2690 Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK Search for more papers by this author Kalle H Sipilä Kalle H Sipilä Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK Search for more papers by this author Julio M Belmonte Julio M Belmonte Developmental Biology Unit and Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Seyedeh Atefeh Mobasseri Seyedeh Atefeh Mobasseri Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK Search for more papers by this author Christina Philippeos Christina Philippeos orcid.org/0000-0001-8654-0291 Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK Search for more papers by this author Rui Dilão Rui Dilão Nonlinear Dynamics Group, Instituto Superior Técnico, Lisbon, Portugal Search for more papers by this author Fiona M Watt Corresponding Author Fiona M Watt [email protected] orcid.org/0000-0001-9151-5154 Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK Search for more papers by this author Emanuel Rognoni Emanuel Rognoni orcid.org/0000-0001-6050-2860 Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK Search for more papers by this author Angela Oliveira Pisco Angela Oliveira Pisco orcid.org/0000-0003-0142-2355 Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK Search for more papers by this author Toru Hiratsuka Toru Hiratsuka orcid.org/0000-0002-5359-2690 Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK Search for more papers by this author Kalle H Sipilä Kalle H Sipilä Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK Search for more papers by this author Julio M Belmonte Julio M Belmonte Developmental Biology Unit and Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Seyedeh Atefeh Mobasseri Seyedeh Atefeh Mobasseri Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK Search for more papers by this author Christina Philippeos Christina Philippeos orcid.org/0000-0001-8654-0291 Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK Search for more papers by this author Rui Dilão Rui Dilão Nonlinear Dynamics Group, Instituto Superior Técnico, Lisbon, Portugal Search for more papers by this author Fiona M Watt Corresponding Author Fiona M Watt [email protected] orcid.org/0000-0001-9151-5154 Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK Search for more papers by this author Author Information Emanuel Rognoni1,‡, Angela Oliveira Pisco1,4,‡, Toru Hiratsuka1, Kalle H Sipilä1, Julio M Belmonte2, Seyedeh Atefeh Mobasseri1, Christina Philippeos1, Rui Dilão3 and Fiona M Watt *,1 1Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK 2Developmental Biology Unit and Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany 3Nonlinear Dynamics Group, Instituto Superior Técnico, Lisbon, Portugal 4Present address: Chan Zuckerberg Biohub, San Francisco, CA, USA ‡These authors contributed equally to this work *Corresponding author. Tel: +44 207188 5608; E-mail: [email protected] Molecular Systems Biology (2018)14:e8174https://doi.org/10.15252/msb.20178174 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Murine dermis contains functionally and spatially distinct fibroblast lineages that cease to proliferate in early postnatal life. Here, we propose a model in which a negative feedback loop between extracellular matrix (ECM) deposition and fibroblast proliferation determines dermal architecture. Virtual-tissue simulations of our model faithfully recapitulate dermal maturation, predicting a loss of spatial segregation of fibroblast lineages and dictating that fibroblast migration is only required for wound healing. To test this, we performed in vivo live imaging of dermal fibroblasts, which revealed that homeostatic tissue architecture is achieved without active cell migration. In contrast, both fibroblast proliferation and migration are key determinants of tissue repair following wounding. The results show that tissue-scale coordination is driven by the interdependence of cell proliferation and ECM deposition, paving the way for identifying new therapeutic strategies to enhance skin regeneration. Synopsis In vivo live imaging of dermal fibroblasts combined with mathematical modeling shows that fibroblast behaviour switching between two distinct states—proliferating and depositing ECM—defines dermal architecture. These findings are relevant for identifying new therapeutic strategies for skin regeneration. Tissue-scale coordination in murine dermis is driven by the interdependence of cell proliferation and ECM deposition. The tissue architecture is set by a negative feedback loop between ECM deposition/remodelling and proliferation. Fibroblast lineages lose segregation with age. Fibroblast migration is the critical discriminator between dermal development and wound healing. Introduction Mammalian skin comprises two mutually dependent layers, the epidermis and the dermis, which form through highly coordinated epithelial–mesenchymal interactions during development (Fuchs & Horsley, 2008; Watt, 2014). At embryonic day 12.5 (E12.5), mouse epidermis comprises one or two cell layers, and the dermis appears homogeneous in composition. During development, the dermis evolves from a multi-potent pool of Pdgfrα+ fibroblasts. These become lineage-restricted at embryonic day 16.5 (E16.5), such that Lrig1 expressing fibroblasts give rise to the upper (papillary) dermis, while Sca1/Dlk1-positive fibroblasts give rise to the lower (reticular) dermis and hypodermis (Driskell et al, 2013). The papillary dermis is distinguishable from the reticular dermis because of its higher cellular density and relative paucity of fibrillar collagen. Functionally, the papillary lineage is required for hair follicle formation in skin reconstitution assays, whereas the lower lineage gives rise to the fibroblasts that mediate the initial phase of wound repair (Driskell et al, 2013; Rognoni et al, 2016). By postnatal day 2 (P2), the hypodermis has formed, comprising differentiated adipocytes and preadipocytes, while fibroblasts from the upper dermis differentiate into the hair follicle arrector pili muscle. By P10, fibroblasts stop proliferating and dermal expansion results in separation of clonally related fibroblasts (Rognoni et al, 2016). In addition, resident immune cells, neuronal cells and endothelial cells are recruited, giving rise to the adult dermis. It has been shown recently that epidermal cells coordinate a tissue-scale behaviour during wound repair, whereby the epithelium organises directional migration and proliferation in overlapping regions oriented towards the wound (Aragona et al, 2017; Park et al, 2017). Upon wounding, dermal fibroblasts become activated, as evidenced by expression of α-smooth muscle actin (α-sma), start proliferating, migrate to the wound and deposit ECM, reconstituting the wound bed (Eming et al, 2014; Shaw & Martin, 2016). The upper and lower lineages enter the wound with different kinetics (Driskell et al, 2013; Rognoni et al, 2016), and adipocytes can be replenished from α-sma+ fibroblasts (Plikus et al, 2017). However, how the growth and spatial organisation of dermal fibroblast subpopulations is regulated is currently unknown. Here, we elucidate how the tissue-scale coordination of fibroblast behaviour is achieved during dermal development and homeostasis. Using a combination of cell biology techniques and mathematical modelling, we were able to demonstrate that fibroblast behaviour switching between two distinct states—proliferating and depositing ECM—is necessary and sufficient to define dermal architecture. These cellular states are balanced by a negative feedback loop between ECM deposition/remodelling and proliferation. Understanding the interdependence of cell behaviour and ECM is potentially important for identifying new therapeutic strategies to enhance skin regeneration. Results Dermal maturation is driven by an inverse correlation between fibroblast proliferation and ECM deposition By combining fibroblast density measurements with dermis volume calculations (Rognoni et al, 2016), we estimated the number of cell divisions during embryonic (E17.5 to P2) and postnatal (P2 to P50) growth (Figs 1A and EV1A, Table EV1). Our data indicated that postnatally the dermis volume increased approximately 13 fold with minimal proliferation, as our model only predicts 1.3 cell divisions. In contrast, during embryonic development, the dermis volume increased proportionally to the change in cell number, indicating that at this stage tissue growth is driven by cell proliferation. Figure 1. Dermal architecture is defined by an inverse correlation between fibroblast proliferation and ECM deposition A. Quantification of fibroblast density (number of PDGFRαH2BEGFP+ cells, right; n = 3 biological replicates per time point) and dermis volume (left) with age (n = 7 for 12.5; n = 8 for 19.5, 25.5; n = 9 for 16.5, 17.5, 23.5; n = 10 for 29.5, 69.5; n = 11 for 10.5; n = 12 for 18.5, 21.5, 40.5; n = 20 for 31.5; biological replicates). B. Changes in fibroblast proliferation and collagen deposition during dermal development in mice. Immunofluorescence staining for Ki67 (red) of PDGFRαH2BEGFP (green) back skin at indicated developmental time points (upper panel). Polarised light images of Picrosirius red stained back skin sections shown in binary images at indicated time points (lower panel). C. Percentage of PDGFRαH2BEGFP+ cells in G1 cell cycle phase with age (n = 1 for 12.5; n = 2 for 21.5, 29.5; n = 3 for 16.5, 17, 19.5; n = 4 for 10.5; n = 5 for 17.5, 18.5, 69.5; n = 7 for 23.5; biological replicates) fitted using an exponential model. D. Quantification of fibroblast proliferation (left; n = 4 for 16.5, 29.5; n = 3 for 10.5, 18.5, 21.5, 41.5, 59.5, 69.5; n = 2 for 17.5, 19.5; biological replicates) and collagen density (right; n = 4 for 16.5, 21.5, 24.5, 29.5, 33.5, 35.5, 41.5, 73.5; n = 3 for 10.5, 17.5, 18.5, 19.5, 31.5, 38.5; biological replicates) with age. Error bars represent standard deviation of the biological replicates. E. Immunofluorescence image of R26Fucci2a x Dermo1Cre back skin at P2 where mVenus-hGem (green) expressing cells are in S/G2/M phase, and mCherry-hCdt1 (red)-positive cells are in G1 phase. F. Percentage of labelled cells in S/G2/M cell cycle phase in the upper and lower dermis at P0 (n = 3 biological replicates) and P2 (n = 4 biological replicates). G–I. Changes in dermal cell proliferation and collagen deposition in human skin. (G) Immunofluorescence staining for vimentin (red) and Ki67 (green) of back skin at indicated time points (upper panel). Polarised light images of Picrosirius red stained back skin section shown as binary image at indicated time points (lower panel). (H) Quantification of proliferating cells in the upper and lower dermis (n = 3 biological replicates per time point). (I) Quantification of dermal cell proliferation (Ki67+) (left) and collagen density (right) with age (n = 3 biological replicates per time point). Note that cells in upper human dermis were more proliferative than cells in the lower dermis at all developmental time points analysed and that fibroblasts entered a quiescent, non-proliferative state before collagen was efficiently deposited. Data information: Data shown are means ± s.d. Nuclei were labelled with DAPI (blue in B, E, G). Scale bars, 100 μm. wk, week. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Characterising dermis expansion and gene expression changes during mouse development (related to Fig 1) Modelling dermal fibroblast cell divisions during development (adapted from Rognoni et al, 2016). Predicted number of dermal fibroblast divisions during the transition from embryonic (E17.5) to neonatal (P2) and neonatal (P2) to adult (P50) mouse. Height, length and dermis diameter were measured (n = 3 mice per time point and gender), and the dermis volume was estimated by representing the mouse trunk as a cylinder. Cell densities were obtained from Fig 1A, and cell number at E17.5 (NE), P2 (NN) and P50 (NA) was estimated by multiplying cell density and dermis volume. The predicted cell division rate is calculated by the log2 of the Nolder/Nyounger ratio. Calculated values are shown in Table EV1. Representative cell cycle flow cytometry profiles for indicated time points. Note the sharp decrease in S-phase with age and the arrest in G1. Comparative analysis of the transcriptomics of neonatal and adult mouse fibroblasts (GSE32966). The volcano plot (left panel) illustrates the differences in fibroblast gene expression at different ages. Colour code indicates entities not statistically significantly changed (grey), statistically significant but not enriched (green) or significantly changed with fold change > 2 and P-value smaller than 0.05. Red corresponds to enriched in neonatal and blue to enriched in adult. Gene ontology (GO) analysis (right panel) of neonatal and adult fibroblasts. GO terms are highlighted in neonates (red) and adults (blue). Download figure Download PowerPoint In line with this observation, we found that most fibroblasts are proliferating (Ki67+) at E10.5, but with age they progressively arrest in the G1 cell cycle phase, which we define as quiescence (Basak et al, 2017), without undergoing apoptosis (Rognoni et al, 2016; Figs 1B upper panel and C, and EV1B). The entry into quiescence coincided with a sharp increase in collagen deposition (quantified by Picrosirius red staining; Fig 1B lower panel). Quantitation of changes in proliferation and ECM deposition suggested an inverse correlation over time (Fig 1D), which led us to hypothesise that dermal growth consists of two phases. During the initial phase, tissue expansion is due to proliferation, as the ratio of dermis volume to cell number is approximately 1:1. The second phase, corresponding to postnatal growth, is strongly associated with ECM deposition and remodelling, as between P2 and P50 the ratio of dermis volume to cell number is 4:1. These observations are supported by publicly available microarray data for neonatal and adult back skin fibroblasts, which show that with age there is a reduction in genes associated with proliferation, together with an enrichment for GO terms for ECM production and remodelling (Fig EV1C; Collins et al, 2011; Rognoni et al, 2016). We further investigated the changes in the ECM by labelling with a collagen hybridising peptide probe (CHP), which recognises the triple helix structure of immature and remodelling collagen fibres (Fig EV2A, Hwang et al, 2017). Collagen fibre bundles were first detectable at E18.5 in the lower dermis, and in agreement with the Picrosirius red staining, further matured and expanded throughout the dermis with age. Moreover, ultrastructural analysis of P2 skin sections revealed that while there was no difference in collagen fibre diameter between upper dermis and lower dermis, collagen fibre bundle formation was evident in the lower dermis, whereas only small and dispersed collagen fibres were present in the upper dermis (Fig EV2B and C). At P2, the fibroblasts closest to the basement membrane were more proliferative than those in the lower dermis (Figs 1E and F, and EV2D). This indicates that the relationship between ECM deposition and fibroblast quiescence holds within different regions of the dermis at any given time point. We observed the same correlation when we examined human foetal and adult skin sections (Fig 1G–I). We therefore conclude that dermal fibroblasts exhibit differential growth behaviour at distinct developmental time points and dermal locations, either dominated by cell proliferation or ECM production. Click here to expand this figure. Figure EV2. Distinct collagen structures in the upper and lower neonatal dermis (related to Fig 1) Immunofluorescence staining of back skin sections at indicated developmental time points for Itga6 (green) and collagen (red, white) using the CHP-biotin probe. Nuclei were labelled with DAPI (blue). Note the appearance of collagen fibres after E18.5. TEM image of upper (papillary layer, left) and lower (reticular layer, right) dermis at P2. Boxed area is magnified below. Quantification of collagen fibre diameter (upper panel; n = 42 fibres) and density (lower panel) in upper and lower dermis at P2 (n = 10 areas). Quantification of proliferating fibroblasts (Ki67+ cells) in the upper and lower dermis at indicated time points (n = 3 E18.5; n = 2 P0 biological replicates). Data information: Scale bars: 100 μm (A), 2 μm (B). Error bars represent standard deviation of the biological replicates. Download figure Download PowerPoint ECM negatively regulates fibroblast proliferation To investigate whether the presence of ECM in a 3D environment is sufficient to prevent fibroblast proliferation, we isolated fibroblasts from P2 mice by flow cytometry and plated them on collagen-coated tissue culture plastic (TCP) or encased them at low density within collagen gels (Fig 2A). We found that culture on TCP resulted in a much greater increase in the number of fibroblasts during the recording period of 4 days than culture within collagen. The negative effect of the 3D collagen environment was reversible, as indicated by the fact that when cells were released by collagenase I treatment, previously non-proliferating fibroblasts resumed a highly proliferative rate soon after being re-plated on TCP (Fig 2B). Figure 2. ECM triggers fibroblast state switching A. Two independent primary mouse fibroblast isolations FB1 (black) and FB2 (grey) were cultured in (triangles) and outside (circles) collagen gels for up to 5 days. Cell number was measured by the CellTiter-Glo assay at indicated time points (n = 3 technical replicates of one representative experiment repeated three times). B. Two independent primary mouse fibroblast isolations FB1 (black) and FB2 (grey) were cultured in collagen gels for 3 days before being released by collagenase treatment and plated on TCP for 4 days (triangles). Controls were kept on TCP for 3 days before plating (circles). Fold increase in confluency 24 h after plating of collagen gel released cells and cells kept on TCP is shown (n = 3 technical replicates). C. Experimental design of decellularised dermal (DED) organotypic cultures for reconstitution (left panel) and ECM degradation (right panel). D, E. Repopulation of DED organotypic cultures recapitulates adult dermal homeostasis. (D) Immunofluorescence staining for Krt14 (green), Ki67 (red) and vimentin (white) of DED organotypic cultures with and without fibroblasts at indicated time points after seeding. (E) Quantification of Ki67-positive fibroblasts at indicated time point after seeding (n = 8 sections per condition of one representative experiment repeated twice). F, G. ECM degradation in DED organotypic cultures by collagenase treatment. (F) Immunofluorescence staining for Krt14 (green), Ki67 (red) and vimentin (white) of DED organotypic cultures at indicated time points after collagenase treatment. (G) Quantification of Ki67-positive fibroblasts at indicated time points after collagenase treatment (n = 8 sections per condition of one representative experiment repeated twice). Note that fibroblasts around the injection site start to proliferate after 48 h. Data information: Nuclei were labelled with DAPI (blue) (D, F). Scale bars, 100 μm (D, F). Data shown are means ± s.d. Fb, fibroblasts; PD, postdigestion; TCP, tissue culture plastic; h, hours. Boxplot horizontal bar represent the median, box limits the 25% percentile and the whiskers the min/max (E, G). Download figure Download PowerPoint To determine whether the negative effect of ECM on fibroblast proliferation held true in a more physiological model, we reconstituted decellularised human dermis (DED) with primary human fibroblasts and keratinocytes and cultured them at the air–medium interface, as described previously (Rikimaru et al, 1997; Philippeos et al, 2018; Fig 2C–G). After 3 weeks, all fibroblasts entered a quiescent state (Fig 2D and E), recapitulating the fibroblast behaviour observed in adult mouse skin (Fig 1B–D). However, when we injected collagenase mixture into the DED, the fibroblasts surrounding the injection sites started to proliferate within 48 h (Fig 2F and G). Thus, we conclude that in both in vitro assays the 3D ECM environment negatively regulates fibroblast proliferation. Nevertheless, the inhibition of proliferation is reversible and occurs in the presence or absence of keratinocytes. Modelling a switch between two fibroblast states To create a mathematical model deconstructing the inverse correlation between proliferation and ECM production, we assumed that fibroblasts switch between two states, proliferating fibroblasts (PF, with proliferating rate κ1) and quiescent fibroblasts (QF), with transition rates κ2 and κ−2, respectively (Fig 3A; Materials and Methods). Following the experimental observations, we conjectured that the existence of ECM would negatively regulate PF (κ4), pushing the equilibrium towards a state where PF were minimal and both QF and ECM deposition/remodelling were maximal. The derived ordinary differential equation (ODE) model is shown in Fig 3B. To fit the experimental data, we defined our multi-objective optimisation problem adapted to the particularity of having two data sets to fit (PF and ECM, Fig 1D) and followed a Monte Carlo technique to find the solutions (Fig 3C; Dilão et al, 2009; Dilão & Muraro, 2010; Dilão & Sainhas, 2011; Muraro & Dilão, 2013). For the parameters obtained, our model had one stable state for which all variables (PF, QF and ECM) were non-negative: that corresponded to the adult dermis. We conclude that a simple model whereby ECM represses fibroblast proliferation via a negative feedback loop is the key element setting dermal architecture. Figure 3. A negative feedback loop between ECM and proliferation determines fibroblast state switching Schematic representation of the assumed relationships between proliferating fibroblasts (PF), quiescent fibroblasts (QF) and extracellular matrix (ECM) during dermal maturation. PF self-replicate with rate κ1. PF and QF can interconvert with rates κ2 and κ−2, respectively. QF deposit ECM at rate κ3. ECM promotes the switch from PF to QF at rate κ4. System of ordinary differential equations (ODE) describing the process of dermal maturation. Because the system is dynamic, all entities can decay over time (κ6 is the degradation rate of QF, κ7 is the degradation rate of ECM, κ5 is the degradation rates of PF and is incorporated into β as β = κ1GF – κ2 – κ5 where GF stands for the growth factors we assume exist). Resulting simulation (black lines) of the system of equations in (B) and experimental data in Fig 1D. Error bars represent standard deviation of the biological replicates. Download figure Download PowerPoint A 3D tissue model recapitulates dermal maturation In order to test the mechanism of fibroblast behaviour and spatial organisation during dermal maturation, we developed a 3D model in CompuCell3D (Belmonte et al, 2016; Hirashima et al, 2017; Swat et al, 2012). We conceptualised the whole animal as a cylindrical object and focused on modelling the dermis between neighbouring hair follicles, to avoid considering changes in the skin associated with the hair growth cycle (Donati et al, 2014). We initialised our body segment model as a simple cylindrical segment, where epithelial tissue (green) surrounded the proliferative fibroblasts (blue) enclosing a lumen representative of the inside of the animal body (white; Fig EV3A). To explore the fundamental dermal architecture, we focused solely on dermal fibroblast behaviour and excluded other cell types, such as immune, neuronal or endothelial cells. Click here to expand this figure. Figure EV3. Skin proliferation kinetics and in vivo fibroblast lineage tracing during dermal maturation (related to Fig 4) A. 3D visualisation of the simulated mouse body segment. Colour code indicates epidermis in green, proliferating fibroblasts in blue and lumen in white. B. Quantification of proliferating (Ki67-positive) keratinocytes and fibroblasts in skin over time (n = 4 for 16.5, 29.5; n = 3 for 10.5, 18.5, 21.5, 30.5, 63.5, 69.5; n = 2 for 17.5, 19.5; biological replicates). Note that the decrease in proliferation of keratinocytes and fibroblasts follows similar kinetics over time. C. Representative simulation images of the epidermal gradients at indicated MCS. Note that the signal concentrates in the immediate surroundings of the epidermal cells and decays over time. D, E. In vivo lineage tracing of upper (Blimp1+ and Lrig1+ cells) and lower dermis (Dlk1+ cells) fibroblasts. (D) Immunofluorescence image of tdtomato or CAG-EGFP-labelled fibroblasts (red) with the indicated Cre lines. Nuclei were labelled with DAPI (blue). (E) Quantification of the percentage of labelled fibroblasts in the lower dermis of adult mice (> P50) (n = 2 biological replicates). Note the increased abundance of Blimp1Cre- and Lrig1CreER-labelled dermal fibroblasts in the lower dermal layer with age. Data information: Data shown are means ± s.d. Scale bar, 100 μm. Download figure Download PowerPoint Since fibroblasts close to the basement membrane were more proliferative (Figs 1E and F, and EV2D) and the switch in fibroblast behaviour also correlated with a decrease in basal keratinocyte proliferation (Fig EV3B), we proposed that the differential spatial proliferation of fibroblasts during development was influenced by an epidermal gradient (Fig EV3C, Collins et al, 2011; Lichtenberger et al, 2016). In agreement with the experimental evidence (Lichtenberger et al, 2016), epidermal signal strength was assumed to decline with age, accounting for the decline in proliferation. Therefore, in this model, the epidermal gradient directly impacted on fibroblast division capability. To account for the effect of ECM on fibroblast proliferation, we linked the transition from the proliferative state to quiescence with the amount of ECM surrounding the cell. While both proliferating and quiescent fibroblast populations can produce ECM, in line with the model in Fig 3A, quiescent cells deposited and remodelled ECM more efficiently. We included an adipocyte layer to represent the process of fibroblast differentiation into adipocytes in the deepest dermis, based on experimental data demonstrating the timing of appearance of the dermal white adipose tissue (DWAT; Rognoni et al, 2016); however, further modelling of DWAT maturation during development was not considered. The fundamental tissue-scale behaviour of the dermis was fully recapitulated in our spatial model (Fig 4A–C, Movie EV1). We started with a pool of proliferative fibroblasts that respond to the presence of the epidermal gradient [Monte Carlo step (MCS) 0, ~E10.5]. When the proliferative cells become surrounded by ECM, the cells stop dividing and deposit ECM more efficiently. The cells in the lower dermis differentiate into adipocytes (MCS > 300) in a spatially controlled manner, that is, only cells in direct contact with the body wall are able to do so. At the first step of dermal maturation, we went through a phase of homogeneous tissue (MCS < 100), where we had minimal ECM and all cells were proliferative. Next, proliferating cells switched to a quiescent state, increasing ECM deposition, and the proliferative rim converged to the region around the epidermis (MCS ~200, ~E17.5). After MCS > 300 (~P0), cells close to the lumen differentiated into adipocytes, forming the hypodermis layer. After MCS > 450 (~P5), all proliferation ceased and the dermis started expanding due to the accumulation of ECM. As a reference, the model progression was optimised such that MCS = 300 coincided with the in vivo proliferation data measured at P0 (for full details please refer to the Materials and Methods section). Figure 4. Development of a 3D tissue model and in vivo live imaging during dermal maturation A. (x,y) Cross-section of the computational model simulation of the dermal maturation process at the indicated Monte Carlo step (MCS). Colour code indicates epidermis (green), proliferating fibroblasts (blue), quiescent fibroblasts (yellow), ECM (grey) and adipocytes (brown) (see accompanying Movie EV1). B. Abund}, number={8}, journal={Molecular Systems Biology}, publisher={EMBO}, author={Rognoni, Emanuel and Pisco, Angela Oliveira and Hiratsuka, Toru and Sipilä, Kalle H and Belmonte, Julio M and Mobasseri, Seyedeh Atefeh and Philippeos, Christina and Dilão, Rui and Watt, Fiona M}, year={2018}, month={Aug} }
 @article{chen_srinivasan_tung_belmonte_wang_murthy_choi_rakhilin_king_varanko_et al._2017, title={A Notch positive feedback in the intestinal stem cell niche is essential for stem cell self‐renewal}, volume={13}, url={https://doi.org/10.15252/msb.20167324}, DOI={10.15252/msb.20167324}, abstractNote={Article28 April 2017Open Access Source DataTransparent process A Notch positive feedback in the intestinal stem cell niche is essential for stem cell self-renewal Kai-Yuan Chen Kai-Yuan Chen orcid.org/0000-0002-9247-8428 School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA Department of Biomedical Engineering, Duke University, Durham, NC, USA Search for more papers by this author Tara Srinivasan Tara Srinivasan Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author Kuei-Ling Tung Kuei-Ling Tung Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author Julio M Belmonte Julio M Belmonte orcid.org/0000-0002-4315-9631 Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, IN, USA Search for more papers by this author Lihua Wang Lihua Wang Department of Biomedical Engineering, Duke University, Durham, NC, USA Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author Preetish Kadur Lakshminarasimha Murthy Preetish Kadur Lakshminarasimha Murthy School of Mechanical Aerospace Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author Jiahn Choi Jiahn Choi Department of Biomedical Engineering, Duke University, Durham, NC, USA Search for more papers by this author Nikolai Rakhilin Nikolai Rakhilin School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA Department of Biomedical Engineering, Duke University, Durham, NC, USA Search for more papers by this author Sarah King Sarah King Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author Anastasia Kristine Varanko Anastasia Kristine Varanko Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author Mavee Witherspoon Mavee Witherspoon School of Mechanical Aerospace Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author Nozomi Nishimura Nozomi Nishimura orcid.org/0000-0003-4342-9416 Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author James A Glazier James A Glazier Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, IN, USA Search for more papers by this author Steven M Lipkin Steven M Lipkin Departments of Medicine, Genetic Medicine and Surgery, Weill Cornell Medical College, New York, NY, USA Search for more papers by this author Pengcheng Bu Corresponding Author Pengcheng Bu [email protected] orcid.org/0000-0001-6001-8665 Department of Biomedical Engineering, Duke University, Durham, NC, USA Key Laboratory of RNA Biology, Key Laboratory of Protein and Peptide Pharmaceutical, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Xiling Shen Corresponding Author Xiling Shen [email protected] orcid.org/0000-0002-4978-3531 School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA Department of Biomedical Engineering, Duke University, Durham, NC, USA Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author Kai-Yuan Chen Kai-Yuan Chen orcid.org/0000-0002-9247-8428 School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA Department of Biomedical Engineering, Duke University, Durham, NC, USA Search for more papers by this author Tara Srinivasan Tara Srinivasan Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author Kuei-Ling Tung Kuei-Ling Tung Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author Julio M Belmonte Julio M Belmonte orcid.org/0000-0002-4315-9631 Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, IN, USA Search for more papers by this author Lihua Wang Lihua Wang Department of Biomedical Engineering, Duke University, Durham, NC, USA Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author Preetish Kadur Lakshminarasimha Murthy Preetish Kadur Lakshminarasimha Murthy School of Mechanical Aerospace Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author Jiahn Choi Jiahn Choi Department of Biomedical Engineering, Duke University, Durham, NC, USA Search for more papers by this author Nikolai Rakhilin Nikolai Rakhilin School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA Department of Biomedical Engineering, Duke University, Durham, NC, USA Search for more papers by this author Sarah King Sarah King Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author Anastasia Kristine Varanko Anastasia Kristine Varanko Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author Mavee Witherspoon Mavee Witherspoon School of Mechanical Aerospace Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author Nozomi Nishimura Nozomi Nishimura orcid.org/0000-0003-4342-9416 Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author James A Glazier James A Glazier Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, IN, USA Search for more papers by this author Steven M Lipkin Steven M Lipkin Departments of Medicine, Genetic Medicine and Surgery, Weill Cornell Medical College, New York, NY, USA Search for more papers by this author Pengcheng Bu Corresponding Author Pengcheng Bu [email protected] orcid.org/0000-0001-6001-8665 Department of Biomedical Engineering, Duke University, Durham, NC, USA Key Laboratory of RNA Biology, Key Laboratory of Protein and Peptide Pharmaceutical, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Xiling Shen Corresponding Author Xiling Shen [email protected] orcid.org/0000-0002-4978-3531 School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA Department of Biomedical Engineering, Duke University, Durham, NC, USA Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA Search for more papers by this author Author Information Kai-Yuan Chen1,2,‡, Tara Srinivasan3,‡, Kuei-Ling Tung4,‡, Julio M Belmonte5, Lihua Wang2,4, Preetish Kadur Lakshminarasimha Murthy6, Jiahn Choi2, Nikolai Rakhilin1,2, Sarah King3, Anastasia Kristine Varanko4, Mavee Witherspoon6, Nozomi Nishimura3, James A Glazier5, Steven M Lipkin7, Pengcheng Bu *,2,8 and Xiling Shen *,1,2,3 1School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA 2Department of Biomedical Engineering, Duke University, Durham, NC, USA 3Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA 4Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA 5Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, IN, USA 6School of Mechanical Aerospace Engineering, Cornell University, Ithaca, NY, USA 7Departments of Medicine, Genetic Medicine and Surgery, Weill Cornell Medical College, New York, NY, USA 8Key Laboratory of RNA Biology, Key Laboratory of Protein and Peptide Pharmaceutical, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China ‡These authors contributed equally to this work *Corresponding author. Tel: +1 919 681 9184; E-mail: [email protected] *Corresponding author. Tel: +1 919 681 9184; E-mail: [email protected] Molecular Systems Biology (2017)13:927https://doi.org/10.15252/msb.20167324 Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract The intestinal epithelium is the fastest regenerative tissue in the body, fueled by fast-cycling stem cells. The number and identity of these dividing and migrating stem cells are maintained by a mosaic pattern at the base of the crypt. How the underlying regulatory scheme manages this dynamic stem cell niche is not entirely clear. We stimulated intestinal organoids with Notch ligands and inhibitors and discovered that intestinal stem cells employ a positive feedback mechanism via direct Notch binding to the second intron of the Notch1 gene. Inactivation of the positive feedback by CRISPR/Cas9 mutation of the binding sequence alters the mosaic stem cell niche pattern and hinders regeneration in organoids. Dynamical system analysis and agent-based multiscale stochastic modeling suggest that the positive feedback enhances the robustness of Notch-mediated niche patterning. This study highlights the importance of feedback mechanisms in spatiotemporal control of the stem cell niche. Synopsis A direct positive feedback loop, in which the intracellular domain of activated Notch receptors binds to an enhancer in the second intron of the Notch1 gene to enhance its expression, is critical for intestinal stem cell self-renewal and niche patterning. Characterization of the Notch signaling response using intestinal organoids shows that the activated Notch receptor binds to an enhancer site in the second intron of the Notch1 gene to form a positive feedback loop in the intestinal stem cell. The Notch1 positive feedback loop is essential for intestinal regeneration and stem cell self-renewal. Computational modeling indicates that the Notch1 positive feedback enhances robustness of stem cell niche patterning. Introduction The stem cell niche provides a spatial environment that regulates stem cell self-renewal and differentiation (Lander et al, 2012). One example is at the base of the intestinal crypt, where self-renewing LGR5+ crypt base columnar (CBC) cells and lysozyme-secreting Paneth cells form a mosaic pattern in which each Paneth cell is separated from others by LGR5+ cells (Barker et al, 2007; Sato et al, 2011a). As proliferative intestinal stem cells (ISCs), CBCs mostly divide symmetrically, compete with each other in a neutral drift process, and regenerate the intestinal epithelium in 3–5 days (Lopez-Garcia et al, 2010; Snippert et al, 2010). The stem cell niche is capable of recovering from radiation or chemical damages to restore tissue homeostasis (Buczacki et al, 2013; Metcalfe et al, 2014). Regulation of the niche is a concerted effort involving various signaling pathways. Paneth cells provide niche factors including epidermal growth factor (EGF), Wnt ligands (WNT3A), Notch ligands, and bone morphogenetic protein (BMP) inhibitor Noggin to support CBC self-renewal, while pericryptal stromal cells underneath the niche also supply additional Wnt ligands (WNT2B) (Barker, 2014). Among the pathways, juxtacrine Notch signaling pathway is often linked to developmental patterning (Artavanis-Tsakonas et al, 1999; Kopan & Ilagan, 2009). Notch signaling is mediated through direct cell-to-cell contact of membrane-bound Notch ligands on one cell and transmembrane Notch receptors on adjacent cells. The extracellular domain of Notch receptors binds Notch ligands, which activates receptor cleavage that releases the Notch receptor intracellular domain (NICD) to translocate to the nucleus. NICD interacts with the DNA-binding protein RBPJk to activate expression of downstream genes, such as the HES family transcription factors. Notch signaling is essential for intestinal stem cell self-renewal and crypt homeostasis (Fre et al, 2005; van der Flier & Clevers, 2009). Among Notch receptors, inhibition of both Notch1 and Notch2 completely depletes proliferative stem/progenitor cells in the intestinal epithelium (Riccio et al, 2008). Inhibition of Notch1 alone is sufficient to cause a defective intestinal phenotype, while inhibition of Notch2 alone causes no significant phenotype (Wu et al, 2010). Notch3 and Notch4 are not expressed in the intestinal epithelium (Fre et al, 2011). Among Notch ligands, DLL1 and DLL4 are essential and function redundantly, and inactivation of both causes loss of stem and progenitor cells; in contrast, JAG1 is not essential (Pellegrinet et al, 2011). In this study, we characterized the response of Notch signaling components in LGR5+ CBCs from intestinal organoids and identified a direct Notch positive feedback loop. Perturbation to the positive feedback by CRISPR/CAS9 mutation of the binding sequence significantly reduced the number of CBCs in the stem cell niche. Computational modeling suggests that the positive feedback may contribute to robustness of the system when proliferation rates are high. Results Notch lateral inhibition and positive feedback We characterized Notch signaling response in CBCs and Paneth cells (Fig 1A) using the in vitro intestinal organoid system (Sato et al, 2009), from which LGR5-EGFP+ CBCs and CD24+ Paneth cells were isolated using an established protocol (Sato et al, 2011a). Immunofluorescence (IF) confirmed that the sorted CD24+ Paneth cells express lysozyme (Fig EV1A). RT–qPCR on purified CBCs and Paneth cells confirmed that Notch receptors (Notch1, Notch2) and signaling effectors (Hes1, Hes5) are enriched in CBCs, while Notch ligands (Dll1, Dll4, Jag1) and the secretory lineage regulator, Atoh1 (Yang et al, 2001), are enriched in Paneth cells, largely consistent with previous microarray measurements (Sato et al, 2011a; Fig 1B). Inhibition of Notch receptor cleavage by the γ-secretase inhibitor DAPT reduced the number of CBCs and increased the number of Paneth cells, whereas Notch activation by recombinant ligand JAG1 embedded in Matrigel (Sato et al, 2009; Van Landeghem et al, 2012; VanDussen et al, 2012; Yamamura et al, 2014; Mahapatro et al, 2016; Srinivasan et al, 2016) or EDTA (Rand et al, 2000) increased the number of CBCs and decreased the number of Paneth cells (Fig 1C). Inhibition of Notch by DAPT up-regulated ligand expression, indicating that active Notch signaling suppresses ligand expression (Fig 1D and E). This is consistent with a lateral inhibition (LI) mechanism previously reported in several developmental systems, where ligands on a “sender” cell (in this case, Paneth cell) activate receptors on a “receiver” cell (in this case, CBC), which, in turn, suppresses ligand expression in the receiver cell (Collier et al, 1996). This intercellular feedback scheme causes bifurcation between adjacent cells, resulting in two opposite Notch signaling states (Fig 1F). Figure 1. Notch levels in niche cells Left: Cross-sectional view of murine intestinal crypt bottoms with co-immunofluorescence (co-IF) showing intermingled LGR5-EGFP+ (green) CBCs and lysozyme+ (LYZ, red) Paneth cells. Scale bar: 50 μm. Right: Schematic illustration of a niche pattern in both longitudinal and cross-sectional views of a crypt. RT–qPCR quantification of Notch signaling components in CBC and Paneth cell populations. The experiment was performed in triplicate and presented mean ± SEM (***P ≤ 0.001, **P ≤ 0.01, *P ≤ 0.05; Student's t-test). Representative FACS plots of organoids treated with DMSO, JAG1 (embedded in Matrigel), EDTA or DAPT for 48 h, including gated analysis to isolate CD24high/SSChigh Paneth cells and LGR5-EGFP+ CBCs according to an established protocol (Sato et al, 2011c). RT–qPCR quantification of Notch signaling components in CBCs and Paneth cell populations after organoids were treated with Matrigel-embedded JAG1 (top), EDTA (middle), or DAPT (bottom). The experiments were performed in triplicate and presented mean ± SEM (**P ≤ 0.01, *P ≤ 0.05; Student's t-test). Western blot analysis of Notch signaling components from conditions described in (D). Actin was used as a loading control. Schematic illustration of lateral inhibition and positive feedback between neighboring cells. Transparent colors and dotted lines represent low expression/activity levels. Source data are available online for this figure. Source Data for Figure 1 [msb167324-sup-0005-SDataFig1.zip] Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Lysozyme expression in isolated Paneth cells and linage tracing of Notch1+ cells Immunofluorescence (IF) image of sorted CD24high/SSChigh cells and LGR5-GFPhigh cells. CD24high/SSChigh cells show expression of Paneth cell-specific marker lysozyme (red) exclusively. DAPI labels nuclei, and scale bar represents 10 μm. Representative images of intestinal tissue derived from tamoxifen-induced Notch1-CreERT2 KI × Rosa26-tdTomato-WPRE mice. Shown are images from 30 days post-tamoxifen induction. Notch1 IF (red). DAPI labels nuclei and scale bar represents 100 μm. Download figure Download PowerPoint Additionally, Notch activation by recombinant JAG1 embedded in Matrigel (Sato et al, 2009; Van Landeghem et al, 2012; VanDussen et al, 2012; Yamamura et al, 2014; Mahapatro et al, 2016; Srinivasan et al, 2016) or EDTA (Rand et al, 2000) significantly increased receptor (Notch1/2) expression, while DAPT significantly reduced receptor expression (Fig 1D and E). This suggests the existence of a positive feedback loop, where activated Notch receptors up-regulate their own expression (Fig 1F). NICD directly activates Notch1 transcription Although both Notch1 and Notch2 form positive autoregulation, Notch1 has a stronger response than Notch2 (Fig 1D and E). This is consistent with previous reports showing that Notch1 and Notch2 are somewhat functionally redundant, but Notch1 is more critical to stem cell self-renewal and crypt homeostasis, while Notch2 is dispensable (Wu et al, 2010). We performed lineage tracing using tamoxifen-inducible Notch1CreER × ROSA26tdTomato transgenic mouse reporter strains (Fre et al, 2011; Oh et al, 2013). After induction, labeled Notch1+ cells showed a similar pattern that largely overlaps with CBCs in the niche (Fig 2A). From day 1 to day 3, marked progeny of Notch1+ cells expanded out of the niche and overtook the trans-amplifying (TA) progenitor compartments; by day 30, the marked clones of the original Notch1+ cells replaced the entire epithelium (Figs 2B and EV1B). These lineage tracing experiments confirmed that Notch1 is active in CBCs, which is consistent with previous findings (Wu et al, 2010; Fre et al, 2011; Pellegrinet et al, 2011; Oh et al, 2013). Figure 2. Notch1 positive feedback Representative image indicating Notch1 expression (red) in intestinal crypt bottoms of tamoxifen-induced Notch1CreER × Rosa26tdTomato mice. Scale bar: 50 μm. Representative images of intestinal crypts showing progeny of Notch1+ cells 1 day (left) and 3 days (right) after tamoxifen induction in Notch1CreER × Rosa26tdTomato mice. Dotted lines label the boundary of cells. Scale bar: 20 μm. Luciferase reporter assay of NICD binding sequences. Left: Luciferase reporter vector map with the wild-type and three mutated NICD binding sequences cloned into the enhancer site. Blue represents wild-type sequence, and red represents mutated sequences. Right: Luciferase activity in the four sequences with normalization to a pRL-SV40 control vector. Jag1 or DAPT was added to stimulate or suppress Notch signaling for 48 h. The experiment was performed in replicates (n = 4) and presented mean + SEM (*P ≤ 0.05, **P ≤ 0.01; Student's t-test compares other conditions to WT sequence in normal condition separately). Western blot following DNA pull-down showing NICD-DNA interaction. DNA pull-down was performed using mouse intestine crypt lysates with biotin-labeled oligonucleotide duplex of wild-type or mutated sequences containing the putative NICD/RBPJk binding site, followed by Western blotting to validate NICD binding on the pull-down sequences. Actin was used for input control. Design of gRNAs for CRISPR/Cas9 mutagenesis to target the putative NICD/RBPJk binding motif on mouse Notch1 sequence. Single LGR5-EGFP+ CBCs were transfected with either an empty vector (control) or CRISPR/Cas9 gRNAs. Shown are representative brightfield images over 15 days and co-IF images indicating LGR5-EGFP (green) and LYZ (red) expression with DAPI nuclear staining. Scale bar represents 100 μm in low magnification and 25 μm in high magnification images, respectively. Single LGR5-EGFP CBCs were transfected with either an empty vector (control) or CRISPR/Cas9 gRNAs. Left: Colony-forming efficiency measured after 5 days. Quantitative analysis calculated from 1,000 cells/replicate. The experiment was performed in triplicate and presented mean ± SEM (**P ≤ 0.01; Student's t-test). Right: Quantitative comparison of organoid diameters after 15 days. The experiment was performed in triplicate and presented mean ± SEM (**P ≤ 0.01; Student's t-test). Single LGR5-EGFP ISCs were transfected with either an empty vector (control) or CRISPR/Cas9 gRNAs. Ratio of LGR5-EGFP+ CBCs/LYZ+ Paneth cells as determined by FACS analysis after 15 days. The experiment was performed in triplicate and presented mean ± SEM (**P ≤ 0.01; Student's t-test). Single empty vector control or CRISPR/Cas9-positive feedback knockout (PF KO) LGR5-EGFP+ CBCs were transfected with an RBPJk-dsRed reporter construct and grown into organoids, which were subsequently treated with DMSO, DAPT, or JAG1 for 48 h. Left: Representative FACS plots for RBPJk-dsRED and LGR5-EGFP expression indicating a gated double positive fraction for each condition. Right: Mean fluorescence intensity (MFI) of RBPJk-dsRed expression of the entire cell population. The experiment was performed in triplicate and presented mean ± SEM (**P ≤ 0.01; Student's t-test). Source data are available online for this figure. Source Data for Figure 2 [msb167324-sup-0006-SDataFig2.zip] Download figure Download PowerPoint We analyzed the LICR ChIP-Seq dataset of mouse small intestinal cells from ENCODE using the UCSC genome browser (Consortium, 2012) to investigate regulation of Notch1 and Notch2 transcription. The second intron region of the Notch1 gene is highly enriched with enhancer histone marks H3K4me1 and H3K27ac, while no such regions were found in the Notch2 sequence (Fig EV2A). Computational analysis of this region with MotifMap (Wang et al, 2014) predicted a putative binding motif for RBPJk, the DNA-binding protein that forms an effector complex with NICD to activate Notch signaling. A unique eight base pair sequence (TTCCCACG, Chr2: 26,349,981–26,349,988) was identified (Fig EV2B). ChIP-PCR shows that NICD binds to this sequence in CBCs, and the binding was enhanced by JAG1 activation of receptors and suppressed by DAPT inhibition of receptor cleavage (Fig EV2C). For further validation, we crossed a LGR5-EGFP strain with a Rosa26-YFP-NICD strain (Oh et al, 2013) to generate a tamoxifen-inducible LGR5-EGFP-CreERT2 × Rosa26-YFP-NICD (NICD-OE) mouse strain. ChIP-PCR analysis of tamoxifen-induced NICD-expressing intestinal cells from NICD-OE mice also showed elevated NICD binding compared to uninduced control (Fig EV2D). Click here to expand this figure. Figure EV2. Notch1 positive feedback in mouse intestine ChIP-Seq signal of LICR histone tracks (H3K4me1 and H3K27ac) on mouse small intestine cells from ENCODE at UCSC Genome Browser. Left: H3K4me1 (top) and H3K27ac (bottom) occupancy related to Notch1. Right: H3K4me1 (top) and H3K27ac (bottom) occupancy related to Notch2. Top: Mouse Notch1 gene. Red line indicates the location of NICD/RBPJk binding motif on mouse Notch1. Bottom: Sequence and chromatogram of NICD binding motif in mouse Notch1 following ChIP-PCR from LGR5-EGFP+ CBCs. Agarose gel analysis of ChIP-PCR products from LGR5-EGFP+ CBCs validating NICD binding to the motif in Notch1 sequence. LGR5-EGFP+ CBCs were sorted from organoids treated with DMSO, DAPT, or JAG1. Organoids extracted from LGR5-EGFP × CreERT2/Rosa26-YFP-NICD mice were treated with tamoxifen to induce NICD overexpression (NICD-OE). Shown is agarose gel analysis of ChIP-PCR products to validate active NICD binding on Notch1. Representative sequences from selected organoid clones transfected with CRISPR/Cas9 gRNAs showing indel mutations in the targeted region of the mouse NICD binding motif. Yellow box represents the putative binding sequence region, where red indicates indel mutations by CRISPR. LGR5-EGFP+ CBCs were transfected with either an empty vector control or CRISPR/Cas9 gRNA and subsequently treated with DMSO, DAPT, or JAG1. Shown is ChIP-qPCR analysis of Notch1, indicating enrichment with NICD antibody compared with IgG control. The experiment was performed in triplicate and presented mean ± SEM (**P ≤ 0.01; Student's t-test). RT–PCR measurements indicating Notch1 expression in LGR5-EGFP+ CBCs. Isolated single LGR5-EGFP+ CBCs were transfected with either an empty vector control or CRISPR/Cas9 gRNAs and subsequently treated with DMSO, DAPT, or JAG1 (embedded in Matrigel). The experiment was performed in triplicate and presented mean ± SEM (*P ≤ 0.05, **P ≤ 0.01; Student's t-test). Single LGR5-EGFP+ CBCs were transfected with either an empty vector control or CRISPR/Cas9 gRNAs and propagated as organoids. Shown is Western blot analysis for NICD expression in sorted LGR5-EGFP+ CBCs from each condition. Actin was used as a loading control. RT–PCR measurements indicating Notch1/2, Hes1/5, and Lgr5 expression in LGR5-EGFP+ ISCs for each condition described in (G). The experiment was performed in triplicate and presented mean ± SEM (**P ≤ 0.01; Student's t-test). Single LGR5-EGFP+ CBCs were transfected with either an empty vector control or CRISPR/Cas9 gRNAs and propagated as organoids for 15 days. Shown are representative FACS plots for each condition including gated analysis to isolate CD24high/SSChigh Paneth cells and LGR5-EGFP+ ISCs. Source data are available online for this figure. Download figure Download PowerPoint To further validate this enhancer sequence motif, we performed a luciferase reporter assay with the enhancer sequence cloned to the pGL4.27 [luc2P/minP] luciferase reporter vector containing a minimal promoter (Fig 2C). We compared the wild-type binding sequence with three mutated sequences (Fig 2C): (i) partial mutation of 3 nts in the binding sequence and 3nts of adjacent flanking region, (ii) partial deletion of 2nts of binding sequence, and (iii) mutation of the entire 8 nt binding sequence. The luciferase reporter vectors were transfected into intestine cells directly isolated from the mouse intestine with a pRL-SV40 control vector containing no binding sequence. The luciferase signal from the wild-type enhancer sequence was significantly higher than those from the mutated sequences or the control vector (Fig 2C). Jag1 and DAPT treatments also elicited stronger responses from the wild-type sequence than the mutated sequences or the control vector (Fig 2C). We then performed a pull-down assay to confirm interaction between NICD and the identified binding motif. Oligonucleotides of the wild-type and mutated enhancer sequences were synthesized and labeled with biotin to pull down NICD/RBPJk complex from mouse intestinal crypt lysates, which was validated by Western blot (Fig 2D). The wild-type sequence has stronger NICD binding than the mutated sequences. Positive feedback is critical to self-renewal, niche homeostasis, and recovery Our characterization of Notch signaling pathways in niche cells suggests that both LI and a direct positive feedback are active. The role of LI in the niche can be easily rationalized, because LI is known to regulate developmental patterns (Collier et al, 1996; Kim et al, 2014). However, it is unclear whether the Notch positive feedback has any function. CRISPR-Cas9 vectors were designed to target the NICD binding sequence (Fig 2E, Table EV1). CRISPR/Cas9 vectors with specific guide RNAs (gRNAs) were transfected into single LGR5-EGFP CBCs, which were subsequently propagated as organoids. After puromycin selection, individual colonies were picked and sequenced separately to confirm CRISPR/Cas9 editing in the cells. Sequencing results indicate the presence of indels in the target NICD binding region formed through non-homologous end joining (NHEJ) (Fig EV2E). The mutated binding motif significantly reduced NICD binding compared to the empty vector (EV) control in CBCs sorted from organoids treated with DMSO (control), JAG1, or DAPT, according to ChIP-qPCR (Fig EV2F), which was consistent with the outcomes of the pull-down assay (Fig 2D). The mutations also significantly decreased Notch1 transcript levels measured by RT–qPCR (Fig EV2G) and NICD levels measured by Western blot (Fig EV2H). Expression levels of Notch signaling components (Notch1, Notch2, Hes1, Hes5) and LGR5 all decreased in CRISPR/Cas9-targeted cells with the mutated binding motif (Fig EV2I). Taken together, the data suggest that, when Notch receptors are activated, the resulting NICD/RBPJk complex bind to the Notch1 gene and enhances its transcription, hence producing more Notch1 receptors and forming a positive feedback loop in intestinal stem cells. CRISPR mutation of the binding motif (PF KO) reduced colony-forming efficiency and growth rate of intestinal organoids markedly (Fig 2F and G). Furthermore, the mutation significantly reduced the number of CBCs and the ratio of CBC to Paneth cell in the niche (Figs 2F and H, and EV2J). Next, to understand how this positive feedback influences Notch sig}, number={4}, journal={Molecular Systems Biology}, publisher={EMBO}, author={Chen, Kai‐Yuan and Srinivasan, Tara and Tung, Kuei‐Ling and Belmonte, Julio M and Wang, Lihua and Murthy, Preetish Kadur Lakshminarasimha and Choi, Jiahn and Rakhilin, Nikolai and King, Sarah and Varanko, Anastasia Kristine and et al.}, year={2017}, month={Apr}, pages={927} }
 @article{belmonte_leptin_nédélec_2017, title={A theory that predicts behaviors of disordered cytoskeletal networks}, volume={13}, url={https://doi.org/10.15252/msb.20177796}, DOI={10.15252/msb.20177796}, abstractNote={Abstract Morphogenesis in animal tissues is largely driven by actomyosin networks, through tensions generated by an active contractile process. Although the network components and their properties are known, and networks can be reconstituted in vitro, the requirements for contractility are still poorly understood. Here, we describe a theory that predicts whether an isotropic network will contract, expand, or conserve its dimensions. This analytical theory correctly predicts the behavior of simulated networks, consisting of filaments with varying combinations of connectors, and reveals conditions under which networks of rigid filaments are either contractile or expansile. Our results suggest that pulsatility is an intrinsic behavior of contractile networks if the filaments are not stable but turn over. The theory offers a unifying framework to think about mechanisms of contractions or expansion. It provides the foundation for studying a broad range of processes involving cytoskeletal networks and a basis for designing synthetic networks.}, number={9}, journal={Molecular Systems Biology}, publisher={EMBO}, author={Belmonte, Julio M and Leptin, Maria and Nédélec, François}, year={2017}, month={Sep}, pages={941} }
 @article{sluka_fu_swat_belmonte_cosmanescu_clendenon_wambaugh_glazier_2016, title={A Liver-Centric Multiscale Modeling Framework for Xenobiotics}, volume={11}, DOI={10.1371/journal.pone.0162428}, abstractNote={We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that span three scales; Physiologically Based Pharmacokinetic (PBPK) modeling of acetaminophen uptake and distribution at the whole body level, cell and blood flow modeling at the tissue/organ level and metabolism at the sub-cellular level. We have used standard modeling modalities at each of the three scales. In particular, we have used the Systems Biology Markup Language (SBML) to create both the whole-body and sub-cellular scales. Our modeling approach allows us to run the individual sub-models separately and allows us to easily exchange models at a particular scale without the need to extensively rework the sub-models at other scales. In addition, the use of SBML greatly facilitates the inclusion of biological annotations directly in the model code. The model was calibrated using human in vivo data for acetaminophen and its sulfate and glucuronate metabolites. We then carried out extensive parameter sensitivity studies including the pairwise interaction of parameters. We also simulated population variation of exposure and sensitivity to acetaminophen. Our modeling framework can be extended to the prediction of liver toxicity following acetaminophen overdose, or used as a general purpose pharmacokinetic model for xenobiotics.}, number={9}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Sluka, James P. and Fu, Xiao and Swat, Maciej and Belmonte, Julio M. and Cosmanescu, Alin and Clendenon, Sherry G. and Wambaugh, John F. and Glazier, James A.}, editor={Schmidt, Edward EEditor}, year={2016}, month={Sep}, pages={e0162428} }
 @article{belmonte_swat_glazier_2016, title={Filopodial-Tension Model of Convergent-Extension of Tissues}, volume={12}, url={http://dx.doi.org/10.1371/journal.pcbi.1004952}, DOI={10.1371/journal.pcbi.1004952}, abstractNote={In convergent-extension (CE), a planar-polarized epithelial tissue elongates (extends) in-plane in one direction while shortening (converging) in the perpendicular in-plane direction, with the cells both elongating and intercalating along the converging axis. CE occurs during the development of most multicellular organisms. Current CE models assume cell or tissue asymmetry, but neglect the preferential filopodial activity along the convergent axis observed in many tissues. We propose a cell-based CE model based on asymmetric filopodial tension forces between cells and investigate how cell-level filopodial interactions drive tissue-level CE. The final tissue geometry depends on the balance between external rounding forces and cell-intercalation traction. Filopodial-tension CE is robust to relatively high levels of planar cell polarity misalignment and to the presence of non-active cells. Addition of a simple mechanical feedback between cells fully rescues and even improves CE of tissues with high levels of polarity misalignments. Our model extends easily to three dimensions, with either one converging and two extending axes, or two converging and one extending axes, producing distinct tissue morphologies, as observed in vivo.}, number={6}, journal={PLOS Computational Biology}, publisher={Public Library of Science (PLoS)}, author={Belmonte, Julio M. and Swat, Maciej H. and Glazier, James A.}, editor={Tusscher, TenEditor}, year={2016}, month={Jun}, pages={e1004952} }
 @article{belmonte_nedelec_2016, title={Large-scale microtubule networks contract quite well}, volume={5}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000371867900001&KeyUID=WOS:000371867900001}, DOI={10.7554/eLife.14076}, abstractNote={The quantitative investigation of how networks of microtubules contract can boost our understanding of actin biology.}, journal={Elife}, author={Belmonte, Julio M. and Nedelec, Francois}, year={2016} }
 @article{belmonte_clendenon_oliveira_swat_greene_jeyaraman_glazier_bacallao_2016, title={Virtual-tissue computer simulations define the roles of cell adhesion and proliferation in the onset of kidney cystic disease}, volume={27}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000387391400027&KeyUID=WOS:000387391400027}, DOI={10.1091/mbc.E16-01-0059}, abstractNote={In autosomal dominant polycystic kidney disease (ADPKD), cysts accumulate and progressively impair renal function. Mutations in PKD1 and PKD2 genes are causally linked to ADPKD, but how these mutations drive cell behaviors that underlie ADPKD pathogenesis is unknown. Human ADPKD cysts frequently express cadherin-8 (cad8), and expression of cad8 ectopically in vitro suffices to initiate cystogenesis. To explore cell behavioral mechanisms of cad8-driven cyst initiation, we developed a virtual-tissue computer model. Our simulations predicted that either reduced cell–cell adhesion or reduced contact inhibition of proliferation triggers cyst induction. To reproduce the full range of cyst morphologies observed in vivo, changes in both cell adhesion and proliferation are required. However, only loss-of-adhesion simulations produced morphologies matching in vitro cad8-induced cysts. Conversely, the saccular cysts described by others arise predominantly by decreased contact inhibition, that is, increased proliferation. In vitro experiments confirmed that cell–cell adhesion was reduced and proliferation was increased by ectopic cad8 expression. We conclude that adhesion loss due to cadherin type switching in ADPKD suffices to drive cystogenesis. Thus, control of cadherin type switching provides a new target for therapeutic intervention.}, number={22}, journal={Molecular Biology of the Cell}, author={Belmonte, Julio M. and Clendenon, Sherry G. and Oliveira, Guilherme M. and Swat, Maciej H. and Greene, Evan V. and Jeyaraman, Srividhya and Glazier, James A. and Bacallao, Robert L.}, year={2016}, pages={3673–3685} }
 @article{thomas_belmonte_graner_glazier_almeida_2015, title={3D simulations of wet foam coarsening evidence a self similar growth regime}, volume={473}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000352959500016&KeyUID=WOS:000352959500016}, DOI={10.1016/j.colsurfa.2015.02.015}, abstractNote={In wet liquid foams, slow diffusion of gas through bubble walls changes bubble pressure, volume and wall curvature. Large bubbles grow at the expenses of smaller ones. The smaller the bubble, the faster it shrinks. As the number of bubbles in a given volume decreases in time, the average bubble size increases: i.e. the foam coarsens. During coarsening, bubbles also move relative to each other, changing bubble topology and shape, while liquid moves within the regions separating the bubbles. Analyzing the combined effects of these mechanisms requires examining a volume with enough bubbles to provide appropriate statistics throughout coarsening. Using a Cellular Potts model, we simulate these mechanisms during the evolution of three-dimensional foams with wetnesses of ϕ = 0.00, 0.05 and 0.20. We represent the liquid phase as an ensemble of many small fluid particles, which allows us to monitor liquid flow in the region between bubbles. The simulations begin with 2 × 105 bubbles for ϕ = 0.00 and 1.25 × 105 bubbles for ϕ = 0.05 and 0.20, allowing us to track the distribution functions for bubble size, topology and growth rate over two and a half decades of volume change. All simulations eventually reach a self-similar growth regime, with the distribution functions time independent and the number of bubbles decreasing with time as a power law whose exponent depends on the wetness.}, journal={Colloids and Surfaces a-Physicochemical and Engineering Aspects}, author={Thomas, Gilberto L. and Belmonte, Julio M. and Graner, Francois and Glazier, James A. and Almeida, Rita M. C.}, year={2015}, pages={109–114} }
 @article{dias_almeida_belmonte_glazier_stern_2014, title={Somites Without a Clock}, volume={343}, DOI={10.1126/science.1247575}, abstractNote={The formation of body segments (somites) in vertebrate embryos is accompanied by molecular oscillations (segmentation clock). Interaction of this oscillator with a wave traveling along the body axis (the clock-and-wavefront model) is generally believed to control somite number, size, and axial identity. Here we show that a clock-and-wavefront mechanism is unnecessary for somite formation. Non-somite mesoderm treated with Noggin generates many somites that form simultaneously, without cyclic expression of Notch-pathway genes, yet have normal size, shape, and fate. These somites have axial identity: The Hox code is fixed independently of somite fate. However, these somites are not subdivided into rostral and caudal halves, which is necessary for neural segmentation. We propose that somites are self-organizing structures whose size and shape is controlled by local cell-cell interactions.}, number={6172}, journal={Science}, author={Dias, Ana S. and Almeida, Irene and Belmonte, Julio M. and Glazier, James A. and Stern, Claudio D.}, year={2014}, pages={791–795} }
 @article{swat_thomas_belmonte_shirinifard_hmeljak_glazier_asthagiri_arkin_2012, title={Multi-Scale Modeling of Tissues Using CompuCell3D}, volume={110}, DOI={10.1016/B978-0-12-388403-9.00013-8}, abstractNote={The study of how cells interact to produce tissue development, homeostasis, or diseases was, until recently, almost purely experimental. Now, multi-cell computer simulation methods, ranging from relatively simple cellular automata to complex immersed-boundary and finite-element mechanistic models, allow in silico study of multi-cell phenomena at the tissue scale based on biologically observed cell behaviors and interactions such as movement, adhesion, growth, death, mitosis, secretion of chemicals, chemotaxis, etc. This tutorial introduces the lattice-based Glazier-Graner-Hogeweg (GGH) Monte Carlo multi-cell modeling and the open-source GGH-based CompuCell3D simulation environment that allows rapid and intuitive modeling and simulation of cellular and multi-cellular behaviors in the context of tissue formation and subsequent dynamics. We also present a walkthrough of four biological models and their associated simulations that demonstrate the capabilities of the GGH and CompuCell3D.}, journal={Computational Methods in Cell Biology}, author={Swat, Maciej H. and Thomas, Gilberto L. and Belmonte, Julio M. and Shirinifard, Abbas and Hmeljak, Dimitrij and Glazier, James A. and Asthagiri, AR and Arkin, AP}, year={2012}, pages={325–366} }
 @article{hester_belmonte_gens_clendenon_glazier_2011, title={A Multi-cell, Multi-scale Model of Vertebrate Segmentation and Somite Formation}, volume={7}, DOI={10.1371/journal.pcbi.1002155}, abstractNote={Somitogenesis, the formation of the body's primary segmental structure common to all vertebrate development, requires coordination between biological mechanisms at several scales. Explaining how these mechanisms interact across scales and how events are coordinated in space and time is necessary for a complete understanding of somitogenesis and its evolutionary flexibility. So far, mechanisms of somitogenesis have been studied independently. To test the consistency, integrability and combined explanatory power of current prevailing hypotheses, we built an integrated clock-and-wavefront model including submodels of the intracellular segmentation clock, intercellular segmentation-clock coupling via Delta/Notch signaling, an FGF8 determination front, delayed differentiation, clock-wavefront readout, and differential-cell-cell-adhesion-driven cell sorting. We identify inconsistencies between existing submodels and gaps in the current understanding of somitogenesis mechanisms, and propose novel submodels and extensions of existing submodels where necessary. For reasonable initial conditions, 2D simulations of our model robustly generate spatially and temporally regular somites, realistic dynamic morphologies and spontaneous emergence of anterior-traveling stripes of Lfng. We show that these traveling stripes are pseudo-waves rather than true propagating waves. Our model is flexible enough to generate interspecies-like variation in somite size in response to changes in the PSM growth rate and segmentation-clock period, and in the number and width of Lfng stripes in response to changes in the PSM growth rate, segmentation-clock period and PSM length.}, number={10}, journal={Plos Computational Biology}, author={Hester, Susan D. and Belmonte, Julio M. and Gens, J. Scott and Clendenon, Sherry G. and Glazier, James A.}, year={2011} }
 @article{belmonte_thomas_brunnet_almeida_chate_2008, title={Self-propelled particle model for cell-sorting phenomena}, volume={100}, DOI={10.1103/PhysRevLett.100.248702}, abstractNote={A self-propelled particle model is introduced to study cell sorting occurring in some living organisms. This allows us to evaluate the influence of intrinsic cell motility separately from differential adhesion with fluctuations, a mechanism previously shown to be sufficient to explain a variety of cell rearrangement processes. We find that the tendency of cells to actively follow their neighbors greatly reduces segregation time scales. A finite-size analysis of the sorting process reveals clear algebraic growth laws as in physical phase-ordering processes, albeit with unusual scaling exponents.}, number={24}, journal={Physical Review Letters}, author={Belmonte, Julio M. and Thomas, Gilberto L. and Brunnet, Leonardo G. and Almeida, Rita M. C. and Chate, Hugues}, year={2008} }