Single-cell microfluidic impedance cytometry: from raw signals to cell phenotypes using data analytics

Sensor technologies for quality control in engineered tissue manufacturing

The use of engineered cells, tissues, and organs has the opportunity to change the way injuries and diseases are treated. Commercialization of these groundbreaking technologies has been limited in part by the complex and costly nature of their manufacture. Process-related variability and even small changes in the manufacturing process of a living product will impact its quality. Without real-time integrated detection, the magnitude and mechanism of that impact are largely unknown. Real-time and non-destructive sensor technologies are key for in-process insight and ensuring a consistent product throughout commercial scale-up and/or scale-out. The application of a measurement technology into a manufacturing process requires cell and tissue developers to understand the best way to apply a sensor to their process, and for sensor manufacturers to understand the design requirements and end-user needs. Furthermore, sensors to monitor component cells' health and phenotype need to be compatible with novel integrated and automated manufacturing equipment. This review summarizes commercially relevant sensor technologies that can detect meaningful quality attributes during the manufacturing of regenerative medicine products, the gaps within each technology, and sensor considerations for manufacturing.

Toward five-part differential of leukocytes based on electrical impedances of single cells and neural network

The five-part differential of leukocytes plays key roles in the diagnosis of a variety of diseases and is realized by optical examinations of single cells, which is prone to various artifacts due to chemical treatments. The classification of leukocytes based on electrical impedances without cell treatments has not been demonstrated because of limitations in approaches of impedance acquisition and data processing. In this study, based on treatment-free single-cell impedance profiles collected from impedance flow cytometry leveraging constriction microchannels, two types of neural pattern recognition were conducted for comparisons with the purpose of realizing the five-part differential of leukocytes. In the first approach, 30 features from impedance profiles were defined manually and extracted automatically, and then a feedforward neural network was conducted, producing a classification accuracy of 84.9% in the five-part leukocyte differential. In the second approach, a customized recurrent neural network was developed to process impedance profiles directly and based on deep learning, a classification accuracy of 97.5% in the five-part leukocyte differential was reported. These results validated the feasibility of the five-part leukocyte differential based on label-free impedance profiles of single cells and thus provide a new perspective of differentiating white blood cells based on impedance flow cytometry.

A 10 M Ω, 50 kHz-40 MHz Impedance Measurement Architecture for Source-Differential Flow Cytometry

A low-power, impedance-based integrated circuit (IC) readout architecture is presented for cell analysis and cytometry applications. A three-electrode layout and source-differential excitation cancels baseline current prior to the sensor front-end, which enables the use of a high-gain readout circuit for the difference current. A lock-in architecture is employed with down-conversion and up-conversion in the feedback loop, enabling high closed-loop gain (up to 10 M Ω) and high bandwidth (up to 40 MHz). A hybrid-RC feedback network mitigates the SNR degradation seen over a wide operating frequency range when using purely capacitive feedback. The effect of phase shift on the closed-loop system gain and noise performance are analyzed in detail, along with optimization strategies, and the design includes fine-grained phase adjustment to minimize phase error. The impedance sensor was fabricated in a 0.18 μ m CMOS process and consumes 9.7 mW with an operating frequency from 50 kHz to 40 MHz and provides adjustable bandwidth. Measurements demonstrate that the impedance sensor achieves 6 pA [Formula: see text] input-referred noise over 200 Hz bandwidth at 0.5 MHz modulation frequency. Combined with a microfluidic flow cell, measured results using this source-differential measurement approach are presented using both monodisperse and polydisperse sample solutions and demonstrate single-cell resolution, detecting 3 μ m diameter particles in solution with 22 dB SNR.

Microfluidics for antibiotic susceptibility testing

The rise of antibiotic resistance is a threat to global health. Rapid and comprehensive analysis of infectious strains is critical to reducing the global use of antibiotics, as informed antibiotic use could slow down the emergence of resistant strains worldwide. Multiple platforms for antibiotic susceptibility testing (AST) have been developed with the use of microfluidic solutions. Here we describe microfluidic systems that have been proposed to aid AST. We identify the key contributions in overcoming outstanding challenges associated with the required degree of multiplexing, reduction of detection time, scalability, ease of use, and capacity for commercialization. We introduce the reader to microfluidics in general, and we analyze the challenges and opportunities related to the field of microfluidic AST.

Automated biophysical classification of apoptotic pancreatic cancer cell subpopulations by using machine learning approaches with impedance cytometry

Unrestricted cell death can lead to an immunosuppressive tumor microenvironment, with dysregulated apoptotic signaling that causes resistance of pancreatic cancer cells to cytotoxic therapies. Hence, modulating cell death by distinguishing the progression of subpopulations under drug treatment from viable towards early apoptotic, late apoptotic, and necrotic states is of interest. While flow cytometry after fluorescent staining can monitor apoptosis with single-cell sensitivity, the background of non-viable cells within non-immortalized pancreatic tumors from xenografts can confound distinction of the intensity of each apoptotic state. Based on single-cell impedance cytometry of drug-treated pancreatic cancer cells that are obtained from tumor xenografts with differing levels of gemcitabine sensitivity, we identify the biophysical metrics that can distinguish and quantify cellular subpopulations at the early apoptotic versus late apoptotic and necrotic states, by using machine learning methods to train for the recognition of each phenotype. While supervised learning has previously been used for classification of datasets with known classes, our advancement is the utilization of optimal positive controls for each class, so that clustering by unsupervised learning and classification by supervised learning can occur on unknown datasets, without human interference or manual gating. In this manner, automated biophysical classification can be used to follow the progression of apoptotic states in each heterogeneous drug-treated sample, for developing drug treatments to modulate cancer cell death and advance longitudinal analysis to discern the emergence of drug resistant phenotypes.

Label-Free Microfluidic Impedance Cytometry for Acrosome Integrity Assessment of Boar Spermatozoa

Microfluidics and lab-on-chip technologies have been used in a wide range of biomedical applications. They are known as versatile, rapid, and low-cost alternatives for expensive equipment and time-intensive processing. The veterinary industry and human fertility clinics could greatly benefit from label-free and standardized methods for semen analysis. We developed a tool to determine the acrosome integrity of spermatozoa using microfluidic impedance cytometry. Spermatozoa from boars were treated with the calcium ionophore A23187 to induce acrosome reaction. The magnitude, phase and opacity of individual treated and non-treated (control) spermatozoa were analyzed and compared to conventional staining for acrosome integrity. The results show that the opacity at 19 MHz over 0.5 MHz is associated with acrosome integrity with a cut-off threshold at 0.86 (sensitivity 98%, specificity 97%). In short, we have demonstrated that acrosome integrity can be determined using opacity, illustrating that microfluidic impedance cytometers have the potential to become a versatile and efficient alternative in semen analysis and for fertility treatments in the veterinary industry and human fertility clinics.

Branched immunochip-integrated pairwise barcoding amplification exploring the spatial proximity of two post-translational modifications in distinct cell subpopulations

Investigating the spatial information of post-translational modifications (PTMs) in distinct cell subpopulations represents a new direction toward single-cell analysis. The specific capture of cell populations combined with PTM spatial proximity visualization making it practically challenging. Here, we develop branched immunochip-integrated pairwise barcoding amplification, termed biChip-PBA, which can perform the respective capture of cell subpopulations expressing different membrane proteins and successive PBA-based fluorescence imaging of PTM proximities. Our work may provide multilevel information for new insights into epigenetic regulation and cell function.

Single-cell assessment of the modulation of macrophage activation by ex vivo intervertebral discs using impedance cytometry

Measurement of macrophage activation and its modulation for immune regulation is of great interest to arrest inflammatory responses associated with degeneration of intervertebral discs that cause chronic back pain, and with transplants that face immune rejection. Due to the phenotypic plasticity of macrophages that serve multiple immune functions, the net disease outcome is determined by a balance of subpopulations with competing functions, highlighting the need for single-cell methods to quantify heterogeneity in their activation phenotypes. However, since macrophage activation can follow several signaling pathways, cytometry after fluorescent staining of markers with antibodies does not often provide dose-dependent information on activation dynamics. We present high throughput single-cell impedance cytometry for multiparametric measurement of biophysical changes to individual macrophages for quantifying activation in a dose and duration dependent manner, without relying on a particular signaling pathway. Impedance phase metrics measured at two frequencies and the electrical diameter from impedance magnitude at lower frequencies are used in tandem to benchmark macrophage activation by degenerated discs against that from lipopolysaccharide stimulation at varying dose and duration levels, so that reversal of the activation state by curcumin can be ascertained. This label-free single-cell measurement method can form the basis for platforms to screen therapies for inflammation, thereby addressing the chronic problem of back pain.

Machine learning for microfluidic design and control

Microfluidics has developed into a mature field with applications across science and engineering, having particular commercial success in molecular diagnostics, next-generation sequencing, and bench-top analysis. Despite its ubiquity, the complexity of designing and controlling custom microfluidic devices present major barriers to adoption, requiring intuitive knowledge gained from years of experience. If these barriers were overcome, microfluidics could miniaturize biological and chemical research for non-experts through fully-automated platform development and operation. The intuition of microfluidic experts can be captured through machine learning, where complex statistical models are trained for pattern recognition and subsequently used for event prediction. Integration of machine learning with microfluidics could significantly expand its adoption and impact. Here, we present the current state of machine learning for the design and control of microfluidic devices, its possible applications, and current limitations.

Recent Advances in Microscale Electroporation

Electroporation (EP) is a commonly used strategy to increase cell permeability for intracellular cargo delivery or irreversible cell membrane disruption using electric fields. In recent years, EP performance has been improved by shrinking electrodes and device structures to the microscale. Integration with microfluidics has led to the design of devices performing static EP, where cells are fixed in a defined region, or continuous EP, where cells constantly pass through the device. Each device type performs superior to conventional, macroscale EP devices while providing additional advantages in precision manipulation (static EP) and increased throughput (continuous EP). Microscale EP is gentle on cells and has enabled more sensitive assaying of cells with novel applications. In this Review, we present the physical principles of microscale EP devices and examine design trends in recent years. In addition, we discuss the use of reversible and irreversible EP in the development of therapeutics and analysis of intracellular contents, among other noteworthy applications. This Review aims to inform and encourage scientists and engineers to expand the use of efficient and versatile microscale EP technologies.

Impedance-Enabled Camera-Free Intrinsic Mechanical Cytometry

Mechanical properties of single cells are important label-free biomarkers normally measured by expensive and complex imaging systems. To unlock this limit and allow mechanical properties comparable across different measurement platforms, camera-free intrinsic mechanical cytometry (CFIMC) is proposed for on-the-fly measurement of two major intrinsic mechanical parameters, that is, Young's modulus E and fluidity β, of single cells. CFIMC adopts a framework that couples the impedance electrodes with the constriction channel spatially, so that the impedance signals contain the dynamic deformability information of the cell squeezing through the constriction channel. Deformation of the cell is thus extracted from the impedance signals and used to derive the intrinsic mechanical parameters. With reasonably high throughput (>500 cells min^-1 ), CFIMC can successfully reveal the mechanical difference in cancer and normal cells (i.e., human breast cell lines MCF-10A, MCF-7, and MDA-MB-231), living and fixed cells, and pharmacological perturbations of the cytoskeleton. It is further found that 1 µM level concentration of Cytochalasin B may be the threshold for the treated cells to induce a significant cytoskeleton effect reflected by the mechanical parameters. It is envisioned that CFIMC provides an alternative avenue for high-throughput and real-time single-cell intrinsic mechanical analysis.

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.

Deciphering impedance cytometry signals with neural networks

Microfluidic impedance cytometry is a label-free technique for high-throughput single-cell analysis. Multi-frequency impedance measurements provide data that allows full characterisation of cells, linking electrical phenotype to individual biophysical properties. To efficiently extract the information embedded in the electrical signals, potentially in real-time, tailored signal processing is needed. Artificial intelligence approaches provide a promising new direction. Here we demonstrate the ability of neural networks to decipher impedance cytometry signals in two challenging scenarios: (i) to determine the intrinsic dielectric properties of single cells directly from raw impedance data streams, (ii) to capture single-cell signals that are hidden in the measured signals of coincident cells. The accuracy of the results and the high processing speed (fractions of ms per cell) demonstrate that neural networks can have an important role in impedance-based single-cell analysis.

Microfluidic Impedance-Deformability Cytometry for Label-Free Single Neutrophil Mechanophenotyping

The intrinsic biophysical states of neutrophils are associated with immune dysfunctions in diseases. While advanced image-based biophysical flow cytometers can probe cell deformability at high throughput, it is nontrivial to couple different sensing modalities (e.g., electrical) to measure other critical cell attributes including cell viability and membrane integrity. Herein, an "optics-free" impedance-deformability cytometer for multiparametric single cell mechanophenotyping is reported. The microfluidic platform integrates hydrodynamic cell pinching, and multifrequency impedance quantification of cell size, deformability, and membrane impedance (indicative of cell viability and activation). A newly-defined "electrical deformability index" is validated by numerical simulations, and shows strong correlations with the optical cell deformability index of HL-60 experimentally. Human neutrophils treated with various biochemical stimul are further profiled, and distinct differences in multimodal impedance signatures and UMAP analysis are observed. Overall, the integrated cytometer enables label-free cell profiling at throughput of >1000 cells min^-1 without any antibodies labeling to facilitate clinical diagnostics.

Inherent Single-Cell Bioelectrical Parameters of Thousands of Neutrophils, Eosinophils and Basophils Derived from Impedance Flow Cytometry

Single-cell bioelectrical properties are commonly used for blood cell phenotyping in a label-free manner. However, previously reported inherent single-cell bioelectrical parameters (e.g., diameter D_c , specific membrane capacitance C_sm and cytoplasmic conductivity σ_cy ) of neutrophils, eosinophils and basophils were obtained from only tens of individual cells with limited statistical significance. In this study, granulocytes were separated into neutrophils, eosinophils and basophils based on fluorescent flow cytometry, which were further aspirated through a constriction-microchannel impedance flow cytometry for electrical property characterization. Based on this microfluidic impedance flow cytometry, single-cell values of D_c , C_sm and σ_cy were measured as 10.25 ± 0.66 μm, 2.17 ± 0.30 μF/cm² , and 0.37 ± 0.05 S/m for neutrophils (n_cell = 9 442); 9.73 ± 0.51 μm, 2.07 ± 0.19 μF/cm² , and 0.30 ± 0.04 S/m for eosinophils (n_cell = 2 982); 9.75 ± 0.49 μm, 2.06 ± 0.17 μF/cm² , and 0.31 ± 0.04 S/m for basophils (n_cell = 5 377). Based on these inherent single-cell bioelectrical parameters, neural pattern recognition was conducted, producing classification rates of 80.8% (neutrophil vs. eosinophil), 77.7% (neutrophil vs. basophil) and 59.3% (neutrophil vs. basophil). These results indicate that as inherent single-cell bioelectrical parameters, D_c , C_sm and σ_cy can be used to classify neutrophils from eosinophils or basophils to some extent while they cannot be used to effectively distinguish eosinophils from basophils.

Controllable Fabrication of Small-Size Holding Pipets for the Nondestructive Manipulation of Suspended Living Single Cells

The capture and manipulation of single cells are an important premise and basis for intracellular delivery, which provides abundant molecular and omics information for biomedical development. However, for intracellular delivery of cargos into/from small-size suspended living single cells, the capture methods are limited by the lack of small-size holding pipets, poor cell activity, and the low spatial accuracy of intracellular delivery. To solve these problems, a method for the controllable fabrication of small-size holding pipets was proposed. A simple, homemade microforge instrument including an imaging device was built to cut and melt the glass capillary tip by controlling the heat production of a nichrome wire. The controllable fabrication of small-size holding pipets was realized by observing the fabrication process in real time. Combined with an electroosmotic drive system and a micromanipulation system with high spatial resolution, the holding pipet achieved the active capture, movement, and sampling of suspended living single cells. Moreover, solid-phase microextraction was performed on captured single pheochromocytoma cells, and the extracted dopamine was successfully detected using an electrochemical method. The homemade microforge instrument overcame the limitations of traditional microforges, resulting in holding pipets that were sufficiently small for small-size suspended single living cells (5-30 μm). This proactive capture method overcame the shortcomings of existing methods to achieve the multiangle, high-precision manipulation of single cells, thereby allowing the intracellular delivery of small-size single cells in suspension with high spatiotemporal resolution.

Machine Learning-Driven Multiobjective Optimization: An Opportunity of Microfluidic Platforms Applied in Cancer Research

Cancer metastasis is one of the primary reasons for cancer-related fatalities. Despite the achievements of cancer research with microfluidic platforms, understanding the interplay of multiple factors when it comes to cancer cells is still a great challenge. Crosstalk and causality of different factors in pathogenesis are two important areas in need of further research. With the assistance of machine learning, microfluidic platforms can reach a higher level of detection and classification of cancer metastasis. This article reviews the development history of microfluidics used for cancer research and summarizes how the utilization of machine learning benefits cancer studies, particularly in biomarker detection, wherein causality analysis is useful. To optimize microfluidic platforms, researchers are encouraged to use causality analysis when detecting biomarkers, analyzing tumor microenvironments, choosing materials, and designing structures.

Modified Red Blood Cells as Multimodal Standards for Benchmarking Single-Cell Cytometry and Separation Based on Electrical Physiology

Biophysical cellular information at single-cell sensitivity is becoming increasingly important within analytical and separation platforms that associate the cell phenotype with markers of disease, infection, and immunity. Frequency-modulated electrically driven microfluidic measurement and separation systems offer the ability to sensitively identify single cells based on biophysical information, such as their size and shape, as well as their subcellular membrane morphology and cytoplasmic organization. However, there is a lack of reliable and reproducible model particles with well-tuned subcellular electrical phenotypes that can be used as standards to benchmark the electrical physiology of unknown cell types or to benchmark dielectrophoretic separation metrics of novel device strategies. Herein, the application of red blood cells (RBCs) as multimodal standard particles with systematically modulated subcellular electrophysiology and associated fluorescence level is presented. Using glutaraldehyde fixation to vary membrane capacitance and by membrane resealing after electrolyte penetration to vary interior cytoplasmic conductivity and fluorescence in a correlated manner, each modified RBC type can be identified at single-cell sensitivity based on phenomenological impedance metrics and fitted to dielectric models to compute biophysical information. In this manner, single-cell impedance data from unknown RBC types can be mapped versus these model RBC types for facile determination of subcellular biophysical information and their dielectrophoretic separation conditions, without the need for time-consuming algorithms that often require unknown fitting parameters. Such internal standards for biophysical cytometry can advance in-line phenotypic recognition strategies.

Inherent Bioelectrical Parameters of Hundreds of Thousands of Single Leukocytes Based on Impedance Flow Cytometry

As label-free biomarkers, bioelectrical properties of single cells have been widely used in hematology analyzers for 3-part differential of leukocytes, in which, however, instrument dependent bioelectrical parameters (e.g., DC/AC impedance values) rather than inherent bioelectrical parameters (e.g., diameter D_c , specific membrane capacitance C_sm and cytoplasmic conductivity σ_cy ) were used, leading to poor comparisons among different instruments. In order to address this issue, this study collected inherent bioelectrical parameters from hundreds of thousands of white blood cells based on a home-developed impedance flow cytometry with corresponding 3-part differential of leukocytes realized. More specifically, leukocytes were separated into three major subtypes of granulocytes, monocytes and lymphocytes based on density gradient centrifugation. Then these separated cells were aspirated through a constriction-microchannel based impedance flow cytometry where inherent bioelectrical parameters of D_c , C_sm and σ_cy were quantified as 9.8 ± 0.7 μm, 2.06 ± 0.26 μF/cm² , and 0.34 ± 0.05 S/m for granulocytes (n_cell = 134 829); 10.4 ± 1.0 μm, 2.45 ± 0.48 μF/cm² , and 0.42 ± 0.08 S/m for monocytes (n_cell = 40 226); 8.0 ± 0.5 μm, 2.23 ± 0.34 μF/cm² , and 0.35 ± 0.08 S/m for lymphocytes (n_cell = 129 193). Based on these inherent bioelectrical parameters, neural pattern recognition was conducted, producing a high "classification accuracy" of 93.5% in classifying these three subtypes of leukocytes. These results indicate that as inherent bioelectrical parameters, D_c , C_sm and σ_cy can be used to electrically phenotype white blood cells in a label-free manner.

Neural network-enhanced real-time impedance flow cytometry for single-cell intrinsic characterization

Single-cell impedance flow cytometry (IFC) is emerging as a label-free and non-invasive method for characterizing the electrical properties and revealing sample heterogeneity. At present, most IFC studies utilize phenomenological parameters (e.g., impedance amplitude, phase and opacity) to characterize single cells instead of intrinsic biophysical metrics (e.g., radius r, cytoplasm conductivity σ_i and specific membrane capacitance C_sm). Intrinsic parameters are normally calculated off-line by time-consuming model-fitting methods. Here, we propose to employ neural network (NN)-enhanced IFC to achieve both real-time single-cell intrinsic characterization and intrinsic parameter-based cell classification at high throughput. Three intrinsic parameters (r, σ_i and C_sm) can be obtained online and in real-time via a trained NN at 0.3 ms per single-cell event, achieving significant improvement in calculation speed. Experiments involving four cancer cells and one lymphocyte cell demonstrated 91.5% classification accuracy in the cell type for a test group of 9751 cell samples. By performing a viability assay, we provide evidence that the IFC test per se would not substantially affect the cell property. We envision that the NN-enhanced real-time IFC will provide a new platform for high-throughput, real-time and online cell intrinsic electrical characterization.

Assessment of the electrical penetration of cell membranes using four-frequency impedance cytometry

The electrical penetration of the cell membrane is vital for determining the cell interior via impedance cytometry. Herein, we propose a method for determining the conductivity of the cell membrane through the tilting levels of impedance pulses. When electrical penetration occurs, a high-frequency current freely passes through the cell membrane; thus, the intracellular distribution can directly act on the high-frequency impedance pulses. Numerical simulation shows that an uneven intracellular component distribution can affect the tilting levels of impedance pulses, and the tilting levels start increasing when the cell membrane is electrically penetrated. Experimental evidence shows that higher detection frequencies (>7 MHz) lead to a wider distribution of the tilting levels of impedance pulses when measuring cell populations with four-frequency impedance cytometry. This finding allows us to determine that a detection frequency of 7 MHz is able to pass through the membrane of Euglena gracilis (E. gracilis) cells. Additionally, we provide a possible application of four-frequency impedance cytometry in the biomass monitoring of single E. gracilis cells. High-frequency impedance (≥7 MHz) can be applied to monitor these biomass changes, and low-frequency impedance (<7 MHz) can be applied to track the corresponding biovolume changes. Overall, this work demonstrates an easy determination method for the electrical penetration of the cell membrane, and the proposed platform is applicable for the multiparameter assessment of the cell state during cultivation.

What is the future of electrical impedance spectroscopy in flow cytometry?

More than 20 years ago, electrical impedance spectroscopy (EIS) was proposed as a potential characterization method for flow cytometry. As the setup is comparably simple and the method is label-free, EIS has attracted considerable interest from the research community as a potential alternative to standard optical methods, such as fluorescence-activated cell sorting (FACS). However, until today, FACS remains by and large the laboratory standard with highly developed capabilities and broad use in research and clinical settings. Nevertheless, can EIS still provide a complement or alternative to FACS in specific applications? In this Perspective, we will give an overview of the current state of the art of EIS in terms of technologies and capabilities. We will then describe recent advances in EIS-based flow cytometry, compare the performance to that of FACS methods, and discuss potential prospects of EIS in flow cytometry.

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.

UV-Vis Spectra-Activated Droplet Sorting for Label-Free Chemical Identification and Collection of Droplets

We introduce the UV-vis spectra-activated droplet sorter (UVADS) for high-throughput label-free chemical identification and enzyme screening. In contrast to previous absorbance-based droplet sorters that relied on single-wavelength absorbance in the visible range, our platform collects full UV-vis spectra from 200 to 1050 nm at up to 2100 spectra per second. Our custom-built open-source software application, "SpectraSorter," enables real-time data processing, analysis, visualization, and selection of droplets for sorting with any set of UV-vis spectral features. An optimized UV-vis detection region extended the absorbance path length for droplets and allowed for the direct protein quantification down to 10 μM of bovine serum albumin at 280 nm. UV-vis spectral data can distinguish a variety of different chemicals or spurious events (such as air bubbles) that are inaccessible at a single wavelength. The platform is used to measure ergothionase enzyme activity from monoclonal microcolonies isolated in droplets. In a label-free manner, we directly measure the ergothioneine substrate to thiourocanic acid product conversion while tracking the microcolony formation. UVADS represents an important new tool for high-throughput label-free in-droplet chemical analysis.

Determining Particle Size and Position in a Coplanar Electrode Setup Using Measured Opacity for Microfluidic Cytometry

Microfluidic impedance flow cytometers enable high-throughput, non-invasive, and label-free detection of single-cells. Cytometers with coplanar electrodes are easy and cheap to fabricate, but are sensitive to positional differences of passing particles, owing to the inhomogeneous electric field. We present a novel particle height compensation method, which employs the dependence of measured electrical opacity on particle height. The measured electrical opacity correlates with the particle height as a result of the constant electrical double layer series capacitance of the electrodes. As an alternative to existing compensation methods, we use only two coplanar electrodes and multi-frequency analysis to determine the particle size of a mixture of 5, 6, and 7 µm polystyrene beads with an accuracy (CV) of 5.8%, 4.0%, and 2.9%, respectively. Additionally, we can predict the bead height with an accuracy of 1.5 µm (8% of channel height) using the measured opacity and we demonstrate its application in flow cytometry with yeast. The use of only two electrodes is of special interest for simplified, easy-to-use chips with a minimum amount of instrumentation and of limited size.

Electro-optical classification of pollen grains via microfluidics and machine learning

In aerobiological monitoring and agriculture there is a pressing need for accurate, label-free and automated analysis of pollen grains, in order to reduce the cost, workload and possible errors associated to traditional approaches. Methods: We propose a new multimodal approach that combines electrical sensing and optical imaging to classify pollen grains flowing in a microfluidic chip at a throughput of 150 grains per second. Electrical signals and synchronized optical images are processed by two independent machine learning-based classifiers, whose predictions are then combined to provide the final classification outcome. Results: The applicability of the method is demonstrated in a proof-of-concept classification experiment involving eight pollen classes from different taxa. The average balanced accuracy is 78.7 % for the electrical classifier, 76.7 % for the optical classifier and 84.2 % for the multimodal classifier. The accuracy is 82.8 % for the electrical classifier, 84.1 % for the optical classifier and 88.3 % for the multimodal classifier. Conclusion: The multimodal approach provides better classification results with respect to the analysis based on electrical or optical features alone. Significance: The proposed methodology paves the way for automated multimodal palynology. Moreover, it can be extended to other fields, such as diagnostics and cell therapy, where it could be used for label-free identification of cell populations in heterogeneous samples.

An easy-fabricated and disposable polymer-film microfluidic impedance cytometer for cell sensing

We report here an easy-fabricated and disposable polymer-film microfluidic impedance cytometer (PMIC) integrated with inertial focusing and parallel facing electrodes for cell sensing. The cells are first focused in an asymmetric serpentine channel, and then their impedance signals are measured when passing through the electrode region. The proposed PMIC device is the first impedance cytometer that is fabricated into a flexible sheet (with a thickness of 0.45 mm) by using the materials of commonly-available ITO-coated polymer films and double-sided adhesive tapes, the whole fabrication process is shortened from traditional 3-4 days to less than 5 min by using UV laser cutting. To verify the feasibility of our device for cell sensing, we explore the focusing behaviors of three differently sized particles and two types of tumor cells, and analyze their impedance signals. The results show that our device is capable of obtaining impedance information on numbers, diameters, and longitudinal positions of cells. We envision that our PMIC device is promising in label-free cell sensing owning to the advantages of low cost, small footprint, and simple fabrication.

Microfluidic impedance cytometry for single-cell sensing: Review on electrode configurations

Single-cell analysis has gained considerable attention for disease diagnosis, drug screening, and differentiation monitoring. Compared to the well-established flow cytometry, which uses fluorescent-labeled antibodies, microfluidic impedance cytometry (MIC) offers a simple, label-free, and noninvasive method for counting, classifying, and monitoring cells. Superior features including a small footprint, low reagent consumption, and ease of use have also been reported. The MIC device detects changes in the impedance signal caused by cells passing through the sensing/electric field zone, which can extract information regarding the size, shape, and dielectric properties of these cells. According to recent studies, electrode configuration has a remarkable effect on detection accuracy, sensitivity, and throughput. With the improvement in microfabrication technology, various electrode configurations have been reported for improving detection accuracy and throughput. However, the various electrode configurations of MIC devices have not been reviewed. In this review, the theoretical background of the impedance technique for single-cell analysis is introduced. Then, two-dimensional, three-dimensional, and liquid electrode configurations are discussed separately; their sensing mechanisms, fabrication processes, advantages, disadvantages, and applications are also described in detail. Finally, the current limitations and future perspectives of these electrode configurations are summarized. The main aim of this review is to offer a guide for researchers on the ongoing advancement in electrode configuration designs.

Apoptotic Bodies in the Pancreatic Tumor Cell Culture Media Enable Label-Free Drug Sensitivity Assessment by Impedance Cytometry

The ability to rapidly and sensitively predict drug response and toxicity using in vitro models of patient-derived tumors is essential for assessing chemotherapy efficacy. Currently, drug sensitivity assessment for solid tumors relies on imaging adherent cells or by flow cytometry of cells lifted from drug-treated cultures after fluorescent staining for apoptotic markers. Subcellular apoptotic bodies (ABs), including microvesicles that are secreted into the culture media under drug treatment can potentially serve as markers for drug sensitivity, without the need to lift cells under culture. However, their stratification to quantify cell disassembly is challenging due to their compositional diversity, with tailored labeling strategies currently needed for the recognition and cytometry of each AB type. It is shown that the high frequency impedance phase versus size distribution of ABs determined by high-throughput single-particle impedance cytometry of supernatants in the media of gemcitabine-treated pancreatic tumor cultures exhibits phenotypic resemblance to lifted apoptotic cells and enables shape-based stratification within distinct size ranges, which is not possible by flow cytometry. It is envisioned that this tool can be applied in conjunction with the appropriate pancreatic tumor microenvironment model to assess drug sensitivity and toxicity of patient-derived tumors, without the need to lift cells from cultures.

Closed-loop feedback control of microfluidic cell manipulation via deep-learning integrated sensor networks

Microfluidic technologies have long enabled the manipulation of flow-driven cells en masse under a variety of force fields with the goal of characterizing them or discriminating the pathogenic ones. On the other hand, a microfluidic platform is typically designed to function under optimized conditions, which rarely account for specimen heterogeneity and internal/external perturbations. In this work, we demonstrate a proof-of-principle adaptive microfluidic system that consists of an integrated network of distributed electrical sensors for on-chip tracking of cells and closed-loop feedback control that modulates chip parameters based on the sensor data. In our system, cell flow speed is measured at multiple locations throughout the device, the data is interpreted in real-time via deep learning-based algorithms, and a proportional-integral feedback controller updates a programmable pressure pump to maintain a desired cell flow speed. We validate the adaptive microfluidic system with both static and dynamic targets and also observe a fast convergence of the system under continuous external perturbations. With an ability to sustain optimal processing conditions in unsupervised settings, adaptive microfluidic systems would be less prone to artifacts and could eventually serve as reliable standardized biomedical tests at the point of care.

Self-aligned sequential lateral field non-uniformities over channel depth for high throughput dielectrophoretic cell deflection

Dielectrophoresis (DEP) enables the separation of cells based on subtle subcellular phenotypic differences by controlling the frequency of the applied field. However, current electrode-based geometries extend over a limited depth of the sample channel, thereby reducing the throughput of the manipulated sample (sub-μL min-1 flow rates and <105 cells per mL). We present a flow through device with self-aligned sequential field non-uniformities extending laterally across the sample channel width (100 μm) that are created by metal patterned over the entire depth (50 μm) of the sample channel sidewall using a single lithography step. This enables single-cell streamlines to undergo progressive DEP deflection with minimal dependence on the cell starting position, its orientation versus the field and intercellular interactions. Phenotype-specific cell separation is validated (>μL min-1 flow and >106 cells per mL) using heterogeneous samples of healthy and glutaraldehyde-fixed red blood cells, with single-cell impedance cytometry showing that the DEP collected fractions are intact and exhibit electrical opacity differences consistent with their capacitance-based DEP crossover frequency. This geometry can address the vision of an "all electric" selective cell isolation and cytometry system for quantifying phenotypic heterogeneity of cellular systems.