Significance: The primary method of COVID-19 detection is reverse transcription polymerase chain reaction (RT-PCR) testing. PCR test sensitivity may decrease as more variants of concern arise and reagents may become less specific to the virus.
Aim: We aimed to develop a reagent-free way to detect COVID-19 in a real-world setting with minimal constraints on sample acquisition. The machine learning (ML) models involved could be frequently updated to include spectral information about variants without needing to develop new reagents.
Approach: We present a workflow for collecting, preparing, and imaging dried saliva supernatant droplets using a non-invasive, label-free technique—Raman spectroscopy—to detect changes in the molecular profile of saliva associated with COVID-19 infection.
Results: We used an innovative multiple instance learning-based ML approach and droplet segmentation to analyze droplets. Amongst all confounding factors, we discriminated between COVID-positive and COVID-negative individuals yielding receiver operating coefficient curves with an area under curve (AUC) of 0.8 in both males (79% sensitivity and 75% specificity) and females (84% sensitivity and 64% specificity). Taking the sex of the saliva donor into account increased the AUC by 5%.
Conclusion: These findings may pave the way for new rapid Raman spectroscopic screening tools for COVID-19 and other infectious diseases.
There is a growing effort in the biomicrosystems community to develop a personalized treatment response assay for
cancer patients using primary cells, patient-derived spheroids, or live tissues on-chip. Recently, our group has developed
a technique to cut tumors in 350 μm diameter microtissues and keep them alive on-chip, enabling multiplexed in vitro
drug assays on primary tumor tissue. Two-photon microscopy, confocal microscopy and flow cytometry are the current
standard to assay tissue chemosensitivity on-chip. While these techniques provide microscopic and molecular
information, they are not adapted for high-throughput analysis of microtissues.
We present a spectroscopic imaging system that allows rapid quantitative measurements of multiple fluorescent viability
markers simultaneously by using a liquid crystal tunable filter to record fluorescence and transmittance spectra. As a
proof of concept, 24 spheroids composed of ovarian cancer cell line OV90 were formed in a microfluidic chip, stained
with two live cell markers (CellTrackerTM Green and Orange), and imaged. Fluorescence images acquired were
normalized to the acquisition time and gain of the camera, dark noise was removed, spectral calibration was applied, and
spatial uniformity was corrected. Spectral un-mixing was applied to separate each fluorophore's contribution. We have
demonstrated that rapid and simultaneous viability measurements on multiple spheroids can be achieved, which will
have a significant impact on the prediction of a tumor’s response to multiple treatment options. This technique may be
applied as well in drug discovery to assess the potential of a drug candidate directly on human primary tissue.
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