MicMode-I2T
Modular image analysis platform for the integration of microscopic image-based data from biopsies into mathematical models of interactions between immune and target cells
The overall aim of MicMode-I2T is to create a modular and flexible image analysis platform that gains image-based information from diagnostic biopsies as well as healthy human tissue sections to make these data available for mathematical models. The focus of the planned work is to comprehensively study the spatial information on interaction between immune cells and their target structures. Thereby, the information hidden in microscopic images will be analyzed and implemented using new methods of object-related and knowledge-based image analysis. So far, this information can only be captured with descriptive or roughly quantifying evaluation procedures.
In particular, networking between the e:Med consortia SYSIMIT, e:Kid and SYS-Stomach will be intensified through MicMode-I2T and promoted by a joint workshop that will take place in the first year of the project. Aim of the workshop is to develop new strategies to work with pilot datasets.
The network MicMode-I2T is promoted by the networking fonds of e:Med. In general, the networking project MicMode-I2T should strengthen the bioinformatics and modeling activities in e:Med.
Publications
Alfonso, J. C. L., L. A. Papaxenopoulou, P. Mascheroni, M. Meyer-Hermann, and H. Hatzikirou (2020). "On the Immunological Consequences of Conventionally Fractionated Radiotherapy." ISCIENCE 23(3). doi.org/10.1016/j.isci.2020.100897.
Barua, A., S. Syga, P. Mascheroni, N. Kavallaris, M. Meyer-Hermann, A. Deutsch, and H. Hatzikirou (2020). "Entropy-driven cell decision-making predicts ‘fluid-to-solid’ transition in multicellular systems." New J Phys 22(12): 123034. doi.org/10.1088/1367-2630/abcb2e.
Chlis, N.-K., A. Karlas, N.-A. Fasoula, M. Kallmayer, H.-H. Eckstein, F. J. Theis, V. Ntziachristos, and C. Marr (2020). "A sparse deep learning approach for automatic segmentation of human vasculature in multispectral optoacoustic tomography." Photoacoustics 20: 100203. doi.org/10.1016/j.pacs.2020.100203.
Graeser, M., F. Feuerhake, O. Gluz, V. Volk, M. Hauptmann, K. Jozwiak, M. Christgen, S. Kuemmel, E.-M. Grischke, H. Forstbauer, M. Braun, M. Warm, J. Hackmann, C. Uleer, B. Aktas, C. Schumacher, C. Kolberg-Liedtke, R. Kates, R. Wuerstlein, U. Nitz, H. H. Kreipe, and N. Harbeck (2021). "Immune cell composition and functional marker dynamics from multiplexed immunohistochemistry to predict response to neoadjuvant chemotherapy in the WSG-ADAPT-TN trial." Journal for ImmunoTherapy of Cancer 9(5): e002198. jitc.bmj.com/content/jitc/9/5/e002198.full.pdf.
Lupperger, V., C. Marr, and P. Chapouton (2020). "Reoccurring neural stem cell divisions in the adult zebrafish telencephalon are sufficient for the emergence of aggregated spatiotemporal patterns." PLoS Biol 18(12): e3000708. doi.org/10.1371/journal.pbio.3000708.
Mascheroni, P., J. C. Lopez Alfonso, M. Kalli, T. Stylianopoulos, M. Meyer-Hermann, and H. Hatzikirou (2019). "On the Impact of Chemo-Mechanically Induced Phenotypic Transitions in Gliomas." Cancers (Basel) 11(5). www.ncbi.nlm.nih.gov/pubmed/31137643.
Mascheroni, P., M. Meyer-Hermann, and H. Hatzikirou (2020). "Investigating the Physical Effects in Bacterial Therapies for Avascular Tumors." FRONTIERS IN MICROBIOLOGY 11(1083). doi.org/10.3389/fmicb.2020.01083.
Rizzuti, I. F., P. Mascheroni, S. Arcucci, Z. Ben-Mériem, A. Prunet, C. Barentin, C. Rivière, H. Delanoë-Ayari, H. Hatzikirou, J. Guillermet-Guibert, and M. Delarue (2020). "Mechanical Control of Cell Proliferation Increases Resistance to Chemotherapeutic Agents." Phys Rev Lett 125(12): 128103. doi.org/10.1103/PhysRevLett.125.128103.
Setten, E., A. Castagna, J. M. Nava-Sedeño, J. Weber, R. Carriero, A. Reppas, V. Volk, J. Schmitz, W. Gwinner, H. Hatzikirou, F. Feuerhake, and M. Locati (2022). "Understanding fibrosis pathogenesis via modeling macrophage-fibroblast interplay in immune-metabolic context." Nat Commun 13(6499): 1–22. doi.org/10.1038/s41467-022-34241-5.
Slabik, C., M. Kalbarczyk, S. Danisch, R. Zeidler, F. Klawonn, V. Volk, N. Krönke, F. Feuerhake, C. Ferreira de Figueiredo, R. Blasczyk, H. Olbrich, S. J. Theobald, A. Schneider, A. Ganser, C. von Kaisenberg, S. Lienenklaus, A. Bleich, W. Hammerschmidt, and R. Stripecke (2020). "CAR-T Cells Targeting Epstein-Barr Virus gp350 Validated in a Humanized Mouse Model of EBV Infection and Lymphoproliferative Disease." Mol Ther Oncolytics 18: 504–524. doi.org/10.1016/j.omto.2020.08.005.
Stervbo, U., M. Nienen, J. Hecht, R. Viebahn, K. Amann, T. H. Westhoff, and N. Babel (2020). "Differential Diagnosis of Interstitial Allograft Rejection and BKV Nephropathy by T-cell Receptor Sequencing." Transplantation 104(4): e107-e108. journals.lww.com/transplantjournal/Fulltext/2020/04000/Differential_Diagnosis_of_Interstitial_Allograft.34.aspx.
Thieme, C. J., B. J. D. Weist, A. Mueskes, T. Roch, U. Stervbo, K. Rosiewicz, P. Wehler, M. Stein, P. Nickel, A. Kurtz, N. Lachmann, M. Choi, M. Schmueck-Henneresse, T. H. Westhoff, P. Reinke, and N. Babel (2019). "The TreaT-Assay: A Novel Urine-Derived Donor Kidney Cell-Based Assay for Prediction of Kidney Transplantation Outcome." Sci Rep 9(1): 1--12. www.nature.com/articles/s41598-019-55442-x.
Vasiljević, J., Z. Nisar, F. Feuerhake, C. Wemmert, and T. Lampert (2022). "CycleGAN for virtual stain transfer: Is seeing really believing?" Artif Intell Med: 102420. doi.org/10.1016/j.artmed.2022.102420.
Volk, V., S. J. Theobald, S. Danisch, S. Khailaie, M. Kalbarczyk, A. Schneider, J. Bialek-Waldmann, N. Krönke, Y. Deng, B. Eiz-Vesper, A. C. Dragon, C. von Kaisenberg, S. Lienenklaus, A. Bleich, J. Keck, M. Meyer-Hermann, F. Klawonn, W. Hammerschmidt, H.-J. Delecluse, C. Münz, F. Feuerhake, and R. Stripecke (2021). "PD-1 Blockade Aggravates Epstein–Barr Virus+ Post-Transplant Lymphoproliferative Disorder in Humanized Mice Resulting in Central Nervous System Involvement and CD4+ T Cell Dysregulations." Front Oncol 10. doi.org/10.3389/fonc.2020.614876.
Waibel, D. J. E., S. S. Boushehri, and C. Marr (2021). "InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification." BMC Bioinf. 22(1): 1–15. doi.org/10.1186/s12859-021-04037-3.