Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation.

Ascertaining the collective viability of cells in different cell culture conditions has typically relied on averaging colorimetric indicators and is often reported out in simple binary readouts. Recent research has combined viability assessment techniques with image-based deep-learning models to automate the characterization of cellular properties. However, further development of viability measurements to assess the continuity of possible cellular states and responses to perturbation across cell culture conditions is needed. In this work, we demonstrate an image processing algorithm for quantifying features associated with cellular viability in 3D cultures without the need for assay-based indicators. We show that our algorithm performs similarly to a pair of human experts in whole-well images over a range of days and culture matrix compositions. To demonstrate potential utility, we perform a longitudinal study investigating the impact of a known therapeutic on pancreatic cancer spheroids. Using images taken with a high content imaging system, the algorithm successfully tracks viability at the individual spheroid and whole-well level. The method we propose reduces analysis time by 97% in comparison with the experts. Because the method is independent of the microscope or imaging system used, this approach lays the foundation for accelerating progress in and for improving the robustness and reproducibility of 3D culture analysis across biological and clinical research.

SARS-CoV-2 Specific IgG Antibodies Persist Over a 12-Month Period in Oral Mucosal Fluid Collected From Previously Infected Individuals.

Background: Developing an understanding of the antibody response, seroprevalence, and seroconversion from natural infection and vaccination against SARS-CoV-2 will give way to a critical epidemiological tool to predict reinfection rates, identify vulnerable communities, and manage future viral outbreaks. To monitor the antibody response on a larger scale, we need an inexpensive, less invasive, and high throughput method. Methods: Here we investigate the use of oral mucosal fluids from individuals recovered from SARS-CoV-2 infection to monitor antibody response and persistence over a 12-month period. For this cohort study, enzyme-linked immunosorbent assays (ELISAs) were used to quantify anti-Spike(S) protein IgG antibodies in participants who had prior SARS-CoV-2 infection and regularly (every 2-4 weeks) provided both serum and oral fluid mucosal fluid samples for longitudinal antibody titer analysis. Results: In our study cohort (n=42) with 17 males and 25 females with an average age of 45.6 +/- 19.3 years, we observed no significant change in oral mucosal fluid IgG levels across the time course of antibody monitoring. In oral mucosal fluids, all the participants who initially had detectable antibodies continued to have detectable antibodies throughout the study. Conclusions: Based on the results presented here, we have shown that oral mucosal fluid-based assays are an effective, less invasive tool for monitoring seroprevalence and seroconversion, which offers an alternative to serum-based assays for understanding the protective ability conferred by the adaptive immune response from viral infection and vaccination against future reinfections.

ELISA detection of SARS-CoV-2 antibodies in saliva.

To facilitate containment of the COVID-19 pandemic currently active in the United States and across the world, options for easy, non-invasive antibody testing are required. Here we have adapted a commercially available, serum-based enzyme-linked immunosorbent assay (ELISA) for use with saliva samples, achieving 84.2% sensitivity and 100% specificity in a set of 149 clinical samples. This strategy will enable widespread, affordable testing for patients who experienced this disease, whilst minimizing exposure risk for healthcare workers.