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Fluorescence and, more generally, photoluminescence enable high contrast imaging of targeted regions of interest through the use of photoluminescent probes with high specificity for different targets. Fluorescence can be used for rare cell imaging; however, this often requires a high space-bandwidth product: simultaneous high resolution and large field of view. With bulky traditional microscopes, high space-bandwidth product images require time-consuming mechanical scanning and stitching. Lensfree imaging can compactly and cost-effectively achieve a high space-bandwidth product in a single image through computational reconstruction of images from diffraction patterns recorded over the full field of view of standard image sensors. Many methods of lensfree photoluminescent imaging exist, where the excitation light is filtered before the image sensor, often by placing spectral filters between the sample and sensor. However, the sample-to-sensor distance is one of the limiting factors on resolution in lensfree systems and so more competitive performance can be obtained if this distance is reduced. Here, we show a time-gated lensfree photoluminescent imaging system that can achieve a resolution of 8.77 µm. We use europium chelate fluorophores because of their long lifetime (642 µs) and trigger camera exposure ∼50 µs after excitation. Because the excitation light is filtered temporally, there is no need for physical filters, enabling reduced sample-to-sensor distances and higher resolutions. 

The persistence of the global COVID-19 pandemic caused by the SARS-CoV-2 virus has continued to emphasize the need for point-of-care (POC) diagnostic tests for viral diagnosis. The most widely used tests, lateral flow assays used in rapid antigen tests, and reverse-transcriptase real-time polymerase chain reaction (RT-PCR), have been instrumental in mitigating the impact of new waves of the pandemic, but fail to provide both sensitive and rapid readout to patients. Here, we present a portable lens-free imaging system coupled with a particle agglutination assay as a novel biosensor for SARS-CoV-2. This sensor images and quantifies individual microbeads undergoing agglutination through a combination of computational imaging and deep learning as a way to detect levels of SARS-CoV-2 in a complex sample. SARS-CoV-2 pseudovirus in solution is incubated with acetyl cholinesterase 2 (ACE2)-functionalized microbeads then loaded into an inexpensive imaging chip. The sample is imaged in a portable in-line lens-free holographic microscope and an image is reconstructed from a pixel superresolved hologram. Images are analyzed by a deep-learning algorithm that distinguishes microbead agglutination from cell debris and viral particle aggregates, and agglutination is quantified based on the network output. We propose an assay procedure using two images which results in the accurate determination of viral concentrations greater than the limit of detection (LOD) of 1.27 × 10 3 copies per mL, with a tested dynamic range of 3 orders of magnitude, without yet reaching the upper limit. This biosensor can be used for fast SARS-CoV-2 diagnosis in low-resource POC settings and has the potential to mitigate the spread of future waves of the pandemic.