Centrifugation-Assisted Three-Dimensional Printing of Devices Embedded with Fully Enclosed Microchannels.

The challenges in reliably removing the sacrificial material from fully enclosed microfluidic channels hinder the use of three-dimensional (3D) printing to create microfluidic devices with intricate geometries. With advances in printer resolution, the etching of sacrificial materials from increasingly smaller channels is poised to be a bottleneck using the existing techniques. In this study, we introduce a microfabrication approach that utilizes centrifugation to effortlessly and efficiently remove the sacrificial materials from 3D-printed microfluidic devices with densely packed microfeatures. We characterize the process by measuring the etch rate under different centrifugal forces and developed a theoretical model to estimate process parameters for a given geometry. The effect of the device layout on the centrifugal etching process is also investigated. We demonstrate the applicability of our approach on devices fabricated using inkjet 3D printing and stereolithography. Finally, the advantages of the introduced approach over commonly used injection-based etching of sacrificial material are experimentally demonstrated in direct comparisons. A robust method to postprocess additively manufactured geometries composed of intricate microfluidic channels can help utilize both the large printing volume and high spatial resolution afforded by 3D printing in creating a variety of devices ranging from scaffolds to large-scale microfluidic assays.

High throughput, label-free isolation of circulating tumor cell clusters in meshed microwells.

Extremely rare circulating tumor cell (CTC) clusters are both increasingly appreciated as highly metastatic precursors and virtually unexplored. Technologies are primarily designed to detect single CTCs and often fail to account for the fragility of clusters or to leverage cluster-specific markers for higher sensitivity. Meanwhile, the few technologies targeting CTC clusters lack scalability. Here, we introduce the Cluster-Wells, which combines the speed and practicality of membrane filtration with the sensitive and deterministic screening afforded by microfluidic chips. The >100,000 microwells in the Cluster-Wells physically arrest CTC clusters in unprocessed whole blood, gently isolating virtually all clusters at a throughput of >25 mL/h, and allow viable clusters to be retrieved from the device. Using the Cluster-Wells, we isolated CTC clusters ranging from 2 to 100+ cells from prostate and ovarian cancer patients and analyzed a subset using RNA sequencing. Routine isolation of CTC clusters will democratize research on their utility in managing cancer.

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.

Negative enrichment of circulating tumor cells from unmanipulated whole blood with a 3D printed device.

Reliable and routine isolation of circulating tumor cells (CTCs) from peripheral blood would allow effective monitoring of the disease and guide the development of personalized treatments. Negative enrichment of CTCs by depleting normal blood cells ensures against a biased selection of a subpopulation and allows the assay to be applied on different tumor types. Here, we report an additively manufactured microfluidic device that can negatively enrich viable CTCs from clinically-relevant volumes of unmanipulated whole blood samples. Our device depletes nucleated blood cells based on their surface antigens and the smaller anucleated cells based on their size. Enriched CTCs are made available off the device in suspension making our technique compatible with standard immunocytochemical, molecular and functional assays. Our device could achieve a ~ 2.34-log depletion by capturing > 99.5% of white blood cells from 10 mL of whole blood while recovering > 90% of spiked tumor cells. Furthermore, we demonstrated the capability of the device to isolate CTCs from blood samples collected from patients (n = 15) with prostate and pancreatic cancers in a pilot study. A universal CTC assay that can differentiate tumor cells from normal blood cells with the specificity of clinically established membrane antigens yet require no label has the potential to enable routine blood-based tumor biopsies at the point-of-care.

Capillary flow control in lateral flow assays via delaminating timers.

Lateral flow assays (LFAs) use capillary flow of liquids for simple detection of analytes. While useful for spontaneously wicking samples, the capillary flow inherently limits performing complex reactions that require timely application of multiple solutions. Here, we introduce a technique to control capillary flow on paper by imprinting roadblocks on the flow path with water-insoluble ink and using the gradual formation of a void between a wetted paper and a sheath polymer tape to create timers. Timers are drawn at strategic nodes to hold the capillary flow for a desired period and thereby enable multiple liquids to be introduced into multistep chemical reactions following a programmed sequence. Using our technique, we developed (i) an LFA with built-in signal amplification to detect human chorionic gonadotropin with an order of magnitude higher sensitivity than the conventional assay and (ii) a device to extract DNA from bodily fluids without relying on laboratory instruments.

Point-of-Care Toolkit for Multiplex Molecular Diagnosis of SARS-CoV-2 and Influenza A and B Viruses.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is still spreading around the globe causing immense public health and socioeconomic problems. As the infection can progress with mild symptoms that can be misinterpreted as the flu, self-testing methods that can positively identify SARS-CoV-2 are needed to effectively track and prevent the transmission of the virus. In this work, we report a point-of-care toolkit for multiplex molecular diagnosis of SARS-CoV-2 and influenza A and B viruses in saliva samples. Our assay is physically programmed to run a sequence of chemical reactions on a paper substrate and internally generate heat to drive these reactions for an autonomous extraction, purification, and amplification of the viral RNA. Using our assay, we could reliably detect SARS-CoV-2 and influenza viruses at concentrations as low as 50 copies/µL visually from a colorimetric analysis. The capability to autonomously perform a traditionally labor-intensive genetic assay on a disposable platform will enable frequent, on-demand self-testing, a critical need to track and contain this and future outbreaks.

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.

Integrated sensor networks with error correction for multiplexed particle tracking in microfluidic chips.

Spatial manipulation of suspended cells based on their properties is an essential part of numerous microfluidic assays. To further read and analyze the manipulation result, a microscopy system is typically required, which, however, increases the cost and reduces the portability of the entire system. As an alternative, a network of integrated Coulter sensors, distributed over a microfluidic chip, provide rapid and reliable detection of spatially-manipulated cells. Code-multiplexing of distributed Coulter sensors enables simplification of such integration by offloading the hardware complexity into advanced signal processing techniques that are needed to interpret the coded sensor outputs. In this work, we combine code-multiplexed Coulter sensor networks with an error-correction technique, a strategy typically used in telecommunication systems for controlling errors in data over unreliable communication channels. Specifically, we include redundancy in the physical sensor design to alleviate the ambiguity in the signal-decoding process, so that interfering sensor signals due to coincidently-detected cells can be resolved reliably. The presented sensor technology not only tracks the spatiotemporal state of cells under test but also measures their sizes and flow speeds. To demonstrate the sensor concept experimentally, we fabricated a microfluidic device with 10 distributed Coulter sensors designed to produce distinct signal waveforms and performed experiments with suspended human cancer cells to characterize the performance of the sensor platform.

Circulating Tumor Cell Enrichment Technologies.

Circulating tumor cells (CTCs) are responsible for the metastatic spread of cancer and therefore are extremely valuable not only for basic research on cancer metastasis but also as potential biomarkers in diagnosing and managing cancer in the clinic. While relatively non-invasive access to the blood tissue presents an opportunity, CTCs are mixed with approximately billion-times more-populated blood cells in circulation. Therefore, the accuracy of technologies for reliable enrichment of the rare CTC population from blood samples is critical to the success of downstream analyses. The focus of this chapter is to provide the reader an overview of significant advances made in the development of diverse CTC enrichment technologies by presenting the strengths of individual techniques in addition to specific challenges remaining to be addressed.

Combinatorial Immunophenotyping of Cell Populations with an Electronic Antibody Microarray.

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

Processing code-multiplexed Coulter signals via deep convolutional neural networks.

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

Hybrid negative enrichment of circulating tumor cells from whole blood in a 3D-printed monolithic device.

Isolation and analysis of circulating tumor cells (CTCs) from blood samples present exciting opportunities for basic cancer research and personalized treatment of the disease. While microchip-based negative CTC enrichment offers both sensitive microfluidic cell screening and unbiased selection, conventional microchips are inherently limited by their capacity to deplete a large number of normal blood cells. In this paper, we use 3D printing to create a monolithic device that combines immunoaffinity-based microfluidic cell capture and a commercial membrane filter for negative enrichment of CTCs directly from whole blood. In our device, stacked layers of chemically-functionalized microfluidic channels capture millions of white blood cells (WBCs) in parallel without getting saturated and the leuko-depleted blood is post-filtered with a 3 µm-pore size membrane filter to eliminate anucleated blood cells. This hybrid negative enrichment approach facilitated direct extraction of viable CTCs off the chip on a membrane filter for downstream analysis. Immunofluorescence imaging of enriched cells showed ∼90% tumor cell recovery rate from simulated samples spiked with prostate, breast or ovarian cancer cells. We also demonstrated the feasibility of our approach for processing clinical samples by isolating prostate cancer CTCs directly from a 10 mL whole blood sample.

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

A typical microfluidic device sorts, captures or fractionates sample constituents by exposing them to discriminating microenvironments. Direct electronic acquisition of such manipulation by a network of integrated sensors can provide a fast, integrated readout, replacing otherwise required microscopy. We have recently introduced a sensor technology, Microfluidic CODES, which allows us to multiplex resistive pulse sensors on a microfluidic device. Microfluidic CODES employs a network of micromachined coplanar electrodes such that particles passing over these electrodes produce distinguishable code sequences. In this paper, we explain the design process to specifically generate an orthogonal digital code set for an efficient and accurate demultiplexing of the sensor signals. We also introduce an equivalent circuit model for a network of code-multiplexed resistive pulse sensors by utilizing the Foster-Schwan model and conformal mapping, to model dynamic cell-electrode interaction in a non-uniform electric field. Our results closely match with both experimental measurements using cell lines and finite element analysis. The coding and modeling framework presented here will enable the design of code-division multiplexed resistive pulse sensors optimized to produce desired waveform patterns to ensure reliable and efficient decoding.