{"doi":"10.1039/d5bm00630a","title":"Seeing deep to map cell–biomaterial interactions <i>via</i> whole mount light sheet imaging and automated analytics in decellularized extracellular matrix models","abstract":"cancer microenvironments. We first combined a series of established sample preparation methods including tissue clearing agents and cell labeling dyes, to optimize dECM scaffold compatibility with volumetric light sheet fluorescence microscopy (LSFM) imaging. We then developed image analysis algorithms capable of overcoming the segmentation limitations of established methods to accurately quantify scaffold porosity as well as cellular occupation and migration at the single cell level within dECM scaffolds. We automated this analysis to increase usability for large data sets and applied the imaging methods to a porcine liver-derived dECM scaffold model, called a biomatrix. Biomatrices recellularized with colorectal cancer spheroids model liver metastasis. The LSFM imaging and analysis successfully detected increased cell numbers between 3 and 5 days of culture on the dECM biomatrix, and the loss of cells in oxaliplatin-treated biomatrices. With the ability to resolve these key changes in proliferation, invasion and therapeutic response, this optimized set of imaging and computational tools will aid in the mechanistic and therapeutic discovery of colorectal cancer liver metastasis. Broadly, the increased volume and resolution of imaging data from our methods can extract spatially relevant scaffold and cellular information within the context of any dECM model, increasing its adoptability to probe complex biological behaviors across diseases.","journal":"Biomaterials Science","year":2025,"id":542085,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.962,"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":875303,"name":"Sabrina N. VandenHeuvel","orcid":"0000-0002-1540-3510","position":1,"is_corresponding":false},{"id":1264247,"name":"Sanjana Roy","orcid":null,"position":2,"is_corresponding":false},{"id":1407849,"name":"Brinlee Goggans","orcid":null,"position":3,"is_corresponding":false},{"id":1430778,"name":"Shubha Holla","orcid":"0009-0004-0979-2187","position":4,"is_corresponding":false},{"id":1206385,"name":"Varsha Rajavel","orcid":null,"position":5,"is_corresponding":false},{"id":1231752,"name":"Joseph Duran","orcid":null,"position":6,"is_corresponding":false},{"id":1407848,"name":"Lucia L. Nash","orcid":null,"position":7,"is_corresponding":false},{"id":904893,"name":"Daniel L. Alge","orcid":"0000-0002-8129-2871","position":8,"is_corresponding":false},{"id":253453,"name":"Alex J. Walsh","orcid":"0000-0003-3832-8207","position":9,"is_corresponding":false},{"id":353036,"name":"Shreya Raghavan","orcid":"0000-0002-4631-7762","position":10,"is_corresponding":false},{"id":784488,"name":"Oscar R. Benavides","orcid":"0000-0001-8212-2535","position":0,"is_corresponding":true}],"reference_count":60,"raw_metadata":null,"created_at":"2026-07-19T02:52:51.593043Z","pmid":"40838317","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":[]}