Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning
Year: 2021
Type: article
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.
Source: Scientific Reports
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,990
Awards: G1523888516, (PQD5) Mass Profiling Melanoma Responses to Improve Therapy Choices and Prognosis, G1820511087, G1981715750, RNA trafficking in mitochondria, G2341660198, G2768817213, SPOTs: Optical Technologies for Instantly Quantifying Multicellular Response Profiles, ENGINEERING CARDIOMYOCYTE FUNCTION BY CONTROLLED MITOCHONDRIAL TRANSFER, Reverse Mitochondrial Genetics Enabled by Blast, G4805816520, G5753219315, G6318647045, G6487559069, (PQ5) Mitochondrial Transfer in Tumor Heterogeneity and Acquired Resistence to Therapy, G8923277708, Cancer Center Support Grant
OpenAlex ID: W3204559139