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Diffraction-limited molecular cluster quantification with Bayesian nonparametrics

Nature Computational Science volume 2, pages 102–111 (2022)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

Life’s fundamental processes involve multiple molecules operating in close proximity within cells. To probe the molecular composition of such small (diffraction-limited) regions, experiments often report on the total fluorescence intensity emitted from labeled molecules within. Methods exist to enumerate total fluorophore numbers (for example, step counting by photobleaching); however, these methods cannot treat photophysical dynamics nor learn their associated kinetic rates. Here we propose a method to simultaneously enumerate fluorophores and determine their photophysical properties. Although our focus here is on photophysical dynamics, such dynamics can also serve as a proxy for other types of dynamics such as the kinetics of assembly and disassembly of clusters. As the number of active fluorescent molecules at any given time is unknown, we rely on Bayesian nonparametrics to derive our kinetic estimates. We provide a versatile framework for enumerating up to 100 fluorophores from brightness time traces, benchmarked on synthetic and real datasets.

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Fig. 1: Description of the data generation.
Fig. 2: A graphical representation of the inverse model.
Fig. 3: Inference on simulated data.
Fig. 4: Comparison against other methods.
Fig. 5: Inference on real data.

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Data availability

The data analyzed in this project from ref. 23 were provided by D.-P. Herten, K. Yserentant and J. Hummert of the University of Birmingham, and can be obtained from D.-P. Herten (D.Herten@bham.ac.uk). Brightness time traces of the ROIs can be found on Zenodo50. Original movies are also available on Zenodo51,52,53,54,55. Source data are provided with this paper.

Code availability

Code can be found on Zenodo50.

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Acknowledgements

We thank D.-P. Herten, K. Yserentant and J. Hummert of University of Birmingham for their collaboration and excellent data. S.P. acknowledges support from the NIH (grant numbers R01GM134426 and R01GM130745) and NSF (award number 1719537).

Author information

Authors and Affiliations

  1. Center for Biological Physics, Arizona State University, Phoenix, AZ, USA

    J. Shepard Bryan IV & Steve Pressé

  2. Department of Mathematics, University of Tennessee Knoxville, Knoxville, TN, USA

    Ioannis Sgouralis

  3. School of Molecular Sciences, Arizona State University, Phoenix, AZ, USA

    Steve Pressé

Authors
  1. J. Shepard Bryan IV
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Contributions

S.P. and I.S. conceived of the project. J.S.B. performed the coding and development. S.P. oversaw all aspects of the project.

Corresponding author

Correspondence to Steve Pressé.

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The authors declare no competing interests.

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Peer review information

Nature Computational Science thanks Jean-Baptiste Masson, Ruobo Zhou and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jie Pan, in collaboration with the Nature Computational Science team.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–45 and Sections 1–4.

Source data

Source Data Fig. 3

Contains five csv files plus a README explaining how to use the csv files to reconstruct the figure. The five csvs are as follows: fig3_data_trace.txt, measured brightness trace for an ROI; fig3_MAP_trace.txt, inferred MAP brightness trace; fig3_num_flor_ground_truth.txt, ground truth number of fluorophores in each ROI; fig3_num_flor_samples.txt, MCMC samples of the number of fluorophores in each ROI; fig3_error.txt, difference between the MCMC samples and the ground truth.

Source Data Fig. 4

Contains 40 csv files plus a README explaining how to use the csv files to reconstruct the figure. The 40 csv files are organized as follows: all files start with ‘fig4_’. After that, the file name will include either ‘base’, ‘startdark’ or ‘highnoise’, indicating which dataset it is from. After that, the file name will include either ‘trace’, ‘numflor’ or ‘error’, indicating what type of data it is. The file name will end with either ‘change_point’, ‘our_method’, ‘ruler_method’, ‘two_state’ or ‘data’, indicating where the data comes from. All ‘trace’ csv files are brightness versus time traces. The data trace is the measurement for an ROI. All ‘numflor’ csv files are estimates for the number of fluorophores in each ROI, with ‘data’ indicating the ground truth. (Note that ‘our_method’ is larger than the others because we provide estimates for many iterations of the MCMC chain) All ‘error’ csv files are the differences between the method estimate for the number of fluorophores in the ROI and the actual number of fluorophores.

Source Data Fig. 5

Contains 12 csv files plus a README explaining how to use the csv files to generate the figure. The csv files are organized as follows: there are six ‘fig5_XX_site_ground_truth.txt’ files, which have the expected distribution for the number of fluorophores; there are six ‘fig5_XX_site_samples.txt’, which are MCMC samples for the number of fluorophores in each ROI. XX indicates which dataset the file is from and can be one of the following: 20, 40, 80, 35, 70, 140.

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Bryan IV, J.S., Sgouralis, I. & Pressé, S. Diffraction-limited molecular cluster quantification with Bayesian nonparametrics. Nat Comput Sci 2, 102–111 (2022). https://doi.org/10.1038/s43588-022-00197-1

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