We report an integrated system for rapid sample-to-answer detection of a viral pathogen in a droplet of whole blood comprised of a two-stage microfluidic cartridge for sample processing and nucleic acid amplification, and a clip-on detection instrument that interfaces with the image sensor of a smartphone. The cartridge is designed to release RNA from the Zika virus in whole blood using chemical lysis, followed by mixing with the assay buffer for performing reverse-transcriptase loop-mediated isothermal amplification (RT-LAMP) reactions in six parallel microfluidic compartments. The battery-powered instrument heats the compartments from below, while LEDs illuminate from above. Fluorescence generation in the compartments is dynamically monitored by a smartphone camera. We characterize the assay time and detection limits for detecting Zika RNA and gamma-irradiated Zika virus spiked into buffer and whole blood and compare the performance of the same assay when conducted in conventional PCR tubes. Our approach for kinetic monitoring of the fluorescence-generating process in the microfluidic compartments enables spatial analysis of early fluorescent “bloom” events for positive samples. We show that dynamic image analysis reduces the time required to designate an assay as a positive test to 22 minutes, compared to ~30-45 minutes for conventional analysis of the average fluorescent intensity of the entire compartment. We achieve a total sample-to-answer time in the range of 17-32 minutes, while demonstrating a viral RNA detection as low as 2.70×102 copies/ul, and a gamma-irradiated virus of 103 virus particles in a single 12.5 microliter droplet blood sample.
The COVID pandemic prompted the need for rapid detection of the SARS-CoV-2 virus and potentially other pathogens. In this study, we report a rapid, label-free optical detection method for SARS-CoV-2 that is aimed at detecting the virus in the patient’s breath condensates. We show in the published pre-clinical study that, through phase imaging with computational specificity (PICS), we can detect and classify SARS-CoV-2 versus other viruses (H1N1, HAdV and ZIKV) with 96% accuracy, within a minute after sample collection. PICS combines ultrasensitive quantitative phase imaging (QPI) with advanced deep-learning algorithms to detect and classify viral particles. The second stage of our project, currently under development, involves clinical validation of our proposed testing technique. Breath samples collected from patients in the clinic will be imaged with QPI and a U-Net model trained on the breath samples will identify the SARS-CoV-2 in the sample within a minute.
In this study, we use phase imaging with computational specificity (PICS) to detect single Adenovirus and SARS-CoV2 particles. These viruses are sub-diffraction particles, with maximum diameter of approximately 120nm, which implies that we cannot fully visualize their internal structure. However, due to the very high spatial sensitivity of SLIM (0.3 nm pathlength), we can detect and localize individual viruses and, furthermore, using deep learning, classify them with high accuracy.
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