High-Throughput Multi-parametric Imaging Flow Cytometry

High-speed optical imaging with sCMOS pixel reassignment

Fluorescence microscopy has undergone rapid advancements, offering unprecedented visualization of biological events and shedding light on the intricate mechanisms governing living organisms. However, the exploration of rapid biological dynamics still poses a significant challenge due to the limitations of current digital camera architectures and the inherent compromise between imaging speed and other capabilities. Here, we introduce sHAPR, a high-speed acquisition technique that leverages the operating principles of sCMOS cameras to capture fast cellular and subcellular processes. sHAPR harnesses custom fiber optics to convert microscopy images into one-dimensional recordings, enabling acquisition at the maximum camera readout rate, typically between 25 and 250 kHz. We have demonstrated the utility of sHAPR with a variety of phantom and dynamic systems, including high-throughput flow cytometry, cardiomyocyte contraction, and neuronal calcium waves, using a standard epi-fluorescence microscope. sHAPR is highly adaptable and can be integrated into existing microscopy systems without requiring extensive platform modifications. This method pushes the boundaries of current fluorescence imaging capabilities, opening up new avenues for investigating high-speed biological phenomena.

Raman Flow Cytometry and Its Biomedical Applications

Raman flow cytometry (RFC) uniquely integrates the "label-free" capability of Raman spectroscopy with the "high-throughput" attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.

Imaging Flow Cytometry: Development, Present Applications, and Future Challenges

Imaging flow cytometry (ImFC) represents a significant technological advancement in the field of cytometry, effectively merging the high-throughput capabilities of flow analysis with the detailed imaging characteristics of microscopy. In our comprehensive review, we adopt a historical perspective to chart the development of ImFC, highlighting its origins and current state of the art and forecasting potential future advancements. The genesis of ImFC stemmed from merging the hydraulic system of a flow cytometer with advanced camera technology. This synergistic coupling facilitates the morphological analysis of cell populations at a high-throughput scale, effectively evolving the landscape of cytometry. Nevertheless, ImFC's implementation has encountered hurdles, particularly in developing software capable of managing its sophisticated data acquisition and analysis needs. The scale and complexity of the data generated by ImFC necessitate the creation of novel analytical tools that can effectively manage and interpret these data, thus allowing us to unlock the full potential of ImFC. Notably, artificial intelligence (AI) algorithms have begun to be applied to ImFC, offering promise for enhancing its analytical capabilities. The adaptability and learning capacity of AI may prove to be essential in knowledge mining from the high-dimensional data produced by ImFC, potentially enabling more accurate analyses. Looking forward, we project that ImFC may become an indispensable tool, not only in research laboratories, but also in clinical settings. Given the unique combination of high-throughput cytometry and detailed imaging offered by ImFC, we foresee a critical role for this technology in the next generation of scientific research and diagnostics. As such, we encourage both current and future scientists to consider the integration of ImFC as an addition to their research toolkit and clinical diagnostic routine.

Light-field flow cytometry for high-resolution, volumetric and multiparametric 3D single-cell analysis

Imaging flow cytometry (IFC) combines flow cytometry and fluorescence microscopy to enable high-throughput, multiparametric single-cell analysis with rich spatial details. However, current IFC techniques remain limited in their ability to reveal subcellular information with a high 3D resolution, throughput, sensitivity, and instrumental simplicity. In this study, we introduce a light-field flow cytometer (LFC), an IFC system capable of high-content, single-shot, and multi-color acquisition of up to 5,750 cells per second with a near-diffraction-limited resolution of 400-600 nm in all three dimensions. The LFC system integrates optical, microfluidic, and computational strategies to facilitate the volumetric visualization of various 3D subcellular characteristics through convenient access to commonly used epi-fluorescence platforms. We demonstrate the effectiveness of LFC in assaying, analyzing, and enumerating intricate subcellular morphology, function, and heterogeneity using various phantoms and biological specimens. The advancement offered by the LFC system presents a promising methodological pathway for broad cell biological and translational discoveries, with the potential for widespread adoption in biomedical research.

PXPermute reveals staining importance in multichannel imaging flow cytometry

Imaging flow cytometry (IFC) allows rapid acquisition of numerous single-cell images per second, capturing information from multiple fluorescent channels. However, the traditional process of staining cells with fluorescently labeled conjugated antibodies for IFC analysis is time consuming, expensive, and potentially harmful to cell viability. To streamline experimental workflows and reduce costs, it is crucial to identify the most relevant channels for downstream analysis. In this study, we introduce PXPermute, a user-friendly and powerful method for assessing the significance of IFC channels, particularly for cell profiling. Our approach evaluates channel importance by permuting pixel values within each channel and analyzing the resulting impact on machine learning or deep learning models. Through rigorous evaluation of three multichannel IFC image datasets, we demonstrate PXPermute's potential in accurately identifying the most informative channels, aligning with established biological knowledge. PXPermute can assist biologists with systematic channel analysis, experimental design optimization, and biomarker identification.

Spiral Large-Dimension Microfluidic Channel for Flow-Rate- and Particle-Size-Insensitive Focusing by the Stabilization and Acceleration of Secondary Flow

Inertial microfluidics has demonstrated its ability to focus particles in a passive and straightforward manner. However, achieving flow-rate- and particle-size-insensitive focusing in large-dimension channels with a simple design remains challenging. In this study, we developed a spiral microfluidic with a large-dimension channel to achieve inertial focusing. By designing a unique "big buffering area" and a "small buffering area" in the spiral microchannel, we observed the stabilization and acceleration of secondary flow. Our optimized design allowed for efficient (>99.9%) focusing of 15 μm particles within a wide range of flow rates (0.5-4.5 mL/min) during a long operation duration (0-60 min). Additionally, we achieved effective (>95%) focusing of different-sized particles (7, 10, 15, and 30 μm) and three types of tumor cells (K562, HeLa, and MCF-7) near the inner wall of the 1 mm wide outlet when applying different flow rates (1-3 mL/min). Finally, successful 3D cell focusing was achieved within an optimized device, with the cells positioned at a distance of 50 μm from the wall. Our strategy of stabilizing and accelerating Dean-like secondary flow through the unique configuration of a "big buffering area" and a "small buffering area" proved to be highly effective in achieving inertial focusing that is insensitive to the flow rate and particle size, particularly in large-dimension channels. Consequently, it shows great potential for use in hand-operated microfluidic tools for flow cytometry.

Structured-light-sheet imaging in an integrated optofluidic platform

Heterogeneity investigation at the single-cell level reveals morphological and phenotypic characteristics in cell populations. In clinical research, heterogeneity has important implications in the correct detection and interpretation of prognostic markers and in the analysis of patient-derived material. Among single-cell analysis, imaging flow cytometry allows combining information retrieved by single cell images with the throughput of fluidic platforms. Nevertheless, these techniques might fail in a comprehensive heterogeneity evaluation because of limited image resolution and bidimensional analysis. Light sheet fluorescence microscopy opened new ways to study in 3D the complexity of cellular functionality in samples ranging from single-cells to micro-tissues, with remarkably fast acquisition and low photo-toxicity. In addition, structured illumination microscopy has been applied to single-cell studies enhancing the resolution of imaging beyond the conventional diffraction limit. The combination of these techniques in a microfluidic environment, which permits automatic sample delivery and translation, would allow exhaustive investigation of cellular heterogeneity with high throughput image acquisition at high resolution. Here we propose an integrated optofluidic platform capable of performing structured light sheet imaging flow cytometry (SLS-IFC). The system encompasses a multicolor directional coupler equipped with a thermo-optic phase shifter, cylindrical lenses and a microfluidic network to generate and shift a patterned light sheet within a microchannel. The absence of moving parts allows a stable alignment and an automated fluorescence signal acquisition during the sample flow. The platform enables 3D imaging of an entire cell in about 1 s with a resolution enhancement capable of revealing sub-cellular features and sub-diffraction limit details.

Smartphone Imaging Flow Cytometry for High-Throughput Single-Cell Analysis

We present a portable imaging flow cytometer comprising a smartphone, a small-footprint optical framework, and a PDMS-based microfluidic device. Flow cytometric analysis is performed in a sheathless manner via elasto-inertial focusing with a custom-written Android program, integrating a graphical user interface (GUI) that provides a high degree of user control over image acquisition. The proposed system offers two different operational modes. First, "post-processing" mode enables particle/cell sizing at throughputs of up to 67 000 particles/s. Alternatively, "real-time" mode allows for integrated cell/particle classification with machine learning at throughputs of 100 particles/s. To showcase the efficacy of our platform, polystyrene particles are accurately enumerated within heterogeneous populations using the post-processing mode. In real-time mode, an open-source machine learning algorithm is deployed within a custom-developed Android application to classify samples containing cells of similar size but with different morphologies. The flow cytometer can extract high-resolution bright-field images with a spatial resolution <700 nm using the developed machine learning-based algorithm, achieving classification accuracies of 97% and 93% for Jurkat and EL4 cells, respectively. Our results confirm that the smartphone imaging flow cytometer (sIFC) is capable of both enumerating single particles in flow and identifying morphological features with high resolution and minimal hardware.

Computer vision meets microfluidics: a label-free method for high-throughput cell analysis

In this paper, we review the integration of microfluidic chips and computer vision, which has great potential to advance research in the life sciences and biology, particularly in the analysis of cell imaging data. Microfluidic chips enable the generation of large amounts of visual data at the single-cell level, while computer vision techniques can rapidly process and analyze these data to extract valuable information about cellular health and function. One of the key advantages of this integrative approach is that it allows for noninvasive and low-damage cellular characterization, which is important for studying delicate or fragile microbial cells. The use of microfluidic chips provides a highly controlled environment for cell growth and manipulation, minimizes experimental variability and improves the accuracy of data analysis. Computer vision can be used to recognize and analyze target species within heterogeneous microbial populations, which is important for understanding the physiological status of cells in complex biological systems. As hardware and artificial intelligence algorithms continue to improve, computer vision is expected to become an increasingly powerful tool for in situ cell analysis. The use of microelectromechanical devices in combination with microfluidic chips and computer vision could enable the development of label-free, automatic, low-cost, and fast cellular information recognition and the high-throughput analysis of cellular responses to different compounds, for broad applications in fields such as drug discovery, diagnostics, and personalized medicine.

Machine learning implementation strategy in imaging and impedance flow cytometry

Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.

Real-time fluorescence imaging flow cytometry enabled by motion deblurring and deep learning algorithms

Fluorescence imaging flow cytometry (IFC) has been demonstrated as a crucial biomedical technique for analyzing specific cell subpopulations from heterogeneous cellular populations. However, the high-speed flow of fluorescent cells leads to motion blur in cell images, making it challenging to identify cell types from the raw images. In this study, we present a real-time single-cell imaging and classification system based on a fluorescence microscope and deep learning algorithm, which is able to directly identify cell types from motion-blur images. To obtain annotated datasets of blurred images for deep learning model training, we developed a motion deblurring algorithm for the reconstruction of blur-free images. To demonstrate the ability of this system, deblurred images of HeLa cells with various fluorescent labels and HeLa cells at different cell cycle stages were acquired. The trained ResNet achieved a high accuracy of 96.6% for single-cell classification of HeLa cells in three different mitotic stages, with a short processing time of only 2 ms. This technology provides a simple way to realize single-cell fluorescence IFC and real-time cell classification, offering significant potential in various biological and medical applications.

ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics

ANPELA is widely used for quantifying traditional bulk proteomic data. Recently, there is a clear shift from bulk proteomics to the single-cell ones (SCP), for which powerful cytometry techniques demonstrate the fantastic capacity of capturing cellular heterogeneity that is completely overlooked by traditional bulk profiling. However, the in-depth and high-quality quantification of SCP data is still challenging and severely affected by the large numbers of quantification workflows and extreme performance dependence on the studied datasets. In other words, the proper selection of well-performing workflow(s) for any studied dataset is elusory, and it is urgently needed to have a significantly enhanced and accelerated tool to address this issue. However, no such tool is developed yet. Herein, ANPELA is therefore updated to its 2.0 version (https://idrblab.org/anpela/), which is unique in providing the most comprehensive set of quantification alternatives (>1000 workflows) among all existing tools, enabling systematic performance evaluation from multiple perspectives based on machine learning, and identifying the optimal workflow(s) using overall performance ranking together with the parallel computation. Extensive validation on different benchmark datasets and representative application scenarios suggest the great application potential of ANPELA in current SCP research for gaining more accurate and reliable biological insights.

Recent advances in microfluidics for single-cell functional proteomics

Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic view of biological details underlying cellular processes, disease onset and progression, as well as facilitates unique biomarker identification from individual cells. Microfluidic-based strategies have become methods of choice for single-cell analysis because they allow facile assay integrations, such as cell sorting, manipulation, and content analysis. Notably, they have been serving as an enabling technology to improve the sensitivity, robustness, and reproducibility of recently developed SCP methods. Critical roles of microfluidics technologies are expected to further expand rapidly in advancing the next phase of SCP analysis to reveal more biological and clinical insights. In this review, we will capture the excitement of the recent achievements of microfluidics methods for both targeted and global SCP, including efforts to enhance the proteomic coverage, minimize sample loss, and increase multiplexity and throughput. Furthermore, we will discuss the advantages, challenges, applications, and future prospects of SCP.

Engineered Polymer-siRNA Polyplexes Provide Effective Treatment of Lung Inflammation

Uncontrolled inflammation is responsible for acute and chronic diseases in the lung. Regulating expression of pro-inflammatory genes in pulmonary tissue using small interfering RNA (siRNA) is a promising approach to combatting respiratory diseases. However, siRNA therapeutics are generally hindered at the cellular level by endosomal entrapment of delivered cargo and at the organismal level by inefficient localization in pulmonary tissue. Here we report efficient anti-inflammatory activity in vitro and in vivo using polyplexes of siRNA and an engineered cationic polymer (PONI-Guan). PONI-Guan/siRNA polyplexes efficiently deliver siRNA cargo to the cytosol for highly efficient gene knockdown. Significantly, these polyplexes exhibit inherent targeting to inflamed lung tissue following intravenous administration in vivo. This strategy achieved effective (>70%) knockdown of gene expression in vitro and efficient (>80%) silencing of TNF-α expression in lipopolysaccharide (LPS)-challenged mice using a low (0.28 mg/kg) siRNA dosage.

Portable light-sheet optofluidic microscopy for 3D fluorescence imaging flow cytometry

Imaging flow cytometry (IFC) combines conventional flow cytometry with optical microscopy, allowing for high-throughput, multi-parameter screening of single-cell specimens with morphological and spatial information. However, current 3D IFC systems are limited by instrumental complexity and incompatibility with available microfluidic devices or operations. Here, we report portable light-sheet optofluidic microscopy (PLSOM) for 3D fluorescence cytometric imaging. PLSOM exploits a compact, open-top light-sheet configuration compatible with commonly adopted microfluidic chips. The system offers a subcellular resolution (2-4 μm) in all three dimensions, high throughput (∼1000 cells per s), and portability (30 cm (l) × 10 cm (w) × 26 cm (h)). We demonstrated PLSOM for 3D IFC using various phantom and cell systems. The low-cost and custom-built architecture of PLSOM permits easy adaptability and dissemination for broad 3D flow cytometric investigations.

Live imaging of apoptotic signaling flow using tunable combinatorial FRET-based bioprobes for cell population analysis of caspase cascades

Understanding cellular signaling flow is required to comprehend living organisms. Various live cell imaging tools have been developed but challenges remain due to complex cross-talk between pathways and response heterogeneities among cells. We have focused on multiplex live cell imaging for statistical analysis to address the difficulties and developed simple multiple fluorescence imaging system to quantify cell signaling at single-cell resolution using Förster Resonance Energy Transfer (FRET)-based chimeric molecular sensors comprised of fluorescent proteins and dyes. The dye-fluorescent protein conjugate is robust for a wide selection of combinations, facilitating rearrangement for coordinating emission profile of molecular sensors to adjust for visualization conditions, target phenomena, and simultaneous use. As the molecular sensor could exhibit highly sensitive in detection for protease activity, we customized molecular sensor of caspase-9 and combine the established sensor for caspase-3 to validate the system by observation of caspase-9 and -3 dynamics simultaneously, key signaling flow of apoptosis. We found cumulative caspase-9 activity rather than reaction rate inversely regulated caspase-3 execution times for apoptotic cell death. Imaging-derived statistics were thus applied to discern the dominating aspects of apoptotic signaling unavailable by common live cell imaging and proteomics protein analysis. Adopted to various visualization targets, the technique can discriminate between rivalling explanations and should help unravel other protease involved signaling pathways.

Ultrathroughput immunomagnetic cell sorting platform

High-throughput phenotypic cell sorting is critical to the development of cell-based therapies and cell screening discovery platforms. However, current cytometry platforms are limited by throughput, number of fractionated populations that can be isolated, cell viability, and cost. We present an ultrathroughput microfluidic cell sorter capable of processing hundreds of millions of live cells per hour per device based on protein expression. This device, a next-generation microfluidic cell sorter (NG-MICS), combines multiple technologies, including 3D printing, reversible clamp sealing, and superhydrophobic treatments to create a reusable and user-friendly platform ready for deployment. The utility of such a platform is demonstrated through the rapid isolation of mature natural killer cells from peripheral blood mononuclear cells, for use in CAR-NK therapies at clinically-relevant scale.

Inertial microfluidics: current status, challenges, and future opportunities

Inertial microfluidics uses the hydrodynamic effects induced at finite Reynolds numbers to achieve passive manipulation of particles, cells, or fluids and offers the advantages of high-throughput processing, simple channel geometry, and label-free and external field-free operation. Since its proposal in 2007, inertial microfluidics has attracted increasing interest and is currently widely employed as an important sample preparation protocol for single-cell detection and analysis. Although great success has been achieved in the inertial microfluidics field, its performance and outcome can be further improved. From this perspective, herein, we reviewed the current status, challenges, and opportunities of inertial microfluidics concerning the underlying physical mechanisms, available simulation tools, channel innovation, multistage, multiplexing, or multifunction integration, rapid prototyping, and commercial instrument development. With an improved understanding of the physical mechanisms and the development of novel channels, integration strategies, and commercial instruments, improved inertial microfluidic platforms may represent a new foundation for advancing biomedical research and disease diagnosis.

Low-cost programable stroboscopic illumination with sub-microsecond pulses for high-throughput microfluidic applications

To visualize fast-moving objects in microfluidic applications, the image acquisition time must be on the order of a microsecond or less. Commercial imaging systems capable of such short exposure times may be too expensive for many research laboratories. We have therefore developed a low-cost stroboscopic illumination for transmitted-light microscopy based on a high-power LED that can be coupled to a standard industrial camera and provides exposure times on the order of 500 ns. The system is designed to be easily mounted on a standard condenser of an inverted microscope. The illumination is controlled by a fast Arduino-compatible Teensy® 4.0 development board, and the illumination parameters can be set from a PC via a graphical user interface written in Python. The system has been successfully used for high-throughput cell phenotyping using deformability cytometry on a Nikon TE2000 microscope, as well as for droplet microfluidic on an old Olympus inverted microscope.

Developments of Conventional and Microfluidic Flow Cytometry Enabling High-Throughput Characterization of Single Cells

This article first reviews scientific meanings of single-cell analysis by highlighting two key scientific problems: landscape reconstruction of cellular identities during dynamic immune processes and mechanisms of tumor origin and evolution. Secondly, the article reviews clinical demands of single-cell analysis, which are complete blood counting enabled by optoelectronic flow cytometry and diagnosis of hematologic malignancies enabled by multicolor fluorescent flow cytometry. Then, this article focuses on the developments of optoelectronic flow cytometry for the complete blood counting by comparing conventional counterparts of hematology analyzers (e.g., DxH 900 of Beckman Coulter, XN-1000 of Sysmex, ADVIA 2120i of Siemens, and CELL-DYN Ruby of Abbott) and microfluidic counterparts (e.g., microfluidic impedance and imaging flow cytometry). Future directions of optoelectronic flow cytometry are indicated where intrinsic rather than dependent biophysical parameters of blood cells must be measured, and they can replace blood smears as the gold standard of blood analysis in the near future.

Cytosolic Protein Delivery Using Modular Biotin-Streptavidin Assembly of Nanocomposites

Current strategies for the delivery of proteins into cells face general challenges of endosomal entrapment and concomitant degradation of protein cargo. Efficient delivery directly to the cytosol overcomes this obstacle: we report here the use of biotin-streptavidin tethering to provide a modular approach to the generation of nanovectors capable of a cytosolic delivery of biotinylated proteins. This strategy uses streptavidin to organize biotinylated protein and biotinylated oligo(glutamate) peptide into modular complexes that are then electrostatically self-assembled with a cationic guanidinium-functionalized polymer. The resulting polymer-protein nanocomposites demonstrate efficient cytosolic delivery of six biotinylated protein cargos of varying size, charge, and quaternary structure. Retention of protein function was established through efficient cell killing via delivery of the chemotherapeutic enzyme granzyme A. This platform represents a versatile and modular approach to intracellular delivery through the noncovalent tethering of multiple components into a single delivery vector.

High-speed fluorescence image-enabled cell sorting

Fast and selective isolation of single cells with unique spatial and morphological traits remains a technical challenge. Here, we address this by establishing high-speed image-enabled cell sorting (ICS), which records multicolor fluorescence images and sorts cells based on measurements from image data at speeds up to 15,000 events per second. We show that ICS quantifies cell morphology and localization of labeled proteins and increases the resolution of cell cycle analyses by separating mitotic stages. We combine ICS with CRISPR-pooled screens to identify regulators of the nuclear factor κB (NF-κB) pathway, enabling the completion of genome-wide image-based screens in about 9 hours of run time. By assessing complex cellular phenotypes, ICS substantially expands the phenotypic space accessible to cell-sorting applications and pooled genetic screening.

B-cell receptor isotypes differentially associate with cell signaling, kinetics, and outcome in chronic lymphocytic leukemia

In chronic lymphocytic leukemia (CLL), the B cell receptor (BCR) plays a critical role in disease development and progression, as indicated by the therapeutic efficacy of drugs blocking BCR signaling. However, the mechanism(s) underlying BCR responsiveness are not completely defined. Selective engagement of membrane IgM or IgD on CLL cells, each coexpressed by more than 90% of cases, leads to distinct signaling events. Since both IgM and IgD carry the same antigen-binding domains, the divergent actions of the receptors are attributed to differences in immunoglobulin (Ig) structure or the outcome of signal transduction. We showed that IgM, not IgD, level and organization associated with CLL-cell birth rate and the type and consequences of BCR signaling in humans and mice. The latter IgM-driven effects were abrogated when BCR signaling was inhibited. Collectively, these studies demonstrated a critical, selective role for IgM in BCR signaling and B cell fate decisions, possibly opening new avenues for CLL therapy.

A weakly supervised deep learning approach for label-free imaging flow-cytometry-based blood diagnostics

The application of machine learning approaches to imaging flow cytometry (IFC) data has the potential to transform the diagnosis of hematological diseases. However, the need for manually labeled single-cell images for machine learning model training has severely limited its clinical application. To address this, we present iCellCnn, a weakly supervised deep learning approach for label-free IFC-based blood diagnostics. We demonstrate the capability of iCellCnn to achieve diagnosis of Sézary syndrome (SS) from patient samples on the basis of bright-field IFC images of T cells obtained after fluorescence-activated cell sorting of human peripheral blood mononuclear cell specimens. With a sample size of four healthy donors and five SS patients, iCellCnn achieved a 100% classification accuracy. As iCellCnn is not restricted to the diagnosis of SS, we expect such weakly supervised approaches to tap the diagnostic potential of IFC by providing automatic data-driven diagnosis of diseases with so-far unknown morphological manifestations.

Inertial microfluidics for high-throughput cell analysis and detection: a review

Since it was first proposed in 2007, inertial microfluidics has been extensively studied in terms of theory, design, fabrication, and application. In recent years, with the rapid development of microfabrication technologies, a variety of channel structures that can focus, concentrate, separate, and capture bioparticles or fluids have been designed and manufactured to extend the range of potential biomedical applications of inertial microfluidics. Due to the advantages of high throughput, simplicity, and low device cost, inertial microfluidics is a promising candidate for rapid sample processing, especially for large-volume samples with low-abundance targets. As an approach to cellular sample pretreatment, inertial microfluidics has been widely employed to ensure downstream cell analysis and detection. In this review, a comprehensive summary of the application of inertial microfluidics for high-throughput cell analysis and detection is presented. According to application areas, the recent advances can be sorted into label-free cell mechanical phenotyping, sheathless flow cytometric counting, electrical impedance cytometer, high-throughput cellular image analysis, and other methods. Finally, the challenges and prospects of inertial microfluidics for cell analysis and detection are summarized.

A flow cytometric journey into cell cycle analysis

Cell cycle involves a series of changes that lead to cell growth and division. Cell cycle analysis is crucial to understand cellular responses to changing environmental conditions. Since its inception, flow cytometry has been particularly useful for cell cycle analysis at single cell level due to its speed and precision. Previously, flow cytometric cell cycle analysis relied solely on the measurement of cellular DNA content. Later, methods were developed for multiparametric analysis. This review explains the journey of flow cytometry to understand different molecular and cellular events underlying cell cycle using various protocols. Recent advances in the field that overcome the shortcomings of traditional flow cytometry and expand its scope for cell cycle studies are also discussed.

Progressive use of multispectral imaging flow cytometry in various research areas

Multi-spectral imaging flow cytometry (MIFC) has become one of the most powerful technologies for investigating general analytics, molecular and cell biology, biotechnology, medicine, and related fields. It combines the capabilities of the morphometric and photometric analysis of single cells and micrometer-sized particles in flux with regard to thousands of events. It has become the tool of choice for a wide range of research and clinical applications. By combining the features of flow cytometry and fluorescence microscopy, it offers researchers the ability to couple the spatial resolution of multicolour images of cells and organelles with the simultaneous analysis of a large number of events in a single system. This provides the opportunity to visually confirm findings and collect novel data that would otherwise be more difficult to obtain. This has led many researchers to design innovative assays to gain new insight into important research questions. To date, it has been successfully used to study cell morphology, surface and nuclear protein co-localization, protein-protein interactions, cell signaling, cell cycle, cell death, and cytotoxicity, intracellular calcium, drug uptake, pathogen internalization, and other applications. Herein we describe some of the recent advances in the field of multiparametric imaging flow cytometry methods in various research areas.

High-Throughput Raman Flow Cytometry and Beyond

Flow cytometry is a powerful tool with applications in diverse fields such as microbiology, immunology, virology, cancer biology, stem cell biology, and metabolic engineering. It rapidly counts and characterizes large heterogeneous populations of cells in suspension (e.g., blood cells, stem cells, cancer cells, and microorganisms) and dissociated solid tissues (e.g., lymph nodes, spleen, and solid tumors) with typical throughputs of 1,000-100,000 events per second (eps). By measuring cell size, cell granularity, and the expression of cell surface and intracellular molecules, it provides systematic insights into biological processes. Flow cytometers may also include cell sorting capabilities to enable subsequent additional analysis of the sorted sample (e.g., electron microscopy and DNA/RNA sequencing), cloning, and directed evolution. Unfortunately, traditional flow cytometry has several critical limitations as it mainly relies on fluorescent labeling for cellular phenotyping, which is an indirect measure of intracellular molecules and surface antigens. Furthermore, it often requires time-consuming preparation protocols and is incompatible with cell therapy. To overcome these difficulties, a different type of flow cytometry based on direct measurements of intracellular molecules by Raman spectroscopy, or "Raman flow cytometry" for short, has emerged. Raman flow cytometry obtains a chemical fingerprint of the cell in a nondestructive manner, allowing for single-cell metabolic phenotyping. However, its slow signal acquisition due to the weak light-molecule interaction of spontaneous Raman scattering prevents the throughput necessary to interrogate large cell populations in reasonable time frames, resulting in throughputs of about 1 eps. The remedy to this throughput limit lies in coherent Raman scattering methods such as stimulated Raman scattering (SRS) and coherent anti-Stokes Raman scattering (CARS), which offer a significantly enhanced light-sample interaction and hence enable high-throughput Raman flow cytometry, Raman imaging flow cytometry, and even Raman image-activated cell sorting (RIACS). In this Account, we outline recent advances, technical challenges, and emerging opportunities of coherent Raman flow cytometry. First, we review the principles of various types of SRS and CARS and introduce several techniques of coherent Raman flow cytometry such as CARS, multiplex CARS, Fourier-transform CARS, SRS, SRS imaging flow cytometry, and RIACS. Next, we discuss a unique set of applications enabled by coherent Raman flow cytometry, from microbiology and lipid biology to cancer detection and cell therapy. Finally, we describe future opportunities and challenges of coherent Raman flow cytometry including increasing sensitivity and throughput, integration with droplet microfluidics, utilizing machine learning techniques, or achieving in vivo flow cytometry. This Account summarizes the growing field of high-throughput Raman flow cytometry and the bright future it can bring.

Toward deep biophysical cytometry: prospects and challenges

The biophysical properties of cells reflect their identities, underpin their homeostatic state in health, and define the pathogenesis of disease. Recent leapfrogging advances in biophysical cytometry now give access to this information, which is obscured in molecular assays, with a discriminative power that was once inconceivable. However, biophysical cytometry should go 'deeper' in terms of exploiting the information-rich cellular biophysical content, generating a molecular knowledge base of cellular biophysical properties, and standardizing the protocols for wider dissemination. Overcoming these barriers, which requires concurrent innovations in microfluidics, optical imaging, and computer vision, could unleash the enormous potential of biophysical cytometry not only for gaining a new mechanistic understanding of biological systems but also for identifying new cost-effective biomarkers of disease.

Effects of exogenous vitamin B12 on nutrient removal and protein expression of algal-bacterial consortium

Chlorella vulgaris and Bacillus licheniformis consortium was added to synthetic wastewater with exogenous vitamin B₁₂. In the presence of 100 ng/L vitamin B₁₂, removal efficiencies of TN, NH₃-N, PO₄^3-P, and COD were 80.1%, 76.8%, 87.9%, and 76.7%, respectively. The functional groups on the cell surface of the consortium, including -NH, -CH₃, C=O, C=C, and P-O-C, increased with 100 ng/L vitamin B₁₂. These functional groups improved the biological adsorption of the consortium; however, higher concentrations of vitamin B₁₂ resulted in an occlusion of the functional groups. Furthermore, there were 5 significantly enriched protein pathways, namely carbon fixation in photosynthetic organisms; amino acid metabolic pathways; the pathway of one carbon pool by folate; nitrogen metabolism; and photosynthesis. Most proteins in these pathways were upregulated, which enhanced carbon fixation and photosynthesis in the algal cells. Simultaneously, B₁₂ promoted significant upregulation of proteins associated with the quorum-sensing pathway, which promoted the interaction between algae and bacteria.

Optical Detection Methods for High-Throughput Fluorescent Droplet Microflow Cytometry

High-throughput microflow cytometry has become a focal point of research in recent years. In particular, droplet microflow cytometry (DMFC) enables the analysis of cells reacting to different stimuli in chemical isolation due to each droplet acting as an isolated microreactor. Furthermore, at high flow rates, the droplets allow massive parallelization, further increasing the throughput of droplets. However, this novel methodology poses unique challenges related to commonly used fluorometry and fluorescent microscopy techniques. We review the optical sensor technology and light sources applicable to DMFC, as well as analyze the challenges and advantages of each option, primarily focusing on electronics. An analysis of low-cost and/or sufficiently compact systems that can be incorporated into portable devices is also presented.

High-throughput multiparametric imaging flow cytometry: toward diffraction-limited sub-cellular detection and monitoring of sub-cellular processes

We present a sheathless, microfluidic imaging flow cytometer that incorporates stroboscopic illumination for blur-free fluorescence detection at ultra-high analytical throughput. The imaging platform is capable of multiparametric fluorescence quantification and sub-cellular localization of these structures down to 500 nm with microscopy image quality. We demonstrate the efficacy of the approach through the analysis and localization of P-bodies and stress granules in yeast and human cells using fluorescence and bright-field detection at analytical throughputs in excess of 60,000 and 400,000 cells/s, respectively. Results highlight the utility of our imaging flow cytometer in directly investigating phase-separated compartments within cellular environments and screening rare events at the sub-cellular level for a range of diagnostic applications.

Deformation of leukaemia cell lines in hyperbolic microchannels: investigating the role of shear and extensional components

The mechanical properties of cells are of enormous interest in a diverse range of physio and pathological situations of clinical relevance. Unsurprisingly, a variety of microfluidic platforms have been developed in recent years to study the deformability of cells, most commonly employing pure shear or extensional flows, with and without direct contact of the cells with channel walls. Herein, we investigate the effects of shear and extensional flow components on fluid-induced cell deformation by means of three microchannel geometries. In the case of hyperbolic microchannels, cell deformation takes place in a flow with constant extensional rate, under non-zero shear conditions. A sudden expansion at the microchannel terminus allows one to evaluate shape recovery subsequent to deformation. Comparison with other microchannel shapes, that induce either pure shear (straight channel) or pure extensional (cross channel) flows, reveals different deformation modes. Such an analysis is used to confirm the softening and stiffening effects of common treatments, such as cytochalasin D and formalin on cell deformability. In addition to an experimental analysis of leukaemia cell deformability, computational fluid dynamic simulations are used to deconvolve the role of the aforementioned flow components in the cell deformation dynamics. In general terms, the current study can be used as a guide for extracting deformation/recovery dynamics of leukaemia cell lines when exposed to various fluid dynamic conditions.

Label-free imaging flow cytometer for analyzing large cell populations by line-field quantitative phase microscopy with digital refocusing

We propose a line-field quantitative phase-imaging flow cytometer for analyzing large populations of label-free cells. Hydrodynamical focusing brings cells into the focus plane of an optical system while diluting the cell suspension, resulting in decreased throughput rate. To overcome the trade-off between throughput rate and in-focus imaging, our cytometer involves digitally extending the depth-of-focus on loosely hydrodynamically focusing cell suspensions. The cells outside the depth-of-focus range in the 70-µm diameter of the core flow were automatically digitally refocused after image acquisition. We verified that refocusing was successful with our cytometer through statistical analysis of image quality before and after digital refocusing.

Virtual-freezing fluorescence imaging flow cytometry

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s⁻¹ without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.

High throughput imaging flow cytometry suffers from trade-offs between throughput, sensitivity and spatial resolution. Here the authors introduce a method to virtually freeze cells in the image acquisition window to enable 1000 times longer signal integration time and improve signal-to-noise ratio.

Implementing machine learning methods for imaging flow cytometry

In this review, we focus on the applications of machine learning methods for analyzing image data acquired in imaging flow cytometry technologies. We propose that the analysis approaches can be categorized into two groups based on the type of data, raw imaging signals or features explicitly extracted from images, being analyzed by a trained model. We hope that this categorization is helpful for understanding uniqueness, differences and opportunities when the machine learning-based analysis is implemented in recently developed ‘imaging’ cell sorters.

Deep Learning-Based Single-Cell Optical Image Studies

Optical imaging technology that has the advantages of high sensitivity and cost-effectiveness greatly promotes the progress of nondestructive single-cell studies. Complex cellular image analysis tasks such as three-dimensional reconstruction call for machine-learning technology in cell optical image research. With the rapid developments of high-throughput imaging flow cytometry, big data cell optical images are always obtained that may require machine learning for data analysis. In recent years, deep learning has been prevalent in the field of machine learning for large-scale image processing and analysis, which brings a new dawn for single-cell optical image studies with an explosive growth of data availability. Popular deep learning techniques offer new ideas for multimodal and multitask single-cell optical image research. This article provides an overview of the basic knowledge of deep learning and its applications in single-cell optical image studies. We explore the feasibility of applying deep learning techniques to single-cell optical image analysis, where popular techniques such as transfer learning, multimodal learning, multitask learning, and end-to-end learning have been reviewed. Image preprocessing and deep learning model training methods are then summarized. Applications based on deep learning techniques in the field of single-cell optical image studies are reviewed, which include image segmentation, super-resolution image reconstruction, cell tracking, cell counting, cross-modal image reconstruction, and design and control of cell imaging systems. In addition, deep learning in popular single-cell optical imaging techniques such as label-free cell optical imaging, high-content screening, and high-throughput optical imaging cytometry are also mentioned. Finally, the perspectives of deep learning technology for single-cell optical image analysis are discussed. © 2020 International Society for Advancement of Cytometry.

A practical guide to intelligent image-activated cell sorting

Intelligent image-activated cell sorting (iIACS) is a machine-intelligence technology that performs real-time intelligent image-based sorting of single cells with high throughput. iIACS extends beyond the capabilities of fluorescence-activated cell sorting (FACS) from fluorescence intensity profiles of cells to multidimensional images, thereby enabling high-content sorting of cells or cell clusters with unique spatial chemical and morphological traits. Therefore, iIACS serves as an integral part of holistic single-cell analysis by enabling direct links between population-level analysis (flow cytometry), cell-level analysis (microscopy), and gene-level analysis (sequencing). Specifically, iIACS is based on a seamless integration of high-throughput cell microscopy (e.g., multicolor fluorescence imaging, bright-field imaging), cell focusing, cell sorting, and deep learning on a hybrid software-hardware data management infrastructure, enabling real-time automated operation for data acquisition, data processing, intelligent decision making, and actuation. Here, we provide a practical guide to iIACS that describes how to design, build, characterize, and use an iIACS machine. The guide includes the consideration of several important design parameters, such as throughput, sensitivity, dynamic range, image quality, sort purity, and sort yield; the development and integration of optical, microfluidic, electrical, computational, and mechanical components; and the characterization and practical usage of the integrated system. Assuming that all components are readily available, a team of several researchers experienced in optics, electronics, digital signal processing, microfluidics, mechatronics, and flow cytometry can complete this protocol in ~3 months.

Optical Imaging Approaches to Monitor Static and Dynamic Cell-on-Chip Platforms: A Tutorial Review

Miniaturized laboratories on chip platforms play an important role in handling life sciences studies. The platforms may contain static or dynamic biological cells. Examples are a fixed medium of an organ-on-a-chip and individual cells moving in a microfluidic channel, respectively. Due to feasibility of control or investigation and ethical implications of live targets, both static and dynamic cell-on-chip platforms promise various applications in biology. To extract necessary information from the experiments, the demand for direct monitoring is rapidly increasing. Among different microscopy methods, optical imaging is a straightforward choice. Considering light interaction with biological agents, imaging signals may be generated as a result of scattering or emission effects from a sample. Thus, optical imaging techniques could be categorized into scattering-based and emission-based techniques. In this review, various optical imaging approaches used in monitoring static and dynamic platforms are introduced along with their optical systems, advantages, challenges, and applications. This review may help biologists to find a suitable imaging technique for different cell-on-chip studies and might also be useful for the people who are going to develop optical imaging systems in life sciences studies.

Cell Cytometry: Review and Perspective on Biotechnological Advances

Cell identification and enumeration are essential procedures within clinical and research laboratories. For over 150 years, quantitative investigation of body fluids such as counts of various blood cells has been an important tool for diagnostic analysis. With the current evolution of point-of-care diagnostics and precision medicine, cheap and precise cell counting technologies are in demand. This article reviews the timeline and recent notable advancements in cell counting that have occurred as a result of improvements in sensing including optical and electrical technology, enhancements in image processing capabilities, and contributions of micro and nanotechnologies. Cell enumeration methods have evolved from the use of manual counting using a hemocytometer to automated cell counters capable of providing reliable counts with high precision and throughput. These developments have been enabled by the use of precision engineering, micro and nanotechnology approaches, automation and multivariate data analysis. Commercially available automated cell counters can be broadly classified into three categories based on the principle of detection namely, electrical impedance, optical analysis and image analysis. These technologies have many common scientific uses, such as hematological analysis, urine analysis and bacterial enumeration. In addition to commercially available technologies, future technological trends using lab-on-a-chip devices have been discussed in detail. Lab-on-a-chip platforms utilize the existing three detection technologies with innovative design changes utilizing advanced nano/microfabrication to produce customized devices suited to specific applications.

Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow

Single-cell analysis commonly requires the confinement of cell suspensions in an analysis chamber or the precise positioning of single cells in small channels. Hydrodynamic flow focusing has been broadly utilized to achieve stream confinement in microchannels for such applications. As imaging flow cytometry gains popularity, the need for imaging-compatible microfluidic devices that allow for precise confinement of single cells in small volumes becomes increasingly important. At the same time, high-throughput single-cell imaging of cell populations produces vast amounts of complex data, which gives rise to the need for versatile algorithms for image analysis. In this work, we present a microfluidics-based platform for single-cell imaging in-flow and subsequent image analysis using variational autoencoders for unsupervised characterization of cellular mixtures. We use simple and robust Y-shaped microfluidic devices and demonstrate precise 3D particle confinement towards the microscope slide for high-resolution imaging. To demonstrate applicability, we use these devices to confine heterogeneous mixtures of yeast species, brightfield-image them in-flow and demonstrate fully unsupervised, as well as few-shot classification of single-cell images with 88% accuracy.