Mapping spatio-temporal dynamics of single biomolecules in living cells

Abstract

We present a Bayesian framework for inferring spatio-temporal maps of diffusivity and potential fields from recorded trajectories of single molecules inside living cells. The framework naturally lets us regularise the high-dimensional inference problem using prior distributions in order to obtain robust results. To overcome the computational complexity of inferring thousands of map parameters from large single particle tracking datasets, we developed a stochastic optimisation method based on local mini-batches and parsimonious gradient calculation. We quantified the gain in convergence speed on numerical simulations, and we demonstrated for the first time temporal regularisation and aligned values of the inferred potential fields across multiple time segments. As a proof-of-concept, we mapped the dynamics of HIV-1 Gag proteins involved in the formation of virus-like particles (VLPs) on the plasma membrane of live T cells at high spatial and temporal resolutions. We focused on transient aggregation events lasting only on tenth of the time required for full VLP formation. The framework and optimisation methods are implemented in the TRamWAy open-source software platform for analysing single biomolecule dynamics.

ExTrack characterizes transition kinetics and diffusion in noisy single-particle tracks

“…The histogram approach is also relevant when the transitions are non-Markovian, for example if transition rates are spatially dependent (28, 47) or if states have minimum durations. In summary, the histogram module can help identify hidden or non-Markovian states and thus guide model choice.…”

Bayesian inference: The comprehensive approach to analyzing single-molecule experiments

“…Not only is Bayesian inference incredibly effective as an analysis tool for single-molecule experiments, but it is also the most optimal tool, as it enables a scientist to account for the large uncertainty in singlemolecule data. Additionally, it allows a scientist to do so in a way that is rigorously consistent with the scientific method, to be transparent about the underlying assumptions used in the modeling, progress in the field demonstrates that addressing these shortcomings is a very active area of research (13,16,22,23,41). Implementing the BMS approach as we have described in Section 4 makes use of all the benefits that probability theory affords and is an exciting avenue to explore…”

Bayesian Inference: The Comprehensive Approach to Analyzing Single-Molecule Experiments

“…While this may be at least partly due to the misconception that Bayesian inference, and particularly the use of priors, might introduce 'non-scientific bias' into an analysis (see Section 3.2), it is clear that further work is still required to make Bayesian inference-based methods more accessible, computationally efficient, and capable of modeling more complex single-molecule data. Fortunately, recent progress in the field demonstrates that addressing these shortcomings is a very active area of research (13,16,22,23,41). Implementing the BMS approach as we have described in Section 4 makes use of all the benefits that probability theory affords and is an exciting avenue to explore for single-molecule analysis methods under current or future development.…”