Low Cost, Easily-Assembled Centrifugal Buoyancy-Based Emulsification and Digital PCR

Deep-qGFP: A Generalist Deep Learning Assisted Pipeline for Accurate Quantification of Green Fluorescent Protein Labeled Biological Samples in Microreactors

Absolute quantification of biological samples provides precise numerical expression levels, enhancing accuracy, and performance for rare templates. Current methodologies, however, face challenges-flow cytometers are costly and complex, whereas fluorescence imaging, relying on software or manual counting, is time-consuming and error-prone. It is presented that Deep-qGFP, a deep learning-aided pipeline for the automated detection and classification of green fluorescent protein (GFP) labeled microreactors, enables real-time absolute quantification. This approach achieves an accuracy of 96.23% and accurately measures the sizes and occupancy status of microreactors using standard laboratory fluorescence microscopes, providing precise template concentrations. Deep-qGFP demonstrates remarkable speed, quantifying over 2000 microreactors across ten images in just 2.5 seconds, with a dynamic range of 56.52-1569.43 copies µL-1 . The method demonstrates impressive generalization capabilities, successfully applied to various GFP-labeling scenarios, including droplet-based, microwell-based, and agarose-based applications. Notably, Deep-qGFP is the first all-in-one image analysis algorithm successfully implemented in droplet digital polymerase chain reaction (PCR), microwell digital PCR, droplet single-cell sequencing, agarose digital PCR, and bacterial quantification, without requiring transfer learning, modifications, or retraining. This makes Deep-qGFP readily applicable in biomedical laboratories and holds potential for broader clinical applications.

Interfacing centrifugal microfluidics with linear-oriented 8-tube strips and multichannel pipettes for increased throughput of digital assays

We present a centrifugal microfluidic cartridge for the eight-fold parallel generation of monodisperse water-in-oil droplets using standard laboratory equipment. The key element is interfacing centrifugal microfluidics with its design based on polar coordinates to the linear structures of standard high-throughput laboratory automation. Centrifugal step emulsification is used to simultaneously generate droplets from eight samples directly into standard 200 μl PCR 8-tube strips. To ensure minimal manual liquid handling, the design of the inlets allows the user to load the samples and the oil via a standard multichannel pipette. Simulation-based design of the cartridge ensures that the performance is consistent in each droplet generation unit despite the varying radial positions that originate from the interface to the linear oriented PCR 8-tube strip and from the integration of linear oriented inlet holes for the multichannel pipettes. Within 10 minutes, sample volumes of 50 μl per droplet generation unit are emulsified at a fixed rotation speed of 960 rpm into 1.47 × 105 monodisperse droplets with a mean diameter of 86 μm. The overall coefficient of variation (CV) of the droplet diameter was below 4%. Feasibility is demonstrated by an exemplary digital droplet polymerase chain reaction (ddPCR) assay which showed high linearity (R2 ≥ 0.999) across all of the eight tubes of the strip.

Bent-Capillary-Centrifugal-Driven Monodisperse Droplet Generator with Its Application for Digital LAMP Assay

We developed a bent-capillary-centrifugal-driven (BCCD) monodisperse droplet generator, which could achieve a perfect combination of driving and segmentation for the dispersed phase only using a rotating bent capillary immersed in the continuous phase (mineral oil). The sample could flow continuously to the bent-capillary outlet to form the droplet precursors, which were segmented into homogeneous droplets in the continuous phase. Through the investigation of influence factors on droplet size and stability, we found that the droplet size could be conveniently controlled by the rotational speed of the bent capillary. The droplet volumes could be adjusted with the range from 34 pL to 1 μL, and the coefficient variations (CVs) were less than 3%. Meanwhile, the BCCD droplet generator could realize the controllable droplet output with a high-efficiency sample utilization of 99.75 ± 1.15%, which offered a significant advantage in reducing the waste of precious samples in the droplet generation process. We validated this system with a digital loop-mediated isothermal amplification (dLAMP) assay for the absolute quantification of Mycobacterium tuberculosis complex nucleic acids. The results demonstrated that the BCCD droplet generator was easy to build, was of low cost, and was convenient to operate, as well as avoided sample loss and cross-contamination by coupling with a 96-well plate. Overall, the present platform, as a simple chip-free droplet generator, will provide an especially valuable droplet generation solution for biochemical applications based on droplets.