FRET Tools

This repository contains tools for the analysis of single-molecule blinking data.

Installation

No installation is required, this repository is just a group of self-contained scripts. If desired, the package can be installed with ./setup.py install.

Requirements

Python 3, Scipy, Numpy and tifffile. All are available from PyPI and can be installed as described in the pip documentation. If necessary, a more up-to-date installer for tifffile is maintained here.

Usage

The scripts can be used directly, or, if installed with setup.py, with blink_analysis <script> <options>.

A number of scripts are meant to be used in the following order. Each has it's own options to fine-tune behaviour, which can be described by passing the --help flag to the appropriate script.

The following examples illustrate picking spots from a video split over 2 TIFF files (video_1.tif and video_2.tif).

1. The video(s) are maximum-intensity projected into a 2D image (projection.tif) using the tiffutil package. See that package's documentation for available options, such as how to select sub-sequences:

   tiffutil project --projection max video_1.tif video_2.tif projection.tif
   

2. The peaks are picked using the picking script from the blob detection repository. The most important parameter is the threshold value.

   The edge parameter limits how close to the edge of the frame peaks are accepted, and should be set to (at least) the same size as used for the extraction in step 3.

   blob find --edge 4 --threshold 40 projection.tif > peaks.csv
   

3. For further analysis, regions of interest (ROI) with a user-defined size are extracted from the video. The size should be adjusted to include the diffraction limited spot and the neighbouring background. The ROIs will be of size 2 * size + 1, centered on the peak.

   blink_analysis extract --size 4 peaks.csv video_1.tif video_2.tif > rois.pickle
   

4. The frames are categorized into 'on-state' and 'off-state'. This simply compares the background (outer 1/4 of the ROI) to the signal (remaining area), on a frame-by-frame basis. A smoothing can be applied that requires a number of consecutive 'off-state' and 'on-state' frames to end and start a blink respectively.

   blink_analysis categorize run --smoothing 4 2 rois.pickle on.pickle
   

   a. The categorization can be plotted for inspection.

   blink_analysis categorize plot rois.pickle on.pickle
   

5. The following statistics are extracted for each ROI:

   • location
   • mean signal per frame
   • mean total signal per switching event
   • total signal of ROI
   • mean 'on-state' length
   • number of switching events
   • total 'on-state' time

   Location is given in pixels, signal is measured in analog-to-digital units (ADU) and time is measured in frames.

   blink_analysis analyse rois.pickle on.pickle > stats.csv
   

If background correction is necessary, tiffutil provides a smooth function that performs rolling-ball background correction. However, the methods used in the picking and categorization software should be robust to low-frequency variation.