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Virology through numbers: Converting TCID50 to IU

The plaque assay directly counts virus particles by the plaques they form, and the TCID50 assay estimates virus concentration based on the dilution at which 50% of wells show infection.

 

But does the TCID50 assay return the same results as the plaque assay? Not exactly. They use different units. The plaque assay uses infectious units per milliliter (IU/ml) and the TCID50 assay uses Tissue Culture Infectious Dose 50 per milliliter (TCID50/ml).

 

But what if we need to translate a TCID50/ml titre into an IU/ml titre? No need to panic, it’s easily done. We simply need to multiply the TCID50/ml value by 0.7.

 

But where does this 0.7 come from? Remember that element of probability behind the TCID50 method discussed in our previous blog article? That is why. Let’s look in more detail into this.

 

MOI and probability of infection

By Multiplicity of Infection (MOI) we mean the number of infectious viruses added per cell. So, an MOI of 1 means that we add one virus for every cell; an MOI of 10 means that we add 10 viruses per cell; an MOI of 0.1 means 1 virus for every 10 cells.

 

We will be now looking at the probability of cells in a monolayer being infected at different MOIs. For this purpose, we need to assume that the cells in question are completely permissive to virus infection and that infection of one cell does not interfere with infection of other cells, or with infection of the same cell by multiple viruses. The biology of virus infection is not really important here, and in fact, while it makes sense for us to keep referring to viruses and cells, from a mathematical perspective, it doesn’t really matter what objects we are talking about.

 

Due purely to probability, at an MOI of 1, some cells of the monolayer will receive one virus, but some cells will receive two viruses, some three viruses, and some possibly more. Statistically, the probabilities of these events are 36.79% (one virus), 18.39% (two viruses), and 6.13% (three viruses), respectively.

 

The Poisson distribution

How do we know, at different MOIs, the probability of multiple viruses infecting the same cell? The rule describing this phenomenon is the Poisson distribution, which is used to model the probability of a certain number of events occurring within a fixed interval of time or space:

 

 P(n) = (MOIⁿ × e^-MOI )/ n!

 

This is saying that the probability (P) of observing n events (where the event here is an infection per cell) is a function of the MOI, with a correction downwards (the multiplication by e^-MOI).