Another interesting feature of ExTrack is its capacity to plot the histograms of the lifetimes of the different states. That can be particularly useful to detect non-Markovian state transitions. On a log scale Attention: To obtain relevant lifetime distributions, it is important to pick the widest range of track lengths as possible (example: 3 to actual maximum track length).

Like for state predictions, we first need to determine the model parameters. This can be done by running the fitting method or by using parameters previously determined on other data.

State lifetime histograms

When chosing State lifetime histograms, clicking on Next will open the lifetime histogram window:

Number of states and model parameters

The number of states and model parameters can be informed manually or by a prior fitting step.

Frame time (in s)

Provide the time in between frames.

Window length

Provide the window length for which no approximations will be made. Outside the window, similar sequences of states will be merged according to their similarity. A higher window length increases the computation time. Compared to fitting, the window length can be higher as it requires less computation time.

Maximum number of sequences

Maximum number of sequences of states to consider. ExTrack will determine the window length based on that number.

Depth of field

Dept of field of the sample. This metric can be important to quantify the probability of tracks to leave the field of view and to therefor avoid the defocalization bias. This parameter assums wide field (or HALO) illumination that allows cytoplasmice tracks to leave from the top or bottom of the depth of field. If you are imaging in TIRF, correct the field of view by multiplying it by 0.7. If you are imaging particles that never leave the depth of field, put a high number (example: 100 * sqrt(2Dtime step)). If you are imaging membrane proteins, you can try to estimate the distance that a particle needs to cross to leave the field of view. If the depth of field is unknown, you can simply put an high value and ignore it. The best way to avoid the defocalization bias is to consider a maximum of track lengths (examples tracks of length 3 to 50).

Plot lifetime histograms

If yes plot the lifetime histograms in linear scale and in log scale. The log scale is particularly useful to detect non-Markovian lifetimes. Indeed, the lifetime shows an exponential decay if Markovian which appears linear in log scale. Non-linear behaviors in log scale indicate non-Markovian lifetimes. A pattern that can often be detect is a mixture of linear behaviors that either results into a bimodal curve or into an heavy-tailed curve. If a state shows a biomodal lifetime curve, that most likely indicates sub-populations of the state. The kinetics of each sub-state might be obtained by adding a state or by quantifying it from the slop of the histogram curve. In this graph obtained from a single movie, the curves are little smooth which indicates that the number of tracks is too low to provide very reliable lifetimes histograms. However, the immobile state (state 0) seem to show a bimodal behavior with a more important off-rate from 0 to 0.5 seconds and a reduced off-rate after 0.5 seconds. This trend needs to be confirmed by increasing the number of tracks used to produce the lifetime histogram. That can be done by acquiring several movies for each replicates. Observing such trend over multiple biological replicates would solidify these conclusions.

Save path

Save path for the lifetime histograms of each state. Format: csv.