An Imaging and Computational Algorithm for Efficient Identification and Quantification of Neutrophil Extracellular Traps.

Neutrophil extracellular traps (NETs) are associated with multiple disease pathologies including sepsis, asthma, rheumatoid arthritis, cancer, systemic lupus erythematosus, acute respiratory distress syndrome, and COVID-19. NETs, being a disintegrated death form, suffered inconsistency in their identification, nomenclature, and quantifications that hindered therapeutic approaches using NETs as a target. Multiple strategies including microscopy, ELISA, immunoblotting, flow cytometry, and image-stream-based methods have exhibited drawbacks such as being subjective, non-specific, error-prone, and not being high throughput, and thus demand the development of innovative and efficient approaches for their analyses. Here, we established an imaging and computational algorithm using high content screening (HCS)-cellomics platform that aid in easy, rapid, and specific detection as well as analyses of NETs. This method employed membrane-permeable and impermeable DNA dyes in situ to identify NET-forming cells. Automated algorithm-driven single-cell analysis of change in nuclear morphology, increase in nuclear area, and change in intensities provided precise detection of NET-forming cells and eliminated user bias with other cell death modalities. Further combination with Annexin V staining in situ detected specific death pathway, e.g., apoptosis, and thus, discriminated between NETs, apoptosis, and necrosis. Our approach does not utilize fixation and permeabilization steps that disturb NETs, and thus, allows the time-dependent monitoring of NETs. Together, this specific imaging-based high throughput method for NETs analyses may provide a good platform for the discovery of potential inhibitors of NET formation and/or agents to modulate neutrophil death, e.g., NETosis-apoptosis switch, as an alternative strategy to enhance the resolution of inflammation.

SARS-CoV-2 IgG Surveillance in Asymptomatic Blood Donors and Health Workers.

MATERIALS AND METHODS: 2085 blood donors were allowed to donate blood only after fulfilling all the criteria laid down by the FDA of India with additional history of excluding COVID-19 suspects. IgG antibody testing was performed by chemiluminescence, and results were noted along with their reactive status. Their reactive status was analyzed with donor information to get an idea of the risk parameters for COVID-19. Medical healthcare workers in whom the study was carried out were 560, out of which 114 had worked in COVID-19 duties and 446 had worked in non-COVID-19 emergencies areas. COVID-19 area duties were further subdivided into triage, holding area, isolation, and COVID-19-related duties. The samples were run on architect i2000 and evaluated for their plasma immunoglobulin G. RESULTS: Amongst the asymptomatic blood donors, 1.9% was found to be COVID-19 IgG antibody positive. It was observed that maximum COVID-19 IgG positivity (57.1%) was seen in the age group 18-29 years followed by 26.2% in the age group 30-39 years. Donors in the age group 40-49 years showed antibody positivity of 16.7%, and no antibody-positive donors were found above 50 years of age. COVID-19 IgG positivity was maximum in replacement donors (61.9%) followed by family donors (28.6%) and least involuntary donors (0.6%) Blood donors who showed high IgG positivity were mainly of labor class. Antibody IgG testing on medical healthcare workers showed 2.3% positivity. The healthcare workers who were posted in COVID-19 duties showed 4.8% positivity in the holding area (waiting area with the treatment of patients till their RT PCR report comes) and 5.7% in other COVID-19 areas related to laboratory work. Healthcare workers doing duties in COVID-19 areas showed 2.7% positivity, while those doing duties in non-COVID-19 emergency areas showed a positivity of 2.2%. CONCLUSION: Our study shows that the prevalence of detectable antibodies was low in the general population in India and many patients were asymptomatic as seen in the blood donors, especially the labor class. Maximum exposure was present in young healthy males of labor class who remained asymptomatic. The healthcare workers were more exposed to COVID-19 as compared to the general population probably due to lack of precaution and awareness. Those doing non-COVID-19 duties were also exposed appreciably and needed to take all the precautions required for COVID-19 duties.