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bioRxiv · 10.1101/2020.07.23.217745

Learning to count: determining the stoichiometry of bio-molecular complexes using fluorescence microscopy and statistical modelling

Mersmann, S. F.·Johns, E.·Yong, T.·McEwan, W. A.·James, L. C.·Cohen, E. A. K.·Grove, J.
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Abstract

Cellular biology occurs through myriad interactions between diverse molecular components, many of which assemble in to specific complexes. Various techniques can provide a qualitative survey of which components are found in a given complex. However, quantitative analysis of the absolute number of molecules within a complex (known as stoichiometry) remains challenging. Here we provide a novel method that combines fluorescence microscopy and statistical modelling to derive accurate molecular counts. We have devised a system in which a given biomolecule is differentially labelled with spectrally distinct fluorescent dyes (label A or B), which are then mixed such that B-labelled molecules are vastly outnumbered by those with label A. Complexes, containing this component, are then simply scored as either being positive or negative for label B. The frequency of positive complexes is directly related to the stoichiometry of interaction and molecular counts can be inferred by statistical modelling. We demonstrate this method using complexes of Adenovirus particles and monoclonal antibodies, achieving counts that are in excellent agreement with previous estimates. Beyond virology, this approach is readily transferable to other experimental systems and, therefore, provides a powerful tool for quantitative molecular biology. The statistical models used in our analysis are available here: https://github.com/sophiamersmann/molecular-counting, the raw data used for molecular counting can be found here: 10.5281/zenodo.3955142.

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Mersmann, S. F., Johns, E., Yong, T., McEwan, W. A., James, L. C., Cohen, E. A. K., Grove, J.. 2020-07-24. Learning to count: determining the stoichiometry of bio-molecular complexes using fluorescence microscopy and statistical modelling. https://doi.org/10.1101/2020.07.23.217745

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