A high-throughput microfluidic nanoimmunoassay for detecting anti-SARS-CoV-2 antibodies in serum or ultralow-volume blood samples.

Novel technologies are needed to facilitate large-scale detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) specific antibodies in human blood samples. Such technologies are essential to support seroprevalence studies and vaccine clinical trials, and to monitor quality and duration of immunity. We developed a microfluidic nanoimmunoassay (NIA) for the detection of anti-SARS-CoV-2 IgG antibodies in 1,024 samples per device. The method achieved a specificity of 100% and a sensitivity of 98% based on the analysis of 289 human serum samples. To eliminate the need for venipuncture, we developed low-cost, ultralow-volume whole blood sampling methods based on two commercial devices and repurposed a blood glucose test strip. The glucose test strip permits the collection, shipment, and analysis of 0.6 µL of whole blood easily obtainable from a simple finger prick. The NIA platform achieves high throughput, high sensitivity, and specificity based on the analysis of 289 human serum samples, and negligible reagent consumption. We furthermore demonstrate the possibility to combine NIA with decentralized and simple approaches to blood sample collection. We expect this technology to be applicable to current and future SARS-CoV-2 related serological studies and to protein biomarker analysis in general.

Automated long-term monitoring of parallel microfluidic operations applying a machine vision-assisted positioning method.

As microfluidics has been applied extensively in many cell and biochemical applications, monitoring the related processes is an important requirement. In this work, we design and fabricate a high-throughput microfluidic device which contains 32 microchambers to perform automated parallel microfluidic operations and monitoring on an automated stage of a microscope. Images are captured at multiple spots on the device during the operations for monitoring samples in microchambers in parallel; yet the device positions may vary at different time points throughout operations as the device moves back and forth on a motorized microscopic stage. Here, we report an image-based positioning strategy to realign the chamber position before every recording of microscopic image. We fabricate alignment marks at defined locations next to the chambers in the microfluidic device as reference positions. We also develop image processing algorithms to recognize the chamber positions in real-time, followed by realigning the chambers to their preset positions in the captured images. We perform experiments to validate and characterize the device functionality and the automated realignment operation. Together, this microfluidic realignment strategy can be a platform technology to achieve precise positioning of multiple chambers for general microfluidic applications requiring long-term parallel monitoring of cell and biochemical activities.