ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2022
Identification of rare cell populations in autofluorescence lifetime image data.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Illuminating Immunity: A Systematic Review of Immune Cell Autofluorescence.Journal of biophotonics · 2025Pooled it
- Multispectral autofluorescence for label free classification of immune cell type and activation/polarization status.Scandinavian journal of immunology · 2025Article
- Evaluation of Cellpose segmentation with sequential thresholding for instance segmentation of cytoplasms within autofluorescence images.Computers in biology and medicine · 2024Article
- Unlocking the potential of T-cell metabolism reprogramming: Advancing single-cell approaches for precision immunotherapy in tumour immunity.Clinical and translational medicine · 2024Review
- Applications of machine learning in time-domain fluorescence lifetime imaging: a review.Methods and applications in fluorescence · 2024Review
- Label-free spatially maintained measurements of metabolic phenotypes in cells.Frontiers in bioengineering and biotechnology · 2023Article
- Comparison of phasor analysis and biexponential decay curve fitting of autofluorescence lifetime imaging data for machine learning prediction of cellular phenotypes.Frontiers in bioinformatics · 2023Article
- POSEA: A novel algorithm to evaluate the performance of multi-object instance image segmentation.PloS one · 2023Article
- Identification of rare cell populations in autofluorescence lifetime image data.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2022Article
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Authors and funding
2 authors.
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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.
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