Nanocarrier imaging at single-cell resolution across entire mouse bodies with deep learning
Open accessAbstract
Efficient and accurate nanocarrier development for targeted drug delivery is hindered by a lack of methods to analyze its cell-level biodistribution across whole organisms. Here we present Single Cell Precision Nanocarrier Identification (SCP-Nano), an integrated experimental and deep learning pipeline to comprehensively quantify the targeting of nanocarriers throughout the whole mouse body at single-cell resolution. SCP-Nano reveals the tissue distribution patterns of lipid nanoparticles (LNPs) after different injection routes at doses as low as 0.0005 mg kg −1 —far below the detection limits of conventional whole body imaging techniques. We demonstrate that intramuscularly injected LNPs carrying SARS-CoV-2 spike mRNA reach heart tissue, leading to proteome changes, suggesting immune activation and blood vessel damage. SCP-Nano generalizes to various types of nanocarriers, including liposomes, polyplexes, DNA origami and adeno-associated viruses (AAVs), revealing that an AAV2 variant transduces adipocytes throughout the body. SCP-Nano enables comprehensive three-dimensional mapping of nanocarrier distribution throughout mouse bodies with high sensitivity and should accelerate the development of precise and safe nanocarrier-based therapeutics.
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Several approaches for image analysis of LSFM datasets have been introduced in recent years, but these methods are typically limited to selected organs and do not provide integrated whole-body quantification of elongated structures.
“Although several approaches for image analysis of light-sheet fluorescence microscopy (LSFM) datasets have been introduced in recent years6–10, these methods are typically limited to selected organs and do not provide integrated whole-body quantification of elongated structures.”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [10]
Tools for comprehensive, high-resolution analysis of disease-associated changes at the whole-body scale have been lacking.
“However, tools for comprehensive, high-resolution analysis of disease-associated changes at the whole-body scale have been lacking.”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [10]
MouseMapper performs true 3D sliding-window inference on uncropped, multiterabyte datasets, rather than relying on 2D projections or precropped volumes.
“In contrast to previous methods for whole-body analysis6,10,18, MouseMapper performs true 3D sliding-window inference on uncropped, multiterabyte datasets, rather than relying on 2D projections or precropped volumes, ensuring unbiased system-wide screening of anatomical changes for elongated structures.”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [10]
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V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931 · see citing works
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00889-5_1 · Cited by 10,642 · see citing works
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945 · see citing works
Engineering precision nanoparticles for drug delivery
Mitchell, M. J., Billingsley, M. M., Haley, R. M., Wechsler, M. E., Peppas, N. A., & Langer, R. (2021) · Nature Reviews Drug Discovery · doi:10.1038/s41573-020-0090-8 · Cited by 7,933 · see citing works
A Simple Practice Guide for Dose Conversion between Animals and Human
Nair, A., & Jacob, S. (2016) · Journal of Basic and Clinical Pharmacy · doi:10.4103/0976-0105.177703 · Cited by 6,174 · see citing works
Attention U-Net: Learning Where to Look for the Pancreas
Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., et al. (2018) · arXiv · doi:10.48550/arxiv.1804.03999 · Cited by 5,458 · see citing works
Lipid nanoparticles for mRNA delivery
Hou, X., Zaks, T., Langer, R., & Dong, Y. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00358-0 · Cited by 4,076 · see citing works
UNETR: Transformers for 3D Medical Image Segmentation
Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H. R., & Xu, D. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00181 · Cited by 3,586 · cited 2× · see citing works
“To achieve the best segmentation performance for nanocarriers, we tested several state-of-the-art https://doi.org/10.1038/s41587-024-02528-1 segmentation networks on our annotated dataset, including VNet 32 , U-Net++ 33 , Attention U-Net 34 , UNETR 35 , SwinUNETR 36 , nnFormer 36 and 3D U-Net 37 .”
InfluentialDIA-NN: Neural networks and interference correction enable deep proteome coverage in high throughput
Demichev, V., Messner, C., Vernardis, S., Lilley, K., & Ralser, M. (2019) · Nature Methods · doi:10.1038/s41592-019-0638-x · Cited by 2,961 · see citing works
Cheng, Q., Wei, T., Farbiak, L., Johnson, L. T., Dilliard, S. A., & Siegwart, D. J. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0669-6 · Cited by 2,383 · see citing works
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H. R., & Xu, D. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-08999-2_22 · Cited by 2,273 · see citing works
Systemic RNA delivery to dendritic cells exploits antiviral defence for cancer immunotherapy
Kranz, L. M., Diken, M., Haas, H., Kreiter, S., Loquai, C., Reuter, K. C., Meng, M., Fritz, D., Vascotto, F., Hefesha, H., et al. (2016) · Nature · doi:10.1038/nature18300 · Cited by 1,771 · see citing works
The Onpattro story and the clinical translation of nanomedicines containing nucleic acid-based drugs
Akinc, A., Maier, M. A., Manoharan, M., Fitzgerald, K., Jayaraman, M., Barros, S., Ansell, S., Du, X., Hope, M. J., Madden, T. D., et al. (2019) · Nature Nanotechnology · doi:10.1038/s41565-019-0591-y · Cited by 1,636 · see citing works
Chan, K. Y., Jang, M. J., Yoo, B. B., Greenbaum, A., Ravi, N., Wu, W.-L., Sánchez-Guardado, L., Lois, C., Mazmanian, S. K., Deverman, B. E., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4593 · Cited by 1,578 · see citing works
Mulligan, M. J., Lyke, K. E., Kitchin, N., Absalon, J., Gurtman, A., Lockhart, S., Neuzil, K., Raabe, V., Bailey, R., Swanson, K. A., et al. (2020) · doi:10.1101/2020.06.30.20142570 · Cited by 1,569 · see citing works
Summerford, C., & Samulski, R. J. (1998) · Journal of Virology · doi:10.1128/jvi.72.2.1438-1445.1998 · Cited by 1,409 · see citing works
The current landscape of nucleic acid therapeutics
Kulkarni, J. A., Witzigmann, D., Thomson, S. B., Chen, S., Leavitt, B. R., Cullis, P. R., & van der Meel, R. (2021) · Nature Nanotechnology · doi:10.1038/s41565-021-00898-0 · Cited by 1,371 · see citing works
Drug delivery systems for RNA therapeutics
Paunovska, K., Loughrey, D., & Dahlman, J. E. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00439-4 · Cited by 1,349 · see citing works
Mutant MHC class II epitopes drive therapeutic immune responses to cancer
Kreiter, S., Vormehr, M., van de Roemer, N., Diken, M., Löwer, M., Diekmann, J., Boegel, S., Schrörs, B., Vascotto, F., Castle, J. C., et al. (2015) · Nature · doi:10.1038/nature14426 · Cited by 1,243 · see citing works
A Designer AAV Variant Permits Efficient Retrograde Access to Projection Neurons
Tervo, D. G. R., Hwang, B., Viswanathan, S., Gaj, T., Lavzin, M., Ritola, K., Lindo, S. E., Michael, S. C., Кулешова, Е. П., Ojala, D. S., et al. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.021 · Cited by 1,170 · see citing works
Cre-dependent selection yields AAV variants for widespread gene transfer to the adult brain
Deverman, B. E., Pravdo, P. L., Simpson, B. P., Kumar, S. R., Chan, K. Y., Banerjee, A., Wu, W.-L., Yang, B., Huber, N., Pasca, S. P., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3440 · Cited by 1,061 · see citing works
Safety of the BNT162b2 mRNA Covid-19 Vaccine in a Nationwide Setting
Barda, N., Dagan, N., Ben-Shlomo, Y., Kepten, E., Waxman, J., Ohana, R., Hernán, M. A., Lipsitch, M., Kohane, I., Netzer, D., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2110475 · Cited by 1,027 · see citing works
On the mechanism of tissue-specific mRNA delivery by selective organ targeting nanoparticles
Dilliard, S. A., Cheng, Q., & Siegwart, D. J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2109256118 · Cited by 1,018 · see citing works
Three-dimensional imaging of solvent-cleared organs using 3DISCO
Ertürk, A., Becker, K., Jährling, N., Mauch, C. P., Hojer, C. D., Egen, J. G., Hellal, F., Bradke, F., Sheng, M., & Dodt, H. (2012) · Nature Protocols · doi:10.1038/nprot.2012.119 · Cited by 997 · see citing works
Self-citationOptimization of lipid nanoparticles for intramuscular administration of mRNA vaccines
Hassett, K. J., Benenato, K. E., Jacquinet, E., Lee, A., Woods, A., Yuzhakov, O., Himansu, S., Deterling, J., Geilich, B. M., Ketova, T., et al. (2019) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2019.01.013 · Cited by 927 · see citing works
Yin, H., Song, C.-Q., Dorkin, J. R., Zhu, L. J., Li, Y., Wu, Q., Park, A., Yang, J., Suresh, S., Bizhanova, A., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3471 · Cited by 876 · see citing works
Myocarditis With COVID-19 mRNA Vaccines
Bozkurt, B., Kamat, I., & Hotez, P. J. (2021) · Circulation · doi:10.1161/circulationaha.121.056135 · Cited by 854 · see citing works
Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles
Walkey, C., Olsen, J. B., Song, F., Liu, R., Guo, H., Olsen, D. W. H., Cohen, Y., Emili, A., & Chan, W. C. W. (2014) · ACS Nano · doi:10.1021/nn406018q · Cited by 824 · see citing works
Dey, S., Fan, C., Gothelf, K. V., Li, J., Lin, C., Liu, L., Liu, N., Nijenhuis, M. A. D., Saccà, B., Simmel, F. C., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00009-8 · Cited by 745 · see citing works
αVβ5 integrin: a co-receptor for adeno-associated virus type 2 infection
Summerford, C., Bartlett, J. S., & Samulski, R. J. (1999) · Nature Medicine · doi:10.1038/4768 · Cited by 711 · see citing works
BNT162b vaccines protect rhesus macaques from SARS-CoV-2
Vogel, A. B., Kanevsky, I., Che, Y., Swanson, K. A., Muik, A., Vormehr, M., Kranz, L. M., Walzer, K. C., Hein, S., Güler, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03275-y · Cited by 694 · see citing works
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image SegmentationPreprint
Isensee, F., Petersen, J., Klein, A., Zimmerer, D., Jaeger, P. F., Köhl, S., Wasserthal, J., Koehler, G., Norajitra, T., Wirkert, S., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.10486 · Cited by 683 · see citing works
DNA Origami as an In Vivo Drug Delivery Vehicle for Cancer Therapy
Zhang, Q., Jiang, Q., Li, N., Dai, L., Liu, Q. L., Song, L., Wang, J., Li, Y., Tian, J., Ding, B., et al. (2014) · ACS Nano · doi:10.1021/nn502058j · Cited by 648 · see citing works
Patone, M., Mei, X. W., Handunnetthi, L., Dixon, S., Zaccardi, F., Shankar‐Hari, M., Watkinson, P., Khunti, K. K., Harnden, A., Coupland, C. A. C., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01630-0 · Cited by 641 · see citing works
Myocarditis Following Immunization With mRNA COVID-19 Vaccines in Members of the US Military
Montgomery, J., Ryan, M., Engler, R., Hoffman, D., McClenathan, B., Collins, L., Loran, D., Hrncir, D., Herring, K., Platzer, M., et al. (2021) · JAMA Cardiology · doi:10.1001/jamacardio.2021.2833 · Cited by 536 · see citing works
An essential receptor for adeno-associated virus infection
Pillay, S., Meyer, N. L., Puschnik, A. S., Davulcu, O., Diep, J., Ishikawa, Y., Jae, L. T., Wosen, J. E., Nagamine, C. M., Chapman, M. S., et al. (2016) · Nature · doi:10.1038/nature16465 · Cited by 532 · see citing works
Virus-Inspired Membrane Encapsulation of DNA Nanostructures To Achieve In Vivo Stability
Perrault, S. D., & Shih, W. M. (2014) · ACS Nano · doi:10.1021/nn5011914 · Cited by 524 · see citing works
Biotechnological mass production of DNA origami
Praetorius, F., Kick, B., Behler, K. L., Honemann, M. N., Weuster‐Botz, D., & Dietz, H. (2017) · Nature · doi:10.1038/nature24650 · Cited by 517 · see citing works
Self-citationPonnuswamy, N., Bastings, M. M. C., Nathwani, B., Ryu, J. H., Chou, L. Y. T., Vinther, M., Li, A. W., Anastassacos, F. M., Mooney, D. J., & Shih, W. M. (2017) · Nature Communications · doi:10.1038/ncomms15654 · Cited by 509 · see citing works
Cai, R., Pan, C., Ghasemigharagoz, A., Todorov, M. I., Förstera, B., Zhao, S., Bhatia, H. S., Parra-Damas, A., Mrowka, L., Theodorou, D., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0301-3 · Cited by 470 · see citing works
Self-citationHuman fibroblast growth factor receptor 1 is a co-receptor for infection by adeno-associated virus 2
Qing, K., Mah, C., Hansen, J., Zhou, S., Dwarki, V., & Srivastava, A. (1999) · Nature Medicine · doi:10.1038/4758 · Cited by 443 · see citing works
Addressing the Instability of DNA Nanostructures in Tissue Culture
Hahn, J., Wickham, S. F. J., Shih, W. M., & Perrault, S. D. (2014) · ACS Nano · doi:10.1021/nn503513p · Cited by 391 · see citing works
Polyethylenimine (PEI) in gene therapy: Current status and clinical applications
Casper, J., Schenk, S., Parhizkar, E., Detampel, P., Dehshahri, A., & Huwyler, J. (2023) · Journal of Controlled Release · doi:10.1016/j.jconrel.2023.09.001 · Cited by 351 · see citing works
Evolution of the nanoparticle corona
Hadjidemetriou, M., & Kostarelos, K. (2017) · Nature Nanotechnology · doi:10.1038/nnano.2017.61 · Cited by 340 · see citing works
Advances in PET imaging of cancer
Schwenck, J., Sonanini, D., Cotton, J. M., Rammensee, H., la Fougère, C., Zender, L., & Pichler, B. J. (2023) · Nature reviews. Cancer · doi:10.1038/s41568-023-00576-4 · Cited by 322 · see citing works
Framework nucleic acids as programmable carrier for transdermal drug delivery
Wiraja, C., Zhu, Y. J., Lio, D. C. S., Yeo, D., Xie, M., Fang, W., Li, Q., Zheng, M., Steensel, M. A. M. V., Wang, L., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09029-9 · Cited by 296 · see citing works
Susaki, E. A., Shimizu, C., Kuno, A., Tainaka, K., Li, X., Nishi, K., Morishima, K., Ono, H., Ode, K. L., Saeki, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15906-5 · Cited by 286 · see citing works
Sequence-programmable covalent bonding of designed DNA assemblies
Gerling, T., Kube, M., Kick, B., & Dietz, H. (2018) · Science Advances · doi:10.1126/sciadv.aau1157 · Cited by 275 · see citing works
Self-citationViral <scp>vector‐based</scp> gene therapies in the clinic
Zhao, Z., Anselmo, A. C., & Mitragotri, S. (2022) · Bioengineering & Translational Medicine · doi:10.1002/btm2.10258 · Cited by 269 · see citing works
Visualization of the Cellular Uptake and Trafficking of DNA Origami Nanostructures in Cancer Cells
Wang, P., Rahman, M. A., Zhao, Z., Weiss, K., Zhang, C., Chen, Z., Hurwitz, S. J., Chen, Z. G., Shin, D. M., & Ke, Y. (2018) · Journal of the American Chemical Society · doi:10.1021/jacs.7b09024 · Cited by 269 · see citing works
Facile and Scalable Preparation of Pure and Dense DNA Origami Solutions
Stahl, E., Martin, T. G., Praetorius, F., & Dietz, H. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201405991 · Cited by 263 · see citing works
Self-citationModulation of the Cellular Uptake of DNA Origami through Control over Mass and Shape
Bastings, M. M. C., Anastassacos, F. M., Ponnuswamy, N., Leifer, F., Cuneo, G. J., Lin, C., Ingber, D. E., Ryu, J. H., & Shih, W. M. (2018) · Nano Letters · doi:10.1021/acs.nanolett.8b00660 · Cited by 251 · see citing works
Mathiesen, S. N., Lock, J. L., Schoderboeck, L., Abraham, W. C., & Hughes, S. M. (2020) · Molecular Therapy — Methods & Clinical Development · doi:10.1016/j.omtm.2020.10.011 · Cited by 214 · see citing works
Programmable icosahedral shell system for virus trapping
Sigl, C., Willner, E. M., Engelen, W., Kretzmann, J. A., Sachenbacher, K., Liedl, A., Kolbe, F., Wilsch, F., Aghvami, S. A., Protzer, U., et al. (2021) · Nature Materials · doi:10.1038/s41563-021-01020-4 · Cited by 209 · see citing works
Self-citationDeep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body
Pan, C., Schoppe, O., Parra-Damas, A., Cai, R., Todorov, M. I., Gondi, G., von Neubeck, B., Böğürcü-Seidel, N., Seidel, S., Sleiman, K., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.013 · Cited by 206 · see citing works
Self-citationRosenblum, H. G., Gee, J. M., Liu, R., Marquez, P. L., Zhang, B., Strid, P., Abara, W. E., McNeil, M. M., Myers, T. R., Hause, A. M., et al. (2022) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(22)00054-8 · Cited by 176 · see citing works
Self-citationWang, C., Zhang, R., He, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39607-x · Cited by 172 · see citing works
Fine tuning of CpG spatial distribution with DNA origami for improved cancer vaccination
Zeng, Y. C., Young, O. J., Wintersinger, C. M., Anastassacos, F. M., MacDonald, J. I., Isinelli, G., Dellacherie, M. O., Sobral, M. C., Bai, H., Graveline, A. R., et al. (2024) · Nature Nanotechnology · doi:10.1038/s41565-024-01615-3 · Cited by 172 · see citing works
Pericyte-to-endothelial cell signaling via vitronectin-integrin regulates blood-CNS barrier
Ayloo, S., Lazo, C. G., Sun, S., Zhang, W., Cui, B., & Gu, C. (2022) · Neuron · doi:10.1016/j.neuron.2022.02.017 · Cited by 166 · see citing works
Schober, G. B., Story, S., & Arya, D. P. (2024) · Scientific Reports · doi:10.1038/s41598-024-52685-1 · Cited by 163 · see citing works
Heidecker, B., Dagan, N., Balicer, R., Eriksson, U., Rosano, G., Coats, A., Tschöpe, C., Kelle, S., Poland, G. A., Frustaci, A., et al. (2022) · European Journal of Heart Failure · doi:10.1002/ejhf.2669 · Cited by 159 · see citing works
Dilliard, S. A., Sun, Y., Brown, M. O., Sung, Y., Chatterjee, S., Farbiak, L., Vaidya, A., Lian, X., Wang, X., Lemoff, A. S., et al. (2023) · Journal of Controlled Release · doi:10.1016/j.jconrel.2023.07.058 · Cited by 156 · see citing works
An Analysis of the Binding Function and Structural Organization of the Protein Corona
Zhang, Y., Wu, J. L. Y., Lazarovits, J., & Chan, W. C. W. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c01853 · Cited by 151 · see citing works
Throughput-scalable manufacturing of SARS-CoV-2 mRNA lipid nanoparticle vaccines
Shepherd, S. J., Han, X., Mukalel, A. J., El-Mayta, R. D., Thatte, A. S., Wu, J., Padilla, M. S., Alameh, M., Srikumar, N., Lee, D., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2303567120 · Cited by 141 · see citing works
Adeno-associated virus (AAV) cell entry: structural insights
Meyer, N., & Chapman, M. S. (2021) · Trends in Microbiology · doi:10.1016/j.tim.2021.09.005 · Cited by 139 · see citing works
Spatial proteomics in three-dimensional intact specimens
Bhatia, H. S., Brunner, A.-D., Öztürk, F., Kapoor, S., Rong, Z., Mai, H., Thielert, M., Ali, M., Al-Maskari, R., Paetzold, J. C., et al. (2022) · Cell · doi:10.1016/j.cell.2022.11.021 · Cited by 129 · see citing works
Self-citationDai, Z., Gjetting, T., Mattebjerg, M. A., Wu, C. C., & Andresen, T. L. (2011) · Biomaterials · doi:10.1016/j.biomaterials.2011.07.044 · Cited by 114 · see citing works
Myocarditis and Pericarditis Following mRNA COVID-19 Vaccination: What Do We Know So Far?
Das, B. B., Moskowitz, W. B., Taylor, M., & Palmer, A. (2021) · Children · doi:10.3390/children8070607 · Cited by 109 · see citing works
Programmable multispecific DNA-origami-based T-cell engagers
Wagenbauer, K. F., Pham, N., Gottschlich, A., Kick, B., Kozina, V., Frank, C., Trninic, D., Stömmer, P., Grünmeier, R., Carlini, E., et al. (2023) · Nature Nanotechnology · doi:10.1038/s41565-023-01471-7 · Cited by 109 · see citing works
Self-citationIdentifying cell receptors for the nanoparticle protein corona using genome screens
Ngo, W., Wu, J. L. Y., Lin, Z. P., Zhang, Y., Bussin, B., Farias, A. G., Syed, A. M., Chan, K., Habsid, A., Moffat, J., et al. (2022) · Nature Chemical Biology · doi:10.1038/s41589-022-01093-5 · Cited by 105 · see citing works
Wamhoff, E., Ronsard, L., Feldman, J. E., Knappe, G. A., Hauser, B. M., Romanov, A. M., Case, J. B., Sanapala, S., Lam, E. C., Denis, K. S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-44869-0 · Cited by 97 · see citing works
Divergent engagements between adeno-associated viruses with their cellular receptor AAVR
Zhang, R., Xu, G., Cao, L., Sun, Z., He, Y., Cui, M., Sun, Y., Li, S., Li, H., Qin, L., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11668-x · Cited by 83 · see citing works
Khalin, I., Heimburger, D., Melnychuk, N., Collot, M., Groschup, B., Hellal, F., Reisch, A., Plesnila, N., & Klymchenko, A. S. (2020) · ACS Nano · doi:10.1021/acsnano.0c01505 · Cited by 82 · see citing works
Self-citationSuzuki, Y., Miyazaki, T., Muto, H., Kubara, K., Mukai, Y., Watari, R., Sato, S., Kondo, K., Tsukumo, S., Yasutomo, K., et al. (2022) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2022.09.017 · Cited by 81 · see citing works
A DNA robotic switch with regulated autonomous display of cytotoxic ligand nanopatterns
Wang, Y., Baars, I., Bērziņa, I., Rocamonde-Lago, I., Shen, B., Yang, Y., Lolaico, M., Waldvogel, J., Smyrlaki, I., Zhu, K., et al. (2024) · Nature Nanotechnology · doi:10.1038/s41565-024-01676-4 · Cited by 79 · see citing works
Bernard, M., Bazin, E., Petiot, N., Lemdani, K., Commandeur, S., Verdelet, C., Margot, S., Perkov, V., Ripoll, M., Garinot, M., et al. (2023) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2023.05.004 · Cited by 62 · see citing works
Adipose Tissue: An Emerging Target for Adeno-associated Viral Vectors
Bates, R., Huang, W., & Cao, L. (2020) · Molecular Therapy — Methods & Clinical Development · doi:10.1016/j.omtm.2020.09.009 · Cited by 49 · see citing works
Tsilingiris, D., Vallianou, N. G., Karampela, Ι., Liu, J., & Dalamaga, Μ. (2021) · Metabolism Open · doi:10.1016/j.metop.2021.100159 · Cited by 49 · see citing works
Li, R., Ren, M., Ni, C., Luo, M., Zhang, Z., & Wu, J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037195 · Cited by 47 · see citing works
Biancacci, I., Sun, Q., Möckel, D., Gremse, F., Rosenhain, S., Kiessling, F. M., Bartneck, M., Hu, Q., Thewissen, M. M., Storm, G., et al. (2020) · Journal of Controlled Release · doi:10.1016/j.jconrel.2020.09.046 · Cited by 47 · see citing works
Alami, A., Krewski, D., Farhat, N., Mattison, D., Wilson, K., Gravel, C. A., Farrell, P. J., Crispo, J. A. G., Haddad, N., Perez-Lloret, S., et al. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-065687 · Cited by 44 · see citing works
Stowe, J., Miller, E., Andrews, N. J., & Whitaker, H. (2023) · PLoS Medicine · doi:10.1371/journal.pmed.1004245 · Cited by 42 · see citing works
Chen, Z., Fan, G., Li, A., Yuan, J., & Xu, T. (2020) · Molecular Therapy - Methods & Clinical Development · doi:10.1016/j.omtm.2019.11.006 · Cited by 41 · see citing works
Virtual reality-empowered deep-learning analysis of brain cells
Kaltenecker, D., Al-Maskari, R., Negwer, M., Hoeher, L., Kofler, F., Zhao, S., Todorov, M., Rong, Z., Paetzold, J. C., Wiestler, B., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02245-2 · Cited by 38 · see citing works
Self-citationExtracellular calcium modulates brown adipocyte differentiation and identity
Pramme-Steinwachs, I., Jastroch, M., & Ussar, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-09025-3 · Cited by 38 · see citing works
Self-citationMo, X., Liu, W., Yao, Y., İmani, S., Lu, S., Dan, G., Nie, X., Yan, J., Zhan, R., Li, X., et al. (2019) · Journal of Cellular Biochemistry · doi:10.1002/jcb.29128 · Cited by 37 · see citing works
Liposome Imaging in Optically Cleared Tissues
Syed, A. M., MacMillan, P., Ngai, J., et al. (2020) · Nano Letters · doi:10.1021/acs.nanolett.9b04853 · Cited by 37 · see citing works
Harumiya, S., Takeda, K., Sugiura, T., et al. (1996) · The Journal of Biochemistry · doi:10.1093/oxfordjournals.jbchem.a021474 · Cited by 36 · see citing works
Intramuscular Delivery of Gene Therapy for Targeting the Nervous System
Tosolini, A. P., & Sleigh, J. N. (2020) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2020.00129 · Cited by 32 · see citing works
Brogi, S. R., Derrien, M., & Hur, J. (2019) · Journal of Fluorescence · doi:10.1007/s10895-019-02398-w · Cited by 29 · see citing works
Wamhoff, E., Ronsard, L., Feldman, J. E., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.16.504128 · Cited by 20 · see citing works
BNT162b2 Vaccine Encoding the SARS-CoV-2 P2 S Protects Transgenic hACE2 Mice against COVID-19
Ji, R.-R., Qu, Y., Zhu, H., Yang, Y., Vogel, A. B., Şahin, U., Qin, C., & Hui, A. (2021) · Vaccines · doi:10.3390/vaccines9040324 · Cited by 19 · see citing works
Pericyte-to-endothelial cell signaling via vitronectin-integrin regulates blood-CNS barrierPreprint
Ayloo, S., Lazo, C. G., Sun, S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.22.441019 · Cited by 10 · see citing works
Pulmonary fat embolism: a potentially new fatal complication of SARS-CoV-2 infection. A case report
Kong, J., Yang, T., Zhang, F., et al. (2023) · BMC Infectious Diseases · doi:10.1186/s12879-023-08559-4 · Cited by 6 · see citing works
AB1516 A CASE OF SEVERE ANCA ASSOCIATED VASCULITIS AFTER COVID-19 VACCINATION
Qaisar, I., & Sunmboye, K. O. (2022) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2022-eular.2748 · Cited by 3 · see citing works
Virtual reality empowered deep learning analysis of brain activityPreprint
Kaltenecker, D., Al-Maskari, R., Negwer, M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.18.540970 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- [29]Al-Maskari, R., Paetzold, J. C., Horvath, I. & Erturk, A. On the pitfalls of deep image segmentation for lightsheet microscopy. In Medical Imaging with Deep Learning. MIDL 2022 Short Papers https://openreview.net/pdf?id=3Krfu84W-Wx (2022).
- [95]Luo, J. et al. Nanocarrier imaging at single-cell resolution across entire mouse bodies with deep learning. PRIDE https://www.ebi.ac.uk/pride/archive/projects/PXD056871 (2024). PMC12700832 39809933
- [96]Luo, J. et al. Nanocarrier imaging at single-cell resolution across entire mouse bodies with deep learning. GitHub https://github.com/erturklab/SCP-Nano (2024). PMC12700832 39809933
Cited by
Sorted by citations
A deep-learning framework reveals whole-body perturbations at cell level
Kaltenecker, D., Horvath, I., Al-Maskari, R., Chen, Y., Kolabas, Z. I., Hoeher, L., Todorov, M., Minde, D.-P., Kapoor, S., Turhan, S. G., et al. (2026) · Nature · doi:10.1038/s41586-026-10535-2 · Cited by 5 · see citing works
Self-citation
- Authors
- Luo, J., Molbay, M., Chen, Y., Horvath, I., Kadletz, K., Kick, B., Zhao, S., Al-Maskari, R., Singh, I., Ali, M., Bhatia, H. S., Minde, D.-P., Negwer, M., Hoeher, L., Calandra, G. M., Groschup, B., Su, J., Kimna, C., Rong, Z., Galensowske, N., Todorov, M. I., Jeridi, D., Ohn, T.-L., Roth, S., Simats, A., Singh, V., Khalin, I., Pan, C., Arús, B. A., Bruns, O. T., Zeidler, R., Liesz, A., Protzer, U., Plesnila, N., Ussar, S., Hellal, F., Paetzold, J., Elsner, M., Dietz, H., & Erturk, A.
- Affiliations
- Helmholtz Munich (DE)
- Technical University of Munich (DE)
- University of Zurich (CH)
- Munich Cluster for Systems Neurology (DE)
- Institut für Schlaganfall- und Demenzforschung (DE)
- Inserm (FR)
- German Cancer Research Center (DE)
- Corresponding author
- Dietz, Hendrik
- ORCID
- Luo, Jie
- Chen, Ying
- Horvath, Izabela
- Zhao, Shan
- Al-Maskari, Rami
- Ali, Mayar
- Bhatia, Harsharan Singh
- Minde, David-Paul
- Negwer, Moritz
- Hoeher, Luciano
- Su, Jinpeng
- Kimna, Ceren
- Rong, Zhouyi
- Galensowske, Nikolas
- Todorov, Mihail Ivilinov
- Ohn, Tzu-Lun
- Roth, Stefan
- Simats, Alba
- Singh, Vikramjeet
- Khalin, Igor
- Pan, Chenchen
- Arús, Bernardo A.
- Zeidler, Reinhard
- Liesz, Arthur
- Protzer, Ulrike
- Plesnila, Nikolaus
- Ussar, Siegfried
- Hellal, Farida
- Paetzold, Johannes
- Elsner, Markus
- Dietz, Hendrik
- Erturk, Ali
- Venue
- Nature Biotechnology
- Published
- 2025-01-14
- Volume
- 43
- Issue
- 12
- Pages
- 2009-2022
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC12700832
- OpenAlex
- W4406374671
- Cited by
- 53
- References
- 96
- Citations by year
- 2026: 35 · 2025: 17
- FWCI
- 10.90
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Biotechnology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (FOR2879, SFB 1052, EXC 2145, 457586042, EXC 2145SyNergy, 390857198, LI-2534/5-1)
- Bundesministerium für Bildung und Forschung (01KX2121)
- China Scholarship Council
- NOMIS Stiftung
- Stiftung zur Erforschung der Vaskulären Demenz
- Helmholtz Artificial Intelligence Cooperation Unit
- Helmholtz Zentrum München
- H2020 European Research Council (802305)
- Topics
- Physical Sciences › Materials Science › Biomaterials › Nanoparticle-Based Drug Delivery
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Interference and Gene Delivery
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmaceutical Science › Advancements in Transdermal Drug Delivery
- Keywords
- single-cell imaging, lipid nanoparticles, deep learning, targeted drug delivery, intramuscular injection, immune activation, liposomes, polyplexes, DNA origami, adeno-associated virus, whole-body imaging, mouse model
- MeSH terms
- Deep Learning, COVID-19, SARS-CoV-2, Dependovirus, Animals, Drug Carriers, Humans, Lipids, Liposomes, Tissue Distribution, Drug Delivery Systems, Mice, Nanoparticles, Single-Cell Analysis, Spike Glycoprotein, Coronavirus
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