Related Experiment Video
Updated: Apr 3, 2026
Simultaneous Label-Free Autofluorescence Multi-Harmonic Microscopy
Published on: August 29, 2025
Cell-MICS: Detecting Immune Cells With Label-Free Two-Photon Autofluorescence and Deep Learning
Lucas Kreiss1,2,3, Amey Chaware1, Maryam Roohian4
1Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
Insights
Multiphoton imaging gains computational specificity using deep learning to classify immune cells. This advance enhances label-free imaging for inflammation research without traditional markers.
Area of Science:
- Biomedical Optics
- Computational Biology
- Immunology
Background:
- Multiphoton imaging offers deep-tissue visualization with metabolic contrast, valuable for studying inflammation.
- Label-free two-photon autofluorescence (2P-AF) lacks specificity compared to antibody-based methods.
- Identifying specific immune cells in unstained tissues remains a challenge for current imaging techniques.
Purpose of the Study:
- To investigate the potential of multiphoton imaging with computational specificity (MICS) for reliable immune cell classification.
- To develop and validate a deep learning model for distinguishing immune cell types using label-free 2P-AF.
- To assess the contribution of metabolic cofactors (NADH and FAD) to classification accuracy.
Main Methods:
- Training a convolutional neural network (CNN) utilizing a low-complexity SqueezeNet architecture on images of immune cells.
- Evaluating classification performance using metrics such as ROC-AUC, PR-AUC, F1 score, precision, and recall.
- Conducting perturbation tests to assess model robustness against extracellular environmental factors.
Main Results:
- The CNN achieved high accuracy in binary classification (0.89 ROC-AUC, 0.95 PR-AUC) between T cells and neutrophils.
- The model demonstrated moderate performance in multi-class classification of six isolated cell types (0.689 F1 score).
- Perturbation tests confirmed the model's independence from the extracellular environment, highlighting the equal importance of NADH and FAD autofluorescence.
Conclusions:
- Deep learning can provide computational specificity for identifying immune cells in label-free multiphoton imaging.
- MICS holds significant potential for advancing unstained tissue analysis and in vivo endomicroscopy.
- This approach could revolutionize label-free inflammation research by enabling precise immune cell identification.
Abstract:
Multiphoton imaging has been widely used for deep-tissue imaging. Although its label-free, metabolic contrast is ideal for investigating inflammation, the label-free two-photon induced autofluorescence is often regarded as less specific compared to conventional antibody markers. In this work, we investigate the potential for multiphoton imaging with computational specificity (MICS) by training a convolutional neural network on images of different immune cells. A low-complexity squeezeNet architecture was able to achieve reliable immune cell classification results (0.89 ROC-AUC, 0.95 PR-AUC for binary classification between T cells and neutrophils; 0.689 F1 score, 0.697 precision, 0.748 recall for multi-class classification between six isolated cell types). Perturbation tests confirmed that the model was not confused by the extracellular environment and that 2P-AF from NADH and FAD is equally important for the classification. In the future, deep learning could provide computational specificity for specific immune cells in unstained tissues, with great potential for label-free in vivo endomicroscopy.
