Author: Johan Hummert
Organization: Herten Lab for Single Molecule Spectroscopy, University of Birmingham, UK
License: GPLv3
Version: 2020.0.1
Python Package providing a framework for photo-bleaching step analysis. The details of the algorithm used and extensive validation with experimental data are described in a bioRxiv preprint:
https://doi.org/10.1101/2020.08.26.268086
please cite this publication if you found this package useful.
Parallelisation The trace analysis can now be run on multiple cores, implemented using multiprocessing. In the analysis functions this can be specified by calling:
pbsa.pbsa_file(file, threshold, maxiter, num_cores=8)One core is reserved for queueing and formatting outputs, so if you specify 8 cores, 7 will be used for the analysis. If the code runs on a compute cluster note that the parallisation does not support parallelisation over multiple nodes.
Although the package was tested with specific versions of these packages, other relatively new versions will likely work as well. If you have issues with newer versions of these packages get in touch. If you have issues with older versions please consider updating.
If you want to use the package not only for analysis of photobleaching traces but also for trace extraction from .tiff stacks there are additional dependencies:
The recommended way to install is via pip:
pip install quickpbsaAlternatively you can clone / download the git repository and place the directory quickpbsa/quickpbsa in your $PYTHONPATH.
If you already have photobleaching traces which you would like to analize, running the analysis is a one-liner:
import quickpbsa as pbsa
pbsa.pbsa_file(file, threshold, maxiter)For this to work the file should be a .csv file, where each row is one photobleaching trace, which ideally, but not necessarily, should be background subtracted. Then there are two additional parameters to set in the analysis, threshold and maxiter. There are many additional optional parameters to set, detailed below, but in many cases the default parameters should be alright.
threshold should be set to approximately half the intensity difference of a typical photobleaching step. This is most easily accomplished by plotting a few traces and finding steps towards the end of the trace, where steps can most easily be found by eye.
maxiter is the maximum number of iterations (i.e. maximum number of steps found). This should be significantly higher than the expected number of steps. In the validation experiments we performed this would typically be set to 200 for samples with up to 35 fluorophores.
The result is exported as a _result.csv file with 7 rows per photobleaching trace. The final result of the complete analysis for each trace is the one with the type 'fluors_full' in the output file. Furthermore traces where the column 'flag' is not 1 should be discarded. The complete structure of the output file and the individual flags are detailed in the Concept below. Examples on how to use the output are also provided under Examples.
If you have a stack of .tif images and want to extract and analyze traces from it make sure that you have tifffile installed. Then there are two options to get traces from your image stack, both of which will yield a _difference.csv file containing background corrected traces ready for the analysis:
If the structures from which you want to extract fluorophore numbers are diffraction-limited, you can extract traces based on a .csv file with coordinates (in nanometers) for each diffraction limited spot. The file should contain at least 2 columns named 'x [nm]' and 'y [nm]'. Our recommended way of obtaining such a file is the Fiji plugin ThunderSTORM.
The trace extraction can then be accomplished with
import quickpbsa as pbsa
pbsa.trace_extraction.extract_traces_localization(tiffstack, locfile, r_peak, r_bg1, r_bg2, min_dist)with tiffstack being the path to your image stack and locfile the path to the localization file. r_peak is the radius (in pixels) of the area around the localization from which the trace is extracted. r_bg1 and r_bg2 define a ring around the localization from which the background for background correction is extracted. min_dist is the minimum distance from one localization to the next. Localizations which are spaced less than min_dist apart are not considered in the trace extraction.
If you have larger structures from which to extract photobleaching traces and fluorophore numbers, you can use a mask image which should be an 8bit Tiff with a white selection on black background. The traces are then extracted from non-connected white regions of interest (ROIs or RsOI ...?) in the mask image.
import quickpbsa as pbsa
pbsa.trace_extraction.extract_traces_mask(tiffstack, maskfile, dist, r_bg)where a background ROI with a distance of dist (in pixels) to the selected ROI and a width of r_bg is defined for background correction.
This photobleaching step analysis is a combination of a preliminary step detection and a following refinement of the preliminary result based on a bayesian approach. A full run of the analysis after trace extraction consists of 3 parts.
The preliminary step detection is based on the work of Kalafut and Visscher (10.1016/j.cpc.2008.06.008). In the algorithm steps are sucessively placed and the Schwarz Information Criterion is evaluated with and without the added steps. In the modified version used here steps with a step height below the threshold parameter are rejected.
The optional parameters of the preliminary step detection can be set by providing a dictionary as an optional argument in quickpbsa.pbsa_file():
import quickpbsa as pbsa
pardict = {'norm': 1000,
'crop': False}
pbsa.pbsa_file(file, threshold, maxiter, preliminary_optional=pardict)Possible parameters are:
norm (default 1) The traces will be divided by norm prior to analysis, mainly for visualization. threshold should be set lower accordingly.
crop (default True) If True the last frames of the trace are cropped for analysis purposes based on threshold. Traces are cropped at the last frame where the difference in intensity exceeds half the value of threshold + bgframes.
bgframes (default 500) How many frames to include in the analysis after the crop point.
max_memory (default 2.0) Maximum available memory for the preliminary step detection. The analysis defaults to a slower, but less memory consuming implementation if the necessary memory exceeds this value. For the default of 4 GB the fast implementation is used for traces with up to ~40000 frames.
Based on the result of the preliminary step detection traces are excluded from the analysis. Assuming that the last two steps are correctly identified in a majority of traces, traces are flagged out. The used flags in the output file are:
| flag | Meaning |
|---|---|
| -1 | No steps found in preliminary step detection |
| -2 | Background intensity out of bounds |
| -3 | Single fluorophore intensity out of bounds |
| -4 | Trace goes into negative values |
| -5 | Fluorophore number becomes negative |
| -6 | interval between final two steps is too short |
The optional parameters of the preliminary step detection can be set by providing a dictionary as an optional argument in quickpbsa.pbsa_file():
import quickpbsa as pbsa
pardict = {'subtracted': False,
'percentile_step': [20, 80]}
pbsa.pbsa_file(file, threshold, maxiter, filter_optional=pardict)Possible parameters are:
subtracted (default True) If True it is assumed that traces are background corrected. This sets the bounds on the background intensity to [-threshold, threshold] and the default lower bound on the single fluorophore intensity to threshold. If False the bounds on the background intensity are set based on the minimum background intensity in the dataset min_bg: [min_bg, min_bg + threshold]. If False the default lower bound on the single fluorophore intensity is also min_bg + threshold.
percentile_step (default 90) Sets the bounds on the single fluorophore intensity. If one value is provided, the upper bound on the single fluorophore intensity is set at this percentile. If two values are provided, as in the example above, lower and upper bounds are set at the percentiles respectively.
length_laststep (default 20) Minimum number of frames between the last two steps.
The step refinement is based on the posterior as defined in the work of Teskouras et al. (10.1091/mbc.e16-06-0404). The refinement function iteratively minimizes this posterior starting from the result of the preliminary step detection.
Most of the optional parameters aim to reduce runtime by reducing the number of possible step arrangements to test. The optional parameters of the step refinement can also be set by providing a dictionary as an optional argument in quickpbsa.pbsa_file():
import quickpbsa as pbsa
pardict = {'mult_threshold': 1.5,
'combcutoff': int(5e6)}
pbsa.pbsa_file(file, threshold, maxiter, refinement_optional=pardict)Possible parameters are:
multstep_fraction (default 0.5) Maximum fraction of steps with an occupancy higher than 1.
nonegatives (default False) If True, no negative double steps are considered. This means that arrangements where 2 or more fluorophore turn back on at the same time are not considered.
mult_threshold (1.0) Only steps where the difference in the mean is above mult_threshold multiplied by the last fluorophore intensity are considered as steps with occupancy higher than 1.
combcutoff (default 2000000) Maximum number of arrangements to test. If this is exceeded, the trace is flagged out with flag -7. If this happens a lot, consider increasing this value, which will increase runtime.
splitcomb (default 30000) How many arrangements to test simultaneously (vectorized). On systems with a large memory this can be increased to speed up the analysis.
maxmult (default 5) Maximum considered occupancy, i.e. how many fluorophores can bleach simultaneously.
maxadded (default 10) Maximum number of added single steps if no steps are removed to yield an improved posterior.
lambda (default 0.1) Hyperparameter $\lambda$ in equation (1).
gamma0 (default 0.5) Hyperparameter $\gamma_0$ in equation (1).
Detailed examples with a more in-depth explanation of the algorithm are available in two jupyter notebooks explaining how the analysis works on an example trace (Examples/Example_Trace.ipynb) and an example tiff-stack (Examples/Example_Stack.ipynb) with the included experimental example data. You will need to install jupyter and matplotlib to run the examples.