{"doi":"10.1101/2025.04.13.648512","title":"Enhanced delivery of lipid nanoparticle-based immunotherapy by modulating the tumor tissue stiffness using ultrasound-activated nanobubbles","abstract":"Abstract Tumors often exhibit an extracellular matrix with elevated stiffness due to excessive accumulation and crosslinking of proteins, particularly collagen. This elevated stiffness acts as a physical barrier, impeding the infiltration of immune cells and the effective delivery of various immunotherapeutic agents, such as lipid nanoparticle-based RNA therapeutics. Here, we investigate the ability of ultrasound-activated nanobubbles (US-NBs) to increase the permeability and immunogenicity of tumors. Our results show that US-NBs physically remodel the tumor tissue by decreasing its stiffness by 60% five days after a single treatment. US-NB-treated tumors display randomly oriented collagen with a 5.47-fold lower deposition compared to untreated tumors. This leads to the effective delivery and widespread distribution of lipid nanoparticles (LNPs) in the tumor. When LNPs are assisted by US-NB, they have higher gene-transfection across pan-immune cells relative to LNPs alone. Notably, US-NB enables LNPs to genetically modify T cells directly in vivo. By effectively engaging both arms of the immune system, US-NB-assisted LNPs enhance the tumor immunogenicity and infiltration of cytotoxic cells by 4-fold when compared to LNPs alone. These results indicate that gentle mechanical stimulation of the tumor using US-NB offers a promising strategy to augment the delivery and efficacy of existing immunotherapies. Graphical Abstract","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":564680,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9446,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":486059,"name":"Pinunta Nittayacharn","orcid":"0000-0003-1114-4328","position":1,"is_corresponding":false},{"id":270031,"name":"Meghna Mehta","orcid":"0000-0001-7636-4749","position":2,"is_corresponding":false},{"id":1468353,"name":"Arya Iyer","orcid":null,"position":3,"is_corresponding":false},{"id":1246626,"name":"Diarmuid W. Hutchinson","orcid":null,"position":4,"is_corresponding":false},{"id":1468354,"name":"Andrew Cheplyansky","orcid":null,"position":5,"is_corresponding":false},{"id":1468355,"name":"Kayo Takizawa","orcid":null,"position":6,"is_corresponding":false},{"id":1468356,"name":"Abraham Nidhiry","orcid":null,"position":7,"is_corresponding":false},{"id":703001,"name":"Ramamurthy Gopalakrishnan","orcid":null,"position":8,"is_corresponding":false},{"id":1209158,"name":"Theresa Kosmides","orcid":null,"position":9,"is_corresponding":false},{"id":296390,"name":"Agata A. Exner","orcid":"0000-0003-3913-7066","position":10,"is_corresponding":false},{"id":325798,"name":"Efstathios Karathanasis","orcid":"0000-0001-7484-7552","position":11,"is_corresponding":false},{"id":1105306,"name":"Anubhuti Bhalotia","orcid":null,"position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":null,"created_at":"2026-07-19T02:56:24.872312Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}