Improvements in methods for calculating virus titer estimates from TCID50 and plaque assays

• Published in: Journal of virological methods 2001-08, Vol.96 (2), p.107-126
• Main Authors: LABARRE, David D, LOWY, R. Joel
• Format: Article
• Language: English
• Subjects: Animal viral diseases; Animals; Bias; Biological and medical sciences; Cell Line; Cytopathogenic Effect, Viral; Epidemiology; Fundamental and applied biological sciences. Psychology; Gamma Rays; Infectious diseases; Influenza A virus - growth & development; Influenza A virus - physiology; Influenza A virus - radiation effects; Logistic Models; Medical sciences; Microbiology; Viral diseases; Viral Plaque Assay - methods; Virology; Virology - methods
• ISSN: 0166-0934; 1879-0984
• DOI: 10.1016/s0166-0934(01)00316-0

Calculation of titer estimates and use of titer reduction assays are fundamental approaches used by virologists. Titer assays being biological assays and based on limiting dilution methods require good error control, both methodologically and analytically. The need for good statistical analysis is likely to become even greater as in clinical, manufacturing, as well as the research settings, improved analytical criteria, quality control, and assurance standards are adopted. Furthermore, increasingly, virus titer assays are based on high throughput methods, which generate continuous rather than traditional quantal data. Described here are two different weighted linear regression methods to determine TCID50 and PFU titers from CPE assays. The TCID50 analysis makes use of a generalized least squares approach using continuous colorimetric data. The plaque analysis makes use of weighted least squares forced through the origin using quantal plaque data generated by serial dilutions. Both methods are improvements in titer and error estimation compared to simpler calculation methods. These methods may have greatest value when lack of experimental material or costs of analysis precludes extensive replicate titer determinations but good estimates of titers and/or treatment differences are essential.