{"doi":"10.1126/sciadv.ade8513","title":"Tissue-embedded stretchable nanoelectronics reveal endothelial cell–mediated electrical maturation of human 3D cardiac microtissues","abstract":"Clinical translation of stem cell therapies for heart disease requires electrical integration of transplanted cardiomyocytes. Generation of electrically matured human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) is critical for electrical integration. Here, we found that hiPSC-derived endothelial cells (hiPSC-ECs) promoted the expression of selected maturation markers in hiPSC-CMs. Using tissue-embedded stretchable mesh nanoelectronics, we achieved a long-term stable map of human three-dimensional (3D) cardiac microtissue electrical activity. The results revealed that hiPSC-ECs accelerated the electrical maturation of hiPSC-CMs in 3D cardiac microtissues. Machine learning-based pseudotime trajectory inference of cardiomyocyte electrical signals further revealed the electrical phenotypic transition path during development. Guided by the electrical recording data, single-cell RNA sequencing identified that hiPSC-ECs promoted cardiomyocyte subpopulations with a more mature phenotype, and multiple ligand-receptor interactions were up-regulated between hiPSC-ECs and hiPSC-CMs, revealing a coordinated multifactorial mechanism of hiPSC-CM electrical maturation. Collectively, these findings show that hiPSC-ECs drive hiPSC-CM electrical maturation via multiple intercellular pathways.","journal":"Science Advances","year":2023,"id":320959,"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":46,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9416,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":494216,"name":"Jessica C. Garbern","orcid":"0000-0002-2397-5180","position":1,"is_corresponding":false},{"id":663783,"name":"Ren Liu","orcid":"0000-0001-5994-4338","position":2,"is_corresponding":false},{"id":640071,"name":"Qiang Li","orcid":"0000-0001-6716-4630","position":3,"is_corresponding":false},{"id":1032832,"name":"Estela Mancheño Juncosa","orcid":null,"position":4,"is_corresponding":false},{"id":1011736,"name":"Hannah Elwell","orcid":"0000-0002-8827-3438","position":5,"is_corresponding":false},{"id":1032370,"name":"Morgan Sokol","orcid":"0000-0003-1261-2146","position":6,"is_corresponding":false},{"id":1032371,"name":"Junya Aoyama","orcid":"0000-0003-0024-4067","position":7,"is_corresponding":false},{"id":1032372,"name":"Undine-Sophie Deumer","orcid":"0000-0002-1148-4675","position":8,"is_corresponding":false},{"id":1032833,"name":"Emma Hsiao","orcid":null,"position":9,"is_corresponding":false},{"id":808050,"name":"Hao Sheng","orcid":"0000-0001-7477-7830","position":10,"is_corresponding":false},{"id":294915,"name":"Richard Lee","orcid":"0000-0003-4687-1381","position":11,"is_corresponding":false},{"id":319593,"name":"Jia Liu","orcid":"0000-0003-2217-6982","position":12,"is_corresponding":false},{"id":640070,"name":"Zuwan Lin","orcid":"0000-0001-5786-9768","position":0,"is_corresponding":true}],"reference_count":84,"raw_metadata":null,"created_at":"2026-07-19T01:07:27.434133Z","pmid":"36888704","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":[]}