6 files

Data for ''RecA finds homologous DNA by reduced dimensionality search’

posted on 22.08.2021, 14:31 by Jakub WiktorJakub Wiktor, Arvid Heden Gynnå, Prune Leroy, Jimmy Larsson, Giovanna Coceano, Ilaria TestaIlaria Testa, Johan ElfJohan Elf
### General information
Author: Johan Elf, Jakub Wiktor, Arvid Gynna
Contact e-mail: johan.elf@icm.uu.se
DOI: 10.17044/scilifelab.14815802
DOI_publication: 10.1038/s41586-021-03877-6
License: CC BY 4.0
This readme file was last updated: 24-06-2021

### Dataset description
The data here is provided to support the publication 'RecA finds homologous DNA by reduced dimensionality search’. The supporting data is largely of two types: (1) Image sequences from automated widefield microscopy of live cells in a microfluidic device, and (2) STED superresolution images of fixed and immunostained cells

For automated widefield microscopy, the 'data' folder contains the raw microscopy images, together with the output from the image processing pipeline - stabilised and cropped phase channel ('PreprocessedPhase' directory) and segmentation mask done on the 'PreprocessedPhase' images ('SegmentedChannels' contains a single mask of growth channels, 'SegmentedPhase' has cell segmentation output for each frame of the 'PreprocessedPhase').

Our custom image processing pipeline referred to above is found in the folder 'image_analysis_code/ImAnalysis'.

The 'plotting_code' directory contains subdirectories with the figure number and panel. In each subdirectory there is a code used to generate the panel. To run those scripts, the paths pointing at the data in the scripts will have to be changed to match the location of the 'data' directory on the machine at which the script is executed.

The 'image_analysis_code' directory has to be added to the MATLAB path in order for the code to work.

The images and data from selected cells in each relevant experiment is stored in a .mat file with the name starting with 'Tt'. This structure can be loaded into the Matlab workspace and the images, segmentation outlines, and the DSB annotation (if relevant) can be accessed.

Fig. S2c script 'plot_spots_and_sace_table.m' uses 'detectSpotsSingleCell' function that relies on the path hardcoded in expInfoObj structure. The path in 'detectSpotsSingleCell' line 311 of the function will have to be modified to match the directory with the microscope images. Same for Fig. S5f, function 'detectFilamentsSingleCell' has to be modified on line 203

The code was developed and run using Matlab R2020b, python3.8.5, pytorch1.5.1, some plots require Matlab gramm library or OriginPro 2020.

### experiments, short descriptions, and folders structure
(the names of the experiments were automatically generated by the BIOVIA electronic lab notebook software)

EXP-20-BV3202 - DSB repair measurements in ParB cells
exp2 exp5, exp6, exp7

EXP-20-BV3206 - control - DSB repair measurements in cells without chromosomal cut-site
exp1, exp3 exp4

EXP-20-BV3207 - DSB repair measurements in recA-SYFP2 background
exp1, exp5, exp6

EXP-20-BV3209 - control - DSB repair in strain with recG and ruvC deletions

EXP-20-BV3210 - DSB measurements in mutants - focus on automatic spot counting
exp1 (wt), exp3 (recA), exp4 (recB), exp5 (wt), exp6 (recA), exp7 (recB), exp8 (wt)

EXP-20-BV3214 - DAPI staining of chromosome

EXP-20-BV3219 - fast (20s/frame) imaging of RecA-SYFP2 during DSB repair

EXP-20-BV3220 - malI experiments during DSB
exp2, exp3, exp4 - malO at -45 kb (yahA)
exp5, exp6 - malO at 170 kb (ybbD)
exp7, exp9 - malO at ygaY

EXP-20-BV3221 - control - measuring number of RecA filaments in cells with recB deletion (vs wt strain on the same chip)
exp1, exp2

EXP-20-BV3224 - control - measuring DSB repair dynamics in the RecA-alfa background
exp1, exp2

EXP-21-BT2884 - control - measuring DSB repair dynamics in a strain with malO-cs (instead of ParS-cs)
therun, exp2

EXP-21-BV3233 - control - measuring repair dynamics in a strain with pars-cs-malO
List of figures and experiments

Figure 1:
a - EXP-20-BV3210
b - n/a
c - EXP-20-BV3202
d - EXP-20-BV3202,EXP-20-BV3206,EXP-20-BV3210
e - EXP-20-BV3202
f - EXP-20-BV3202
g - EXP-20-BV3202

Figure 2:
a - EXP-20-BV3220
b - n/a
c - EXP-20-BV3220
d - EXP-20-BV3220
e - EXP-20-BV3220

Figure 3:
a - EXP-20-BV3207
b - EXP-20-BV3219
c - EXP-20-BV3207
f - EXP-20-BR5273
g - EXP-20-BR5273
h - EXP-20-BR5273
i - EXP-20-BR5273
j - EXP-20-BR5273

Figure 4:
a - n/a
b - EXP-20-BV3202, EXP-20-BV3207, EXP-20-BV3220

Figure ED1:
a - EXP-20-BV3210

Figure ED2:
a - EXP-20-BV3210
b - EXP-20-BV3206, EXP-20-BV3210
c - EXP-20-BV3210
d - EXP-20-BV3202
e - EXP-20-BV3202
f - EXP-20-BV3209

Figure ED3:
a - EXP-21-BT2884
b - EXP-21-BV3233

Figure ED4:
a - EXP-20-BV3220
b - EXP-20-BV3220
c - EXP-20-BV3220
d - EXP-20-BV3220
e - none

Figure ED5:
a - EXP-20-BV3207
b - n/a
c - EXP-20-BV3207
d - EXP-20-BV3207
e - EXP-20-BV3207
f - EXP-20-BV3221
g - EXP-20-BV3202, EXP-20-BV3207, EXP-20-BV3224
h - EXP-20-BV3207
i - EXP-20-BV3207

Figure ED6:
a - EXP-20-BR5273
b - n/a
c - EXP-20-BR5273
d - EXP-20-BR5273
e - EXP-20-BR5273
f - EXP-20-BR5273
g - EXP-20-BR5273
h - EXP-20-BR5273
i - EXP-20-BR5273
k - EXP-20-BR5273

Figure ED7:

Figure ED8:



Uppsala University

Contact email