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.

Paper-based multi-well depletion ELISA

Enzyme-linked immunosorbent assay (ELISA) is widely employed for detecting target molecules in bioassays including the serological assays that measure specific antibody titers. However, ELISA tests are inherently limited to centralized laboratories staffed with trained personnel as the assay workflow requires multiple steps to be performed in a specific sequence. Here, we report a dipstick ELISA test that automates this otherwise laborious process and reports the titer of a target molecule in a digital manner without the need for an external instrument or operator. Our assay measures titer by gradually immuno-depleting the target analyte from a flowing sample effectively diluting the residual target - a process conventionally achieved through serially diluting the whole sample in numerous, time-consuming pipetting steps performed manually. Furthermore, the execution of the depletion ELISA process is automated by a built-in flow controller which sequentially delivers different reagents with preset delays. We apply the technology to develop assays measuring (1) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody titers (IgM/IgG antibodies to nucleocapsid and spike protein) and (2) troponin I, a cardiac biomarker.

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.

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.

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

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