Cross-Modality Imaging of Murine Tumor Vasculature—a Feasibility Study
Tóm tắt
Tumor vasculature and angiogenesis play a crucial role in tumor progression. Their visualization is therefore of utmost importance to the community. In this proof-of-principle study, we have established a novel cross-modality imaging (CMI) pipeline to characterize exactly the same murine tumors across scales and penetration depths, using orthotopic models of melanoma cancer. This allowed the acquisition of a comprehensive set of vascular parameters for a single tumor. The workflow visualizes capillaries at different length scales, puts them into the context of the overall tumor vessel network and allows quantification and comparison of vessel densities and morphologies by different modalities. The workflow adds information about hypoxia and blood flow rates. The CMI approach includes well-established technologies such as magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), and ultrasound (US), and modalities that are recent entrants into preclinical discovery such as optical coherence tomography (OCT) and high-resolution episcopic microscopy (HREM). This novel CMI platform establishes the feasibility of combining these technologies using an extensive image processing pipeline. Despite the challenges pertaining to the integration of microscopic and macroscopic data across spatial resolutions, we also established an open-source pipeline for the semi-automated co-registration of the diverse multiscale datasets, which enables truly correlative vascular imaging. Although focused on tumor vasculature, our CMI platform can be used to tackle a multitude of research questions in cancer biology.
Từ khóa
Tài liệu tham khảo
Lanitis E, Irving M, Coukos G (2015) Targeting the tumor vasculature to enhance T cell activity. Curr Opin Immunol 33:55–63
Carmeliet P, Jain RK (2011) Molecular mechanisms and clinical applications of angiogenesis. Nature 473:298–307
Niccoli Asabella A, Di Palo A, Altini C, Ferrari C, Rubini G (2017) Multimodality imaging in tumor angiogenesis: present status and perspectives. Int J Mol Sci 18
Bergers G, Hanahan D (2008) Modes of resistance to anti-angiogenic therapy. Nat Rev Cancer 8:592–603
Yankeelov TE, Abramson RG, Quarles CC (2014) Quantitative multimodality imaging in cancer research and therapy. Nat Rev Clin Oncol 11:670–680
Goel S, Duda DG, Xu L, Munn LL, Boucher Y, Fukumura D, Jain RK (2011) Normalization of the vasculature for treatment of cancer and other diseases. Physiol Rev 91:1071–1121
Penet MF, Krishnamachary B, Chen Z, Jin J, Bhujwalla ZM (2014) Molecular imaging of the tumor microenvironment for precision medicine and theranostics. Adv Cancer Res 124:235–256
Cebulla J, Kim E, Rhie K, Zhang J, Pathak AP (2014) Multiscale and multi-modality visualization of angiogenesis in a human breast cancer model. Angiogenesis 17:695–709
Keuenhof et al. (2021) Multimodality imaging beyond CLEM: Showcases of combined in-vivo preclinical imaging and ex-vivo microscopy to detect murine mural vascular lesions, MCB, Vol 162. https://doi.org/10.1016/bs.mcb.2020.10.002
Müller BL L, Dominietto M, Rudin M, et al. (2008) High-resolution tomographic imaging of microvessels. Proc of SPIE
Walter A, Paul-Gilloteaux P, Plochberger B, et al. (2020) Correlated multimodal imaging in life sciences: expanding the biomedical horizon. Front Phys 8
Meeth K, Wang JX, Micevic G, et al (2016) The YUMM lines: a series of congenic mouse melanoma cell lines with defined genetic alterations. Pigment Cell Melanoma Research 29:590–597. https://doi.org/10.1111/pcmr.12498
Chen Z, Rank E, Meiburger KM, Sinz C, Hodul A, Zhang E, Hoover E, Minneman M, Ensher J, Beard PC, Kittler H, Leitgeb RA, Drexler W, Liu M (2017) Non-invasive multimodal optical coherence and photoacoustic tomography for human skin imaging, Scientific Reports 7;17975
Monsky WL, Carreira CM, Tsuzuki Y, Gohongi T, Fukumura D, Jain RK (2002) Role of host microenvironment in angiogenesis and microvascular functions in human breast cancer xenografts: mammary fat pad versus cranial tumors. Clin Cancer Res 8:1008–1013
Mohun TJ, Weninger WJ (2012) Embedding embryos for high-resolution episcopic microscopy (HREM). Cold Spring Harb Protoc 2012:678–680
Weninger WJ, Geyer SH, Mohun TJ, Rasskin-Gutman D, Matsui T, Ribeiro I, Costa LF, Izpisúa-Belmonte JC, Müller GB (2006) High-resolution episcopic microscopy: a rapid technique for high detailed 3D analysis of gene activity in the context of tissue architecture and morphology. Anat Embryol (Berl) 211:213–221
Mohun TJ, Weninger WJ (2012) Generation of volume data by episcopic three-dimensional imaging of embryos. Cold Spring Harb Protoc 2012:681–682
Novikov AA, Major D, Wimmer M, Sluiter G, Buhler K (2017) Automated Anatomy-Based Tracking of Systemic Arteries in Arbitrary Field-of-View CTA Scans. IEEE Trans Med Imaging 36:1359–1371
Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez JY, White DJ, Hartenstein V, Eliceiri K, Tomancak P, Cardona A (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682
Rueden CT, Schindelin J, Hiner MC, DeZonia BE, Walter AE, Arena ET, Eliceiri KW (2017) ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics 18:529
ImageJ2/FIJI (https://imagej.net/Fiji
Askeland C, Solberg OV, Bakeng JB et al (2016) CustusX: an open-source research platform for image-guided therapy. Int J Comput Assist Radiol Surg 11:505–519
CustusX (https://www.custusx.org/)
SlicerIGT (http://www.slicerigt.org)
Ungi T, Lasso A, Fichtinger G (2016 Oct) Open-source platforms for navigated image-guided interventions. Med Image Anal. 33:181–186. https://doi.org/10.1016/j.media.2016.06.011
3D Slicer (https://www.slicer.org/)
Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323–1341
Obenauf A, Zou Y, Ji A et al (2015) Therapy-induced tumour secretomes promote resistance and tumour progression. Nature 520:368–372
Yankeelov TE, Arlinghaus LR, Li X, Gore JC (2011) The role of magnetic resonance imaging biomarkers in clinical trials of treatment response in cancer. Semin Oncol 38:16–25
The Vascular Modeling Toolkit. www.vmtk.org
Tomviz www.tomviz.org.
Walter et al. (2021) Correlative multimodal imaging: Building a community, MCB, Vol 162. https://doi.org/10.1016/bs.mcb.2020.12.010
Moccia S, De Momi E, El Hadji S, and Mattos LS (2018) “Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metrics,” Comput Methods Programs Biomed 158;71–91
Walter et al. (2010) Visualization of image data from cells to organisms. Nature Methods 7:S26‐S41. https://doi.org/10.1038/nmeth.1431
Lawonn K, Smit NN, Bühler K, and Preim B (2018) “A Survey on Multimodal Medical Data Visualization.,” CGF, 2018
Sharma DBAVEBLJRCPM (2020) Deep learning techniques for biomedical and health informatics. Academic Press
Cai W, Chen X (2008) Multimodality molecular imaging of tumor angiogenesis. J Nucl Med 49(Suppl 2):113S–128S