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.

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...

Deep learning-enhanced Coulter counter networks for electronic tracking of particles in microfluidic devices.

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.

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