Project description:Flow cytometry is an indispensable tool in biology for counting and analyzing single cells in large heterogeneous populations. However, it predominantly relies on fluorescent labeling to differentiate cells and, hence, comes with several fundamental drawbacks. Here, we present a high-throughput Raman flow cytometer on a microfluidic chip that chemically probes single live cells in a label-free manner. It is based on a rapid-scan Fourier-transform coherent anti-Stokes Raman scattering spectrometer as an optical interrogator, enabling us to obtain the broadband molecular vibrational spectrum of every single cell in the fingerprint region (400 to 1600 cm-1) with a record-high throughput of ~2000 events/s. As a practical application of the method not feasible with conventional flow cytometry, we demonstrate high-throughput label-free single-cell analysis of the astaxanthin productivity and photosynthetic dynamics of Haematococcus lacustris.
Project description:Three-part white blood cell differentials which are key to routine blood workups are typically performed in centralized laboratories on conventional hematology analyzers operated by highly trained staff. With the trend of developing miniaturized blood analysis tool for point-of-need in order to accelerate turnaround times and move routine blood testing away from centralized facilities on the rise, our group has developed a highly miniaturized holographic imaging system for generating lens-free images of white blood cells in suspension. Analysis and classification of its output data, constitutes the final crucial step ensuring appropriate accuracy of the system. In this work, we implement reference holographic images of single white blood cells in suspension, in order to establish an accurate ground truth to increase classification accuracy. We also automate the entire workflow for analyzing the output and demonstrate clear improvement in the accuracy of the 3-part classification. High-dimensional optical and morphological features are extracted from reconstructed digital holograms of single cells using the ground-truth images and advanced machine learning algorithms are investigated and implemented to obtain 99% classification accuracy. Representative features of the three white blood cell subtypes are selected and give comparable results, with a focus on rapid cell recognition and decreased computational cost.
Project description:Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer lacking specific biomarkers that can be correlated to disease onset, promotion and progression. To assess whether tumor cell electrophysiology may serve as a marker for PDAC tumorigenicity, we use multi-frequency impedance cytometry at high throughput (∼350 cells/s) to measure the electrical phenotype of single PDAC tumor cells from xenografts, which are derived from primary pancreatic tumors versus those from liver metastases of different patients. A novel phase contrast metric based on variations in the high and low frequency impedance phase responses that is related to electrophysiology of the cell interior is found to be systematically altered as a function of tumorigenicity. PDAC cells of higher tumorigenicity exhibited lowered interior conductivity and enhanced permittivity, which is validated by the dielectrophoresis on the respective cell types. Using genetic analysis, we suggest the role of dysregulated Na+ transport and removal of Ca2+ ions from the cytoplasm on key oncogenic KRAS-driven processes that may be responsible for lowering of the interior cell conductivity. We envision that impedance cytometry can serve as a tool to quantify phenotypic heterogeneity for rapidly stratifying tumorigenicity. It can also aid in protocols for dielectrophoretic isolation of cells with a particular phenotype for prognostic studies on patient survival and to tailor therapy selection to specific patients.
Project description:Flow cytometry is one of the most important technologies for high-throughput single-cell analysis. Fluorescent labeling acts as the primary approach for cellular analysis in flow cytometry. Nevertheless, the fluorescent tags are not applicable to all cases, especially to small molecules, for which labeling may significantly perturb the biological functionality. Spontaneous Raman scattering flow cytometry offers the capability to non-invasively detect chemical contents of cells but suffers from slow data acquisition. In order to achieve label-free high-throughput single-particle analysis using Raman scattering, we developed a 32-channel multiplex stimulated Raman scattering flow cytometry (SRS-FC) technique that can measure chemical contents of single particles at a speed of 5 μs per Raman spectrum. Using mixed polymer beads, we demonstrate the discrimination of different particles at a throughput of up to 11,000 particles per second. This is a four orders of magnitude improvement in throughput compared to conventional spontaneous Raman flow cytometry. As a proof of concept, we show the differentiation of 3T3-L1 cells at different states by SRS-FC according to the difference in cellular chemical content. The SRS-FC technique opens new opportunities for high-throughput and high-content chemical analysis of live cells in a label-free manner.
Project description:Stem cell therapies hold great promise for repairing tissues damaged due to disease or injury. However, a major obstacle facing this field is the difficulty in identifying cells of a desired phenotype from the heterogeneous population that arises during stem cell differentiation. Conventional fluorescence flow cytometry and magnetic cell purification require exogenous labeling of cell surface markers which can interfere with the performance of the cells of interest. Here, we describe a non-genetic, label-free cell cytometry method based on electrophysiological response to stimulus. As many of the cell types relevant for regenerative medicine are electrically-excitable (e.g. cardiomyocytes, neurons, smooth muscle cells), this technology is well-suited for identifying cells from heterogeneous stem cell progeny without the risk and expense associated with molecular labeling or genetic modification. Our label-free cell cytometer is capable of distinguishing clusters of undifferentiated human induced pluripotent stem cells (iPSC) from iPSC-derived cardiomyocyte (iPSC-CM) clusters. The system utilizes a microfluidic device with integrated electrodes for both electrical stimulation and recording of extracellular field potential (FP) signals from suspended cells in flow. The unique electrode configuration provides excellent rejection of field stimulus artifact while enabling sensitive detection of FPs with a noise floor of 2 ?V(rms). Cells are self-aligned to the recording electrodes via hydrodynamic flow focusing. Based on automated analysis of these extracellular signals, the system distinguishes cardiomyocytes from non-cardiomyocytes. This is an entirely new approach to cell cytometry, in which a cell's functionality is assessed rather than its expression profile or physical characteristics.
Project description:Automation and quality control (QC) are critical in manufacturing safe and effective cell and gene therapy products. However, current QC methods, reliant on molecular staining, pose difficulty in in-line testing and can increase manufacturing costs. Here we demonstrate the potential of using label-free ghost cytometry (LF-GC), a machine learning-driven, multidimensional, high-content, and high-throughput flow cytometry approach, in various stages of the cell therapy manufacturing processes. LF-GC accurately quantified cell count and viability of human peripheral blood mononuclear cells (PBMCs) and identified non-apoptotic live cells and early apoptotic/dead cells in PBMCs (ROC-AUC: area under receiver operating characteristic curve = 0.975), T cells and non-T cells in white blood cells (ROC-AUC = 0.969), activated T cells and quiescent T cells in PBMCs (ROC-AUC = 0.990), and particulate impurities in PBMCs (ROC-AUC ≧ 0.998). The results support that LF-GC is a non-destructive label-free cell analytical method that can be used to monitor cell numbers, assess viability, identify specific cell subsets or phenotypic states, and remove impurities during cell therapy manufacturing. Thus, LF-GC holds the potential to enable full automation in the manufacturing of cell therapy products with reduced cost and increased efficiency.
Project description:Imaging flow cytometry combines the high-throughput capabilities of conventional flow cytometry with single-cell imaging. Here we demonstrate label-free prediction of DNA content and quantification of the mitotic cell cycle phases by applying supervised machine learning to morphological features extracted from brightfield and the typically ignored darkfield images of cells from an imaging flow cytometer. This method facilitates non-destructive monitoring of cells avoiding potentially confounding effects of fluorescent stains while maximizing available fluorescence channels. The method is effective in cell cycle analysis for mammalian cells, both fixed and live, and accurately assesses the impact of a cell cycle mitotic phase blocking agent. As the same method is effective in predicting the DNA content of fission yeast, it is likely to have a broad application to other cell types.
Project description:The development of reliable and cost-efficient methods to assess the toxicity of nanomaterials (NMs) is critical for the proper identification of their impact on human health and for ensuring a safe progress of nanotechnology. In this study, we investigated the reliability and applicability of label-free impedance flow cytometry (IFC) for in vitro nanotoxicity screening, which avoids time-consuming labelling steps and minimizes possible NM-induced interferences. U937 human lymphoma cells were exposed for 24 h to eight different nanomaterials at five concentrations (2, 10, 20, 50, and 100 μg/mL). The NMs' effect on viability was measured using IFC and the results were compared to those obtained by trypan blue (TB) dye exclusion and conventional flow cytometry (FC). To discriminate viable from necrotic cells, the IFC measurement settings regarding signal trigger level and frequency, as well as the buffer composition, were optimised. A clear discrimination between viable and necrotic cells was obtained at 6 MHz in a sucrose-based measurement buffer. Nanomaterial-induced interferences were not detected for IFC. The IFC and TB assay results were in accordance for all NMs. The IFC was found to be robust, reliable and less prone to interferences due to the advantage of being label-free.
Project description:Circulating tumor cell clusters (CTCCs) are rare cellular events found in the blood stream of metastatic tumor patients. Despite their scarcity, they represent an increased risk for metastasis. Label-free detection methods of these events remain primarily limited to in vitro microfluidic platforms. Here, we expand on the use of confocal backscatter and fluorescence flow cytometry (BSFC) for label-free detection of CTCCs in whole blood using machine learning for peak detection/classification. BSFC uses a custom-built flow cytometer with three excitation wavelengths (405 nm, 488 nm, and 633 nm) and five detectors to detect CTCCs in whole blood based on corresponding scattering and fluorescence signals. In this study, detection of CTCC-associated GFP fluorescence is used as the ground truth to assess the accuracy of endogenous back-scattered light-based CTCC detection in whole blood. Using a machine learning model for peak detection/classification, we demonstrated that the combined use of backscattered signals at the three wavelengths enable detection of ~ 93% of all CTCCs larger than two cells with a purity of > 82% and an overall accuracy of > 95%. The high level of performance established through BSFC and machine learning demonstrates the potential for label-free detection and monitoring of CTCCs in whole blood. Further developments of label-free BSFC to enhance throughput could lead to important applications in the isolation of CTCCs in whole blood with minimal disruption and ultimately their detection in vivo.
Project description:Cell viability is an essential physiological status for drug screening. While cell staining is a conventional cell viability analysis method, dye staining is usually cytotoxic. Alternatively, impedance cytometry provides a straightforward and label-free sensing approach for the assessment of cell viability. A key element of impedance cytometry is its sensing electrodes. Most state-of-the-art electrodes are made of expensive metals, microfabricated by lithography, with a typical size of ten microns. In this work, we proposed a low-cost microfluidic impedance cytometry device with 100-micron wide indium tin oxide (ITO) electrodes to achieve a comparable performance to the 10-micron wide Au electrodes. The effectiveness was experimentally verified as 7 μm beads can be distinguished from 10 μm beads. To the best of our knowledge, this is the lowest geometry ratio of the target to the sensing unit in the impedance cytometry technology. Furthermore, a cell viability test was performed on MCF-7 cells. The proposed double differential impedance cytometry device has successfully differentiated the living and dead MCF-7 cells with a throughput of ~1000 cells/s. The label-free and low-cost, high-throughput impedance cytometry could benefit drug screening, fundamental biological research and other biomedical applications.