System Maintenance Notice

Please note that maintenance work is scheduled between 4 AM and 5 AM - CET. During this time, you may experience reduced performance. Thanks for your patience.

Publications

Journal Article Research Support, Non-U.S. Gov't Epublish 2 citations

PubMed: 39479453 DOI: 10.7150/thno.100298

Real-time monitoring of single dendritic cell maturation using deep learning-assisted surface-enhanced Raman spectroscopy.

Theranostics 14(17):6818-6830

Authors: Cai Zhang, Mengling Wang, Houde Wu, Hongmei Gao, Pengyun Wu, Li Guo, Dingbin Liu

Affiliations

  • Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Immunology and Biotherapy, Tianjin 300060, China.
  • School of Medical Imaging, Tianjin Medical University, Tianjin 300203, China.
  • Department of Intensive Care Unit, Key Laboratory for Critical Care Medicine of the Ministry of Health, Emergency Medicine Research Institute, Tianjin First Center Hospital, Nankai University, Tianjin 300192, China.
  • Department of Nuclear Medicine, Tianjin Medical University General Hospital, Tianjin, 300052, China.
  • College of Chemistry, Research Center for Analytical Sciences, State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Molecular Recognition and Biosensing, Nankai University, Tianjin 300071, China.

Abstract

Background: Dynamic real-time detection of dendritic cell (DC) maturation is pivotal for accurately predicting immune system activation, assessing vaccine efficacy, and determining the effectiveness of immunotherapy. The heterogeneity of cells underscores the significance of assessing the maturation status of each individual cell, while achieving real-time monitoring of DC maturation at the single-cell level poses significant challenges. Surface-enhanced Raman spectroscopy (SERS) holds great potential for providing specific fingerprinting information of DCs to detect biochemical alterations and evaluate their maturation status. Methods: We developed Au@CpG@PEG nanoparticle as a self-reporting nanovaccine for DC activation and maturation state assessment, utilizing a label-free SERS strategy. Fingerprint vibrational spectra of the biological components in different states of DCs were collected and analyzed using deep learning Convolutional Neural Networks (CNN) algorithms, aiding in the rapid and efficient identification of DC maturation. Results: This approach enables dynamic real-time detection of DC maturation, maintaining accuracy levels above 98.92%. Conclusion: By employing molecular profiling, we revealed that the signal ratio of tryptophan-to-carbohydrate holds potential as a prospective marker for distinguishing the maturation status of DCs.

MeSH

Cells, Nanomedicine, Vaccines, Deep Learning, Dendritic Cells, Learning