{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Schmitz RL"],"funding":["NIAID NIH HHS","NCI NIH HHS","NIGMS NIH HHS"],"pubmed_abstract":["New non-destructive tools are needed to reliably assess lymphocyte function for immune profiling and adoptive cell therapy. Optical metabolic imaging (OMI) is a label-free method that measures the autofluorescence intensity and lifetime of metabolic cofactors NAD(P)H and FAD to quantify metabolism at a single-cell level. Here, we investigate whether OMI can resolve metabolic changes between human quiescent versus IL4/CD40 activated B cells and IL12/IL15/IL18 activated memory-like NK cells. We found that quiescent B and NK cells were more oxidized compared to activated cells. Additionally, the NAD(P)H mean fluorescence lifetime decreased and the fraction of unbound NAD(P)H increased in the activated B and NK cells compared to quiescent cells. Machine learning classified B cells and NK cells according to activation state (CD69+) based on OMI parameters with up to 93.4% and 92.6% accuracy, respectively. Leveraging our previously published OMI data from activated and quiescent T cells, we found that the NAD(P)H mean fluorescence lifetime increased in NK cells compared to T cells, and further increased in B cells compared to NK cells. Random forest models based on OMI classified lymphocytes according to subtype (B, NK, T cell) with 97.8% accuracy, and according to activation state (quiescent or activated) and subtype (B, NK, T cell) with 90.0% accuracy. Our results show that autofluorescence lifetime imaging can accurately assess lymphocyte activation and subtype in a label-free, non-destructive manner."],"journal":["bioRxiv : the preprint server for biology"],"pagination":["2023.01.23.525260"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9900834"],"repository":["biostudies-literature"],"pubmed_title":["Autofluorescence lifetime imaging classifies human lymphocyte activation and subtype."],"pmcid":["PMC9900834"],"funding_grant_id":["R01 CA215461","U24 AI152177","R01 CA211082","T32 GM135066","P30 CA014520","R01 CA205101"],"pubmed_authors":["Walsh AJ","Riendeau J","Tweed KE","Maly EM","Samimi K","Forsberg MH","Shahi A","Schmitz RL","Rehani P","Jones I","Guzman EC","Skala MC","Capitini CM"],"additional_accession":[]},"is_claimable":false,"name":"Autofluorescence lifetime imaging classifies human lymphocyte activation and subtype.","description":"New non-destructive tools are needed to reliably assess lymphocyte function for immune profiling and adoptive cell therapy. Optical metabolic imaging (OMI) is a label-free method that measures the autofluorescence intensity and lifetime of metabolic cofactors NAD(P)H and FAD to quantify metabolism at a single-cell level. Here, we investigate whether OMI can resolve metabolic changes between human quiescent versus IL4/CD40 activated B cells and IL12/IL15/IL18 activated memory-like NK cells. We found that quiescent B and NK cells were more oxidized compared to activated cells. Additionally, the NAD(P)H mean fluorescence lifetime decreased and the fraction of unbound NAD(P)H increased in the activated B and NK cells compared to quiescent cells. Machine learning classified B cells and NK cells according to activation state (CD69+) based on OMI parameters with up to 93.4% and 92.6% accuracy, respectively. Leveraging our previously published OMI data from activated and quiescent T cells, we found that the NAD(P)H mean fluorescence lifetime increased in NK cells compared to T cells, and further increased in B cells compared to NK cells. Random forest models based on OMI classified lymphocytes according to subtype (B, NK, T cell) with 97.8% accuracy, and according to activation state (quiescent or activated) and subtype (B, NK, T cell) with 90.0% accuracy. Our results show that autofluorescence lifetime imaging can accurately assess lymphocyte activation and subtype in a label-free, non-destructive manner.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2026-04-08T03:16:06.125Z","creation":"2026-04-07T21:19:11.452Z"},"accession":"S-EPMC9900834","cross_references":{"pubmed":["36747690"],"doi":["10.1101/2023.01.23.525260"]}}