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

Particle manipulation under X-force fields

Particle manipulation is a central technique that enhances numerous scientific and medical applications by exploiting micro- and nanoscale control within fluidic environments. In this review, we systematically explore the multifaceted domain of particle manipulation under the influence of various X-force fields, integral to lab-on-a-chip technologies. We dissect the fundamental mechanisms of hydrodynamic, gravitational, optical, magnetic, electrical, and acoustic forces and detail their individual and synergistic applications. In particular, our discourse extends to advanced multi-modal manipulation strategies that harness the combined power of these forces, revealing their enhanced efficiency and precision in complex assays and diagnostic frameworks. The integration of cutting-edge technologies such as artificial intelligence and autonomous systems further enhances the capabilities of these microfluidic platforms, leading to transformative innovations in personalized medicine and point-of-care diagnostics. This review not only highlights current technological advances, but also forecasts the trajectory of future developments, emphasizing the escalating precision and scalability essential for advancing lab-on-a-chip applications.

Cognitively Driven Autonomous Flow Chemistry for Producing On-Demand Perovskite Quantum Dots Via Advanced Closed-Loop Feedback Control

Recent developments in the synthesis of hybrid organic-inorganic halide perovskite quantum dots (HP-QDs) through compositional adjustments have highlighted their potential applications in the fields of photovoltaics and light sources due to their unique optoelectronic properties. However, traditional methods to fine-tune their composition involve repetitive, labor-intensive, and costly processes. Herein, the utilization of a continuous flow chemistry approach is developed, in combination with a Proportional-Integral (PI) feedback control system as an effective method for producing on-demand methylammonium lead bromoiodide (MAPbBrxI3-x) HP-QDs. The PI feedback control allows for real-time optimization of the flow rates of halide precursor solutions (halide PSs), enabling the precise tuning of the emission wavelength of HP-QDs. HP-QDs having an emission wavelength of 550 and 650 nm are synthesized through a blue-shifted and red-shifted algorithm, respectively, from any arbitrary reaction condition within 400 s. The iterative process through the PI feedback control produces the target HP-QDs with short rise time and low overshoot. The proposed automatic flow chemistry system integrated with a universal and accessible control algorithm of PI can generate the target HP-QDs with high accuracy, stability, and robustness, demonstrating a significant advancement in constructing an autonomous flow chemistry synthetic system.

Advancing Intelligent Organ-on-a-Chip Systems with Comprehensive In Situ Bioanalysis

In vitro models are essential to a broad range of biomedical research, such as pathological studies, drug development, and personalized medicine. As a potentially transformative paradigm for 3D in vitro models, organ-on-a-chip (OOC) technology has been extensively developed to recapitulate sophisticated architectures and dynamic microenvironments of human organs by applying the principles of life sciences and leveraging micro- and nanoscale engineering capabilities. A pivotal function of OOC devices is to support multifaceted and timely characterization of cultured cells and their microenvironments. However, in-depth analysis of OOC models typically requires biomedical assay procedures that are labor-intensive and interruptive. Herein, the latest advances toward intelligent OOC (iOOC) systems, where sensors integrated with OOC devices continuously report cellular and microenvironmental information for comprehensive in situ bioanalysis, are examined. It is proposed that the multimodal data in iOOC systems can support closed-loop control of the in vitro models and offer holistic biomedical insights for diverse applications. Essential techniques for establishing iOOC systems are surveyed, encompassing in situ sensing, data processing, and dynamic modulation. Eventually, the future development of iOOC systems featuring cross-disciplinary strategies is discussed.

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications

High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.

Impedance-Based Multimodal Electrical-Mechanical Intrinsic Flow Cytometry

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

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.

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.

Deep learning with microfluidics for on-chip droplet generation, control, and analysis

Droplet microfluidics has gained widespread attention in recent years due to its advantages of high throughput, high integration, high sensitivity and low power consumption in droplet-based micro-reaction. Meanwhile, with the rapid development of computer technology over the past decade, deep learning architectures have been able to process vast amounts of data from various research fields. Nowadays, interdisciplinarity plays an increasingly important role in modern research, and deep learning has contributed greatly to the advancement of many professions. Consequently, intelligent microfluidics has emerged as the times require, and possesses broad prospects in the development of automated and intelligent devices for integrating the merits of microfluidic technology and artificial intelligence. In this article, we provide a general review of the evolution of intelligent microfluidics and some applications related to deep learning, mainly in droplet generation, control, and analysis. We also present the challenges and emerging opportunities in this field.

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.

Conformational-switch biosensors as novel tools to support continuous, real-time molecular monitoring in lab-on-a-chip devices

Recent years have seen continued expansion of the functionality of lab on a chip (LOC) devices. Indeed LOCs now provide scientists and developers with useful and versatile platforms across a myriad of chemical and biological applications. The field still fails, however, to integrate an often important element of bench-top analytics: real-time molecular measurements that can be used to "guide" a chemical response. Here we describe the analytical techniques that could provide LOCs with such real-time molecular monitoring capabilities. It appears to us that, among the approaches that are general (i.e., that are independent of the reactive or optical properties of their targets), sensing strategies relying on binding-induced conformational change of bioreceptors are most likely to succeed in such applications.

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.

Automatic feedback control by image processing for mixing solutions in a microfluidic device

Two approaches of an automatic control were studied through mathematical fitting obtained from color mixing saturation curves in polydimethylsiloxane microfluidic devices: The integrative control with variable integral gain and integrative control with constant integral gain. The aim of this work is to control the color percentage decrement when dye is injected. The results indicate that microfluidic systems are very sensitive to changes in flow and the control variable needs to change slowly; that is, it must be small (at least 100 times less than the theoretically calculated values). The control and stabilization of the microfluidic system were achieved for dye percentages above 60%. The controlling color percentage could provide a tool to regulate other parameters' concentration applied to cell culture and alkalinity control (pH) of solutions in microfluidic devices.

Machine learning for microfluidic design and control

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

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.

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.

Microfluidic particle separation and detection system based on standing surface acoustic wave and lensless imaging

OBJECTIVE

Separation and detection of micro-particles or cells from bio-samples by point-of-care (POC) systems are critical for biomedical and healthcare diagnostic applications. Among the microfluidic separation techniques, the acoustophoresis-based microfluidic separation technique has the advantages of label-free, contactless, and good biocompatibility. However, most of the separation techniques are bulky, requiring additional equipment for analysis, not suitable for POC-based in-field real-time applications. Therefore, we proposed a platform, which integrates an acoustophoresis-based separation device and a lensless imaging sensor into a compact standalone system to solve the problem.

METHODS

In this system, Standing Surface Acoustic Wave (SSAW) is utilized for label-free particle separation, while lensless imaging is employed for seamless particle detection and counting using self-developed dual-threshold motion detection algorithms. In particular, the microfluidic channel and interdigital transducers (IDTs) were specially optimized; a heat dissipation system was custom designed to suppress the rise of the fluid temperature; a novel frequency-temperature-curve based method was proposed to determine the appropriate signal driving frequency for the system; an effective treatment protocol that improves the bonding strength between LiNbO3 and PDMS was proposed.

RESULTS

At 2 L/min sample flow rate, the separation efficiency of 93.52% and purity of 94.29% for 15 m microbead were achieved in mixed 5m and 15m microbead solution at a 25 dBm RF driving power, the separation efficiency of 92.75% and purity of 91.43% were obtained for 15 m microbead from mixed 10 m and 15 m microbead solution at a driving power of 24 dBm.

CONCLUSIONS

The results showed that the integrated platform has an excellent capability to seamlessly separate, distinguish, and count microbeads of different sizes.

SIGNIFICANCE

Such a platform and the design methodologies offer a promising POC solution for label-free cell separation and detection in biomedical diagnostics.

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.