We present a quantitative phase image (QPI) reconstruction method using generative deep learning (with high similarity of 91% and low error rate of < 1%), and its ability to integrate with a high-throughput microfluidic multimodal imaging flow cytometry platform (called multi-ATOM) that can consistently classify cancer cells in heterogeneous tumors from human non-small cell lung cancer patients at large scale (~200,000 cells) and high accuracy (~98%); and can reveal biophysical heterogeneity of tumors. This work represents another groundwork of synergizing high-throughput QPI and deep learning for future label-free intelligent clinical cancer diagnosis.
Using a high-throughput imaging flow cytometer (10,000 cells/sec) multi-ATOM, we established a hierarchical biophysical phenotyping approach for label-free single-cell analysis. We demonstrate that the label-free multi-ATOM contrasts can be derived into a set of spatially hierarchical biophysical features that reflect optical density and dry mass density distributions in local and global scales. This phenotypic profile enables us to delineate subtle cellular response of molecularly targeted drug even at an early time point after the drug administration (6 hours). Based on fluorescence image analysis, we further interpreted how these biophysical phenotypes correlate with specific intracellular organelles alteration upon drug treatment.
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