We developed a large-scale morphological profiling approach that reads out a multitude of high-resolution biophysical fractal properties of single cells, based on our high-throughput quantitative phase imaging (QPI) platform (at 10,000 cells/sec). We showed that the single-cell morphological profile constructed by the sub-cellular fractal characteristics could be harnessed to implicate cell types and states in the context of identifying lung cancer cell line subtypes, assessing cell morphology in response to drug treatment, and tracking cell cycle progression.
We developed a large-scale single-cell intrinsic morphological profiling strategy using ultrahigh-throughput quantitative phase imaging (QPI) combined with a novel spinning on-the-fly cell-based assay platform. This integrated system demonstrates the unprecedented functional assay capability of QPI in not only scaling up the assay throughput, but also empowering new cytometric power to perform multiplexed live-cell drug screening (96 conditions in a single run) and genetic perturbation assay (by CRISPR). This platform thus allows generation of large cellular QPI datasets (4.85 TByte in this study) that could spearhead cost-effective label-free solutions for identifying disease-or gene-related cellular morphological phenotypes in therapeutics screening.
We present an unprecedented, generative deep learning model (named beGAN) in reconstructing batch-effect-free quantitative phase image (QPI). By employing the high-throughput microfluidic multimodal imaging flow cytometry platform (i.e. multi-ATOM), our model demonstrated a robust QPI prediction from brightfield on various lung cancer cell lines (>800,000 cells). With batch-free QPI, biophysical phenotypes of cells are unified across batches and a significant improvement from 33.61% to 91.34% is achieved on the cross-batches cancer cell lines classification. This work unveil an avenue on overcoming batch effect with deep learning at single-cell imaging level.
Using high-throughput quantitative phase imaging (QPI) flow cytometry, we demonstrate that label-free single-cell image-based analysis allows the classification of the key human T cell subpopulations, CD4+ and CD8+ cells. Going beyond the existing QPI cytometers, we show that the high-dimensional biophysical phenotypic profiles extracted from this large-scale QPI platform display the label-free statistical power to unambiguously reveal the respective activation changes of the CD4+ and CD8+ cells subpopulations which are costimulated with anti-CD3/CD28. The findings are validated with the standard activation marker CD25. This work has further substantiated the potential of adopting label-free QPI cytometry for in-depth functional immune cell profiling.
We report an arrayed optofluidic imaging platform that allows multiplexed image-based cell assay at ultrahigh imaging throughput. The assay platform, consisting of 96 fluidic sample chambers arranged in a circular symmetry, operates in a reconfigurable spinning motion synchronized with an ultrafast laser-scanning microscope (a line-scan rate >10 MHz). Based on a stable through-focus spinning mechanism, the assay platform allows ultralarge field-of-view imaging (18.8 cm2, >160 Gpixels) with high image fidelity and subcellular resolution (in both the bright-field and quantitative phase image contrasts). We further validated that the continuous spinning operation has minimal impact on the cell morphology and viability.
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.
We report a robust method based on generative deep learning to reconstruct quantitative phase image (QPI). By employing multiplexed asymmetric-detection time-stretch optical microscopy (multi-ATOM), we simultaneously captured multiple intensity image contrasts of the same cell in microfluidic flow, revealing different phase-gradient orientations at high throughput (10,000 cells/sec). Using conditional generative adversarial networks (cGAN), we performed a systematic analysis of how different orientations of the phase-gradient contrasts and their combinations influence the QPI prediction performance, which overall general achieves a high similarity (structural similarity index > 0.91) and low error rate (mean squared error < 0.01).
Continuing development of image-based bioassay is mainly hampered by the lack of throughput to systematically screen a large cell/tissue population under extensive experimental conditions; and the overwhelming reliance on biochemical markers, which are not always effective, especially when there is poor prior knowledge of the markers. Here we demonstrate ultralarge-scale, high-resolution “on-the-fly” quantitative phase imaging (QPI) of single-cells and whole-tissue-slide on a spinning-disk assay platform at an imaging rate of at least 100-times faster than current assays – mitigating the imaging throughput limitation hindered by the fundamental space-bandwidth-product limit of classical optical imaging. The concept takes advantage of the high-speed spinning motion, which naturally provides imaging at an ultrafast rate (<10MHz) that can only be made possible with time-stretch imaging.
To demonstrate the capability of the system, we imaged both label-free adherent cells and tissue slices, prepared on the functionalized digital versatile discs (DVDs), across a giga-pixel-FOV exceeding 10mm2 at a resolution of ~ 1μm. Both bright-field and QPI images are generated in real-time with this FOV at a spinning speed of <1,000 rpm. In contrast to the vast majority of current QPI modalities, our platform requires no interferometry and no computationally-intensive iterative method for phase retrieval, favouring continuous high-speed QPI operation in real-time. More importantly, this spinning imaging platform allows generation of a catalogue of label-free biophysical phenotypes of cells/tissues, e.g. cell size, dry mass density, morphology as well as light scattering properties, which could enable a new generation of large-scale in-depth label-free image-based bioassays.
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