{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Shi J"],"funding":["NIBIB NIH HHS","U.S. Department of Health & Human Services | National Institutes of Health (NIH)"],"pagination":["157"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11790971"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["8(1)"],"pubmed_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. Correlati"],"journal":["Communications biology"],"pubmed_title":["Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning."],"pmcid":["PMC11790971"],"funding_grant_id":["P41EB031772","P41 EB031772"],"pubmed_authors":["Spillman DR","Boppart SA","Snyder CE","Chaney EJ","Finka G","Ho A","Alex A","Sorrells JE","Doan M","Hood SR","Shi J","Marjanovic M","Talaban R"],"additional_accession":[]},"is_claimable":false,"name":"Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning.","description":"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. Correlati","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Feb","modification":"2025-04-04T00:56:08.562Z","creation":"2025-04-04T00:56:08.562Z"},"accession":"S-EPMC11790971","cross_references":{"pubmed":["39900674"],"doi":["10.1038/s42003-025-07596-w"]}}