Identification of rare cell populations in autofluorescence lifetime image data

Elizabeth N. Cardona, Alex J. Walsh

Research output: Contribution to journalArticlepeer-review

Abstract

Drug-resistant cells and anti-inflammatory immune cells within tumor masses contribute to tumor aggression, invasion, and worse patient outcomes. These cells can be a small proportion (<10%) of the total cell population of the tumor. Due to their small quantity, the identification of rare cells is challenging with traditional assays. Single cell analysis of autofluorescence images provides a live-cell assay to quantify cellular heterogeneity. Fluorescence intensities and lifetimes of the metabolic coenzymes reduced nicotinamide adenine dinucleotide and oxidized flavin adenine dinucleotide allow quantification of cellular metabolism and provide features for classification of cells with different metabolic phenotypes. In this study, Gaussian distribution modeling and machine learning classification algorithms are used for the identification of rare cells within simulated autofluorescence lifetime image data of a large tumor comprised of tumor cells and T cells. A Random Forest machine learning algorithm achieved an overall accuracy of 95% for the identification of cell type from the simulated optical metabolic imaging data of a heterogeneous tumor of 20,000 cells consisting of 70% drug responsive breast cancer cells, 5% drug resistant breast cancer cells, 20% quiescent T cells and 5% activated T cells. High resolution imaging methods combined with single-cell quantitative analyses allows identification and quantification of rare populations of cells within heterogeneous cultures.

Original languageEnglish (US)
Pages (from-to)497-506
Number of pages10
JournalCytometry Part A
Volume101
Issue number6
DOIs
StatePublished - Jun 2022

Keywords

  • NADH
  • breast cancer
  • cell analysis
  • drug response
  • fluorescence lifetime imaging
  • heterogeneity
  • modeling

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

Fingerprint

Dive into the research topics of 'Identification of rare cell populations in autofluorescence lifetime image data'. Together they form a unique fingerprint.

Cite this