biostudies-literatureShi JNIBIB NIH HHSU.S. Department of Health & Human Services | National Institutes of Health (NIH)157https://www.ebi.ac.uk/biostudies/studies/S-EPMC11790971biostudies-literatureUnknown8(1)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. CorrelatiCommunications biologyAccelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning.PMC11790971P41EB031772P41 EB031772Spillman DRBoppart SASnyder CEChaney EJFinka GHo AAlex ASorrells JEDoan MHood SRShi JMarjanovic MTalaban RfalseAccelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning.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. Correlati2025-01-01T00:00:00Z2025 Feb2025-04-04T00:56:08.562Z2025-04-04T00:56:08.562ZS-EPMC117909713990067410.1038/s42003-025-07596-w