Reagent-Free and Rapid Assessment of T Cell Activation State Using Diffraction Phase Microscopy and Deep Learning.
Sukrut Hemant KarandikarChi ZhangAkilan MeiyappanIshan BarmanChristine FinckPramod Kumar SrivastavaRishikesh PandeyPublished in: Analytical chemistry (2019)
CD8+ T cells constitute an essential compartment of the adaptive immune system. During immune responses, naı̈ve T cells become functional, as they are primed with their cognate determinants by the antigen presenting cells. Current methods of identifying activated CD8+ T cells are laborious, time-consuming and expensive due to the extensive list of required reagents. Here, we demonstrate an optical imaging approach featuring quantitative phase imaging to distinguish activated CD8+ T cells from naı̈ve CD8+ T cells in a rapid and reagent-free manner. We measured the dry mass of live cells and employed transport-based morphometry to better understand their differential morphological attributes. Our results reveal that, upon activation, the dry cell mass of T cells increases significantly in comparison to that of unstimulated cells. By employing deep learning formalism, we are able to accurately predict the population ratios of unknown mixed population based on the acquired quantitative phase images. We envision that, with further refinement, this label-free method of T cell phenotyping will lead to a rapid and cost-effective platform for assaying T cell responses to candidate antigens in the near future.
Keyphrases
- deep learning
- high resolution
- induced apoptosis
- cell cycle arrest
- label free
- immune response
- high throughput
- endoplasmic reticulum stress
- single cell
- artificial intelligence
- machine learning
- stem cells
- cell death
- high speed
- mass spectrometry
- toll like receptor
- mesenchymal stem cells
- oxidative stress
- gene expression
- current status
- genome wide
- dna methylation
- bone marrow
- heat shock
- sensitive detection
- heat stress