Identifying cancer cell metabolic states in autofluorescence lifetime images with machine learning

Linghao Hu, Alex J. Walsh

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Cancer cells switch to glycolytic metabolic states even in aerobic environments to support enhanced growth and cellular functions. This phenomenon is known as the Warburg effect, and it inspires advancing interests in targeting metabolism for cancer therapy. Optical metabolic imaging (OMI) measures the fluorescence intensity and lifetime of the co-enzymes reduced nicotinamide adenine dinucleotide (NADH) and oxidized flavin adenine dinucleotide (FAD). OMI can quantitatively distinguish cellular metabolic activities in a label-free manner. The goal of this study is to identify key metabolic pathways of cancer cells using NADH and FAD fluorescence lifetime imaging and machine learning methods. MCF-7 breast cancer cells were exposed to different culture media and inhibitors to disturb their metabolic activities, and NADH and FAD fluorescence lifetime imaging were performed by a multi-photon microscope. Here, we proposed a potential method of training convolutional neural networks to predict cellular metabolic states. Adapting convolutional neural networks for the prediction of cancer cell metabolic conditions was anticipated to provide substantially better performance than traditional models with extracted features. In summary, this investigation offers a non-invasive, quantitative technology to detect metabolic perturbations at a cellular level, which improves the identification of different metabolic states of cancer cells.

Original languageEnglish (US)
Title of host publicationLabel-free Biomedical Imaging and Sensing (LBIS) 2023
EditorsNatan T. Shaked, Oliver Hayden
ISBN (Electronic)9781510658875
StatePublished - 2023
EventLabel-free Biomedical Imaging and Sensing (LBIS) 2023 - San Francisco, United States
Duration: Jan 28 2023Jan 31 2023

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceLabel-free Biomedical Imaging and Sensing (LBIS) 2023
Country/TerritoryUnited States
CitySan Francisco


  • cancer cell
  • convolutional neural network
  • fluorescence lifetime
  • metabolism

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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