A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry

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.

Recent developments and future perspectives of microfluidics and smart technologies in wearable devices

Wearable devices are gaining popularity in the fields of health monitoring, diagnosis, and drug delivery. Recent advances in wearable technology have enabled real-time analysis of biofluids such as sweat, interstitial fluid, tears, saliva, wound fluid, and urine. The integration of microfluidics and emerging smart technologies, such as artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT), into wearable devices offers great potential for accurate and non-invasive monitoring and diagnosis. This paper provides an overview of current trends and developments in microfluidics and smart technologies in wearable devices for analyzing body fluids. The paper discusses common microfluidic technologies in wearable devices and the challenges associated with analyzing each type of biofluid. The paper emphasizes the importance of combining smart technologies with microfluidics in wearable devices, and how they can aid diagnosis and therapy. Finally, the paper covers recent applications, trends, and future developments in the context of intelligent microfluidic wearable devices.

Convolutional Neural Network-Driven Impedance Flow Cytometry for Accurate Bacterial Differentiation

Impedance flow cytometry (IFC) has been demonstrated to be an efficient tool for label-free bacterial investigation to obtain the electrical properties in real time. However, the accurate differentiation of different species of bacteria by IFC technology remains a challenge owing to the insignificant differences in data. Here, we developed a convolutional neural networks (ConvNet) deep learning approach to enhance the accuracy and efficiency of the IFC toward distinguishing various species of bacteria. First, more than 1 million sets of impedance data (comprising 42 characteristic features for each set) of various groups of bacteria were trained by the ConvNet model. To improve the efficiency for data analysis, the Spearman correlation coefficient and the mean decrease accuracy of the random forest algorithm were introduced to eliminate feature interaction and extract the opacity of impedance related to the bacterial wall and membrane structure as the predominant features in bacterial differentiation. Moreover, the 25 optimized features were selected with differentiation accuracies of >96% for three groups of bacteria (bacilli, cocci, and vibrio) and >95% for two species of bacilli (Escherichia coli and Salmonella enteritidis), compared to machine learning algorithms (complex tree, linear discriminant, and K-nearest neighbor algorithms) with a maximum accuracy of 76.4%. Furthermore, bacterial differentiation was achieved on spiked samples of different species with different mixing ratios. The proposed ConvNet deep learning-assisted data analysis method of IFC exhibits advantages in analyzing a huge number of data sets with capacity for extracting predominant features within multicomponent information and will bring about progress and advances in the fields of both biosensing and data analysis.

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.

Machine learning classification of cellular states based on the impedance features derived from microfluidic single-cell impedance flow cytometry

Mitosis is a crucial biological process where a parental cell undergoes precisely controlled functional phases and divides into two daughter cells. Some drugs can inhibit cell mitosis, for instance, the anti-cancer drugs interacting with the tumor cell proliferation and leading to mitosis arrest at a specific phase or cell death eventually. Combining machine learning with microfluidic impedance flow cytometry (IFC) offers a concise way for label-free and high-throughput classification of drug-treated cells at single-cell level. IFC-based single-cell analysis generates a large amount of data related to the cell electrophysiology parameters, and machine learning helps establish correlations between these data and specific cell states. This work demonstrates the application of machine learning for cell state classification, including the binary differentiations between the G1/S and apoptosis states and between the G2/M and apoptosis states, as well as the classification of three subpopulations comprising a subgroup insensitive to the drug beyond the two drug-induced states of G2/M arrest and apoptosis. The impedance amplitudes and phases used as input features for the model training were extracted from the IFC-measured datasets for the drug-treated tumor cells. The deep neural network (DNN) model was exploited here with the structure (e.g., hidden layer number and neuron number in each layer) optimized for each given cell type and drug. For the H1650 cells, we obtained an accuracy of 78.51% for classification between the G1/S and apoptosis states and 82.55% for the G2/M and apoptosis states. For HeLa cells, we achieved a high accuracy of 96.94% for classification between the G2/M and apoptosis states, both of which were induced by taxol treatment. Even higher accuracy approaching 100% was achieved for the vinblastine-treated HeLa cells for the differentiation between the viable and non-viable states, and between the G2/M and apoptosis states. We also demonstrate the capability of the DNN model for high-accuracy classification of the three subpopulations in a complete cell sample treated by taxol or vinblastine.

When microplastics meet electroanalysis: future analytical trends for an emerging threat

Microplastics are a major modern challenge that must be addressed to protect the environment, particularly the marine environment. Microplastics, defined as particles ≤5 mm, are ubiquitous in the environment. Their small size for a relatively large surface area, high persistence and easy distribution in water, soil and air require the development of new analytical methods to monitor their presence. At present, the availability of analytical techniques that are easy to use, automated, inexpensive and based on new approaches to improve detection remains an open challenge. This review aims to outline the evolution and novelties of classical and advanced methods, in particular the recently reported electroanalytical detectors, methods and devices. Among all the studies reviewed here, we highlight the great advantages of electroanalytical tools over spectroscopic and thermal analysis, especially for the rapid and accurate detection of microplastics in the sub-micron range. Finally, the challenges faced in the development of automated analytical methods are discussed, highlighting recent trends in artificial intelligence (AI) in microplastics analysis.

Advances in current in vitro models on neurodegenerative diseases

Many neurodegenerative diseases are identified but their causes and cure are far from being well-known. The problem resides in the complexity of the neural tissue and its location which hinders its easy evaluation. Although necessary in the drug discovery process, in vivo animal models need to be reduced and show relevant differences with the human tissues that guide scientists to inquire about other possible options which lead to in vitro models being explored. From organoids to organ-on-a-chips, 3D models are considered the cutting-edge technology in cell culture. Cell choice is a big parameter to take into consideration when planning an in vitro model and cells capable of mimicking both healthy and diseased tissue, such as induced pluripotent stem cells (iPSC), are recognized as good candidates. Hence, we present a critical review of the latest models used to study neurodegenerative disease, how these models have evolved introducing microfluidics platforms, 3D cell cultures, and the use of induced pluripotent cells to better mimic the neural tissue environment in pathological conditions.

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.

Structural Optimization Design of Microfluidic Chips Based on Fast Sequence Pair Algorithm

The market for microfluidic chips is experiencing significant growth; however, their development is hindered by a complex design process and low efficiency. Enhancing microfluidic chips' design quality and efficiency has emerged as an integral approach to foster their advancement. Currently, the existing structural design schemes lack careful consideration regarding the impact of chip area, microchannel length, and the number of intersections on chip design. This inadequacy leads to redundant chip structures resulting from the separation of layout and wiring design. This study proposes a structural optimization method for microfluidic chips to address these issues utilizing a simulated annealing algorithm. The simulated annealing algorithm generates an initial solution in advance using the fast sequence pair algorithm. Subsequently, an improved simulated annealing algorithm is employed to obtain the optimal solution for the device layout. During the wiring stage, an advanced wiring method is used to designate the high wiring area, thereby increasing the success rate of microfluidic chip wiring. Furthermore, the connection between layout and routing is reinforced through an improved layout adjustment method, which reduces the length of microchannels and the number of intersections. Finally, the effectiveness of the structural optimization approach is validated through six sets of test cases, successfully achieving the objective of enhancing the design quality of microfluidic chips.

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.

Supervised learning on impedance cytometry data for label-free biophysical distinction of pancreatic cancer cells versus their associated fibroblasts under gemcitabine treatment

Chemotherapy failure in pancreatic cancer patients is widely attributed to cancer cell reprogramming towards drug resistance by cancer associated fibroblasts (CAFs), which are the abundant cell type in the tumor microenvironment. Association of drug resistance to specific cancer cell phenotypes within multicellular tumors can advance isolation protocols for enabling cell-type specific gene expression markers to identify drug resistance. This requires the distinction of drug resistant cancer cells versus CAFs, which is challenging since permeabilization of CAF cells during drug treatment can cause non-specific uptake of cancer cell-specific stains. Cellular biophysical metrics, on the other hand, can provide multiparametric information to assess the gradual alteration of target cancer cells towards drug resistance, but these phenotypes need to be distinguished versus CAFs. Using pancreatic cancer cells and CAFs from a metastatic patient-derived tumor that exhibits cancer cell drug resistance under CAF co-culture, the biophysical metrics from multifrequency single-cell impedance cytometry are utilized for distinction of the subpopulation of viable cancer cells versus CAFs, before and after gemcitabine treatment. This is accomplished through supervised machine learning after training the model using key impedance metrics for cancer cells and CAFs from transwell co-cultures, so that an optimized classifier model can recognize each cell type and predict their respective proportions in multicellular tumor samples, before and after gemcitabine treatment, as validated by their confusion matrix and flow cytometry assays. In this manner, an aggregate of the distinguishing biophysical metrics of viable cancer cells after gemcitabine treatment in co-cultures with CAFs can be used in longitudinal studies, to classify and isolate the drug resistant subpopulation for identifying markers.

Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning

Individual cells have many unique properties that can be quantified to develop a holistic understanding of a population. This can include understanding population characteristics, identifying subpopulations, or elucidating outlier characteristics that may be indicators of disease. Electrical impedance measurements are rapid and label-free for the monitoring of single cells and generate large datasets of many cells at single or multiple frequencies. To increase the accuracy and sensitivity of measurements and define the relationships between impedance and biological features, many electrical measurement systems have incorporated machine learning (ML) paradigms for control and analysis. Considering the difficulty capturing complex relationships using traditional modelling and statistical methods due to population heterogeneity, ML offers an exciting approach to the systemic collection and analysis of electrical properties in a data-driven way. In this work, we discuss incorporation of ML to improve the field of electrical single cell analysis by addressing the design challenges to manipulate single cells and sophisticated analysis of electrical properties that distinguish cellular changes. Looking forward, we emphasize the opportunity to build on integrated systems to address common challenges in data quality and generalizability to save time and resources at every step in electrical measurement of single cells.

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.

Advances in Integration, Wearable Applications, and Artificial Intelligence of Biomedical Microfluidics Systems

Microfluidics attracts much attention due to its multiple advantages such as high throughput, rapid analysis, low sample volume, and high sensitivity. Microfluidics has profoundly influenced many fields including chemistry, biology, medicine, information technology, and other disciplines. However, some stumbling stones (miniaturization, integration, and intelligence) strain the development of industrialization and commercialization of microchips. The miniaturization of microfluidics means fewer samples and reagents, shorter times to results, and less footprint space consumption, enabling a high throughput and parallelism of sample analysis. Additionally, micro-size channels tend to produce laminar flow, which probably permits some creative applications that are not accessible to traditional fluid-processing platforms. The reasonable integration of biomedical/physical biosensors, semiconductor microelectronics, communications, and other cutting-edge technologies should greatly expand the applications of current microfluidic devices and help develop the next generation of lab-on-a-chip (LOC). At the same time, the evolution of artificial intelligence also gives another strong impetus to the rapid development of microfluidics. Biomedical applications based on microfluidics normally bring a large amount of complex data, so it is a big challenge for researchers and technicians to analyze those huge and complicated data accurately and quickly. To address this problem, machine learning is viewed as an indispensable and powerful tool in processing the data collected from micro-devices. In this review, we mainly focus on discussing the integration, miniaturization, portability, and intelligence of microfluidics technology.

A low-cost, label-free microfluidic scanning flow cytometer for high-accuracy quantification of size and refractive index of particles

Flow cytometers and fluorescence activated cells sorters (FCM/FACS) represent the gold standard for high-throughput single-cell analysis, but their usefulness for label-free applications is limited by the unreliability of forward and side scatter measurements. Scanning flow cytometers represent an appealing alternative, as they exploit measurements of the angle-resolved scattered light to provide accurate and quantitative estimates of cellular properties, but the requirements of current setups are unsuitable for integration with other lab-on-chip technologies or for point-of-care applications. Here we present the first microfluidic scanning flow cytometer (μSFC), able to achieve accurate angle-resolved scattering measurements within a standard polydimethylsiloxane microfluidic chip. The system exploits a low cost linearly variable optical density (OD) filter to reduce the dynamic range of the signal and to increase its signal-to-noise ratio. We present a performance comparison between the μSFC and commercial machines for the label free characterization of polymeric beads with different diameters and refractive indices. In contrast to FCM and FACS, the μSFC yields size estimates linearly correlated with nominal particle sizes (R² = 0.99) and quantitative estimates of particle refractive indices. The feasibility of using the μSFC for the characterization of biological samples is demonstrated by analyzing a population of monocytes identified based on the morphology of a peripheral blood mononuclear cells sample, which yields values in agreement with the literature. The proposed μSFC combines low setup requirements with high performance, and has great potential for integration within other lab-on-chip systems for multi-parametric cell analysis and for next-generation point-of-care diagnostic applications.

Microsystem Advances through Integration with Artificial Intelligence

Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.

Floating-Electrode-Enabled Impedance Cytometry for Single-Cell 3D Localization

As a label-free, low-cost, and noninvasive tool, impedance measurement has been widely used in single-cell characterization analysis. However, due to the tiny volume of cells, the uncertainty of the spatial position in the microchannel will bring measurement errors in single-cell electrical parameters. To overcome the issue, we designed a novel microdevice configured with a coplanar differential electrode structure to accurately resolve the spatial position of single cells without constraining techniques such as additional sheath fluids or narrow microchannels. The device precisely localizes single cells by measuring the induced current generated by the combined action of the floating electrode and the differential electrodes when single cells flow through the electrode-sensing area. The device was experimentally validated by measuring 6 μm yeast cells and 10 μm particles, achieving spatial localization with a resolution down to 2.1 μm (about 5.3% of the channel width) in lateral direction and 1.2 μm (about 5.9% of the channel height) in the vertical direction at a flow rate of 1.2 μL/min. In addition, by comparing measurement of yeast cells and particles, it was demonstrated that the device not only localizes the single cells or particles but also simultaneously characterizes their status properties such as velocity and size. The device offers a competitive electrode configuration in impedance cytometry with the advantages of simple structure, low cost, and high throughput, promising cell localization and thus electrical characterization.

Concepts, electrode configuration, characterization, and data analytics of electric and electrochemical microfluidic platforms: a review

Microfluidic cytometry (MC) and electrical impedance spectroscopy (EIS) are two important techniques in biomedical engineering. Microfluidic cytometry has been utilized in various fields such as stem cell differentiation and cancer metastasis studies, and provides a simple, label-free, real-time method for characterizing and monitoring cellular fates. The impedance microdevice, including impedance flow cytometry (IFC) and electrical impedance spectroscopy (EIS), is integrated into MC systems. IFC measures the impedance of individual cells as they flow through a microfluidic device, while EIS measures impedance changes during binding events on electrode regions. There have been significant efforts to improve and optimize these devices for both basic research and clinical applications, based on the concepts, electrode configurations, and cell fates. This review outlines the theoretical concepts, electrode engineering, and data analytics of these devices, and highlights future directions for development.

A Review of Advanced Multifunctional Magnetic Nanostructures for Cancer Diagnosis and Therapy Integrated into an Artificial Intelligence Approach

The new era of nanomedicine offers significant opportunities for cancer diagnostics and treatment. Magnetic nanoplatforms could be highly effective tools for cancer diagnosis and treatment in the future. Due to their tunable morphologies and superior properties, multifunctional magnetic nanomaterials and their hybrid nanostructures can be designed as specific carriers of drugs, imaging agents, and magnetic theranostics. Multifunctional magnetic nanostructures are promising theranostic agents due to their ability to diagnose and combine therapies. This review provides a comprehensive overview of the development of advanced multifunctional magnetic nanostructures combining magnetic and optical properties, providing photoresponsive magnetic platforms for promising medical applications. Moreover, this review discusses various innovative developments using multifunctional magnetic nanostructures, including drug delivery, cancer treatment, tumor-specific ligands that deliver chemotherapeutics or hormonal agents, magnetic resonance imaging, and tissue engineering. Additionally, artificial intelligence (AI) can be used to optimize material properties in cancer diagnosis and treatment, based on predicted interactions with drugs, cell membranes, vasculature, biological fluid, and the immune system to enhance the effectiveness of therapeutic agents. Furthermore, this review provides an overview of AI approaches used to assess the practical utility of multifunctional magnetic nanostructures for cancer diagnosis and treatment. Finally, the review presents the current knowledge and perspectives on hybrid magnetic systems as cancer treatment tools with AI models.

Supervised machine learning in microfluidic impedance flow cytometry for improved particle size determination

The assessment of particle and cell size in electrical microfluidic flow cytometers has become common practice. Nevertheless, in flow cytometers with coplanar electrodes accurate determination of particle size is difficult, owing to the inhomogeneous electric field. Pre-defined signal templates and compensation methods have been introduced to correct for this positional dependence, but are cumbersome when dealing with irregular signal shapes. We introduce a simple and accurate post-processing method without the use of pre-defined signal templates and compensation functions using supervised machine learning. We implemented a multiple linear regression model and show an average reduction of the particle diameter variation by 37% with respect to an earlier processing method based on a feature extraction algorithm and compensation function. Furthermore, we demonstrate its application in flow cytometry by determining the size distribution of a population of small (4.6 ± 0.9 μm) and large (5.9 ± 0.8 μm) yeast cells. The improved performance of this coplanar, two electrode chip enables precise cell size determination in easy to fabricate impedance flow cytometers.

Label-free microfluidic cell sorting and detection for rapid blood analysis

Blood tests are considered as standard clinical procedures to screen for markers of diseases and health conditions. However, the complex cellular background (>99.9% RBCs) and biomolecular composition often pose significant technical challenges for accurate blood analysis. An emerging approach for point-of-care blood diagnostics is utilizing "label-free" microfluidic technologies that rely on intrinsic cell properties for blood fractionation and disease detection without any antibody binding. A growing body of clinical evidence has also reported that cellular dysfunction and their biophysical phenotypes are complementary to standard hematoanalyzer analysis (complete blood count) and can provide a more comprehensive health profiling. In this review, we will summarize recent advances in microfluidic label-free separation of different blood cell components including circulating tumor cells, leukocytes, platelets and nanoscale extracellular vesicles. Label-free single cell analysis of intrinsic cell morphology, spectrochemical properties, dielectric parameters and biophysical characteristics as novel blood-based biomarkers will also be presented. Next, we will highlight research efforts that combine label-free microfluidics with machine learning approaches to enhance detection sensitivity and specificity in clinical studies, as well as innovative microfluidic solutions which are capable of fully integrated and label-free blood cell sorting and analysis. Lastly, we will envisage the current challenges and future outlook of label-free microfluidics platforms for high throughput multi-dimensional blood cell analysis to identify non-traditional circulating biomarkers for clinical diagnostics.

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.

Single-Cell Classification Based on Population Nucleus Size Combining Microwave Impedance Spectroscopy and Machine Learning

Many recent efforts in the diagnostic field address the accessibility of cancer diagnosis. Typical histological staining methods identify cancer cells visually by a larger nucleus with more condensed chromatin. Machine learning (ML) has been incorporated into image analysis for improving this process. Recently, impedance spectrometers have been shown to generate all-inclusive lab-on-a-chip platforms to detect nucleus abnormities. In this paper, a wideband electrical sensor and data analysis paradigm that can identify nuclear changes shows the realization of a single-cell microfluidic device to detect nuclei of altered sizes. To model cells of altered nucleus, Jurkat cells were treated to enlarge or shrink their nucleus followed by broadband sensing to obtain the S-parameters of single cells. The ability to deduce important frequencies associated with nucleus size is demonstrated and used to improve classification models in both binary and multiclass scenarios, despite a heterogeneous and overlapping cell population. The important frequency features match those predicted in a double-shell circuit model published in prior work, demonstrating a coherent new analytical technique for electrical data analysis. The electrical sensing platform assisted by ML with impressive accuracy of cell classification looks forward to a label-free and flexible approach to cancer diagnosis.

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.

Microfluidic Systems for Blood and Blood Cell Characterization

A laboratory blood test is vital for assessing a patient's health and disease status. Advances in microfluidic technology have opened the door for on-chip blood analysis. Currently, microfluidic devices can reproduce myriad routine laboratory blood tests. Considerable progress has been made in microfluidic cytometry, blood cell separation, and characterization. Along with the usual clinical parameters, microfluidics makes it possible to determine the physical properties of blood and blood cells. We review recent advances in microfluidic systems for measuring the physical properties and biophysical characteristics of blood and blood cells. Added emphasis is placed on multifunctional platforms that combine several microfluidic technologies for effective cell characterization. The combination of hydrodynamic, optical, electromagnetic, and/or acoustic methods in a microfluidic device facilitates the precise determination of various physical properties of blood and blood cells. We analyzed the physical quantities that are measured by microfluidic devices and the parameters that are determined through these measurements. We discuss unexplored problems and present our perspectives on the long-term challenges and trends associated with the application of microfluidics in clinical laboratories. We expect the characterization of the physical properties of blood and blood cells in a microfluidic environment to be considered a standard blood test in the future.

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.

Classification between Normal and Cancerous Human Urothelial Cells by Using Micro-Dimensional Electrochemical Impedance Spectroscopy Combined with Machine Learning

Although the high incidence and recurrence rates of urothelial cancer of the bladder (UCB) are heavy burdens, a noninvasive tool for effectively detecting UCB as an alternative to voided urine cytology, which has low sensitivity, is yet to be reported. Herein, we propose an intelligent discrimination method between normal (SV-HUC-1) and cancerous (TCCSUP) urothelial cells by using a combination of micro-dimensional electrochemical impedance spectroscopy (µEIS) with machine learning (ML) for a noninvasive and high-accuracy UCB diagnostic tool. We developed a unique valved flow cytometry, equipped with a pneumatic valve to increase sensitivity without cell clogging. Since contact between a cell and electrodes is tight with a high volume fraction, the electric field can be effectively confined to the cell. This enables the proposed sensor to highly discriminate different cell types at frequencies of 10, 50, 100, 500 kHz, and 1 MHz. A total of 236 impedance spectra were applied to six ML models, and systematic comparisons of the ML models were carried out. The hyperparameters were estimated by conducting a grid search or Bayesian optimization. Among the ML models, random forest strongly discriminated between SV-HUC-1 and TCCSUP, with an accuracy of 91.7%, sensitivity of 92.9%, precision of 92.9%, specificity of 90%, and F1-score of 93.8%.

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.

Optimization of Microchannels and Application of Basic Activation Functions of Deep Neural Network for Accuracy Analysis of Microfluidic Parameter Data

The fabrication of microflow channels with high accuracy in terms of the optimization of the proposed designs, minimization of surface roughness, and flow control of microfluidic parameters is challenging when evaluating the performance of microfluidic systems. The use of conventional input devices, such as peristaltic pumps and digital pressure pumps, to evaluate the flow control of such parameters cannot confirm a wide range of data analysis with higher accuracy because of their operational drawbacks. In this study, we optimized the circular and rectangular-shaped microflow channels of a 100 μm microfluidic chip using a three-dimensional simulation tool, and analyzed concentration profiles of different regions of the microflow channels. Then, we applied a deep learning (DL) algorithm for the dense layers of the rectified linear unit (ReLU), Leaky ReLU, and Swish activation functions to train and test 1600 experimental and interpolation of data samples which obtained from the microfluidic chip. Moreover, using the same DL algorithm, we configured three models for each of these three functions by changing the internal middle layers of these models. As a result, we obtained a total of 9 average accuracy values of ReLU, Leaky ReLU, and Swish functions for a defined threshold value of 6×10-5 using the trial-and-error method. We applied single-to-five-fold cross-validation technique of deep neural network to avoid overfitting and reduce noises from data-set to evaluate better average accuracy of data of microfluidic parameters.

Biosensors and machine learning for enhanced detection, stratification, and classification of cells: a review

Biological cells, by definition, are the basic units which contain the fundamental molecules of life of which all living things are composed. Understanding how they function and differentiating cells from one another, therefore, is of paramount importance for disease diagnostics as well as therapeutics. Sensors focusing on the detection and stratification of cells have gained popularity as technological advancements have allowed for the miniaturization of various components inching us closer to Point-of-Care (POC) solutions with each passing day. Furthermore, Machine Learning has allowed for enhancement in the analytical capabilities of these various biosensing modalities, especially the challenging task of classification of cells into various categories using a data-driven approach rather than physics-driven. In this review, we provide an account of how Machine Learning has been applied explicitly to sensors that detect and classify cells. We also provide a comparison of how different sensing modalities and algorithms affect the classifier accuracy and the dataset size required.

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.

Neural Network-Based Optimization of an Acousto Microfluidic System for Submicron Bioparticle Separation

The advancement in microfluidics has provided an excellent opportunity for shifting from conventional sub-micron-sized isolation and purification methods to more robust and cost-effective lab-on-chip platforms. The acoustic-driven separation approach applies differential forces acting on target particles, guiding them towards different paths in a label-free and biocompatible manner. The main challenges in designing the acoustofluidic-based isolation platforms are minimizing the reflected radio frequency signal power to achieve the highest acoustic radiation force acting on micro/nano-sized particles and tuning the bandwidth of the acoustic resonator in an acceptable range for efficient size-based binning of particles. Due to the complexity of the physics involved in acoustic-based separations, the current existing lack in performance predictive understanding makes designing these miniature systems iterative and resource-intensive. This study introduces a unique approach for design automation of acoustofluidic devices by integrating the machine learning and multi-objective heuristic optimization approaches. First, a neural network-based prediction platform was developed to predict the resonator's frequency response according to different geometrical configurations of interdigitated transducers In the next step, the multi-objective optimization approach was executed for extracting the optimum design features for maximum possible device performance according to decision-maker criteria. The results show that the proposed methodology can significantly improve the fine-tuned IDT designs with minimum power loss and maximum working frequency range. The examination of the power loss and bandwidth on the alternation and distribution of the acoustic pressure inside the microfluidic channel was carried out by conducting a 3D finite element-based simulation. The proposed methodology improves the performance of the acoustic transducer by overcoming the constraints related to bandwidth operation, the magnitude of acoustic radiation force on particles, and the distribution of pressure acoustic inside the microchannel.

Aluminum Oxide-Coated Particle Differentiation Employing Supervised Machine Learning and Impedance Cytometry

This article uses a supervised machine learning (ML) system for identifying groups of nanoparticles coated with metal oxides of varying thicknesses using a microfluidic impedance cytometer. These particles generate unique impedance signatures when probed with a multifrequency electric field and finds applications in enabling many multiplexed biosensing technologies. However, current experimental and data processing techniques are unable to sensitively differentiate different metal oxide coated particle types. Here, we employ various machine learning models and collect multiple particle metrics measured. In reported experiments, a 75% accuracy was determined to separate aluminum oxide coated (10nm and 30nm), which is significantly greater than observing only univariate data between different microparticle types. This approach will enable ML models to differentiate such particles with greater accuracies.

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.

Joint Particle Detection and Analysis by a CNN and Adaptive Norm Minimization Approach

Optical flow cytometry is used as the gold standard in single cell function diagnostics with the drawback of involving high complexity and operator costs. Magnetic flow cytometers try to overcome this problem by replacing optical labeling with magnetic nanoparticles to assign each cell a magnetic fingerprint. This allows operators to obtain rich cell information from a biological sample with minimal sample preparation at near in-vivo conditions in a decentralized environment. A central task in flow cytometry is the determination of cell concentrations and cell parameters, e.g. hydrodynamic diameter. For the acquisition of this information, signal processing is an essential component. Previous approaches mainly focus on the processing of one-cell signals, leaving out superimposed signals originating from cells passing the magnetic sensors in close proximity. In this work, we present a framework for joint cell/particle detection and analysis, which is capable of processing one-cell as well as multi-cell signals. We employ deep learning and compressive sensing in this approach, which involves the minimization of an adaptive norm. We evaluate our method on simulated and experimental signals, the latter being obtained with polymer microparticles. Our results show that the framework is capable of counting cells with a relative error smaller than 2 %. Inference of cell parameters works reliably at both low and high noise levels.

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.

Exploiting machine learning for bestowing intelligence to microfluidics

Intelligent microfluidics is an emerging cross-discipline research area formed by combining microfluidics with machine learning. It uses the advantages of microfluidics, such as high throughput and controllability, and the powerful data processing capabilities of machine learning, resulting in improved systems in biotechnology and chemistry. Compared to traditional microfluidics using manual analysis methods, intelligent microfluidics needs less human intervention, and results in a more user-friendly experience with faster processing. There is a paucity of literature reviewing this burgeoning and highly promising cross-discipline. Therefore, we herein comprehensively and systematically summarize several aspects of microfluidic applications enabled by machine learning. We list the types of microfluidics used in intelligent microfluidic applications over the last five years, as well as the machine learning algorithms and the hardware used for training. We also present the most recent advances in key technologies, developments, challenges, and the emerging opportunities created by intelligent microfluidics.

Multiplexed assessment of engineered bacterial constructs for intracellular β-galactosidase expression by redox amplification on catechol-chitosan modified nanoporous gold

Synthetic biology approaches for rewiring of bacterial constructs to express particular intracellular factors upon induction with the target analyte are emerging as sensing paradigms for applications in environmental and in vivo monitoring. To aid in the design and optimization of bacterial constructs for sensing analytes, there is a need for lysis-free intracellular detection modalities that monitor the signal level and kinetics of expressed factors within different modified bacteria in a multiplexed manner, without requiring cumbersome surface immobilization. Herein, an electrochemical detection system on nanoporous gold that is electrofabricated with a biomaterial redox capacitor is presented for quantifying β-galactosidase expressed inside modified Escherichia coli constructs upon induction with dopamine. This nanostructure-mediated redox amplification approach on a microfluidic platform allows for multiplexed assessment of the expressed intracellular factors from different bacterial constructs suspended in distinct microchannels, with no need for cell lysis or immobilization. Since redox mediators present over the entire depth of the microchannel can interact with the electrode and with the E. coli construct in each channel, the platform exhibits high sensitivity and enables multiplexing. We envision its application in assessing synthetic biology-based approaches for comparing specificity, sensitivity, and signal response time upon induction with target analytes of interest.

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.

Biochip with multi-planar electrodes geometry for differentiation of non-spherical bioparticles in a microchannel

A biosensor capable of differentiating cells or other microparticles based on morphology finds significant biomedical applications. Examples may include morphological determination in the cellular division process, differentiation of bacterial cells, and cellular morphological variation in inflammation and cancer etc. Here, we present a novel integrated multi-planar microelectrodes geometry design that can distinguish a non-spherical individual particle flowing along a microchannel based on its electrical signature. We simulated multi-planar electrodes design in COMSOL Multiphysics and have shown that the changes in electrical field intensity corresponding to multiple particle morphologies can be distinguished. Our initial investigation has shown that top-bottom electrodes configuration produces significantly enhanced signal strength for a spherical particle as compared to co-planar configuration. Next, we integrated the co-planar and top-bottom configurations to develop a multi-planar microelectrode design capable of electrical impedance measurement at different spatial planes inside a microchannel by collecting multiple output signatures. We tested our integrated multi-planar electrode design with particles of different elliptical morphologies by gradually changing spherical particle dimensions to the non-spherical. The computed electrical signal ratio of non-spherical to spherical particle shows a very good correlation to predict the particle morphology. The biochip sensitivity is also found be independent of orientation of the particle flowing in the microchannel. Our integrated design will help develop the technology that will allow morphological analysis of various bioparticles in a microfluidic channel in the future.

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.

Organoid Technology: Current Standing and Future Perspectives

Organoids are powerful systems to facilitate the study of individuals' disorders and personalized treatments. Likewise, emerging this technology has improved the chance of translatability of drugs for pre-clinical therapies and mimicking the complexity of organs, while it proposes numerous approaches for human disease modeling, tissue engineering, drug development, diagnosis, and regenerative medicine. In this review, we outline the past/present organoid technology and summarize its faithful applications, then, we discuss the challenges and limitations encountered by 3D organoids. In the end, we offer the human organoids as basic mechanistic infrastructure for "human modelling" systems to prescribe personalized medicines. © AlphaMed Press 2021 SIGNIFICANCE STATEMENT: This concise review concerns about organoids, available methods for in vitro organoid formation and different types of human organoid models. We, then, summarize biological approaches to improve 3D organoids complexity and therapeutic potentials of organoids. Despite the existing incomprehensive review articles in literature that examine partial aspects of the organoid technology, the present review article comprehensively and critically presents this technology from different aspects. It effectively provides a systematic overview on the past and current applications of organoids and discusses the future perspectives and suggestions to improve this technology and its applications.

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.

Label-Free Quantification of Cell Cycle Synchronicity of Human Neural Progenitor Cells Based on Electrophysiology Phenotypes

The ability to coax human-induced pluripotent stem cells (hiPSCs) into human neural progenitor cells (hNPCs) can lead to novel drug discovery and transplant therapy platforms for neurological diseases. Since hNPCs can form organoids that mimic brain development, there is emerging interest in their label-free characterization for controlling cell composition to optimize organoid formation in three-dimensional (3D) cultures. However, this requires the ability to quantify hNPCs in heterogeneous samples with subpopulations of similar phenotype. Using high-throughput (>6000 cells per condition), single-cell impedance cytometry, we present the utilization of electrophysiology for quantification of hNPC subpopulations that are altered in cell cycle synchronicity by camptothecin (CPT) exposure. Electrophysiology phenotypes are determined from impedance magnitude and phase metrics for distinguishing each cell cycle phase, as validated by flow cytometry, for a wide range of subpopulation proportions. Using multishell dielectric models for each cell cycle phase, electrophysiology alterations with CPT dose could be predicted. This label-free detection strategy can prevent loss of cell viability to speed the optimization of cellular compositions for organoid development.

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

The biophysical analysis of single-cells by microfluidic impedance cytometry is emerging as a label-free and high-throughput means to stratify the heterogeneity of cellular systems based on their electrophysiology. Emerging applications range from fundamental life-science and drug assessment research to point-of-care diagnostics and precision medicine. Recently, novel chip designs and data analytic strategies are laying the foundation for multiparametric cell characterization and subpopulation distinction, which are essential to understand biological function, follow disease progression and monitor cell behaviour in microsystems. In this tutorial review, we present a comparative survey of the approaches to elucidate cellular and subcellular features from impedance cytometry data, covering the related subjects of device design, data analytics (i.e., signal processing, dielectric modelling, population clustering), and phenotyping applications. We give special emphasis to the exciting recent developments of the technique (timeframe 2017-2020) and provide our perspective on future challenges and directions. Its synergistic application with microfluidic separation, sensor science and machine learning can form an essential toolkit for label-free quantification and isolation of subpopulations to stratify heterogeneous biosystems.