Microscopic impedance cytometry for quantifying single cell shape

Microfluidic characterization of single-cell biophysical properties and the applications in cancer diagnosis

Single-cell biophysical properties play a crucial role in regulating cellular physiological states and functions, demonstrating significant potential in the fields of life sciences and clinical diagnostics. Therefore, over the last few decades, researchers have developed various detection tools to explore the relationship between the biophysical changes of biological cells and human diseases. With the rapid advancement of modern microfabrication technology, microfluidic devices have quickly emerged as a promising platform for single-cell analysis offering advantages including high-throughput, exceptional precision, and ease of manipulation. Consequently, this paper provides an overview of the recent advances in microfluidic analysis and detection systems for single-cell biophysical properties and their applications in the field of cancer. The working principles and latest research progress of single-cell biophysical property detection are first analyzed, highlighting the significance of electrical and mechanical properties. The development of data acquisition and processing methods for real-time, high-throughput, and practical applications are then discussed. Furthermore, the differences in biophysical properties between tumor and normal cells are outlined, illustrating the potential for utilizing single-cell biophysical properties for tumor cell identification, classification, and drug response assessment. Lastly, we summarize the limitations of existing microfluidic analysis and detection systems in single-cell biophysical properties, while also pointing out the prospects and future directions of their applications in cancer diagnosis and treatment.

Cellular nucleus image-based smarter microscope system for single cell analysis

Cell imaging technology is undoubtedly a powerful tool for studying single-cell heterogeneity due to its non-invasive and visual advantages. It covers microscope hardware, software, and image analysis techniques, which are hindered by low throughput owing to abundant hands-on time and expertise. Herein, a cellular nucleus image-based smarter microscope system for single-cell analysis is reported to achieve high-throughput analysis and high-content detection of cells. By combining the hardware of an automatic fluorescence microscope and multi-object recognition/acquisition software, we have achieved more advanced process automation with the assistance of Robotic Process Automation (RPA), which realizes a high-throughput collection of single-cell images. Automated acquisition of single-cell images has benefits beyond ease and throughout and can lead to uniform standard and higher quality images. We further constructed a single-cell image database-based convolutional neural network (Efficient Convolutional Neural Network, E-CNN) exceeding 20618 single-cell nucleus images. Computational analysis of large and complex data sets enhances the content and efficiency of single-cell analysis with the assistance of Artificial Intelligence (AI), which breaks through the super-resolution microscope's hardware limitation, such as specialized light sources with specific wavelengths, advanced optical components, and high-performance graphics cards. Our system can identify single-cell nucleus images that cannot be artificially distinguished with an accuracy of 95.3%. Overall, we build an ordinary microscope into a high-throughput analysis and high-content smarter microscope system, making it a candidate tool for Imaging cytology.

Electrical micro flow cytometry with LSTM and its application in leukocyte differential

This paper developed an electrical micro flow cytometry to realize leukocyte differentials leveraging a constrictional microchannel and a deep neural network. Firstly, purified granulocytes, lymphocytes or monocytes traveled through the constrictional microchannel with a cross-sectional area marginally larger than individual cells and produced large impedance variations by blocking focused electric field lines. By optimizing key elements (e.g., normalization, learning rate, batch size and neuron number) of the recurrent neural network (RNN), electrical results of purified leukocytes were analyzed to establish a leukocyte differential system with a classification accuracy of 95.2%. Then the leukocyte mixtures were forced to travel through the same constrictional microchannel, producing mixed impedance profiles which were classified into granulocytes, lymphocytes and monocytes based on the aforementioned differential system. As to the classification results, two leukocyte mixtures from the same donor were processed, producing comparable classification results, which were 57% versus 59% of granulocytes, 37% versus 34% of lymphocytes and 6% versus 7% of monocytes. These results validated the established classification system based on the constrictional microchannel and the recurrent neural network, providing a new perspective of differentiating white blood cells by electrical flow cytometry.

Impedance-Based Multimodal Electrical-Mechanical Intrinsic Flow Cytometry

Reflecting various physiological states and phenotypes of single cells, intrinsic biophysical characteristics (e.g., mechanical and electrical properties) are reliable and important, label-free biomarkers for characterizing single cells. However, single-modal mechanical or electrical properties alone are not specific enough to characterize single cells accurately, and it has been long and challenging to couple the conventionally image-based mechanical characterization and impedance-based electrical characterization. In this work, the spatial-temporal characteristics of impedance sensing signal are leveraged, and an impedance-based multimodal electrical-mechanical flow cytometry framework for on-the-fly high-dimensional intrinsic measurement is proposed, that is, Young's modulus E, fluidity β, radius r, cytoplasm conductivity σi , and specific membrane capacitance Csm , of single cells. With multimodal high-dimensional characterization, the electrical-mechanical flow cytometry can better reveal the difference in cell types, demonstrated by the experimental results with three types of cancer cells (HepG2, MCF-7, and MDA-MB-468) with 93.4% classification accuracy and pharmacological perturbations of the cytoskeleton (fixed and Cytochalasin B treated cells) with 95.1% classification accuracy. It is envisioned that multimodal electrical-mechanical flow cytometry provides a new perspective for accurate label-free single-cell intrinsic characterization.

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.

A review on intelligent impedance cytometry systems: Development, applications and advances

Impedance cytometry is a well-established technique for counting and analyzing single cells, with several advantages, such as convenience, high throughput, and no labeling required. A typical experiment consists of the following steps: single-cell measurement, signal processing, data calibration, and particle subtype identification. At the beginning of this article, we compared commercial and self-developed options extensively and provided references for developing reliable detection systems, which are necessary for cell measurement. Then, a number of typical impedance metrics and their relationships to biophysical properties of cells were analyzed with respect to the impedance signal analysis. Given the rapid advances of intelligent impedance cytometry in the past decade, this article also discussed the development of representative machine learning-based approaches and systems, and their applications in data calibration and particle identification. Finally, the remaining challenges facing the field were summarized, and potential future directions for each step of impedance detection were discussed.

An optimized PDMS microfluidic device for ultra-fast and high-throughput imaging flow cytometry

Imaging flow cytometry (IFC) is a powerful tool for cell detection and analysis due to its high throughput and compatibility in image acquisition. Optical time-stretch (OTS) imaging is considered as one of the most promising imaging techniques for IFC because it can realize cell imaging at a flow speed of around 60 m s-1. However, existing PDMS-based microchannels cannot function at flow velocities higher than 10 m s-1; thus the capability of OTS-based IFC is significantly limited. To overcome the velocity barrier for PDMS-based microchannels, we proposed an optimized design of PDMS-based microchannels with reduced hydraulic resistance and 3D hydrodynamic focusing capability, which can drive fluids at an ultra-high flow velocity (of up to 40 m s-1) by using common syringe pumps. To verify the feasibility of our design, we fabricated and installed the microchannel in an OTS IFC system. The experimental results first proved that the proposed microchannel can support a stable flow velocity of up to 40 m s-1 without any leakage or damage. Then, we demonstrated that the OTS IFC is capable of imaging cells at a velocity of up to 40 m s-1 with good quality. To the best of our knowledge, it is the first time that IFC has achieved such a high flow velocity just by using a PDMS-glass chip. Moreover, high velocity can enhance the focusing of cells on the optical focal plane, increasing the number of detected cells and the throughput. This work provides a promising solution for IFC to fully release its capability of advanced imaging techniques by operating at an extremely high screening throughput.

Rapid Assessment of Susceptibility of Bacteria and Erythrocytes to Antimicrobial Peptides by Single-Cell Impedance Cytometry

Antimicrobial peptides (AMPs) represent a promising class of compounds to fight antibiotic-resistant infections. In most cases, they kill bacteria by making their membrane permeable and therefore exhibit low propensity to induce bacterial resistance. In addition, they are often selective, killing bacteria at concentrations lower than those at which they are toxic to the host. However, clinical applications of AMPs are hindered by a limited understanding of their interactions with bacteria and human cells. Standard susceptibility testing methods are based on the analysis of the growth of a bacterial population and therefore require several hours. Moreover, different assays are required to assess the toxicity to host cells. In this work, we propose the use of microfluidic impedance cytometry to explore the action of AMPs on both bacteria and host cells in a rapid manner and with single-cell resolution. Impedance measurements are particularly well-suited to detect the effects of AMPs on bacteria, due to the fact that the mechanism of action involves perturbation of the permeability of cell membranes. We show that the electrical signatures of Bacillus megaterium cells and human red blood cells (RBCs) reflect the action of a representative antimicrobial peptide, DNS-PMAP23. In particular, the impedance phase at high frequency (e.g., 11 or 20 MHz) is a reliable label-free metric for monitoring DNS-PMAP23 bactericidal activity and toxicity to RBCs. The impedance-based characterization is validated by comparison with standard antibacterial activity assays and absorbance-based hemolytic activity assays. Furthermore, we demonstrate the applicability of the technique to a mixed sample of B. megaterium cells and RBCs, which paves the way to study AMP selectivity for bacterial versus eukaryotic cells in the presence of both cell types.

piRT-IFC: Physics-informed real-time impedance flow cytometry for the characterization of cellular intrinsic electrical properties

Real-time transformation was important for the practical implementation of impedance flow cytometry. The major obstacle was the time-consuming step of translating raw data to cellular intrinsic electrical properties (e.g., specific membrane capacitance Csm and cytoplasm conductivity σcyto). Although optimization strategies such as neural network-aided strategies were recently reported to provide an impressive boost to the translation process, simultaneously achieving high speed, accuracy, and generalization capability is still challenging. To this end, we proposed a fast parallel physical fitting solver that could characterize single cells' Csm and σcyto within 0.62 ms/cell without any data preacquisition or pretraining requirements. We achieved the 27000-fold acceleration without loss of accuracy compared with the traditional solver. Based on the solver, we implemented physics-informed real-time impedance flow cytometry (piRT-IFC), which was able to characterize up to 100,902 cells' Csm and σcyto within 50 min in a real-time manner. Compared to the fully connected neural network (FCNN) predictor, the proposed real-time solver showed comparable processing speed but higher accuracy. Furthermore, we used a neutrophil degranulation cell model to represent tasks to test unfamiliar samples without data for pretraining. After being treated with cytochalasin B and N-Formyl-Met-Leu-Phe, HL-60 cells underwent dynamic degranulation processes, and we characterized cell's Csm and σcyto using piRT-IFC. Compared to the results from our solver, accuracy loss was observed in the results predicted by the FCNN, revealing the advantages of high speed, accuracy, and generalizability of the proposed piRT-IFC.

Robust Spontaneous Raman Flow Cytometry for Single-Cell Metabolic Phenome Profiling via pDEP-DLD-RFC

A full-spectrum spontaneous single-cell Raman spectrum (fs-SCRS) captures the metabolic phenome for a given cellular state of the cell in a label-free, landscape-like manner. Herein a positive dielectrophoresis induced deterministic lateral displacement-based Raman flow cytometry (pDEP-DLD-RFC) is established. This robust flow cytometry platform utilizes a periodical positive dielectrophoresis induced deterministic lateral displacement (pDEP-DLD) force that is exerted to focus and trap fast-moving single cells in a wide channel, which enables efficient fs-SCRS acquisition and extended stable running time. It automatically produces deeply sampled, heterogeneity-resolved, and highly reproducible ramanomes for isogenic cell populations of yeast, microalgae, bacteria, and human cancers, which support biosynthetic process dissection, antimicrobial susceptibility profiling, and cell-type classification. Moreover, when coupled with intra-ramanome correlation analysis, it reveals state- and cell-type-specific metabolic heterogeneity and metabolite-conversion networks. The throughput of ≈30-2700 events min^-1 for profiling both nonresonance and resonance marker bands in a fs-SCRS, plus the >5 h stable running time, represent the highest performance among reported spontaneous Raman flow cytometry (RFC) systems. Therefore, pDEP-DLD-RFC is a valuable new tool for label-free, noninvasive, and high-throughput profiling of single-cell metabolic phenomes.

Performance-enhanced clogging-free viscous sheath constriction impedance flow cytometry

As a label-free and high-throughput single cell analysis platform, impedance flow cytometry (IFC) suffers from clogging caused by a narrow microchannel as mechanical constriction (MC). Current sheath constriction (SC) solutions lack systematic evaluation of the performance and proper guidelines for the sheath fluid. Herein, we hypothesize that the viscosity of the non-conductive liquid is the key to the performance of SC, and propose to employ non-conductive viscous sheath flow in SC to unlock the tradeoff between sensitivity and throughput, while ensuring measurement accuracy. By placing MC and SC in series in the same microfluidic chip, we established an evaluation platform to prove the hypothesis. Through modeling analysis and experiments, we confirmed the accuracy (error < 1.60% ± 4.71%) of SC w.r.t. MC, and demonstrated that viscous non-conductive PEG solution achieved an improved sensitivity (7.92×) and signal-to-noise ratio (1.42×) in impedance measurement, with the accuracy maintained and free of clogging. Viscous SC IFC also shows satisfactory ability to distinguish different types of cancer cells and different subtypes of human breast cancer cells. It is envisioned that viscous SC IFC paves the way for IFC to be really usable in practice with clogging-free, accurate, and sensitive performance.

Recent advances in non-optical microfluidic platforms for bioparticle detection

The effective analysis of the basic structure and functional information of bioparticles are of great significance for the early diagnosis of diseases. The synergism between microfluidics and particle manipulation/detection technologies offers enhanced system integration capability and test accuracy for the detection of various bioparticles. Most microfluidic detection platforms are based on optical strategies such as fluorescence, absorbance, and image recognition. Although optical microfluidic platforms have proven their capabilities in the practical clinical detection of bioparticles, shortcomings such as expensive components and whole bulky devices have limited their practicality in the development of point-of-care testing (POCT) systems to be used in remote and underdeveloped areas. Therefore, there is an urgent need to develop cost-effective non-optical microfluidic platforms for bioparticle detection that can act as alternatives to optical counterparts. In this review, we first briefly summarise passive and active methods for bioparticle manipulation in microfluidics. Then, we survey the latest progress in non-optical microfluidic strategies based on electrical, magnetic, and acoustic techniques for bioparticle detection. Finally, a perspective is offered, clarifying challenges faced by current non-optical platforms in developing practical POCT devices and clinical applications.

Parallel Impedance Cytometry for Real-Time Screening of Bacterial Single Cells from Nano- to Microscale

The benefits of impedance cytometry include high-throughput and label-free detection, while long-term calibration is required to remove the effects of the detection circuits. This study presents a novel impedance cytometry system, called parallel impedance cytometry, to simplify the calibration and analysis of the impedance signals. Furthermore, target objects can be detected even when benchmarked against similar objects. Parallel dual microchannels allow the simultaneous detection of reference and target particles in two separate microchannels, without the premixing of reference and target suspensions. The impedance pulses of both can appear separately on the opposite sides of the same time series, which have been verified via simulation and experimental results. Raw impedance signals can easily distinguish target particles from reference ones. Polystyrene beads with different sizes ranging from nano- to microscale (e.g., 500, 750 nm, 1, 2, 3, and 4.5 μm) confirm the nanosensitivity of the system. In addition, the detection of antibiotic-treated Escherichia coli cells demonstrates that our system can be used for the quantitative assessment of the dielectric properties of individual cells, as well as for the proportion of susceptible cells. Through benchmarking against untreated E. coli cells in the other channel, our method enables the discrimination of susceptible cells from others and the comparison of susceptible and insusceptible cells in the target suspension. Those findings indicate that the parallel impedance cytometry can greatly facilitate the measurement and calibration of the impedances of various particles or cells and provide a means to compare their dielectric properties.

Identification of Single Yeast Budding Using Impedance Cytometry with a Narrow Electrode Span

Impedance cytometry is wildly used in single-cell detection, and its sensitivity is essential for determining the status of single cells. In this work, we focus on the effect of electrode gap on detection sensitivity. Through comparing the electrode span of 1 µm and 5 µm, our work shows that narrowing the electrode span could greatly improve detection sensitivity. The mechanism underlying the sensitivity improvement was analyzed via numerical simulation. The small electrode gap (1 µm) allows the electric field to concentrate near the detection area, resulting in a high sensitivity for tiny particles. This finding is also verified with the mixture suspension of 1 µm and 3 µm polystyrene beads. As a result, the electrodes with 1 µm gap can detect more 1 µm beads in the suspension than electrodes with 5 µm gap. Additionally, for single yeast cells analysis, it is found that impedance cytometry with 1 µm electrodes gap can easily distinguish budding yeast cells, which cannot be realized by the impedance cytometry with 5 µm electrodes gap. All experimental results support that narrowing the electrode gap is necessary for tiny particle detection, which is an important step in the development of submicron and nanoscale impedance cytometry.

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.

Microfluidic flow cytometry for blood-based biomarker analysis

Flow cytometry has proven its capability for rapid and quantitative analysis of individual cells and the separation of targeted biological samples from others. The emerging microfluidics technology makes it possible to develop portable microfluidic diagnostic devices for point-of-care testing (POCT) applications. Microfluidic flow cytometry (MFCM), where flow cytometry and microfluidics are combined to achieve similar or even superior functionalities on microfluidic chips, provides a powerful single-cell characterisation and sorting tool for various biological samples. In recent years, researchers have made great progress in the development of the MFCM including focusing, detecting, and sorting subsystems, and its unique capabilities have been demonstrated in various biological applications. Moreover, liquid biopsy using blood can provide various physiological and pathological information. Thus, biomarkers from blood are regarded as meaningful circulating transporters of signal molecules or particles and have great potential to be used as non (or minimally)-invasive diagnostic tools. In this review, we summarise the recent progress of the key subsystems for MFCM and its achievements in blood-based biomarker analysis. Finally, foresight is offered to highlight the research challenges faced by MFCM in expanding into blood-based POCT applications, potentially yielding commercialisation opportunities.

Dual-frequency impedance assays for intracellular components in microalgal cells

Intracellular components (including organelles and biomolecules) at the submicron level are typically analyzed in situ by special preparation or expensive setups. Here, a label-free and cost-effective approach of screening microalgal single-cells at a subcellular resolution is available based on impedance cytometry. To the best of our knowledge, it is the first time that the relationships between impedance signals and submicron intracellular organelles and biomolecules are shown. Experiments were performed on Euglena gracilis (E. gracilis) cells incubated under different incubation conditions (i.e., aerobic and anaerobic) and 15 μm polystyrene beads (reference) at two distinct stimulation frequencies (i.e., 500 kHz and 6 MHz). Based on the impedance detection of tens of thousands of samples at a throughput of about 900 cells per second, three metrics were used to track the changes in biophysical properties of samples. As a result, the electrical diameters of cells showed a clear shrinkage in cell volume and intracellular components, as observed under a microscope. The morphology metric of impedance pulses (i.e., tilt index) successfully characterized the changes in cell shape and intracellular composition distribution. Besides, the electrical opacity showed a stable ratio of the intracellular components to cell volume under the cellular self-regulation. Additionally, simulations were used to support these findings and to elucidate how submicron intracellular components and cell morphology affect impedance signals, providing a basis for future improvements. This work opens up a label-free and high-throughput way to analyze single-cell intracellular components by impedance cytometry.

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.