Keizer et al. "Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics" – Data, software and documentation (5/16)

Data, software and documentation to reproduce the results presented in [Keizer et al. (2022) ‘Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics’ Science, 377:6605, DOI: 10.1126/science.abi9810].

Description

Location

Centralized GitHub repository with:

Local copy of all the code and trajectory/force files

Jupyter notebooks to make all the graphs in Keizer et al.

Pointers to all the datasets also shown in this table

Keizer et al. repository

Raw microscopy data:

Experiments performed with the 30’-PR scheme

Experiment performed with the 100”-PR scheme

Experiment performed with high frame rate (dt = 0.5”)

Zenodo 1 (30’-PR)
Zenodo 2 (30’-PR)
Zenodo 3 (30’-PR)
Zenodo 4 (30’-PR)
Zenodo 5 (30’-PR)
Zenodo 6 (100”-PR)
Zenodo 7 (30’-PR)
Zenodo 8 (30’-PR)
Zenodo 9 (dt = 0.5")
Zenodo 10 (30’-PR)

Concatenated TIFFs and timestamp files for all of the 30’-PR data.

Zenodo 11 (1/2)
Zenodo 12 (2/2)

Python pipeline to generate (i) concatenated movies, (ii) cropped and rotated movies for each cell, and (iii) force time profiles for each cell.

ChroMag-pipeline repository

Final registered and rotated TIFF files:

30’-PR experiments: n = 35 cells

100”-PR experiment, including time projections & kymograph

dt = 0.5” experiments: n = 3 cells

no force: n = 11 cells before manipulation, n = 8 cells after manipulation

Data files with trajectories and force time profiles for all analyzed cells

Instructions and Fiji/Python scripts to reproduce these files.

Zenodo 13

Single-MNPs fluorescence: raw data, Python/Fiji scripts and instructions

Zenodo 14

MagSim, Python library for magnetic simulations

Jupyter notebook for calibrating and generating maps (Fig. S5 & Fig. S6).

MagSim repository

Force calibration – Method 1: Gradient of free GFP-ferritin in solution

Raw microscopy data (6 pillars; Fig. S6B-C)

Calculated force maps, with Fiji scripts and instructions to generate them.

Zenodo 15

Force calibration – Method 2: Attraction of ferritin-coated beads (Fig. S7)

Raw microscopy data (free diffusion and attraction)

Python/Fiji scripts to calculate forces.

Zenodo 16

Python library for force inference using different polymer models

rouselib repository

License: All the code, data and documentation in this repository is under GPLv3 license. The Author Accepted Manuscript of the study [Keizer et al. 2022] is under CC-BY 4.0 license. The Final Published Version, published by AAAS, is not (more information).

Overview of the raw data repositories (Zenodo 1-10)

Refer to the Material and Methods section of the article for details on data production.

Each Zenodo dataset represents one day of acquisition. It includes the data that was not retained for further downstream analysis. Each dataset contains:

The raw MicroManager folder architecture (one folder contains multiple positions on the coverslip). On occasions where placement or removal of the external magnet led to a loss of focus, the acquisition was stopped and restarted, creating a new MicroManager folder each time. For instance:

The various positions were imaged before injection (folder with the _preInjection, _1-pre-inj or _1-inj_1 suffix)

These positions were imaged again after injection (suffix _postInjection, _2-post-inj or _1-inj_2) and before the magnet was added (suffix _beforeexp or _before-attr)

They were imaged again with the magnet added (suffix _attraction1). If acquisition was stopped and restarted an extra folder is created (suffix _attraction2)

They were then imaged after the magnet was removed (suffix _release1)

Finally, the cells were monitored after the experiment (suffix _after-exp or _postexp)

A text file named lab_journal_[...].txt contains extra information the acquisition and experimental procedure

Note: the MicroManager metadata in the TIFF file are fully populated

Overview of the concatenated datasets (Zenodo 11-12)

In these Zenodo repository, each position (acquired in different folders), is concatenated into a single TIFF movie using code available in the ChroMag-pipeline repository. The folder contains:

One TIFF file per selected position

One .xls file per selected position, with one line per frame, and columns with the following information:

path (Relative path): Reference to the original (raw MicroManager) file

start_time (Timestamp): Timestamp saved by MicroManager when the acquisition was started (the «acquire » button was pressed).

time_in_file (seconds): Number of seconds between start_time and the acquisition of the current timepoint

start_time_s (seconds): Variable start_time converted to a number of seconds

time (seconds): Sum of start_time and time_in_file

timestamp (Timestamp): Variable time, back-converted to a timestamp

timeOn (Timestamp): Time(s) when the magnet was added. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline

timeOff (Timestamp): Time(s) when the magnet was removed. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline

forceActivated (Boolean): If the magnet is present during the current frame (calculated from timeOn and timeOff)

seconds_since_first_magnet_ON (seconds): Number of (relative) seconds since the magnet was added for the first time.

Frame (Integer) Frame number (1-indexed)

Positions (Integer): The position number

Processed datasets (Zenodo 13) and calibration datasets (Zenodo 14-16)

These datasets and their analysis are fully described in the Materials and Methods section of the article and in the different README.md files within the various folders of the datasets.