Machine learning based approach to pH imaging and classification of single cancer cells.
Yuri BelottiDoorgesh Sharma JokhunJ S PonnambalamV L M ValerioChwee-Teck LimPublished in: APL bioengineering (2021)
The ability to identify different cell populations in a noninvasive manner and without the use of fluorescence labeling remains an important goal in biomedical research. Various techniques have been developed over the last decade, which mainly rely on fluorescent probes or nanoparticles. On the other hand, their applications to single-cell studies have been limited by the lengthy preparation and labeling protocols, as well as issues relating to reproducibility and sensitivity. Furthermore, some of these techniques require the cells to be fixed. Interestingly, it has been shown that different cell types exhibit a unique intracellular environment characterized by specific acidity conditions as a consequence of their distinct functions and metabolism. Here, we leverage a recently developed pH imaging modality and machine learning-based single-cell segmentation and classification to identify different cancer cell lines based on their characteristic intracellular pH. This simple method opens up the potential to perform rapid noninvasive identification of living cancer cells for early cancer diagnosis and further downstream analyses.
Keyphrases
- single cell
- machine learning
- rna seq
- deep learning
- papillary thyroid
- artificial intelligence
- high resolution
- high throughput
- big data
- induced apoptosis
- squamous cell
- cell therapy
- single molecule
- reactive oxygen species
- fluorescence imaging
- oxidative stress
- convolutional neural network
- living cells
- quantum dots
- risk assessment
- signaling pathway
- mesenchymal stem cells
- squamous cell carcinoma
- endoplasmic reticulum stress
- lymph node metastasis
- molecularly imprinted
- stem cells
- photodynamic therapy
- climate change
- cell proliferation
- mass spectrometry
- human health
- simultaneous determination
- young adults
- walled carbon nanotubes
- loop mediated isothermal amplification
- energy transfer