The Effects of Statistical Multiplicity of Infection on Virus Quantification and Infectivity Assays

Abstract

Many biological assays are employed in virology to quantify parameters of interest. Two such classes of assays, virus quantification assays (VQAs) and infectivity assays (IAs), aim to estimate the number of viruses present in a solution and the ability of a viral strain to successfully infect a host cell, respectively. VQAs operate at extremely dilute concentrations, and results can be subject to stochastic variability in virus-cell interactions. At the other extreme, high viral-particle concentrations are used in IAs, resulting in large numbers of viruses infecting each cell, enough for measurable change in total transcription activity. Furthermore, host cells can be infected at any concentration regime by multiple particles, resulting in a statistical multiplicity of infection and yielding potentially significant variability in the assay signal and parameter estimates. We develop probabilistic models for statistical multiplicity of infection at low and high viral-particle-concentration limits and apply them to the plaque (VQA), endpoint dilution (VQA), and luciferase reporter (IA) assays. A web-based tool implementing our models and analysis is also developed and presented. We test our proposed new methods for inferring experimental parameters from data using numerical simulations and show improvement on existing procedures in all limits.

Time to revisit the endpoint dilution assay and to replace the TCID50 as a measure of a virus sample’s infection concentration

“…Note that this expression is largely equivalent to that obtained by Mistry et al [8] in the context of estimating the TCID 50 of a virus sample, and by many others in the broader context of infection dose quantification [12,13].…”

“…., D 11 ¼ 10 À 7 ), and the total volume of inoculum (diluted virus sample + dilutant) placed in each well is V inoc = 0.1 mL. Now, consider that a virus sample is measured using this ED experiment and one observes (8,8,8,8,8,7,7,5,2,0,0) infected wells out of 8 replicates at each of the 11 dilutions, as illustrated in Fig 1A.…”

“…This makes it problematic to experimentally achieve the desired multiplicity of infection when inoculating from a sample quantified via the RM or SK methods. Many have proposed replacements for the RM and SK calculations based on logit or probit transforms of the data [4,6,7] or on statistical analysis of the ED assay output [7,8] with some implemented as website applications [9,10]. Sadly, none of these improvements were widely adopted to improve estimates of the TCID 50 , available on GitHub (https://github.com/cbeauc/ midSIN) and the midSIN tool is available as a web application (https://midsin.physics.ryerson.ca).…”

Generation of inner ear hair cells by direct lineage conversion of primary somatic cells

“…This result indicated that within this initial group were individual transcription factors, or combinations thereof, able to reprogram MEFs to a hair cell-like state. The low level of reprogramming efficiency is expected when large numbers of factors are infected simultaneously, since only a subset of factors is expected to infect any given cell ( Phan and Wodarz, 2015 ; Mistry et al, 2018 ), and since using large numbers of factors, and/or virus, is likely to challenge cellular transcription/translational machinery, thus further reducing efficiency ( Babos et al, 2019 ).…”

Time to revisit the endpoint dilution assay and to replace TCID$_{50}$ and PFU as measures of a virus sample's infection concentration

“…This makes it problematic to experimentally achieve the desired multiplicity of infection when inoculating from a sample quantified via the SK or RM methods. Many have proposed replacements for the RM and SK calculations with some based on logit or probit transforms of the data [2,4,6] and others on statistical analysis of the ED assay output [6,7]. Sadly, none of these improvements were widely adopted, possibly due to a lack of visibility of these publications, or the lack of widespread awareness of the limitations of the RM and SK methods.…”