A novel method to accurately locate and count large numbers of steps by photobleaching

Abstract

A new Bayesian photobleaching trace analysis method that is computationally inexpensive can be used to treat blinking, reactivation, and overlapping events and reliably detect up to 50 fluorophores even for low signal-to-noise ratios. It can also scale up to 500+ for high signal-to-noise ratios.

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

“…These approaches are particularly sensitive to noise models, 61,86,94–96 and for this reason, it is often best to simultaneously infer the model for the system of interest together with the noise model in a self-consistent fashion. The hidden Markov model that we describe in greater depth in section 3.4.1 is one such example.…”

“…96,266,267 The problem of locating sharp discontinuous changes in noisy data, the purview of step-finding algorithms, is a general problem across science that has been investigated across single molecule biophysics. 59 As always, a precise understanding of the noise characteristics in the data-generating process is required to accurately locate steps (Figure 14).…”

“…62,66 The same is true of all Bayesian methods where priors properly informed by the physics of the problem provide a systematic way to improve inference. 96 …”

“…A step-finding algorithm that does take into account variable noise performs significantly better (red line which overlaps with the theoretical noiseless trace used to generate this synthetic data). 96 (b) An example of a real data trace (red line) showing an RNA hairpin zipping and unzipping (data was obtained through force spectroscopy). 268 The offset green line are the steps found using a step-finding algorithm assuming constant noise.…”

“…269 The time trace in (a) was created via a simple Gillespie algorithm with variable noise ratio. The code implementation of the method in ref 96 can be found online at https://github.com/lavrys/Photobleach. The code for the algorithm that does not take into account variable noise can be found at https://github.com/knyquist/KV_SIC.…”