Cabezas, R. et al. Growth factors and astrocytes metabolism: Possible roles for platelet derived growth factor. Med. Chem. 12, 204–210. https://doi.org/10.2174/1573406411666151019120444 (2016).
Google Scholar
Nag, S. & Walker, J. The Blood–Brain and Other Neural Barriers: Reviews and Protocols (Humana Press, 2011).
Siracusa, R., Fusco, R. & Cuzzocrea, S. Astrocytes: Role and functions in brain pathologies. Front. Pharmacol.https://doi.org/10.3389/fphar.2019.01114 (2019).
Google Scholar
Freeman, M. R. Specification and morphogenesis of astrocytes. Science 330, 774–778. https://doi.org/10.1126/science.1190928 (2010).
Google Scholar
Guillamon-Vivancos, T., Gomez-Pinedo, U. & Matias-Guiu, J. Astrocytes in neurodegenerative diseases (I): Function and molecular description. Neurologia (English Edition) 30, 119–129. https://doi.org/10.1016/j.nrleng.2014.12.005 (2015).
Google Scholar
Zhou, Y. et al. Dual roles of astrocytes in plasticity and reconstruction after traumatic brain injury. Cell Commun. Signal.https://doi.org/10.1186/s12964-020-00549-2 (2020).
Google Scholar
Sloan, S. A. & Barres, B. A. Mechanisms of astrocyte development and their contributions to neurodevelopmental disorders. Curr. Opin. Neurobiol. 27, 75–81. https://doi.org/10.1016/j.conb.2014.03.005 (2014).
Google Scholar
Zhou, B., Zuo, Y.-X. & Jiang, R.-T. Astrocyte morphology: Diversity, plasticity, and role in neurological diseases. CNS Neurosci. Ther. 25, 665–673. https://doi.org/10.1111/cns.13123 (2019).
Google Scholar
Wang, S.S.-H. et al. Functional trade-offs in white matter axonal scaling. J. Neurosci. 28, 4047–4056. https://doi.org/10.1523/jneurosci.5559-05.2008 (2008).
Google Scholar
Middelkamp, H. H. T. et al. Cell type-specific changes in transcriptomic profiles of endothelial cells, IPSC-derived neurons and astrocytes cultured on microfluidic chips. Sci. Rep.https://doi.org/10.1038/s41598-021-81933-x (2021).
Google Scholar
Herland, A. et al. Distinct contributions of astrocytes and pericytes to neuroinflammation identified in a 3D human blood–brain barrier on a chip. PLOS ONE 11, e0150360. https://doi.org/10.1371/journal.pone.0150360 (2016).
Google Scholar
Oudart, M. et al. Astrodot: A new method for studying the spatial distribution of mRNA in astrocytes. J. Cell Sci.https://doi.org/10.1242/jcs.239756 (2020).
Google Scholar
Yu, X., Nagai, J. & Khakh, B. S. Improved tools to study astrocytes. Nat. Rev. Neurosci. 21, 121–138. https://doi.org/10.1038/s41583-020-0264-8 (2020).
Google Scholar
Oschmann, F., Berry, H., Obermayer, K. & Lenk, K. From in silico astrocyte cell models to neuron-astrocyte network models: A review. Brain Res. Bull. 136, 76–84. https://doi.org/10.1016/j.brainresbull.2017.01.027 (2018).
Google Scholar
Savtchenko, L. P. et al. Disentangling astroglial physiology with a realistic cell model in silico. Nat. Commun.https://doi.org/10.1038/s41467-018-05896-w (2018).
Google Scholar
Verisokin, A. Y., Verveyko, D. V., Postnov, D. E. & Brazhe, A. R. Modeling of astrocyte networks: Toward realistic topology and dynamics. Front. Cell. Neurosci.https://doi.org/10.3389/fncel.2021.645068 (2021).
Google Scholar
Nguyen, K.-V., Hernandez-Garzon, E. & Valette, J. Efficient GPU-based Monte-Carlo simulation of diffusion in real astrocytes reconstructed from confocal microscopy. J. Magnet. Reson. 296, 188–199. https://doi.org/10.1016/j.jmr.2018.09.013 (2018).
Google Scholar
Lenk, K. et al. A computational model of interactions between neuronal and astrocytic networks: The role of astrocytes in the stability of the neuronal firing rate. Front. Comput. Neurosci.https://doi.org/10.3389/fncom.2019.00092 (2020).
Google Scholar
Hines, M. L. & Carnevale, N. T. The neuron simulation environment. Neural Comput. 9, 1179–1209. https://doi.org/10.1162/neco.1997.9.6.1179 (1997).
Google Scholar
Allam, S. L. et al. A computational model to investigate astrocytic glutamate uptake influence on synaptic transmission and neuronal spiking. Front. Comput. Neurosci.https://doi.org/10.3389/fncom.2012.00070 (2012).
Google Scholar
Radan, M., Djikic, T., Obradovic, D. & Nikolic, K. Application of in vitro pampa technique and in silico computational methods for blood-brain barrier permeability prediction of novel cns drug candidates. Eur. J. Pharmaceut. Sci. 168, 106056. https://doi.org/10.1016/j.ejps.2021.106056 (2022).
Google Scholar
Abdellah, M. et al. Metaball skinning of synthetic astroglial morphologies into realistic mesh models for in silico simulations and visual analytics. Bioinformatics 37, i426–i433. https://doi.org/10.1093/bioinformatics/btab280 (2021).
Google Scholar
Comes, M. C. et al. Accelerating the experimental responses on cell behaviors: A long-term prediction of cell trajectories using social generative adversarial network. Sci. Rep.https://doi.org/10.1038/s41598-020-72605-3 (2020).
Google Scholar
Goncalves Seabra, A. C., Silva, A. F. D., Stieglitz, T. & Amado-Rey, A. B. In silico blood pressure models comparison. IEEE Sens. J. 22, 23486–23493. https://doi.org/10.1109/jsen.2022.3215597 (2022).
Google Scholar
Ghaffarizadeh, A., Heiland, R., Friedman, S. H., Mumenthaler, S. M. & Macklin, P. Physicell: An open source physics-based cell simulator for 3-D multicellular systems. PLOS Comput. Biol. 14, e1005991. https://doi.org/10.1371/journal.pcbi.1005991 (2018).
Google Scholar
Swat, M. H. et al. Multi-Scale Modeling of Tissues Using CompuCell3D. 325–366 (Elsevier, 2012).
Roy, M. & Finley, S. D. Metabolic reprogramming dynamics in tumor spheroids: Insights from a multicellular, multiscale model. PLOS Comput. Biol. 15, e1007053. https://doi.org/10.1371/journal.pcbi.1007053 (2019).
Google Scholar
Risau, W. Mechanisms of angiogenesis. Nature 386, 671–674. https://doi.org/10.1038/386671a0 (1997).
Google Scholar
Ferozepurwalla, Z., Merzah, J., Thielemans, L. & Birdsey, G. Molecular and Cellular Mechanisms of Angiogenesis. 219–226 (Springer, 2019).
Villa, C., Chaplain, M. A. J., Gerisch, A. & Lorenzi, T. Mechanical models of pattern and form in biological tissues: The role of stress-strain constitutive equations. Bull. Math. Biol.https://doi.org/10.1007/s11538-021-00912-5 (2021).
Google Scholar
Tosin, A., Ambrosi, D. & Preziosi, L. Mechanics and chemotaxis in the morphogenesis of vascular networks. Bull. Math. Biol. 68, 1819–1836. https://doi.org/10.1007/s11538-006-9071-2 (2006).
Google Scholar
Manoussaki, D. A mechanochemical model of angiogenesis and vasculogenesis. ESAIM Math. Model. Numer. Anal. 37, 581–599 https://doi.org/10.1051/m2an:2003046 (2003).
Nakazawa, T., Tasaki, S., Nakai, K. & Suzuki, T. Multicellular model of angiogenesis. AIMS Bioeng. 9, 44–60. https://doi.org/10.3934/bioeng.2022004 (2022).
Google Scholar
Wang, Y. et al. On-chip-angiogenesis based on a high-throughput biomimetic three-dimensional cell spheroid culture system. Analyst 148, 3870–3875. https://doi.org/10.1039/d3an00817g (2023).
Google Scholar
Mada, J. & Tokihiro, T. Pattern formation of vascular network in a mathematical model of angiogenesis. Jpn. J. Indus. Appl. Math. 39, 351–384. https://doi.org/10.1007/s13160-021-00493-9 (2021).
Google Scholar
Wyss-Coray, T. et al. Adult mouse astrocytes degrade amyloid-b in vitro and in situ. Nat. Med. 9, 453–457. https://doi.org/10.1038/nm838 (2003).
Google Scholar
Ogata, K. & Kosaka, T. Structural and quantitative analysis of astrocytes in the mouse hippocampus. Neuroscience 113, 221–233. https://doi.org/10.1016/s0306-4522(02)00041-6 (2002).
Google Scholar
Zhuo, L. et al. Live astrocytes visualized by green fluorescent protein in transgenic mice. Dev. Biol. 187, 36–42. https://doi.org/10.1006/dbio.1997.8601 (1997).
Google Scholar
Aten, S. et al. Ultrastructural view of astrocyte arborization, astrocyte-astrocyte and astrocyte-synapse contacts, intracellular vesicle-like structures, and mitochondrial network. Prog. Neurobiol. 213, 102264. https://doi.org/10.1016/j.pneurobio.2022.102264 (2022).
Google Scholar
Kacem, K., Lacombe, P., Seylaz, J. & Bonvento, G. Structural organization of the perivascular astrocyte endfeet and their relationship with the endothelial glucose transporter: A confocal microscopy study. Glia 23, 1–10. https://doi.org/10.1002/(sici)1098-1136(199805)23:1<1::aid-glia1>3.0.co;2-b (1998).
Google Scholar
Jackson, J. G. & Robinson, M. B. Regulation of mitochondrial dynamics in astrocytes: Mechanisms, consequences, and unknowns. Glia 66, 1213–1234. https://doi.org/10.1002/glia.23252 (2017).
Google Scholar
Lanjakornsiripan, D. et al. Layer-specific morphological and molecular differences in neocortical astrocytes and their dependence on neuronal layers. Nat. Commun.https://doi.org/10.1038/s41467-018-03940-3 (2018).
Google Scholar
Chai, H. et al. Neural circuit-specialized astrocytes: Transcriptomic, proteomic, morphological, and functional evidence. Neuron 95, 531-549.e9. https://doi.org/10.1016/j.neuron.2017.06.029 (2017).
Google Scholar
Beretta, C. et al. Extracellular vesicles from amyloid-b exposed cell cultures induce severe dysfunction in cortical neurons. Sci. Rep.https://doi.org/10.1038/s41598-020-72355-2 (2020).
Google Scholar
Zhang, C. et al. Effects of dimethyl sulfoxide on the morphology and viability of primary cultured neurons and astrocytes. Brain Res. Bull. 128, 34–39. https://doi.org/10.1016/j.brainresbull.2016.11.004 (2017).
Google Scholar
Wakida, N. M. et al. Phagocytic response of astrocytes to damaged neighboring cells. PLOS ONE 13, e0196153. https://doi.org/10.1371/journal.pone.0196153 (2018).
Google Scholar
Wakida, N. M., Cruz, G. M. S., Pouladian, P., Berns, M. W. & Preece, D. Fluid shear stress enhances the phagocytic response of astrocytes. Front. Bioeng. Biotechnol.https://doi.org/10.3389/fbioe.2020.596577 (2020).
Google Scholar
Teh, D. B. L. et al. Transcriptome analysis reveals neuroprotective aspects of human reactive astrocytes induced by interleukin 1b. Sci. Rep.https://doi.org/10.1038/s41598-017-13174-w (2017).
Google Scholar
Allahyari, R. V., Clark, K. L., Shepard, K. A. & Garcia, A. D. R. Sonic hedgehog signaling is negatively regulated in reactive astrocytes after forebrain stab injury. Sci. Rep.https://doi.org/10.1038/s41598-018-37555-x (2019).
Google Scholar
Farhy-Tselnicker, I. & Allen, N. J. Astrocytes, neurons, synapses: A tripartite view on cortical circuit development. Neural Dev.https://doi.org/10.1186/s13064-018-0104-y (2018).
Google Scholar
Vezzoli, E. et al. Ultrastructural evidence for a role of astrocytes and glycogen-derived lactate in learning-dependent synaptic stabilization. Cereb. Cortex 30, 2114–2127. https://doi.org/10.1093/cercor/bhz226 (2019).
Google Scholar
Gavrilov, N. et al. Astrocytic coverage of dendritic spines, dendritic shafts, and axonal boutons in hippocampal neuropil. Front. Cell. Neurosci.https://doi.org/10.3389/fncel.2018.00248 (2018).
Google Scholar
Arizono, M. et al. Structural basis of astrocytic Ca2+ signals at tripartite synapses. Nat. Commun.https://doi.org/10.1038/s41467-020-15648-4 (2020).
Google Scholar
Berg, S. et al. Ilastik: Interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232. https://doi.org/10.1038/s41592-019-0582-9 (2019).
Google Scholar
Comes, M. C. et al. A camera sensors-based system to study drug effects on in vitro motility: The case of pc-3 prostate cancer cells. Sensors 20, 1531. https://doi.org/10.3390/s20051531 (2020).
Google Scholar
D’Orazio, M. et al. Deciphering cancer cell behavior from motility and shape features: Peer prediction and dynamic selection to support cancer diagnosis and therapy. Front. Oncol.https://doi.org/10.3389/fonc.2020.580698 (2020).
Google Scholar
Suleymanova, I. et al. A deep convolutional neural network approach for astrocyte detection. Sci. Rep.https://doi.org/10.1038/s41598-018-31284-x (2018).
Google Scholar
D’Orazio, M. et al. Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response. Sci. Rep.https://doi.org/10.1038/s41598-022-12364-5 (2022).
Google Scholar
Mencattini, A. et al. Deep-manager: A versatile tool for optimal feature selection in live-cell imaging analysis. Commun. Biol.https://doi.org/10.1038/s42003-023-04585-9 (2023).
Google Scholar
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444. https://doi.org/10.1038/nature14539 (2015).
Google Scholar
Guo, Y. et al. Deep learning for visual understanding: A review. Neurocomputing 187, 27–48. https://doi.org/10.1016/j.neucom.2015.09.116 (2016).
Google Scholar
Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inform. Process. Syst. 25, 133 (2012).
Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. https://doi.org/10.48550/ARXIV.1409.1556 (2014).
Zeiler, M. D. & Fergus, R. Visualizing and understanding convolutional networks. In Computer Vision—ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6–12, 2014, Proceedings, Part I 13. 818–833 (Springer, 2014).
Szegedy, C. et al. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1–9 (2015).
Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18. 234–241 (Springer, 2015).
Xia, X. & Kulis, B. W-net: A deep model for fully unsupervised image segmentation. arXiv preprint arXiv:1711.08506 (2017).
Ciresan, D., Giusti, A., Gambardella, L. & Schmidhuber, J. Deep neural networks segment neuronal membranes in electron microscopy images. Adv. Neural Inform. Process. Syst. 25, 14 (2012).
Jung, C. et al. W-net: A CNN-based architecture for white blood cells image classification. arXiv preprint arXiv:1910.01091 (2019).
Shanthi, T. & Sabeenian, R. Modified alexnet architecture for classification of diabetic retinopathy images. Comput. Electr. Eng. 76, 56–64. https://doi.org/10.1016/j.compeleceng.2019.03.004 (2019).
Google Scholar
Kaur, T. & Gandhi, T. K. Automated brain image classification based on VGG-16 and transfer learning. In 2019 International Conference on Information Technology (ICIT). https://doi.org/10.1109/icit48102.2019.00023 (IEEE, 2019).
Lu, T., Han, B. & Yu, F. Detection and classification of marine mammal sounds using alexnet with transfer learning. Ecol. Inform. 62, 101277. https://doi.org/10.1016/j.ecoinf.2021.101277 (2021).
Google Scholar
Mashrur, F. R., Dutta Roy, A. & Saha, D. K. Automatic identification of arrhythmia from ECG using Alexnet convolutional neural network. In 2019 4th International Conference on Electrical Information and Communication Technology (EICT). https://doi.org/10.1109/eict48899.2019.9068806 (IEEE, 2019).
Lu, X., Duan, X., Mao, X., Li, Y. & Zhang, X. Feature extraction and fusion using deep convolutional neural networks for face detection. Math. Probl. Eng. 1–9, 2017. https://doi.org/10.1155/2017/1376726 (2017).
Google Scholar
Yu, B., Yin, H. & Zhu, Z. St-unet: A spatio-temporal u-network for graph-structured time series modeling. arXiv preprint arXiv:1903.05631 (2019).
Jiang, Y., Yang, M., Wang, S., Li, X. & Sun, Y. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun. 40, 154–166. https://doi.org/10.1002/cac2.12012 (2020).
Google Scholar
Chen, T. & Chefd’hotel, C. Deep Learning Based Automatic Immune Cell Detection for Immunohistochemistry Images. 17–24 (Springer, 2014).
Labate, D. & Kayasandik, C. Advances in quantitative analysis of astrocytes using machine learning. Neural Regener. Res. 18, 313. https://doi.org/10.4103/1673-5374.346474 (2023).
Google Scholar
He, K., Gkioxari, G., Dollár, P. & Girshick, R. Mask r-CNN. In Proceedings of the IEEE International Conference on Computer Vision. 2961–2969 (2017).
Tsai, H.-F., Gajda, J., Sloan, T. F., Rares, A. & Shen, A. Q. Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning. SoftwareX 9, 230–237 (2019).
Google Scholar
Girshick, R., Radosavovic, I., Gkioxari, G., Dollár, P. & He, K. Detectron. https://github.com/facebookresearch/detectron (2018).
Kirillov, A. et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 4015–4026 (2023).
Edlund, C. et al. Livecell—A large-scale dataset for label-free live cell segmentation. Nat. Methods 18, 1038–1045 (2021).
Google Scholar
Zhang, W. et al. Tobacco leaf segmentation based on improved mask RCNN algorithm and SAM model. IEEE Access (2023).
He, S., Bao, R., Li, J., Grant, P. E. & Ou, Y. Accuracy of segment-anything model (SAM) in medical image segmentation tasks. arXiv preprint arXiv:2304.09324 (2023).
Russell, B. C., Torralba, A., Murphy, K. P. & Freeman, W. T. Labelme: A database and web-based tool for image annotation. Int. J. Comput. Vis. 77, 157–173 (2008).
Google Scholar
Shijie, J., Ping, W., Peiyi, J. & Siping, H. Research on data augmentation for image classification based on convolution neural networks. In 2017 Chinese Automation Congress (CAC). 4165–4170 (IEEE, 2017).
Kayasandik, C. B. & Labate, D. Improved detection of soma location and morphology in fluorescence microscopy images of neurons. J. Neurosci. Methods 274, 61–70 (2016).
Google Scholar
Labate, D., Laezza, F., Negi, P., Ozcan, B. & Papadakis, M. Efficient processing of fluorescence images using directional multiscale representations. Math. Model. Nat. Phenomena 9, 177–193 (2014).
Google Scholar
Ozcan, B., Labate, D., Jiménez, D. & Papadakis, M. Directional and non-directional representations for the characterization of neuronal morphology. In Wavelets and Sparsity XV. Vol. 8858. 12–22 (SPIE, 2013).
Ozcan, B., Negi, P., Laezza, F., Papadakis, M. & Labate, D. Automated detection of soma location and morphology in neuronal network cultures. PloS one 10, e0121886 (2015).
Google Scholar
Hayashi, M. K., Sato, K. & Sekino, Y. Neurons induce tiled astrocytes with branches that avoid each other. Int. J. Mol. Sci. 23, 4161. https://doi.org/10.3390/ijms23084161 (2022).
Google Scholar
Mencattini, A. et al. Neurites. monitoring neurite changes through transfer entropy and semantic segmentation in bright-field time-lapse microscopy. Patterns 2 (2021).
Weikert, S. et al. Rapid Ca2+-dependent no-production from central nervous system cells in culture measured by no-nitrite/ozone chemoluminescence. Brain Res. 748, 1–11. https://doi.org/10.1016/s0006-8993(96)01241-3 (1997).
Google Scholar
Stel’mashuk, E. V. et al. Vliianie induktora neirovospaleniia na komponenty neirovaskuliarnoi edinitsy golovnogo mozga in vitro. Rossiiskii Fiziol. Zh. IM Sechenova 108, 686–696 (2022).
