Making sense of mass photometry measurements: from single molecules to histograms
Updated: Jan 11, 2022
Mass photometry is a novel way to measure the mass of biomolecules. It works by quantifying the light scattered by an individual molecule in solution, which is directly proportional to the molecule’s mass ,  (read more about how mass photometry works).
The single-molecule nature of mass photometry makes it a powerful bioanalytical technique. It allows you to detect and quantify populations of protein species in your sample. To do so, you need to examine the histograms produced in mass photometry measurements. In this blog post, we explain how to understand and interpret mass photometry histograms.
Mass photometry histograms
In mass photometry, every time a biomolecule in your sample lands on the glass coverslip (the measurement surface), it produces a signal. The typical mass photometry measurement lasts for one minute, and hundreds to thousands of landing events can be detected by the mass photometer during that time. Histograms are a helpful way to visualise those many single-molecule measurements (Fig. 1).
Figure 1. Mass photometry data of a sample of the antibody 2G12. The scatter plot (lower panel) shows the mass measurements associated with the many landing events recorded over a 160-second time period. The mass photometry histogram (upper panel) presents the data as a histogram and the peaks have been fit by Gaussian curves. The peaks correspond to 2G12 monomers, dimers and trimers. 2G12 IgG is a monoclonal antibody against the HIV envelope glycoprotein gp120.
In histograms, the measurements of single-particle landing events are grouped into narrow mass ranges (‘binned’) to make the data easier to interpret. Each bin is represented by a vertical bar, and the height of the bar (the ‘counts’) tells you how many measurements fell into that particular range. If a bar is tall, it means that the mass photometer counted many landing events within that mass range.
As for most biological data, repeated measurements of molecules with the same mass will produce data with some variability that is centred on the true value. In a mass photometry histogram, such data will appear as peaks made up of several bars. Each peak can be fit by a Gaussian curve (Fig. 1), a straightforward statistical approach that is implemented in DiscoverMP, the data analysis software that comes with the Refeyn mass photometry instruments.
This fitting yields two key values: the median of the peak and its standard deviation. The mass of the molecules whose data formed the peak is the median, while the standard deviation indicates how spread out the values are – an indicator of the uncertainty in the measurement.
Often, a mass photometry histogram will have multiple peaks, indicating that there are multiple species present in the sample. Indeed, the single-molecule nature of mass photometry means that you can characterise samples containing many different species across a broad mass range; those species can be detected provided they differ enough in mass (the mass differences must be above the resolution of the instrument – learn more about mass photometry resolution).
We analysed a sample containing the antibody 2G12, which is known to form oligomers. In the histogram, we can see three peaks, indicating that there were three protein species, each with different mass, in the sample. From the median of each peak (which tells us the mass of the molecules in that subpopulation), we can conclude that the peaks correspond to 2G12 monomers, dimers and trimers (Fig. 1). From the heights of the peaks, we can see that the monomers were the most abundant, followed by dimers and then trimers. By calculating the area under the Gaussian curve, we can quantify those abundances.
You could even use the information on the relative abundances to determine proteins’ binding affinities .
An example from the literature
There are numerous examples in the literature of mass photometry being used for biomolecular characterisation, including in research into haemoglobin scavenging , R2TP chaperones , antifungal drug targets  and many other areas. Liebthal et al. recently published another such study, ‘Single molecule mass photometry reveals the dynamic oligomerization of human and plant peroxiredoxins’  (Fig. 2). The authors used mass photometry to quantify the affinity and polydispersity of heterogeneous complexes of 2-Cysteine peroxiredoxins (2CPs), whose diverse functions include chaperone and peroxidase activity. Mass photometry allowed the authors to study the oligomerisation dynamics of the 2CPs, differences in polydispersity between plant and human 2CPs, and the occurrence of intermediates. Ultimately, they gained a clearer picture of the role of oligomerisation in 2CP function and how that oligomerisation depends on redox conditions.
Figure 2. Mass photometry quantifies the affinity and polydispersity of heterogeneous 2-Cysteine peroxiredoxin complexes. Dimers and decamers were revealed primarily in this case (20 nM Arabidopsis 2CPA), along with other states. The schematic shows the known oligomeric species of the protein; interconversion between these species depends on oxidation state .
In summary, mass photometry is a powerful bioanalytical method which allows the characterisation of biomolecules on a single-molecule level. Mass photometry data are typically presented as histograms, where each peak of the histogram represents a subpopulation of molecular species with a particular mass. Analysis of the peaks yields the mass of the subpopulation, along with the uncertainty of that mass measurement and the relative abundances of the species.
If you would like to learn more about mass photometry, we recommend the following resources:
Webinar: Quantifying protein-protein interactions by molecular counting with mass photometry
Fabian Soltermann from the University of Oxford talks about his work on mass photometry and the quantification of protein-protein interactions in antibody-antigen systems. Fabian shows how we go from counting single molecules with mass photometry to obtaining information on the purity of samples, as well as on stoichiometry, affinity and binding kinetics.
Live remote demo: the TwoMP mass photometer
If you are exploring mass photometry and would like to see the instrument or ask some questions, register for our live remote demos. During these demos, one of our mass photometry experts will show how to use the TwoMP mass photometer on some example samples and will be happy to answer any questions about the technology.
 G. Young et al., ‘Quantitative mass imaging of single biological macromolecules’, Science, vol. 360, no. 6387, pp. 423–427, Apr. 2018, doi: 10.1126/science.aar5839.
 G. Young and P. Kukura, ‘Interferometric Scattering Microscopy’, Annu. Rev. Phys. Chem., vol. 70, no. 1, pp. 301–322, Jun. 2019, doi: 10.1146/annurev-physchem-050317-021247.
 F. Soltermann et al., ‘Quantifying Protein–Protein Interactions by Molecular Counting with Mass Photometry’, Angew. Chem. Int. Ed., vol. 59, no. 27, pp. 10774–10779, 2020, doi: 10.1002/anie.202001578.
 S. Tamara, V. Franc, and A. J. R. Heck, ‘A wealth of genotype-specific proteoforms fine-tunes hemoglobin scavenging by haptoglobin’, Proc. Natl. Acad. Sci., vol. 117, no. 27, pp. 15554–15564, Jul. 2020, doi: 10.1073/pnas.2002483117.
 T. V. Seraphim et al., ‘Assembly principles of the human R2TP chaperone complex reveal the presence of R2T and R2P complexes’, Structure, vol. 0, no. 0, Sep. 2021, doi: 10.1016/j.str.2021.08.002.
 S. M. H. Chua et al., ‘Structural features of Cryptococcus neoformans bifunctional GAR/AIR synthetase may present novel antifungal drug targets’, J. Biol. Chem., p. 101091, Aug. 2021, doi: 10.1016/j.jbc.2021.101091.
 M. Liebthal, M. S. Kushwah, P. Kukura, and K.-J. Dietz, ‘Single molecule mass photometry reveals the dynamic oligomerization of human and plant peroxiredoxins’, iScience, vol. 24, no. 11, p. 103258, Nov. 2021, doi: 10.1016/j.isci.2021.103258.