{"doi":"10.1101/2020.09.09.287870","title":"Generation of nonlinear and spatially-organized 3D cultures on a microfluidic chip using photoreactive thiol-ene and methacryloyl hydrogels","abstract":"Abstract Micropatterning techniques for 3D cell cultures enable the recreation of tissue-level structures, but their combination with well-defined, microscale fluidic systems for perfusion remains challenging. To address this technological gap, we developed a user-friendly in-situ micropatterning protocol that integrates photolithography of crosslinkable, cell-laden hydrogels with a simple microfluidic housing, and tested the impact of crosslinking chemistry on stability and spatial resolution. Working with gelatin functionalized with photo-crosslinkable moieties, we found that inclusion of cells at high densities (≥ 10 7 /mL) during crosslinking did not impede thiol-norbornene gelation, but decreased the storage moduli of methacryloyl hydrogels. Hydrogel composition and light dose were selected to match the storage moduli of soft tissues. The cell-laden precursor solution was flowed into a microfluidic chamber and exposed to 405 nm light through a photomask to generate the desired pattern. The on-chip 3D cultures were self-standing, and the designs were interchangeable by simply swapping out the photomask. Thiol-ene hydrogels yielded highly accurate feature sizes from 100 – 900 μm in diameter, whereas methacryloyl hydrogels yielded slightly enlarged features. Furthermore, only thiol-ene hydrogels were mechanically stable under perfusion overnight. Repeated patterning readily generated multi-region cultures, either separately or adjacent, including non-linear boundaries that are challenging to obtain on-chip. As a proof-of-principle, primary human T cells, were patterned on-chip with high regional specificity. Viability remained high (> 85%) after overnight culture with constant perfusion. We envision that this technology will enable researchers to pattern 3D cultures under fluidic control in biomimetic geometries that were previously difficult to obtain.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":130949,"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":0.9535,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":584187,"name":"Jonathan M. Zatorski","orcid":"0000-0003-1930-2183","position":1,"is_corresponding":false},{"id":584188,"name":"Abhinav Arneja","orcid":"0000-0001-7339-0562","position":2,"is_corresponding":false},{"id":584908,"name":"Alyssa N. Montalbine","orcid":null,"position":3,"is_corresponding":false},{"id":584189,"name":"Jennifer M. Munson","orcid":"0000-0002-9477-1505","position":4,"is_corresponding":false},{"id":584190,"name":"Chance John Luckey","orcid":"0000-0002-0787-9121","position":5,"is_corresponding":false},{"id":424899,"name":"Rebecca R. Pompano","orcid":"0000-0002-8644-9313","position":6,"is_corresponding":false},{"id":584907,"name":"Jennifer E. Ortiz-Cárdenas","orcid":null,"position":0,"is_corresponding":true}],"reference_count":70,"raw_metadata":null,"created_at":"2026-07-18T23:16:00.235845Z","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":[]}