Abstract
Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. Previous QPM approaches focused on fluid flow systems or time-lapse images that provide high throughput data for cells at single time points, or of time-lapse images that require delayed post-experiment analyses, respectively. To date, QPM studies have not imaged specific cells over time with rapid, concurrent analyses during image acquisition. In order to study biological phenomena or cellular interactions over time, efficient time-dependent methods that automatically and rapidly identify events of interest are desirable. Here, we present an approach that combines QPM and machine learning to identify tumor-reactive T cell killing of adherent cancer cells rapidly, which could be used for identifying and isolating novel T cells and/or their T cell receptors for studies in cancer immunotherapy. We demonstrate the utility of this method by machine learning model training and validation studies using one melanoma-cognate T cell receptor model system, followed by high classification accuracy in identifying T cell killing in an additional, independent melanoma-cognate T cell receptor model system. This general approach could be useful for studying additional biological systems under label-free conditions over extended periods of examination.
Highlights
Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels
The discovery of tumor antigen-recognizing T cells, their T cell receptors (TCRs), and cognate tumor-specific, cell surface antigens is an area of intense activity in cancer immunotherapy research
Our study goal was to create an automatic, rapid, label-free classification method using QPM data coupled to machine learning for accurate and reproducible identification of cells of interest
Summary
Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. We demonstrate the utility of this method by machine learning model training and validation studies using one melanoma-cognate T cell receptor model system, followed by high classification accuracy in identifying T cell killing in an additional, independent melanoma-cognate T cell receptor model system This general approach could be useful for studying additional biological systems under label-free conditions over extended periods of examination. QPM has shown potential for classifying white blood cells through flow-based methods[15,16,17], currently there is not a time-dependent screening approach to identify T cell killing rapidly during image acquisition Such an advance could span discovery stage studies in identifying new targets and TCRs for therapy, implementation approaches with streamlined processes for personalized therapy, and accessibility, with cost savings and time reductions in translational pipelines and clinical applications
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