Mitchell, M. J. et al. Engineering precision nanoparticles for drug delivery. Nat. Rev. Drug Discov. 20, 101–124 (2021).
Yardley, D. A. nab-Paclitaxel mechanisms of action and delivery. J. Control. Rel. 170, 365–372 (2013).
Wilhelm, S. et al. Analysis of nanoparticle delivery to tumours. Nat. Rev. Mater. https://doi.org/10.1038/natrevmats.2016.14 (2016).
Sindhwani, S. et al. The entry of nanoparticles into solid tumours. Nat. Mater. 19, 566 (2020).
Blanco, E., Shen, H. & Ferrari, M. Principles of nanoparticle design for overcoming biological barriers to drug delivery. Nat. Biotechnol. 33, 941–951 (2015).
Ouyang, B. et al. Impact of tumor barriers on nanoparticle delivery to macrophages. Mol. Pharm. 19, 1917–1925 (2022).
Jiang, W. et al. Designing nanomedicine for immuno-oncology. Nat. Biomed. Eng. 1, 0029 (2017).
Nguyen, L. N. M. et al. The exit of nanoparticles from solid tumours. Nat. Mater. 22, 1261–1272 (2023).
Jiang, W., Kim, B. Y., Rutka, J. T. & Chan, W. C. Nanoparticle-mediated cellular response is size-dependent. Nat. Nanotechnol. 3, 145–150 (2008).
Hatit, M. Z. C. et al. Species-dependent in vivo mRNA delivery and cellular responses to nanoparticles. Nat. Nanotechnol. 17, 310–318 (2022).
Horie, M., Kato, H., Fujita, K., Endoh, S. & Iwahashi, H. In vitro evaluation of cellular response induced by manufactured nanoparticles. Chem. Res. Toxicol. 25, 605–619 (2012).
Jiang, W., Wang, Y., Wargo, J. A., Lang, F. F. & Kim, B. Y. S. Considerations for designing preclinical cancer immune nanomedicine studies. Nat. Nanotechnol. 16, 6–15 (2021).
Hasin, Y., Seldin, M. & Lusis, A. Multi-omics approaches to disease. Genome Biol. 18, 83 (2017).
Baysoy, A., Bai, Z., Satija, R. & Fan, R. The technological landscape and applications of single-cell multi-omics. Nat. Rev. Mol. Cell Biol. 24, 695–713 (2023).
Cai, R. et al. Panoptic imaging of transparent mice reveals whole-body neuronal projections and skull-meninges connections. Nat. Neurosci. 22, 317–327 (2019).
Mai, H. et al. Whole-body cellular mapping in mouse using standard IgG antibodies. Nat. Biotechnol. 42, 617–627 (2023).
Kuett, L. et al. Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues and the tumor microenvironment. Nat. Cancer 3, 122–133 (2022).
Hauser, M. et al. Correlative super-resolution microscopy: new dimensions and new opportunities. Chem. Rev. 117, 7428–7456 (2017).
Navikas, V. et al. Correlative 3D microscopy of single cells using super-resolution and scanning ion-conductance microscopy. Nat. Commun. 12, 4565 (2021).
Grandi, F. C., Modi, H., Kampman, L. & Corces, M. R. Chromatin accessibility profiling by ATAC-seq. Nat. Protoc. 17, 1518–1552 (2022).
Wang, M. et al. Single-nucleus multi-omic profiling of human placental syncytiotrophoblasts identifies cellular trajectories during pregnancy. Nat. Genet. 56, 294–305 (2024).
Hu, Y. et al. Simultaneous profiling of transcriptome and DNA methylome from a single cell. Genome Biol. 17, 88 (2016).
Hou, Y. et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 26, 304–319 (2016).
Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018).
Black, S. et al. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat. Protoc. 16, 3802–3835 (2021).
Rao, A., Barkley, D., Franca, G. S. & Yanai, I. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211–220 (2021).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
Dong, K. & Zhang, S. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nat. Commun. 13, 1739 (2022).
Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792.e21 (2022).
Xiao, Z. et al. 3D reconstruction of a gastrulating human embryo. Cell 187, 2855–2874.e19 (2024).
Simeth, J. et al. Virtual tissue expression analysis. Preprint at bioRxiv https://doi.org/10.1101/2023.11.16.567357 (2023).
Vicari, M. et al. Spatial multimodal analysis of transcriptomes and metabolomes in tissues. Nat. Biotechnol. 42, 1046–1050 (2023).
Zhang, D. et al. Spatial epigenome-transcriptome co-profiling of mammalian tissues. Nature 616, 113–122 (2023).
Zhang, B. et al. Proteogenomic characterization of human colon and rectal cancer. Nature 513, 382–387 (2014).
Larsson, A. J. M. et al. Genomic encoding of transcriptional burst kinetics. Nature 565, 251–254 (2019).
Mund, A., Brunner, A. D. & Mann, M. Unbiased spatial proteomics with single-cell resolution in tissues. Mol. Cell 82, 2335–2349 (2022).
Graves, P. R. & Haystead, T. A. Molecular biologist’s guide to proteomics. Microbiol. Mol. Biol. Rev. 66, 39–63 (2002).
Bennett, H. M., Stephenson, W., Rose, C. M. & Darmanis, S. Single-cell proteomics enabled by next-generation sequencing or mass spectrometry. Nat. Methods 20, 363–374 (2023).
Woo, J. et al. High-throughput and high-efficiency sample preparation for single-cell proteomics using a nested nanowell chip. Nat. Commun. 12, 6246 (2021).
Ahmad, R. & Budnik, B. A review of the current state of single-cell proteomics and future perspective. Anal. Bioanal. Chem. 415, 6889–6899 (2023).
Norris, J. L. & Caprioli, R. M. Analysis of tissue specimens by matrix-assisted laser desorption/ionization imaging mass spectrometry in biological and clinical research. Chem. Rev. 113, 2309–2342 (2013).
Mund, A. et al. Deep visual proteomics defines single-cell identity and heterogeneity. Nat. Biotechnol. 40, 1231–1240 (2022).
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
Liu, Y. et al. High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial CITE-seq. Nat. Biotechnol. 41, 1405–1409 (2023).
Clish, C. B. Metabolomics: an emerging but powerful tool for precision medicine. Cold Spring Harb. Mol. Case Stud. 1, a000588 (2015).
Yang, K. & Han, X. Lipidomics: techniques, applications, and outcomes related to biomedical sciences. Trends Biochem. Sci. 41, 954–969 (2016).
Mapstone, M. et al. Plasma phospholipids identify antecedent memory impairment in older adults. Nat. Med. 20, 415–418 (2014).
Wang, T. J. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448–453 (2011).
Mayers, J. R. et al. Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development. Nat. Med. 20, 1193–1198 (2014).
Johnson, C. H., Ivanisevic, J. & Siuzdak, G. Metabolomics: beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17, 451–459 (2016).
Ingolia, N. T., Brar, G. A., Rouskin, S., McGeachy, A. M. & Weissman, J. S. The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments. Nat. Protoc. 7, 1534–1550 (2012).
Wang, T. et al. Translating mRNAs strongly correlate to proteins in a multivariate manner and their translation ratios are phenotype specific. Nucleic Acids Res. 41, 4743–4754 (2013).
Zhao, J., Qin, B., Nikolay, R., Spahn, C. M. T. & Zhang, G. Translatomics: the global view of translation. Int. J. Mol. Sci. 20, e20010212 (2019).
Ozadam, H. et al. Single-cell quantification of ribosome occupancy in early mouse development. Nature 618, 1057–1064 (2023).
Zeng, H. et al. Spatially resolved single-cell translatomics at molecular resolution. Science 380, eadd3067 (2023).
Guo, Y., Yan, S. & Zhang, W. Translatomics to explore dynamic differences in immunocytes in the tumor microenvironment. Mol. Ther. Nucleic Acids 34, 102037 (2023).
Li, S. D. & Huang, L. Pharmacokinetics and biodistribution of nanoparticles. Mol. Pharm. 5, 496–504 (2008).
Kulkarni, S. A. & Feng, S. S. Effects of particle size and surface modification on cellular uptake and biodistribution of polymeric nanoparticles for drug delivery. Pharm. Res. 30, 2512–2522 (2013).
Li, R. et al. Therapeutically reprogrammed nutrient signalling enhances nanoparticulate albumin bound drug uptake and efficacy in KRAS-mutant cancer. Nat. Nanotechnol. 16, 830–839 (2021).
Chen, Y. et al. Therapeutic remodeling of the tumor microenvironment enhances nanoparticle delivery. Adv. Sci. 6, 1802070 (2019).
Sheth, V. et al. Quantifying intracellular nanoparticle distributions with three-dimensional super-resolution microscopy. ACS Nano 17, 8376–8392 (2023).
Andrian, T. et al. Super-resolution correlative light-electron microscopy using a click-chemistry approach for studying intracellular trafficking. Methods Cell Biol. 162, 303–331 (2021).
Samrot, A. V., Sahithya, C. S., Selvarani, J. A., Purayil, S. K. & Ponnaiah, P. A review on synthesis, characterization and potential biological applications of superparamagnetic iron oxide nanoparticles. Curr. Res. Green Sustain. Chem. 4, 100042 (2021).
Stepanenko, O. V. et al. Comparative studies on the structure and stability of fluorescent proteins EGFP, zFP506, mRFP1, “dimer2”, and DsRed1. Biochemistry 43, 14913–14923 (2004).
Campbell, B. C., Paez-Segala, M. G., Looger, L. L., Petsko, G. A. & Liu, C. F. Chemically stable fluorescent proteins for advanced microscopy. Nat. Methods 19, 1612–1621 (2022).
Gordon, S. & Taylor, P. R. Monocyte and macrophage heterogeneity. Nat. Rev. Immunol. 5, 953–964 (2005).
Dobrowolski, C. et al. Nanoparticle single-cell multiomic readouts reveal that cell heterogeneity influences lipid nanoparticle-mediated messenger RNA delivery. Nat. Nanotechnol. 17, 871–879 (2022). This article reports DNA-barcoded nanoparticles to screen for factors related to lipid nanoparticle delivery of mRNA at single-cell resolution.
Wang, Y. et al. Age-associated disparity in phagocytic clearance affects the efficacy of cancer nanotherapeutics. Nat. Nanotechnol. 19, 255–263 (2024). This article shows that liver clearance of nanoparticles declines with age owing to a change in liver macrophage population during ageing.
Boehnke, N. et al. Massively parallel pooled screening reveals genomic determinants of nanoparticle delivery. Science 377, eabm5551 (2022). This article reports the screening of a library of different nanoparticles and barcoded cell lines, identifying the core materials and surface modifications of nanoparticles influencing their cellular uptake.
La-Beck, N. M. et al. Factors affecting the pharmacokinetics of pegylated liposomal doxorubicin in patients. Cancer Chemother. Pharmacol. 69, 43–50 (2012).
Serpooshan, V. et al. Effect of cell sex on uptake of nanoparticles: the overlooked factor at the nanobio interface. ACS Nano 12, 2253–2266 (2018).
Mahmoudi, N. et al. Sex-specific nanomedicine- and biomaterials-based therapies of chronic wounds. Nat. Rev. Bioeng. 2, 447–449 (2024). This article discusses sex differences in chronic wound healing and the design of sex-specific biomaterials for chronic wound treatment.
He, Y., Wang, Y., Wang, L., Jiang, W. & Wilhelm, S. Understanding nanoparticle-liver interactions in nanomedicine. Expert Opin. Drug Deliv. 21, 829–843 (2024).
Tabula Sapiens, C. et al. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 376, eabl4896 (2022).
Saeed, S. et al. Epigenetic programming of monocyte-to-macrophage differentiation and trained innate immunity. Science 345, 1251086 (2014).
Hall, E. et al. Sex differences in the genome-wide DNA methylation pattern and impact on gene expression, microRNA levels and insulin secretion in human pancreatic islets. Genome Biol. 15, 522 (2014).
Kundakovic, M. & Tickerhoof, M. Epigenetic mechanisms underlying sex differences in the brain and behavior. Trends Neurosci. 47, 18–35 (2024).
Pal, S. & Tyler, J. K. Epigenetics and aging. Sci. Adv. 2, e1600584 (2016).
Ng, C. T. et al. The induction of epigenetic regulation of PROS1 gene in lung fibroblasts by gold nanoparticles and implications for potential lung injury. Biomaterials 32, 7609–7615 (2011).
Zhao, X., Toyooka, T. & Ibuki, Y. Silver nanoparticle-induced phosphorylation of histone H3 at serine 10 is due to dynamic changes in actin filaments and the activation of Aurora kinases. Toxicol. Lett. 276, 39–47 (2017).
Sanei, M., Amirheidari, B. & Satarzadeh, N. Mutuality of epigenetic and nanoparticles: two sides of a coin. Heliyon 10, e23679 (2024).
Lahnemann, D. et al. Eleven grand challenges in single-cell data science. Genome Biol. 21, 31 (2020).
Kiselev, V. Y., Andrews, T. S. & Hemberg, M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20, 273–282 (2019).
Wang, L. et al. Exploring and analyzing the systemic delivery barriers for nanoparticles. Adv. Funct. Mater. 34, 2308446 (2023).
Manco, R. & Itzkovitz, S. Liver zonation. J. Hepatol. 74, 466–468 (2021).
Kietzmann, T. Metabolic zonation of the liver: the oxygen gradient revisited. Redox Biol. 11, 622–630 (2017).
Guilliams, M. et al. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell 185, 379–396.e38 (2022). This article identifies spatially resolved niches of different liver macrophage populations.
Matusiak, M. et al. Spatially segregated macrophage populations predict distinct outcomes in colon cancer. Cancer Discov. 14, 1418–1439 (2024). This article reports that tumour-associated macrophages at different spatial locations have distinct functions, which can be related to disease outcomes.
Wang, W. et al. Identification of hypoxic macrophages in glioblastoma with therapeutic potential for vasculature normalization. Cancer Cell 42, 815–832.e12 (2024).
Mahmoudi, M., Landry, M. P., Moore, A. & Coreas, R. The protein corona from nanomedicine to environmental science. Nat. Rev. Mater. 8, 422–438 (2023).
Lee, D., Huntoon, K., Lux, J., Kim, B. Y. S. & Jiang, W. Engineering nanomaterial physical characteristics for cancer immunotherapy. Nat. Rev. Bioeng. 1, 499–517 (2023). This article discusses how the physical characteristics of nanomaterials affect immunological outcomes in cells.
Ngo, W. et al. Identifying cell receptors for the nanoparticle protein corona using genome screens. Nat. Chem. Biol. 18, 1023–1031 (2022).
Hsiao, I. L. et al. Quantification and visualization of cellular uptake of TiO2 and Ag nanoparticles: comparison of different ICP-MS techniques. J. Nanobiotechnol. 14, 50 (2016).
Wang, H. et al. Study on uptake of gold nanoparticles by single cells using droplet microfluidic chip-inductively coupled plasma mass spectrometry. Talanta 200, 398–407 (2019).
Donahue, N. D. et al. Absolute quantification of nanoparticle interactions with individual human B cells by single cell mass spectrometry. Nano Lett. 22, 4192–4199 (2022).
Gatto, L. et al. Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments. Nat. Methods 20, 375–386 (2023).
Zhu, Z. J., Ghosh, P. S., Miranda, O. R., Vachet, R. W. & Rotello, V. M. Multiplexed screening of cellular uptake of gold nanoparticles using laser desorption/ionization mass spectrometry. J. Am. Chem. Soc. 130, 14139–14143 (2008).
Gioria, S. et al. Proteomics study of silver nanoparticles on Caco-2 cells. Toxicol. In Vitro 50, 347–372 (2018).
Verano-Braga, T. et al. Insights into the cellular response triggered by silver nanoparticles using quantitative proteomics. ACS Nano 8, 2161–2175 (2014).
Ashkarran, A. A. et al. Measurements of heterogeneity in proteomics analysis of the nanoparticle protein corona across core facilities. Nat. Commun. 13, 6610 (2022).
Ostroff, R. et al. The stability of the circulating human proteome to variations in sample collection and handling procedures measured with an aptamer-based proteomics array. J. Proteom. 73, 649–666 (2010).
Faserl, K., Chetwynd, A. J., Lynch, I., Thorn, J. A. & Lindner, H. H. Corona isolation method matters: capillary electrophoresis mass spectrometry based comparison of protein corona compositions following on-particle versus in-solution or in-gel digestion. Nanomaterials https://doi.org/10.3390/nano9060898 (2019).
Horník, Š. et al. Effects of workers exposure to nanoparticles studied by NMR metabolomics. Appl. Sci. 11, 6601 (2021).
He, X. et al. Metabolomics of V2O5 nanoparticles and V2O5 nanofibers in human airway epithelial BEAS-2B cells. Toxicol. Appl. Pharmacol. 459, 116327 (2023).
Bannuscher, A. et al. Metabolomics profiling to investigate nanomaterial toxicity in vitro and in vivo. Nanotoxicology 14, 807–826 (2020).
Tang, H. et al. Cholesterol modulates the physiological response to nanoparticles by changing the composition of protein corona. Nat. Nanotechnol. 18, 1067–1077 (2023).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887.e17 (2019).
Cao, Z. J. & Gao, G. Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nat. Biotechnol. 40, 1458–1466 (2022).
Vandereyken, K., Sifrim, A., Thienpont, B. & Voet, T. Methods and applications for single-cell and spatial multi-omics. Nat. Rev. Genet. 24, 494–515 (2023).
Athieniti, E. & Spyrou, G. M. A guide to multi-omics data collection and integration for translational medicine. Comput. Struct. Biotechnol. J. 21, 134–149 (2023).
Aberg, C., Piattelli, V., Montizaan, D. & Salvati, A. Sources of variability in nanoparticle uptake by cells. Nanoscale 13, 17530–17546 (2021).
Lin, Z. P. et al. Macrophages actively transport nanoparticles in tumors after extravasation. ACS Nano 16, 6080–6092 (2022).
Behzadi, S. et al. Cellular uptake of nanoparticles: journey inside the cell. Chem. Soc. Rev. 46, 4218–4244 (2017).
Zhang, F. et al. Multistage signal-interactive nanoparticles improve tumor targeting through efficient nanoparticle-cell communications. Cell Rep. 35, 109131 (2021).
Ultimo, A. et al. Nanoparticle-cell-nanoparticle communication by stigmergy to enhance poly(I:C) induced apoptosis in cancer cells. Chem. Commun. 56, 7273–7276 (2020).
Chai, J., Zeng, H., Li, A. & Ngai, E. W. T. Deep learning in computer vision: a critical review of emerging techniques and application scenarios. Mach. Learn. Appl. 6, 100134 (2021).
Zhu, M. et al. Machine-learning-assisted single-vessel analysis of nanoparticle permeability in tumour vasculatures. Nat. Nanotechnol. 18, 657–666 (2023). This article reports the use of computer vision and machine learning to quantify and analyse the tumour vasculature and delivery of nanoparticles.
Harrison, P. J. et al. Deep-learning models for lipid nanoparticle-based drug delivery. Nanomedicine 16, 1097–1110 (2021).
Kang, M., Ko, E. & Mersha, T. B. A roadmap for multi-omics data integration using deep learning. Brief. Bioinform. 23, bbab454 (2022).
Li, R. & Yang, X. De novo reconstruction of cell interaction landscapes from single-cell spatial transcriptome data with DeepLinc. Genome Biol. 23, 124 (2022).
Zhang, Z. et al. Graph neural network approaches for drug-target interactions. Curr. Opin. Struct. Biol. 73, 102327 (2022).
Mastropietro, A., Pasculli, G. & Bajorath, J. Learning characteristics of graph neural networks predicting protein-ligand affinities. Nat. Mach. Intell. 5, 1427–1436 (2023).
Saldinger, J. C., Raymond, M., Elvati, P. & Violi, A. Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles. Nat. Comput. Sci. 3, 393–402 (2023).
Xu, Y. et al. AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery. Nat. Commun. 15, 6305 (2024).
Wen, H. et al. Graph neural networks for multimodal single-cell data integration. In Proc. 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 4153–4163 (2022).
Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).
Lutz, I. D. et al. Top-down design of protein architectures with reinforcement learning. Science 380, 266–273 (2023). This article reports the use of reinforced learning to design new complex protein nanomaterials with desired properties.
Repecka, D. et al. Expanding functional protein sequence spaces using generative adversarial networks. Nat. Mach. Intell. 3, 324–333 (2021).
Korshunova, M. et al. Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds. Commun. Chem. 5, 129 (2022).
Popova, M., Isayev, O. & Tropsha, A. Deep reinforcement learning for de novo drug design. Sci. Adv. 4, eaap7885 (2018).
Jyakhwo, S., Serov, N., Dmitrenko, A. & Vinogradov, V. V. Machine learning reinforced genetic algorithm for massive targeted discovery of selectively cytotoxic inorganic nanoparticles. Small 20, e2305375 (2024).
May, J. N. et al. Histopathological biomarkers for predicting the tumour accumulation of nanomedicines. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-024-01197-4 (2024). This article reports the use of supervised machine learning to train an AI model that uses biological factors to predict the tumour delivery of nanoparticles.
Wang, Y., Schrank, B. R., Jiang, W. & Kim, B. Y. S. Learning what keeps nanomedicines in tumours. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-024-01251-1 (2024).
Arora, A. & Arora, A. Generative adversarial networks and synthetic patient data: current challenges and future perspectives. Future Healthc. J. 9, 190–193 (2022).
Kuo, N. I. et al. Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: example using antiretroviral therapy for HIV. J. Biomed. Inf. 144, 104436 (2023).
Lazarovits, J. et al. Supervised learning and mass spectrometry predicts the in vivo fate of nanomaterials. ACS Nano 13, 8023–8034 (2019).
Wang, C., Lue, W., Kaalia, R., Kumar, P. & Rajapakse, J. C. Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma. Sci. Rep. 12, 15425 (2022).
Higdon, R. et al. The promise of multi-omics and clinical data integration to identify and target personalized healthcare approaches in autism spectrum disorders. OMICS 19, 197–208 (2015).
Shen, W. X. et al. AggMapNet: enhanced and explainable low-sample omics deep learning with feature-aggregated multi-channel networks. Nucleic Acids Res. 50, e45 (2022).
Koppad, S., B, A., Gkoutos, G. V. & Acharjee, A. Cloud computing enabled big multi-omics data analytics. Bioinform Biol. Insights 15, 11779322211035921 (2021).
Chen, X., Wang, C., Tang, S., Yu, C. & Zou, Q. CMSA: a heterogeneous CPU/GPU computing system for multiple similar RNA/DNA sequence alignment. BMC Bioinform. 18, 315 (2017).
Hajipour, M. J. et al. Sex as an important factor in nanomedicine. Nat. Commun. 12, 2984 (2021).
Liu, Y., Zhou, J. & White, K. P. RNA-seq differential expression studies: more sequence or more replication. Bioinformatics 30, 301–304 (2014).
Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902 (2014).
Dahlman, J. E. et al. Barcoded nanoparticles for high throughput in vivo discovery of targeted therapeutics. Proc. Natl Acad. Sci. USA 114, 2060–2065 (2017).
Wei, W., Lu, H., Dai, W., Zheng, X. & Dong, H. Multiplexed organelles portrait barcodes for subcellular microRNA array detection in living cells. ACS Nano 16, 20329–20339 (2022).
Rhym, L. H., Manan, R. S., Koller, A., Stephanie, G. & Anderson, D. G. Peptide-encoding mRNA barcodes for the high-throughput in vivo screening of libraries of lipid nanoparticles for mRNA delivery. Nat. Biomed. Eng. 7, 901–910 (2023).
Xue, L. et al. High-throughput barcoding of nanoparticles identifies cationic, degradable lipid-like materials for mRNA delivery to the lungs in female preclinical models. Nat. Commun. 15, 1884 (2024).
Victorious, A., Saha, S., Pandey, R. & Soleymani, L. Enhancing the sensitivity of photoelectrochemical DNA biosensing using plasmonic DNA barcodes and differential signal readout. Angew. Chem. Int. Ed. 60, 7316–7322 (2021).
Edwardson, T. G., Lau, K. L., Bousmail, D., Serpell, C. J. & Sleiman, H. F. Transfer of molecular recognition information from DNA nanostructures to gold nanoparticles. Nat. Chem. 8, 162–170 (2016).
Zhao, J. et al. Core–shell silica nanoparticle-based barcodes combined with a hybridization chain reaction for multiplex quantitative detection of bacterial drug-resistance genes. ACS Appl. Nano Mater. 6, 23114–23121 (2023).
Yang, Y. S. et al. High-throughput quantitation of inorganic nanoparticle biodistribution at the single-cell level using mass cytometry. Nat. Commun. 8, 14069 (2017).
Hofmann, D. et al. Mass spectrometry and imaging analysis of nanoparticle-containing vesicles provide a mechanistic insight into cellular trafficking. ACS Nano 8, 10077–10088 (2014).
Ouyang, B. et al. The dose threshold for nanoparticle tumour delivery. Nat. Mater. 19, 1362–1371 (2020).
Chen, J. et al. Sex differences in the toxicity of polyethylene glycol-coated gold nanoparticles in mice. Int. J. Nanomed. 8, 2409–2419 (2013).
Han, H. Y. et al. Amorphous silica nanoparticle-induced pulmonary inflammatory response depends on particle size and is sex-specific in rats. Toxicol. Appl. Pharmacol. 390, 114890 (2020).
Zamboni, W. C. et al. Bidirectional pharmacodynamic interaction between pegylated liposomal CKD-602 (S-CKD602) and monocytes in patients with refractory solid tumors. J. Liposome Res. 21, 158–165 (2011).
Steppan, J. et al. Commonly used mouse strains have distinct vascular properties. Hypertens. Res. 43, 1175–1181 (2020).
Corder, K. M., Hoffman, J. M., Sogorovic, A. & Austad, S. N. Behavioral comparison of the C57BL/6 inbred mouse strain and their CB6F1 siblings. Behav. Process. 207, 104836 (2023).
Montgomery, M. K. et al. Mouse strain-dependent variation in obesity and glucose homeostasis in response to high-fat feeding. Diabetologia 56, 1129–1139 (2013).
Hamilton, R. F. Jr, Thakur, S. A., Mayfair, J. K. & Holian, A. MARCO mediates silica uptake and toxicity in alveolar macrophages from C57BL/6 mice. J. Biol. Chem. 281, 34218–34226 (2006).
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