DOI: 10.5061/dryad.9p8cz8ww4
title: Data and code from: In vitro reconstitution of heterochromatin compartments reveals the spontaneous formation of tunable liquid-liquid interfaces

description: This dataset contains the time-lapse imaging data collected and analyzed
 in "In vitro reconstitution of heterochromatin compartments reveals
 the spontaneous formation of tunable liquid-liquid interfaces." These
 movies include representative droplet fusions and heat-reversibility of
 chromatin compartmentalization, the quantification of which is shown in
 Figures 2, 3, and 4 of the manuscript. The Python scripts used to analyze
 and visualize this data are included in the PythonNotebooks.zip folder.
 This zip folder contains the Python scripts necessary to replicate the
 plots shown in the main text figures 1-5, and this folder includes the raw
 tabular data visualized in each plot. Specifics on the data structure,
 acquisition methods, and analysis pipelines can be found in the README
 file.  
description: # Data and code from: In vitro reconstitution of heterochromatin
 compartments reveals the spontaneous formation of tunable liquid-liquid
 interfaces Dataset DOI: [10.5061/dryad.9p8cz8ww4](10.5061/dryad.9p8cz8ww4)
 ## Description of the data and file structure Chromatin & HP1a
 condensate fusion and heat-reversibility movies shown in Figures 2, 3,
 & 4 of the corresponding manuscript. Additional 3-dimensional images
 of mutant HP1a-chromatin mixtures are also available here as
 representative image-sets of the single slices shown in Figure 5. All
 movies were collected on a Zeiss 880 confocal and are uploaded here as
 .czi files, which can be opened and viewed in FIJI/ ImageJ. Axial-scans of
 3-dimensional z-stacks were created using the reslice function in FIJI
 (see below). —— The following data were collected and analyzed by LD
 Brennan for the manuscript: "In vitro reconstitution of
 heterochromatin compartments reveals the spontaneous formation of tunable
 liquid-liquid interfaces." ### Figure 2: Chromatin + HP1a condensate
 heating experiments File names:
 "240108_me3-cy5_wt-cy3_25uM-HP1a_22-37_002part-1",
 "240108_me3-cy5_wt-cy3_25uM-HP1a_22-37_002",
 "230802_27to37realtime_1uM-chromatin_25uM-HP1a_005" These movies
 of chromatin and HP1a co-condensates were collected using a Zeiss 880
 Airyscan microscope with a 63x/1.4 oil immersion objective. Time-lapse
 image stacks were collected every 5 seconds at a single imaging plane and
 have a pixel size of 0.132μm X 0.132μm. Channel 1 corresponds with the Cy3
 channel, channel 2 with Alexa-488, and channel 3 with Cy5. Heating starts
 at Frame 11 in file
 "230802_27to37realtime_1uM-chromatin_25uM-HP1a_005" and
 "240108_me3-cy5_wt-cy3_25uM-HP1a_22-37_002part-1" which then
 continues, with heating, in
 "240108_me3-cy5_wt-cy3_25uM-HP1a_22-37_002". Files can be opened
 in FIJI using the BioFormats extension. The Cy3 and Cy5 channels were
 split into individual files in FIJI, saved as a Tif series, and then
 analyzed using the following custom Python scripts: make_segments,
 make_group_segments, and calculate_region_correlations. Data were plotted
 using the seaborn boxplot functionality of Python. ### Figure 3: chromatin
 condensate material properties
 240115_1uM-wt-Chromatin-fusions-63x_DIC_002_crop029.nd2
 240115_1uM-wt-Chromatin-fusions-63x_DIC_002_crop030.nd2
 240115_1uM-me3-Chromatin-fusions-63x_DIC_005_crop025.nd2
 240115_1uM-me3-Chromatin-fusions-63x_DIC_005_crop022-2for.nd2 ### Figure
 4: chromatin + HP1a condensate material properties
 240115_1uM-wt-25uM-HP1a-Chromatin-fusions-63x_DIC_009_crop041.nd2
 240115_1uM-wt-25uM-HP1a-Chromatin-fusions-63x_DIC_009_crop042.nd2
 240115_1uM-me3-25uM-HP1a-Chromatin-fusions-63x_DIC_008_crop019.nd2
 240115_1uM-me3-25uM-HP1a-Chromatin-fusions-63x_DIC_008_crop024.nd2 These
 movies are representative examples of chromatin condensates fusions
 captured on a Nikon Ti2 with a 63x oil objective lens at 0.02 sec per
 frame with brightfield illumination and have a pixel size of 0.108 μm X
 0.108 μm. These files can be opened in FIJI and were manually analyzed
 using the line tool to measure the long-axis of the droplets undergoing
 fusion: pre-fusion, mid-fusion, and post-fusion. See methods. ### Figure
 5: HP1a mutants “AADA-topdown” shows an overlay of both chromatin types,
 shown as a z-stack along the X-Y plane. “AADA-reslice” shows the same
 droplet as in the top-down view, shown along the X-Z planes. “KED-topdown”
 shows an overlay of both chromatin types, shown as a z-stack along the X-Y
 plane. “KED-reslice” shows the same droplet as in the top-down view, shown
 along the X-Z planes. These movies of chromatin and HP1a co-condensates
 were collected using a Zeiss 880 Airyscan microscope with a 63x/1.4 oil
 immersion objective. Z-stacks were collected at an interval of 0.42 μm and
 have a pixel size of 0.132 μm X 0.132 μm. In all movies, Me-chromatin is
 red, and U-chromatin is cyan. HP1a is not shown. Scale bars are 0.5 μm.
 ### Files and variables #### File:
 240108_me3-cy5_wt-cy3_25uM-HP1a_22-37_002.czi **Description:** heating and
 cooling of the chromatin+ HP1a mixtures images in part1, quantitation
 shown in Figure 2 #### File:
 240108_me3-cy5_wt-cy3_25uM-HP1a_22-37_002part-1.czi
 **Description:** pre-heating of chromatin +HP1a mixtures, quantitation
 shown in Figure 2 #### File:
 230802_27to37-realtime_1uM-chromatin_25uM-HP1a_005.czi
 **Description:** duplicate heating experiments of chromatin + HP1a
 mixtures, stilled shown in Figure 2 #### File:
 240115_1uM-wt-25uM-HP1a-Chromatin-fusions-63x_DIC_009_crop041.nd2
 **Description:** Example of brightfield condensate fusions analyzed in
 Figure 4 #### File:
 240115_1uM-wt-25uM-HP1a-Chromatin-fusions-63x_DIC_009_crop042.nd2
 **Description:** Example of brightfield condensate fusions analyzed in
 Figure 4 #### File:
 240115_1uM-wt-Chromatin-fusions-63x_DIC_002_crop029.nd2
 **Description:** Example of brightfield condensate fusions analyzed in
 Figure 3 #### File:
 240115_1uM-wt-Chromatin-fusions-63x_DIC_002_crop030.nd2
 **Description:** Example of brightfield condensate fusions analyzed in
 Figure 3 #### File:
 240115_1uM-me3-Chromatin-fusions-63x_DIC_005_crop022-2for.nd2
 **Description:** Example of brightfield condensate fusions analyzed in
 Figure 3 #### File:
 240115_1uM-me3-Chromatin-fusions-63x_DIC_005_crop025.nd2
 **Description:** Example of brightfield condensate fusions analyzed in
 Figure 3 #### File:
 240115_1uM-me3-25uM-HP1a-Chromatin-fusions-63x_DIC_008_crop019.nd2
 **Description:** Example of brightfield condensate fusions analyzed in
 Figure 4 #### File:
 240115_1uM-me3-25uM-HP1a-Chromatin-fusions-63x_DIC_008_crop024.nd2
 **Description:** Example of brightfield condensate fusions analyzed in
 Figure 4 #### File: AADA-topdown.m4v **Description:** shows an overlay of
 both chromatin types, shown as a z-stack along the X-Y plane. #### File:
 AADA-reslice.m4v **Description:** shows the same droplet as in the
 top-down view, shown along the X-Z planes, analyzed in Figure 5. ####
 File: KED-topDown.m4v **Description:** shows an overlay of both chromatin
 types, shown as a z-stack along the X-Y plane. #### File: KED-reslice.m4v
 **Description:** shows the same droplet as in the topdown view, shown
 along the X-Z planes, analyzed in Figure 5. ## Code/software Python
 analysis code and the plotted datasets can be found in the
 PythonNotebooks.zip file.  This zip file is organized into folders for
 each figure that include the raw, tabular data or Python
 "pickled" data, along with the relevant Python notebook used to
 visualize said data. For ease of use, we suggest that users open the
 Python notebooks first, as they point to the relevant tabular data file
 and provide commented roadmaps for how to interact with the raw data (such
 as variable names and units). ## Tabular datasets: Folder Figure 1&5:
 The tabular data was generated from segmenting single z-plane imaging of
 HP1-chromatin mixtures in Arivis and thus contains columns that are
 labelled such that the segment identity ("name" and
 "ID") can be traced and cross-correlated with the "parent
 droplet" ("parent ID") that may contain multiple segment
 types depending on HP1a content. The units for each column are included in
 the column name (or key if imported into Python) except for the pixel
 intensity values (arbitrary units), which are denoted as follows:  *
 'Intensity #1' is Me-chromatin * 'Intensity #2 ' is
 HP1a * 'Intensity #3' is U-chromatin Folder Figure 2: The raw
 data is stored as a .pickle binary file, which is accessible only through
 the associated Python notebook. This notebook also provides the method
 used for analyzing the pearson-correlation coefficent for each chromatin
 type over the duration of the heating experiments and can be re-run in
 this notebook. The columns in the binary file are labeled as such:
 frame_ch1, frame_ch2, region_label, correlation, and time which correlates
 to: the file name for each frame of the movie separated by channel 1
 (me-chromatin) or channel 2 (wt-chromatin) respectively,
 "region_label" is an arbitrary index of segmented regions
 identified in the initial analysis, "correlation" is the
 pearson-correlation coefficent for the pixel intensities in each frame,
 and "time" in frame-count in the movies for which the frame rate
 is 5 seconds/per frame. Folder Figure 3&4: This folder contains a
 tabular dataset that is for the droplet fusion analysis:
 "231020_DropletFusions," used to plot the fusion times shown
 inFigures 3 and 4. This file has several sheets that correspond to raw
 data (230919 me3, 230713 wt, 230811 me3 hp1a, 230811 wt hp1a, 240115 wt,
 240115 me3, 240115 wt hp1a, and 240115 me3 hp1a) of fusion times measured
 as delineated in the methods section of the manuscript. And the following
 sheets: "comparison", which is the statistical tests comparing
 fusions of the two chromatin types with and without HP1a.
 "pooled-0-HP1a", pooled-25-HP1a", and "pooled",
 which each contain the combined fusion times from each experimental sheet
 for without HP1a, with HP1a, and both experimental conditions,
 respectively. This folder also includes several .pickle binary files,
 which contain the raw data of droplet counts, areas, and HP1a intensities,
 which were used to plot how the addition of increasing HP1a concentrations
 changes droplet morphology and HP1a partitioning into the different
 chromatin condensates. These files can be accessed by loading them as
 indicated in the Python notebooks. This files labeled "...AllDroplet
 data..." contain the following "columns" of data:
 'label', 'area', 'area (micron)',
 'axis_major_length', 'axis_minor_length',
 'eccentricity', 'intensity_max',
 'intensity_mean', 'intensity_min',
 'perimeter', 'centroid-0', 'centroid-1',
 'chromatin_intensity', 'background_intensity',
 'partition_intensity', 'fraction_area',
 'Frame', 'date', 'Chromatin Type',
 'HP1a Conc', 'partition_avgBackground',
 'intensity_correct', 'partition_correct',
 'bleedthrough' where size/ length measurements are provided in
 pixels unless otherwise specified in column key and intensity is in
 arbitrary units summed for each droplet labeled during the segmentation
 analysis. These datasets were used to generate the binary files labeled
 "... HP1a-partitioning," which can be recapitulated using the
 Python notebook in this folder. Or, this data can be directly imported and
 plotted in the notebook as well as is contains the following:
 'Chromatin Type', 'HP1a Conc' = HP1a concentration,
 'mean' = mean intensity of the HP1a channel in the me-3
 chromatin droplet, 'std' = standard deviation of the intensity
 of the HP1a channel in the me-3 chromatin droplet, 'unmod HP1
 mean' = mean intensity of the HP1a channel in the unmod-chromatin
 droplet, 'unmod HP1 std' = standard deviation of the intensity
 of the HP1a channel in the unmod-chromatin droplet, 'HP1
 partition' = ratio of HP1 in the me3-chromatin to the
 unmod-chromatin, 'partition std' = the standard deviation of HP1
 partitioning.