Toward Microfluidic Label-Free Isolation and Enumeration of Circulating Tumor Cells from Blood Samples

Novel thermoplastic microvalves based on an elastomeric cyclic olefin copolymer

Microfluidic systems combine multiple processing steps and components to perform complex assays in an autonomous fashion. To enable the integration of several bio-analytical processing steps into a single system, valving is used as a component that directs fluids and controls introduction of sample and reagents. While elastomer polydimethylsiloxane has been the material of choice for valving, it does not scale well to accommodate disposable integrated systems where inexpensive and fast production is needed. As an alternative to polydimethylsiloxane, we introduce a membrane made of thermoplastic elastomeric cyclic olefin copolymer (eCOC), that displays unique attributes for the fabrication of reliable valving. The eCOC membrane can be extruded or injection molded to allow for high scale production of inexpensive valves. Normally hydrophobic, eCOC can be activated with UV/ozone to produce a stable hydrophilic monolayer. Valves are assembled following in situ UV/ozone activation of eCOC membrane and thermoplastic valve seat and bonded by lamination at room temperature. eCOC formed strong bonding with polycarbonate (PC) and polyethylene terephthalate glycol (PETG) able to hold high fluidic pressures of 75 kPa and 350 kPa, respectively. We characterized the eCOC valves with mechanical and pneumatic actuation and found the valves could be reproducibly actuated >50 times without failure. Finally, an integrated system with eCOC valves was employed to detect minimal residual disease (MRD) from a blood sample of a pediatric acute lymphoblastic leukemia (ALL) patient. The two module integrated system evaluated MRD by affinity-selecting CD19(+) cells and enumerating leukemia cells via immunophenotyping with ALL-specific markers.

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.

Prediction of antibody production performance change in Chinese hamster ovary cells using morphological profiling

Monoclonal antibodies (mAbs) represent a significant segment of biopharmaceuticals, with the market for mAb therapeutics expected to reach $200 billion in 2021. Chinese Hamster Ovary (CHO) cells are the industry standard for large-scale mAb production owing to their adaptability and genetic engineering capabilities. However, maintaining consistent product quality is challenging, primarily because of the inherent genetic instability of CHO cells. In this study, we address the need for advanced technologies for quality monitoring of host cells in biopharmaceuticals. We highlight the limitations of traditional cell assessment techniques such as flow cytometry and propose a noninvasive, label-free image-based analysis method. By utilizing advanced image processing and machine learning, this technique aims to non-invasively and quantitatively evaluate subtle quality changes in suspension cells. The research aims to investigate the use of morphological analysis for identifying subtle alterations in mAb productivity of CHO cells, employing cells stimulated by compounds as a model for this study. Our results show that the mAb productivity of CHO cells (day 8) can be predicted only from their early morphological profile (day 3). Our study also discusses the importance of strategic methods for forecasting host cell mAb productivity using morphological profiles, as inferred from our machine learning models specialized in predictive score prediction and anomaly prediction.

Novel Isolating Approaches to Circulating Tumor Cell Enrichment Based on Microfluidics: A Review

Circulating tumor cells (CTCs), derived from the primary tumor and carrying genetic information, contribute significantly to the process of tumor metastasis. The analysis and detection of CTCs can be used to assess the prognosis and treatment response in patients with tumors, as well as to help study the metastatic mechanisms of tumors and the development of new drugs. Since CTCs are very rare in the blood, it is a challenging problem to enrich CTCs efficiently. In this paper, we provide a comprehensive overview of microfluidics-based enrichment devices for CTCs in recent years. We explore in detail the methods of enrichment based on the physical or biological properties of CTCs; among them, physical properties cover factors such as size, density, and dielectric properties, while biological properties are mainly related to tumor-specific markers on the surface of CTCs. In addition, we provide an in-depth description of the methods for enrichment of single CTCs and illustrate the importance of single CTCs for performing tumor analyses. Future research will focus on aspects such as improving the separation efficiency, reducing costs, and increasing the detection sensitivity and accuracy.

An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images

Biomedical image analysis plays a crucial role in enabling high-performing imaging and various clinical applications. For the proper diagnosis of blood diseases related to red blood cells, red blood cells must be accurately identified and categorized. Manual analysis is time-consuming and prone to mistakes. Analyzing multi-label samples, which contain clusters of cells, is challenging due to difficulties in separating individual cells, such as touching or overlapping cells. High-performance biomedical imaging and several medical applications are made possible by advanced biosensors. We develop an intelligent neural network model that can automatically identify and categorize red blood cells from microscopic medical images using region-based convolutional neural networks (RCNN) and cutting-edge biosensors. Our model successfully navigates obstacles like touching or overlapping cells and accurately recognizes various blood structures. Additionally, we utilized data augmentation as a pre-processing method on microscopic images to enhance the model's computational efficiency and expand the sample size. To refine the data and eliminate noise from the dataset, we utilized the Radial Gradient Index filtering algorithm for imaging data equalization. We exhibit improved detection accuracy and a reduced model loss rate when using medical imagery datasets to apply our proposed model in comparison to existing ResNet and GoogleNet models. Our model precisely detected red blood cells in a collection of medical images with 99% training accuracy and 91.21% testing accuracy. Our proposed model outperformed earlier models like ResNet-50 and GoogleNet by 10-15%. Our results demonstrated that Artificial intelligence (AI)-assisted automated red blood cell detection has the potential to revolutionize and speed up blood cell analysis, minimizing human error and enabling early illness diagnosis.

High-Efficiency Inertial Separation of Microparticles Using Elevated Columned Reservoirs and Vortex Technique for Lab-on-a-Chip Applications

The discovery of circulating tumor cells (CTCs) has envisioned an excellent outlook for cancer diagnosis and prognosis. Among numerous efforts proposed for CTCs isolation, vortex separation is a well-known method for capturing CTCs from blood due to its applicability, low sample volume requirement, and ability to retain cell viability. It is a label-free, passive, low-cost, and automated method, making it an ideal solution for lab-on-a-chip applications. The previous designs that employed vortex technology have shown reaching high throughput and 70% separation efficiency although it was after three processing cycles which are not desired. Inspired by our earlier design, in this work, we redesigned the chip geometry by elevating the columned reservoir height to capture more particles and consequently reduce particle-particle collision, eventually improving efficiency. So, a height-variable chip with fewer elevated columned reservoirs (ECRs) was employed to isolate 20 μm microparticles representing CTCs from 8 μm microparticles. Also, numerical simulations were conducted to investigate the third axis contribution to the separation mechanism. The new design with ECRs resulted in a 14% increase in average efficiency, reaching ∼80% ± 8.3% in microparticle separation and 61% purity. Moreover, the proposed chip geometry demonstrated more than three times higher capacity in retaining orbiting particles up to 1300 in peak performance without sacrificing efficiency compared to earlier single-layer designs. We came up with an upgraded injection system to facilitate this chip characterization. We also presented an effortless and straightforward approach for purging air bubbles trapped inside the reservoirs to preserve regular chip operation, especially where there is a mismatch between channel and reservoir heights.

Multichannel impedance cytometry downstream of cell separation by deterministic lateral displacement to quantify macrophage enrichment in heterogeneous samples

The integration of on-chip biophysical cytometry downstream of microfluidic enrichment for inline monitoring of phenotypic and separation metrics at single-cell sensitivity can allow for active control of separation and its application to versatile sample sets. We present integration of impedance cytometry downstream of cell separation by deterministic lateral displacement (DLD) for enrichment of activated macrophages from a heterogeneous sample, without the problems of biased sample loss and sample dilution caused by off-chip analysis. This required designs to match cell/particle flow rates from DLD separation into the confined single-cell impedance cytometry stage, the balancing of flow resistances across the separation array width to maintain unidirectionality, and the utilization of co-flowing beads as calibrated internal standards for inline assessment of DLD separation and for impedance data normalization. Using a heterogeneous sample with un-activated and activated macrophages, wherein macrophage polarization during activation causes cell size enlargement, on-chip impedance cytometry is used to validate DLD enrichment of the activated subpopulation at the displaced outlet, based on the multiparametric characteristics of cell size distribution and impedance phase metrics. This hybrid platform can monitor separation of specific subpopulations from cellular samples with wide size distributions, for active operational control and enhanced sample versatility.

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 Microfluidic Platform for Physical and Immunological Detection and Capture of Circulating Tumor Cells

CTCs (circulating tumor cells) are well-known for their use in clinical trials for tumor diagnosis. Capturing and isolating these CTCs from whole blood samples has enormous benefits in cancer diagnosis and treatment. In general, various approaches are being used to separate malignant cells, including immunomagnets, macroscale filters, centrifuges, dielectrophoresis, and immunological approaches. These procedures, on the other hand, are time-consuming and necessitate multiple high-level operational protocols. In addition, considering their low efficiency and throughput, the processes of capturing and isolating CTCs face tremendous challenges. Meanwhile, recent advances in microfluidic devices promise unprecedented advantages for capturing and isolating CTCs with greater efficiency, sensitivity, selectivity and accuracy. In this regard, this review article focuses primarily on the various fabrication methodologies involved in microfluidic devices and techniques specifically used to capture and isolate CTCs using various physical and biological methods as well as their conceptual ideas, advantages and disadvantages.

Inertial microfluidics for high-throughput cell analysis and detection: a review

Since it was first proposed in 2007, inertial microfluidics has been extensively studied in terms of theory, design, fabrication, and application. In recent years, with the rapid development of microfabrication technologies, a variety of channel structures that can focus, concentrate, separate, and capture bioparticles or fluids have been designed and manufactured to extend the range of potential biomedical applications of inertial microfluidics. Due to the advantages of high throughput, simplicity, and low device cost, inertial microfluidics is a promising candidate for rapid sample processing, especially for large-volume samples with low-abundance targets. As an approach to cellular sample pretreatment, inertial microfluidics has been widely employed to ensure downstream cell analysis and detection. In this review, a comprehensive summary of the application of inertial microfluidics for high-throughput cell analysis and detection is presented. According to application areas, the recent advances can be sorted into label-free cell mechanical phenotyping, sheathless flow cytometric counting, electrical impedance cytometer, high-throughput cellular image analysis, and other methods. Finally, the challenges and prospects of inertial microfluidics for cell analysis and detection are summarized.

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

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

Inertial Microfluidics Enabling Clinical Research

Fast and accurate interrogation of complex samples containing diseased cells or pathogens is important to make informed decisions on clinical and public health issues. Inertial microfluidics has been increasingly employed for such investigations to isolate target bioparticles from liquid samples with size and/or deformability-based manipulation. This phenomenon is especially useful for the clinic, owing to its rapid, label-free nature of target enrichment that enables further downstream assays. Inertial microfluidics leverages the principle of inertial focusing, which relies on the balance of inertial and viscous forces on particles to align them into size-dependent laminar streamlines. Several distinct microfluidic channel geometries (e.g., straight, curved, spiral, contraction-expansion array) have been optimized to achieve inertial focusing for a variety of purposes, including particle purification and enrichment, solution exchange, and particle alignment for on-chip assays. In this review, we will discuss how inertial microfluidics technology has contributed to improving accuracy of various assays to provide clinically relevant information. This comprehensive review expands upon studies examining both endogenous and exogenous targets from real-world samples, highlights notable hybrid devices with dual functions, and comments on the evolving outlook of the field.

Microscale nonlinear electrokinetics for the analysis of cellular materials in clinical applications: a review

This review article presents a discussion of some of the latest advancements in the field of microscale electrokinetics for the analysis of cells and subcellular materials in clinical applications. The introduction presents an overview on the use of electric fields, i.e., electrokinetics, in microfluidics devices and discusses the potential of electrokinetic-based methods for the analysis of liquid biopsies in clinical and point-of-care applications. This is followed by four comprehensive sections that present some of the newest findings on the analysis of circulating tumor cells, blood (red blood cells, white blood cells, and platelets), stem cells, and subcellular particles (extracellular vesicles and mitochondria). The valuable contributions discussed here (with 131 references) were mainly published during the last 3 to 4 years, providing the reader with an overview of the state-of-the-art in the use of microscale electrokinetic methods in clinical analysis. Finally, the conclusions summarize the main advancements and discuss the future prospects.

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.

A Bayesian Approach for Coincidence Resolution in Microfluidic Impedance Cytometry

OBJECTIVE

Cell counting and characterization is fundamental for medicine, science and technology. Coulter-type microfluidic devices are effective and automated systems for cell/particle analysis, based on the electrical sensing zone principle. However, their throughput and accuracy are limited by coincidences (i.e., two or more particles passing through the sensing zone nearly simultaneously), which reduce the observed number of particles and may lead to errors in the measured particle properties. In this work, a novel approach for coincidence resolution in microfluidic impedance cytometry is proposed.

METHODS

The approach relies on: (i) a microchannel comprising two electrical sensing zones and (ii) a model of the signals generated by coinciding particles. Maximum a posteriori probability (MAP) estimation is used to identify the model parameters and therefore characterize individual particle properties.

RESULTS

Quantitative performance assessment on synthetic data streams shows a counting sensitivity of 97% and a positive predictive value of 99% at concentrations of 2×10⁶ particles/ml. An application to red blood cell analysis shows accurate particle characterization up to a throughput of about 2500 particles/s. An original formula providing the expected number of coinciding particles is derived, and good agreement is found between experimental results and theoretical predictions.

CONCLUSION

The proposed cytometer enables the decomposition of signals generated by coinciding particles into individual particle contributions, by using a Bayesian approach.

SIGNIFICANCE

This system can be profitably used in applications where accurate counting and characterization of cell/particle suspensions over a broad range of concentrations is required.

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

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