{"doi":"10.1038/s42003-025-07596-w","title":"Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning","abstract":"The selection of high-performing cell lines is crucial for biopharmaceutical production but is often time-consuming and labor-intensive. We investigated label-free multimodal nonlinear optical microscopy for non-perturbative profiling of biopharmaceutical cell lines based on their intrinsic molecular contrast. Employing simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy with fluorescence lifetime imaging microscopy (FLIM), we characterized Chinese hamster ovary (CHO) cell lines at early passages (0-2). A machine learning (ML)-assisted analysis pipeline leveraged high-dimensional information to classify single cells into their respective lines. Remarkably, the monoclonal cell line classifiers achieved balanced accuracies exceeding 96.8% as early as passage 2. Correlation features and FLIM modality played pivotal roles in early classification. This integrated optical bioimaging and machine learning approach presents a promising solution to expedite cell line selection process while ensuring identification of high-performing biopharmaceutical cell lines. The techniques have potential for broader single-cell characterization applications in stem cell research, immunology, cancer biology and beyond.","journal":"Communications Biology","year":2025,"id":531039,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9582,"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":1304029,"name":"Alexander Ho","orcid":"0000-0002-1418-4459","position":1,"is_corresponding":false},{"id":1411363,"name":"Corey E. Snyder","orcid":null,"position":2,"is_corresponding":false},{"id":417934,"name":"Eric J. Chaney","orcid":"0000-0002-0966-5450","position":3,"is_corresponding":false},{"id":451761,"name":"Janet E. Sorrells","orcid":"0000-0003-0071-8509","position":4,"is_corresponding":false},{"id":451760,"name":"Aneesh Alex","orcid":"0000-0001-8652-307X","position":5,"is_corresponding":false},{"id":1411364,"name":"Remben Talaban","orcid":null,"position":6,"is_corresponding":false},{"id":451497,"name":"Darold R. Spillman","orcid":"0000-0001-9946-2659","position":7,"is_corresponding":false},{"id":383804,"name":"Marina Marjanović","orcid":"0000-0002-1213-882X","position":8,"is_corresponding":false},{"id":321400,"name":"Minh Doan","orcid":"0000-0002-3235-0457","position":9,"is_corresponding":false},{"id":1411365,"name":"Gary Finka","orcid":null,"position":10,"is_corresponding":false},{"id":802146,"name":"Steve R. Hood","orcid":"0000-0002-7708-7699","position":11,"is_corresponding":false},{"id":366688,"name":"Stephen A. Boppart","orcid":"0000-0002-9386-5630","position":12,"is_corresponding":false},{"id":706680,"name":"Jindou Shi","orcid":"0000-0002-8906-1082","position":0,"is_corresponding":true}],"reference_count":60,"raw_metadata":null,"created_at":"2026-07-19T02:51:10.077559Z","pmid":"39900674","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":[]}