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.
Free-space angular-chirp-enhanced delay (FACED) imaging is proven to offer ultrafast imaging speed needed in a wide range of bioimaging applications. However, this superior performance critically requires stringent hardware mechanical stability, which limits it wide applicability for robust long-term operation. We develop FACED 2.0, that integrates diffractive optical elements with the infinity mirrors for preserving the capability of high-density laser-scanning foci generation, and more importantly, and enabling extended stable continuous operation over days or even a week. As a proof-of-concept demonstration, we showcased the applicability of FACED 2.0 in high-throughput imaging flow cytometry at the throughput equivalent to ~10,000 cells/sec.
We report the use of high-throughput quantitative phase imaging (QPI) flow cytometry (based on multiplexed asymmetric-detection time-stretch optical microscopy (multi-ATOM)) to investigate biophysical profiles of single cells infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This technique reveals the subtle biophysical heterogeneity of SARS-CoV-2 infection under the same multiplicity of infection. Furthermore, analyzing the label-free high-dimensional single-cell biophysical profiles (derived from multi-ATOM images) based on an unsupervised trajectory inference algorithm accurately recovers the infection progression over time. This study could offer biophysical insight of cellular morphogenesis of SARS-CoV-2 and shows the potential of label-free morphological profiling as a complementary drug discovery strategy for SARS-CoV-2.
We report a large-scale light-scattering single-cell characterization enabled by a high-throughput quantitative phase imaging platform (multi-ATOM) (10,000 cells/sec). By virtue of its subcellular resolution, multi-ATOM accesses the light-scattering information from individual cells via Fourier Transform light scattering (FTLS) analysis. Specifically, we applied FTLS analysis on multi-ATOM images to explore the statistical characteristics of single-cell fractal dimension (FD). We demonstrated that FD can be harnessed as an effective label-free phenotype that is indicative of cell types and states. FD can identify the heterogeneity among leukemia/lung cancer cell types and trace the different phases in cell cycle progression.
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).
Multiplexed asymmetric-detection time-stretch optical microscopy (multi-ATOM) has recently been developed to enable high-throughput quantitative phase imaging flow cytometry, from which single-cell biophysical properties can be measured at large scale. However, it lacks the ability to link such biophysical knowledge to biomolecular signatures at the single-cell precision for validation and correlative multi-scale single-cell analysis. We report a high-throughput multimodal system that integrates multi-ATOM with multiplexed 1-D fluorescence imaging/detection, termed FluorATOM; and applied it to perform synchronized biophysical and biomolecular phenotyping of rare breast circulating tumor cells detected in peripheral blood in a mouse xenograft at a throughput of >10,000 cell/sec.
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.
Phytoplankton is highly diversified in species, differing in size, geometries, morphology and biochemical composition. Such diversity plays a critical role in the atmospheric carbon cycle and marine ecosystem. Large-scale quantitation and classification of phytoplankton with taxonomic information is thus of significance in environmental monitoring and even biofuel production. To this end, we report a high-throughput, label-free imaging flow cytometer (>10,000 cells/sec) based on quantitative phase time stretch imaging flow cytometry, combined with a supervised learning strategy for multi-class classification of phytoplankton (13 classes). This is in contrast to the previous demonstrations on integrating machine learning with time-stretch imaging which achieve high-accuracy binary (two-class) image-based classification. We leverage interferometry-free quantitative phase time-stretch imaging which favors generation of high-resolution and high-contrast single-cell (phytoplankton) images with both quantitative phase and amplitude contrasts, we can extract a catalogue of 109 image-content-rich features (44 from the amplitude image and 65 from the phase image), not only limited to sizes, shapes, but also sub-cellular morphology, e.g. local dry mass density statistics. By using the random forest algorithm for feature ranking, we select 30 most significant features for a multi-class SVM model and achieve a high classification accuracy (> 95%) across 13 classes of phytoplankton. Almost 50% of these selected features are derived from the quantitative phase and play an important role in classifying morphologically similar species, e.g. Thalassiosira versus Prorocentrum; Chaetoceros gracilis versus Merismopedia – demonstrating the classification power of this quantitative phase time-stretch imaging flow cytometer required for large-scale high-content screening and analysis.
Biophysical properties of cells could complement and correlate biochemical markers to characterize a multitude of cellular states. Changes in cell size, dry mass and subcellular morphology, for instance, are relevant to cell-cycle progression which is prevalently evaluated by DNA-targeted fluorescence measurements. Quantitative-phase microscopy (QPM) is among the effective biophysical phenotyping tools that can quantify cell sizes and sub-cellular dry mass density distribution of single cells at high spatial resolution. However, limited camera frame rate and thus imaging throughput makes QPM incompatible with high-throughput flow cytometry – a gold standard in multiparametric cell-based assay. Here we present a high-throughput approach for label-free analysis of cell cycle based on quantitative-phase time-stretch imaging flow cytometry at a throughput of > 10,000 cells/s. Our time-stretch QPM system enables sub-cellular resolution even at high speed, allowing us to extract a multitude (at least 24) of single-cell biophysical phenotypes (from both amplitude and phase images). Those phenotypes can be combined to track cell-cycle progression based on a t-distributed stochastic neighbor embedding (t-SNE) algorithm. Using multivariate analysis of variance (MANOVA) discriminant analysis, cell-cycle phases can also be predicted label-free with high accuracy at >90% in G1 and G2 phase, and >80% in S phase. We anticipate that high throughput label-free cell cycle characterization could open new approaches for large-scale single-cell analysis, bringing new mechanistic insights into complex biological processes including diseases pathogenesis.
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