Determination of the Maturation Status of Dendritic Cells by Applying Pattern Recognition to High-Resolution Images

Michael F. Lohrer, Yang Liu, Darrin M. Hanna, Kang Hsin Wang, Fu Tong Liu, Ted A. Laurence, Gang Yu Liu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The maturation or activation status of dendritic cells (DCs) directly correlates with their behavior and immunofunction. A common means to determine the maturity of dendritic cells is from high-resolution images acquired via scanning electron microscopy (SEM) or atomic force microscopy (AFM). While direct and visual, the determination has been made by directly looking at the images by researchers. This work reports a machine learning approach using pattern recognition in conjunction with cellular biophysical knowledge of dendritic cells to determine the maturation status of dendritic cells automatically. The determination from AFM images reaches 100% accuracy. The results from SEM images reaches 94.9%. The results demonstrate the accuracy of using machine learning for accelerating data analysis, extracting information, and drawing conclusions from high-resolution cellular images, paving the way for future applications requiring high-throughput and automation, such as cellular sorting and selection based on morphology, quantification of cellular structure, and DC-based immunotherapy.

Original languageEnglish (US)
Pages (from-to)8540-8548
Number of pages9
JournalThe journal of physical chemistry. B
Volume124
Issue number39
DOIs
StatePublished - Oct 1 2020

ASJC Scopus subject areas

  • Physical and Theoretical Chemistry
  • Surfaces, Coatings and Films
  • Materials Chemistry

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