Design and modeling of electrode networks for code-division multiplexed resistive pulse sensing in microfluidic devices

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.

Rapid Detection of Microparticles Using a Microfluidic Resistive Pulse Sensor Based on Bipolar Pulse-Width Multiplexing

Rapid and accurate analysis of micro/nano bio-objects (e.g., cells, biomolecules) is crucial in clinical diagnostics and drug discovery. While a traditional resistive pulse sensor can provide multiple kinds of information (size, count, surface charge, etc.) about analytes, it has low throughput. We present a unique bipolar pulse-width, multiplexing-based resistive pulse sensor for high-throughput analysis of microparticles. Signal multiplexing is enabled by exposing the central electrode at different locations inside the parallel sensing channels. Together with two common electrodes, the central electrode encodes the electrical signal from each sensing channel, generating specific bipolar template waveforms with different pulse widths. Only one DC source is needed as input, and only one combined electrical output is collected. The combined signal can be demodulated using correlation analysis and a unique iterative cancellation scheme. The accuracy of particle counting and sizing was validated using mixtures of various sized microparticles. Results showed errors of 2.6% and 6.1% in sizing and counting, respectively. We further demonstrated its accuracy for cell analysis using HeLa cells.

Machine learning at the edge for AI-enabled multiplexed pathogen detection

Multiplexed detection of biomarkers in real-time is crucial for sensitive and accurate diagnosis at the point of use. This scenario poses tremendous challenges for detection and identification of signals of varying shape and quality at the edge of the signal-to-noise limit. Here, we demonstrate a robust target identification scheme that utilizes a Deep Neural Network (DNN) for multiplex detection of single particles and molecular biomarkers. The model combines fast wavelet particle detection with Short-Time Fourier Transform analysis, followed by DNN identification on an AI-specific edge device (Google Coral Dev board). The approach is validated using multi-spot optical excitation of Klebsiella Pneumoniae bacterial nucleic acids flowing through an optofluidic waveguide chip that produces fluorescence signals of varying amplitude, duration, and quality. Amplification-free 3× multiplexing in real-time is demonstrated with excellent specificity, sensitivity, and a classification accuracy of 99.8%. These results show that a minimalistic DNN design optimized for mobile devices provides a robust framework for accurate pathogen detection using compact, low-cost diagnostic devices.

An autonomous microchip for real-time, label-free immune cell analysis

Characterization of cell populations and identification of distinct subtypes based on surface markers are needed in a variety of applications from basic research and clinical assays to cell manufacturing. Conventional immunophenotyping techniques such as flow cytometry or fluorescence microscopy require immunolabeling of cells, expensive and complex instrumentation, skilled operators, and are therefore incompatible with field deployment and automated cell manufacturing systems. In this work, we introduce an autonomous microchip that can electronically quantify the immunophenotypical composition of a cell suspension. Our microchip identifies different cell subtypes by capturing each in different microfluidic chambers functionalized against the markers of the target populations. All on-chip activity is electronically monitored by an integrated sensor network, which informs an algorithm determining subpopulation fractions from chip-wide immunocapture statistics in real time. Moreover, optimal operational conditions within the chip are enforced through a closed-loop feedback control on the sensor data and the cell flow speed, and hence, the antibody-antigen interaction time is maintained within its optimal range for selective immunocapture. We apply our microchip to analyze a mixture of unlabeled CD4⁺ and CD8⁺ T cell sub-populations and then validated the results against flow cytometry measurements. The demonstrated capability to quantitatively analyze immune cells with no labels has the potential to enable not only autonomous biochip-based immunoassays for remote testing but also cell manufacturing bioreactors with built-in, adaptive quality control.

Immunomagnetic leukocyte differential in whole blood on an electronic microdevice

Leukocytes are the frontline defense mechanism of the immune system. Their composition dynamically changes as a response to a foreign body, infection, inflammation, or other malignant behavior occurring within the body. Monitoring the composition of leukocytes, namely leukocyte differential, is a crucial assay periodically performed to diagnose an infection or to assess a person's vulnerability for a health anomaly. Currently, leukocyte differential analysis is performed using hematology analyzers or flow cytometers, both of which are bulky instruments that require trained and certified personnel for operation. In this work, we demonstrate a new technique to obtain leukocyte differentials in a highly portable and integrated microfluidic chip by magnetically analyzing the CD33 expression of leukocytes. When benchmarked against conventional laboratory instruments, our technology demonstrated <5% difference on average for all subtypes. Our results show that hematology testing could be performed beyond the centralized laboratories at a low cost and ultimately provide point-of-care and at-home testing opportunities.

Electronic measurement of cell antigen expression in whole blood

Membrane antigens are phenotypic signatures of cells used for distinguishing various subpopulations and, therefore, are of great interest for diagnosis of diseases and monitoring of patients in hematology and oncology. Existing methods to measure antigen expression of a target subpopulation in blood samples require labor-intensive lysis of contaminating cells and subsequent analysis with complex and bulky instruments in specialized laboratories. To address this long-standing limitation in clinical cytometry, we introduce a microchip-based technique that can directly measure surface expression of target cells in hematological samples. Our microchip isolates an immunomagnetically-labeled target cell population from the contaminating background in whole blood and then utilizes the differential responses of target cells to on-chip magnetic manipulation to estimate their antigen expression. Moreover, manipulating cells with chip-sized permanent magnets and performing quantitative measurements via an on-chip electrical sensor network allows the assay to be performed in a portable platform with no reliance on laboratory infrastructure. Using our technique, we could successfully measure expressions of the CD45 antigen that is commonly expressed by white blood cells, as well as CD34 that is expressed by scarce hematopoietic progenitor cells, which constitutes only ∼0.0001% of all blood cells, directly from whole blood. With our technology, flow cytometry can potentially become a rapid bedside or at-home testing method that is available around the clock in environments where this invaluable assay with proven clinical utility is currently either outsourced or not even accessible.

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.

Positional dependence of particles and cells in microfluidic electrical impedance flow cytometry: origin, challenges and opportunities

Microfluidic electrical impedance flow cytometry is now a well-known and established method for single-cell analysis. Given the richness of the information provided by impedance measurements, this non-invasive and label-free approach can be used in a wide field of applications ranging from simple cell counting to disease diagnostics. One of its major limitations is the variation of the impedance signal with the position of the cell in the sensing area. Indeed, identical particles traveling along different trajectories do not result in the same data. The positional dependence can be considered as a challenge for the accuracy of microfluidic impedance cytometers. On the other hand, it has recently been regarded by several groups as an opportunity to estimate the position of particles in the microchannel and thus take a further step in the logic of integrating sensors in so-called "Lab-on-a-chip" devices. This review provides a comprehensive overview of the physical grounds of the positional dependence of impedance measurements. Then, both the developed strategies to reduce position influence in impedance-based assays and the recent reported technologies exploiting that dependence for the integration of position detection in microfluidic devices are reviewed.

Combinatorial Immunophenotyping of Cell Populations with an Electronic Antibody Microarray

Immunophenotyping is widely used to characterize cell populations in basic research and to diagnose diseases from surface biomarkers in the clinic. This process usually requires complex instruments such as flow cytometers or fluorescence microscopes, which are typically housed in centralized laboratories. Microfluidics are combined with an integrated electrical sensor network to create an antibody microarray for label-free cell immunophenotyping against multiple antigens. The device works by fractionating the sample via capturing target subpopulations in an array of microfluidic chambers functionalized against different antigens and by electrically quantifying the cell capture statistics through a network of code-multiplexed electrical sensors. Through a combinatorial arrangement of antibody sequences along different microfluidic paths, the device can measure the prevalence of different cell subpopulations in a sample from computational analysis of the electrical output signal. The device performance is characterized by analyzing heterogeneous samples of mixed tumor cell populations and then the technique is applied to determine leukocyte subpopulations in blood samples and the results are validated against complete blood cell count and flow cytometry results. Label-free immunophenotyping of cell populations against multiple targets on a disposable electronic chip presents opportunities in global health and telemedicine applications for cell-based diagnostics and health monitoring.

Processing code-multiplexed Coulter signals via deep convolutional neural networks

Beyond their conventional use of counting and sizing particles, Coulter sensors can be used to spatially track suspended particles, with multiple sensors distributed over a microfluidic chip. Code-multiplexing of Coulter sensors allows such integration to be implemented with simple hardware but requires advanced signal processing to extract multi-dimensional information from the output waveform. In this work, we couple deep learning-based signal analysis with microfluidic code-multiplexed Coulter sensor networks. Specifically, we train convolutional neural networks to analyze Coulter waveforms not only to recognize certain sensor waveform patterns but also to resolve interferences among them. Our technology predicts the size, speed, and location of each detected particle. We show that the algorithm yields a >90% pattern recognition accuracy for distinguishing non-correlated waveform patterns at a processing speed that can potentially enable real-time microfluidic assays. Furthermore, once trained, the algorithm can readily be applied for processing electrical data from other microfluidic devices integrated with the same Coulter sensor network.

Electronic profiling of membrane antigen expression via immunomagnetic cell manipulation

Membrane antigens control cell function by regulating biochemical interactions and hence are routinely used as diagnostic and prognostic targets in biomedicine. Fluorescent labeling and subsequent optical interrogation of cell membrane antigens, while highly effective, limit expression profiling to centralized facilities that can afford and operate complex instrumentation. Here, we introduce a cytometry technique that computes surface expression of immunomagnetically labeled cells by electrically tracking their trajectory under a magnetic field gradient on a microfluidic chip with a throughput of >500 cells per min. In addition to enabling the creation of a frugal cytometry platform, this immunomagnetic cell manipulation-based measurement approach allows direct expression profiling of target subpopulations from non-purified samples. We applied our technology to measure epithelial cell adhesion molecule expression on human breast cancer cells. Once calibrated, surface expression and size measurements match remarkably well with fluorescence-based measurements from a commercial flow cytometer. Quantitative measurements of biochemical and biophysical cell characteristics with a disposable cytometer have the potential to impact point of care testing of clinical samples particularly in resource limited settings.

Scaling code-multiplexed electrode networks for distributed Coulter detection in microfluidics

Microfluidic devices can discriminate particles based on their properties and map them into different locations on the device. For distributed detection of these particles, we have recently introduced a multiplexed sensing technique called Microfluidic CODES, which combines code division multiple access with Coulter sensing. Our technique relies on micromachined sensor geometries to produce distinct waveforms that can uniquely be linked to specific locations on the microfluidic device. In this work, we investigated the scaling of the code-multiplexed Coulter sensor network through theoretical and experimental analysis. As a model system, we designed and fabricated a microfluidic device integrated with a network of 10 code-multiplexed sensors, each of which was characterized and verified to produce a 31-bit orthogonal digital code. To predict the performance of the sensor network, we developed a mathematical model based on communications and coding theory, and calculated the error rate for our sensor network as a function of the network size and sample properties. We theoretically and experimentally demonstrated the effect of electrical impedance on the signal-to-noise ratio and developed an optimized device. We also introduced a computational approach that can process the sensor network data with minimal input from the user and demonstrated system-level operation by processing suspensions of cultured human cancer cells. Taken together, our results demonstrated the feasibility of deploying large-scale code-multiplexed electrode networks for distributed Coulter detection to realize integrated lab-on-a-chip devices.

Node-Pore Coded Coincidence Correction: Coulter Counters, Code Design, and Sparse Deconvolution

We present a novel method to perform individual particle (e.g. cells or viruses) coincidence correction through joint channel design and algorithmic methods. Inspired by multiple-user communication theory, we modulate the channel response, with Node-Pore Sensing, to give each particle a binary Barker code signature. When processed with our modified successive interference cancellation method, this signature enables both the separation of coincidence particles and a high sensitivity to small particles. We identify several sources of modeling error and mitigate most effects using a data-driven self-calibration step and robust regression. Additionally, we provide simulation analysis to highlight our robustness, as well as our limitations, to these sources of stochastic system model error. Finally, we conduct experimental validation of our techniques using several encoded devices to screen a heterogeneous sample of several size particles.

Lab-on-a-chip electrical multiplexing techniques for cellular and molecular biomarker detection

Signal multiplexing is vital to develop lab-on-a-chip devices that can detect and quantify multiple cellular and molecular biomarkers with high throughput, short analysis time, and low cost. Electrical detection of biomarkers has been widely used in lab-on-a-chip devices because it requires less external equipment and simple signal processing and provides higher scalability. Various electrical multiplexing for lab-on-a-chip devices have been developed for comprehensive, high throughput, and rapid analysis of biomarkers. In this paper, we first briefly introduce the widely used electrochemical and electrical impedance sensing methods. Next, we focus on reviewing various electrical multiplexing techniques that had achieved certain successes on rapid cellular and molecular biomarker detection, including direct methods (spatial and time multiplexing), and emerging technologies (frequency, codes, particle-based multiplexing). Lastly, the future opportunities and challenges on electrical multiplexing techniques are also discussed.