The EV antibody database: An interactive database of curated antibodies for extracellular vesicle and nanoparticle research.

Antibodies are critical tools for research into extracellular vesicles (EVs) and other extracellular nanoparticles (ENPs), where they can be used for their identification, characterization, and isolation. However, the lack of a centralized antibody platform where researchers can share validation results thus minimizing wasted personnel time and reagents, has been a significant obstacle. Moreover, because the performance of antibodies varies among assay types and conditions, detailed information on assay variables and protocols is also of value. To facilitate sharing of results on antibodies that are relevant to EV/ENP research, the EV Antibody Database has been developed by the investigators of the Extracellular RNA Communication Consortium (ERCC). Hosted by the ExRNA Portal (https://exrna.org/resources/evabdb/), this interactive database aggregates and shares results from antibodies that have been tested by research groups in the EV/ENP field. Currently, the EV Antibody Database includes modules for antibodies tested for western Blot, EV Flow Cytometry, and EV Sandwich Assays, and holds 110 records contributed by 6 laboratories from the ERCC. Detailed information on antibody sources, assay conditions, and results is provided, including negative results. We encourage ongoing expert input and community feedback to enhance the database's utility, making it a valuable resource for comprehensive validation data on antibodies and protocols in EV biology.

Comparison of heat-inactivated and infectious SARS-CoV-2 across indoor surface materials shows comparable RT-qPCR viral signal intensity and persistence.

Environmental monitoring in public spaces can be used to identify surfaces contaminated by persons with COVID-19 and inform appropriate infection mitigation responses. Research groups have reported detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) on surfaces days or weeks after the virus has been deposited, making it difficult to estimate when an infected individual may have shed virus onto a SARS-CoV-2 positive surface, which in turn complicates the process of establishing effective quarantine measures. In this study, we determined that reverse transcription-quantitative polymerase chain reaction (RT-qPCR) detection of viral RNA from heat-inactivated particles experiences minimal decay over seven days of monitoring on eight out of nine surfaces tested. The properties of the studied surfaces result in RT-qPCR signatures that can be segregated into two material categories, rough and smooth, where smooth surfaces have a lower limit of detection. RT-qPCR signal intensity (average quantification cycle (Cq)) can be correlated to surface viral load using only one linear regression model per material category. The same experiment was performed with infectious viral particles on one surface from each category, with essentially identical results. The stability of RT-qPCR viral signal demonstrates the need to clean monitored surfaces after sampling to establish temporal resolution. Additionally, these findings can be used to minimize the number of materials and time points tested and allow for the use of heat-inactivated viral particles when optimizing environmental monitoring methods.