Data from: Microtubules are necessary for proper Reticulon localization during mitosis
Data files
Oct 02, 2026 version files 1.29 GB
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EXP1_control_dmso10_percent.7z
246.34 MB
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EXP2_5uM_Colchicine_dmso10.7z
237.91 MB
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EXP3_5uM_Taxol_dmso10.7z
243.29 MB
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EXP4_10uM_BI2536_dmso10.7z
318.35 MB
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EXP5_100uM_CiliobrevinD_dmso10.7z
247.26 MB
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README.md
13.47 KB
Abstract
During mitosis, the structure of the Endoplasmic Reticulum (ER) displays a dramatic reorganization and remodeling, however the mechanism driving these changes is poorly understood. Hairpin-containing ER transmembrane proteins that stabilize ER tubules have been identified as possible factors to promote these drastic changes in ER morphology. Recently, the Reticulon and REEP family of ER shaping proteins have been shown to heavily influence ER morphology by driving the formation of ER tubules, which are known for their close proximity with microtubules. Here, we examine the role of microtubules and other cytoskeletal factors in the dynamics of a Drosophila Reticulon, Reticulon-like 1 (Rtnl1),localization to spindle poles during mitosis in the early embryo. At prometaphase, Rtnl1 is enriched to spindle poles just prior to the ER retention motif KDEL, suggesting a possible recruitment roll for Rtln1 in the bulk localization of ER to spindle poles. Using image analysis based methods and precise temporal injections of cytoskeletal inhibitors in the early syncytial Drosophila embryo, we show that microtubules are necessary for proper Rtnl1 localization to spindles during mitosis. Lastly, we show that astral microtubules, not microfilaments, are necessary for proper Rtnl1 localization to spindle poles, and is largely independent of the minus-end directed motor protein Dynein. This work highlights the role of the microtubule cytoskeleton in Rtnl1 localization to spindles during mitosis and sheds light on a pathway towards inheritance of this major organelle.
https://doi.org/10.7272/Q6125QTD
Overview
This dataset contains quantitative fluorescence microscopy data from Rtln1 and ReepB Drosophila embryo strains imaged under five experimental treatment conditions.
The Dryad data are distributed as five top-level compressed experimental archives. Each archive corresponds to one treatment condition and contains data for both Rtln1 and ReepB, together with the MATLAB scripts used for data collection and analysis.
The experimental archives are self-contained with respect to the deposited research data and the authors' MATLAB analysis scripts. They are not completely self-contained for graph generation, because the third-party MATLAB function shadedErrorBar.m is not included in the Dryad deposit and must be obtained separately before running Spindle_Cyto_Normalization_and_Graphs.m.It can be obtained from the official shadedErrorBar repository or MATLAB File Exchange; after downloading it, place shadedErrorBar.m in the active EXP folder or add its location to the MATLAB path before running Spindle_Cyto_Normalization_and_Graphs.m
The saved experimental graphs and the quantitative-analysis pipeline figure are deposited separately through Zenodo as supplemental information and will be associated with the Dryad dataset as related works.
Maintaining the experimental directory structure and filenames is important because the MATLAB scripts were written to operate using these names and relative file locations.
Top-Level Dryad Archives
The Dryad submission contains the following five compressed experimental archives:
EXP1_control_dmso10_percent.7z
EXP2_5uM_Colchicine_dmso10.7z
EXP3_5uM_Taxol_dmso10.7z
EXP4_10uM_BI2536_dmso10.7z
EXP5_100uM_CiliobrevinD_dmso10.7z
After extraction, these archives create the corresponding experimental folders:
EXP1_control_dmso10_percent/
EXP2_5uM_Colchicine_dmso10/
EXP3_5uM_Taxol_dmso10/
EXP4_10uM_BI2536_dmso10/
EXP5_100uM_CiliobrevinD_dmso10/
The treatment represented by each archive is:
| Dryad archive name | Treatment |
|---|---|
| EXP1_control_dmso10_percent.7z | Control, 10% DMSO |
| EXP2_5uM_Colchicine_dmso10.7z | 5 µM colchicine, 10% DMSO |
| EXP3_5uM_Taxol_dmso10.7z | 5 µM taxol, 10% DMSO |
| EXP4_10uM_BI2536_dmso10.7z | 10 µM BI2536, 10% DMSO |
| EXP5_100uM_CilobrevinD_dmso10.7z | 100 µM ciliobrevin D, 10% DMSO |
The same internal file organization is used within all five extracted experimental folders.
Contents of Each Extracted Experimental Folder
Each extracted EXP folder contains:
- Raw microscopy TIFF files for both strains
- Cytoplasmic and spindle ROI masks saved as JPEG files
- Extracted ROI measurements saved as MATLAB .mat files
- Spindle_Cyto_Data_Collection.m
- Spindle_Cyto_Normalization_and_Graphs.m
There is no separate Scripts folder within the Dryad data. The raw data, ROI masks, extracted measurements, and analysis scripts for each treatment are located directly within the corresponding extracted EXP folder.
1. Raw Microscopy Data
Raw microscopy data are stored as TIFF files directly in each extracted experimental folder.
The naming convention is:
[Strain]_Max_n[Sample].tif
Each experimental folder contains six raw TIFF datasets.
ReepB
ReepB_Max_n1.tif
ReepB_Max_n2.tif
ReepB_Max_n3.tif
Rtln1
Rtln1_Max_n1.tif
Rtln1_Max_n2.tif
Rtln1_Max_n3.tif
Here:
- Rtln1 or ReepB identifies the Drosophila strain.
- n1, n2, and n3 identify the three samples analyzed within that treatment condition.
These TIFF files are the raw microscopy datasets from which the cytoplasmic and spindle measurements were obtained.
2. ROI Binary Mask Images
The regions of interest used for quantitative analysis are stored as JPEG files directly in the same experimental folder.
ROI masks are provided separately for:
- Cyto — cytoplasmic ROI
- Spindle — spindle ROI
The naming convention is:
[Strain]_[ROI]_n[Sample]_Frame[Number].jpg
For example:
ReepB_Cyto_n1_Frame1.jpg
ReepB_Cyto_n1_Frame2.jpg
...
ReepB_Cyto_n1_Frame10.jpg
and:
Rtln1_Spindle_n3_Frame1.jpg
Rtln1_Spindle_n3_Frame2.jpg
...
Rtln1_Spindle_n3_Frame10.jpg
Each strain/sample/ROI combination contains ROI mask images corresponding to Frame1 through Frame10.
The complete set of ROI-mask groups within each experimental folder is:
ReepB_Cyto_n1_Frame1-10
ReepB_Cyto_n2_Frame1-10
ReepB_Cyto_n3_Frame1-10
ReepB_Spindle_n1_Frame1-10
ReepB_Spindle_n2_Frame1-10
ReepB_Spindle_n3_Frame1-10
Rtln1_Cyto_n1_Frame1-10
Rtln1_Cyto_n2_Frame1-10
Rtln1_Cyto_n3_Frame1-10
Rtln1_Spindle_n1_Frame1-10
Rtln1_Spindle_n2_Frame1-10
Rtln1_Spindle_n3_Frame1-10
These JPEG files contain the binary ROI masks used by the MATLAB analysis pipeline to identify the regions measured in the corresponding raw TIFF datasets.
3. Extracted ROI Measurements
Measurements generated from the raw TIFF images using the corresponding ROI masks are saved as MATLAB .mat files directly in each experimental folder.
The general filename structure is:
[Strain]_[ROI]n[Sample][Measurement].mat
For each strain, ROI, and sample combination, the following measurement files are provided:
[Strain]_[ROI]_n[Sample]_err_MaxInt.mat
[Strain]_[ROI]_n[Sample]_err_MeanInt.mat
[Strain]_[ROI]_n[Sample]_MaxIntFullSet.mat
[Strain]_[ROI]_n[Sample]_MeanIntFullSet.mat
[Strain]_[ROI]_n[Sample]_NumberImages.mat
[Strain]_[ROI]_n[Sample]_spindle_count.mat
[Strain]_[ROI]_n[Sample]_Y_Mean_MaxInt.mat
[Strain]_[ROI]_n[Sample]_Y_Mean_MeanInt.mat
For example, the Rtln1 spindle measurements for sample n1 are:
Rtln1_Spindle_n1_err_MaxInt.mat
Rtln1_Spindle_n1_err_MeanInt.mat
Rtln1_Spindle_n1_MaxIntFullSet.mat
Rtln1_Spindle_n1_MeanIntFullSet.mat
Rtln1_Spindle_n1_NumberImages.mat
Rtln1_Spindle_n1_spindle_count.mat
Rtln1_Spindle_n1_Y_Mean_MaxInt.mat
Rtln1_Spindle_n1_Y_Mean_MeanInt.mat
Equivalent files are present for all combinations of:
Strain
- Rtln1
- ReepB
ROI
- Cyto
- Spindle
Sample
- n1
- n2
- n3
Measurement Units
Fluorescence intensity measurements are recorded in arbitrary intensity units (a.u.) in the extracted data. For normalization and graphing, intensity values are expressed as percent increase relative to the intensity at time zero (T0).
Other stored variables, including frame/image number, spindle count, and error values (err), are numeric or count-based values and do not have physical measurement units.
4. MATLAB Data-Collection Script
Each experimental folder contains:
Spindle_Cyto_Data_Collection.m
This script is used to collect measurements from the raw TIFF microscopy data using the selected cytoplasmic or spindle ROIs.
Because the raw data, ROI masks, and script are located in the same experimental directory, the corresponding extracted EXP folder should be used as the MATLAB current working directory before running the script.
5. MATLAB Normalization and Graphing Script
Each experimental folder contains:
Spindle_Cyto_Normalization_and_Graphs.m
This script compiles and normalizes the extracted measurements and generates the corresponding experimental graph.
The script should be run with the corresponding extracted EXP folder set as the MATLAB current working directory.
Required External Dependency
Spindle_Cyto_Normalization_and_Graphs.m requires the third-party MATLAB function:
shadedErrorBar.m
shadedErrorBar.m is not included in this Dryad deposit because it is third-party software distributed separately under its own license.
Before running Spindle_Cyto_Normalization_and_Graphs.m, obtain shadedErrorBar.m from its official source and either:
- place shadedErrorBar.m directly in the extracted EXP folder being used as the MATLAB current working directory, or
- add the location containing shadedErrorBar.m to the MATLAB path.
Thus, each EXP archive is self-contained for the deposited microscopy data, ROI masks, extracted measurements, and authors' MATLAB scripts, but this external dependency must be added separately to reproduce graph generation.
Example Experimental Folder Structure
After extracting EXP1_control_dmso10_percent.7z, the directory has the following general organization:
EXP1_control_dmso10_percent/
│
├── ReepB_Max_n1.tif
├── ReepB_Max_n2.tif
├── ReepB_Max_n3.tif
│
├── Rtln1_Max_n1.tif
├── Rtln1_Max_n2.tif
├── Rtln1_Max_n3.tif
│
├── ReepB_Cyto_n1_Frame1.jpg
├── ReepB_Cyto_n1_Frame2.jpg
├── ...
├── ReepB_Cyto_n1_Frame10.jpg
│
├── [remaining ReepB Cyto/Spindle ROI masks]
├── [remaining Rtln1 Cyto/Spindle ROI masks]
│
├── ReepB_Cyto_n1_err_MaxInt.mat
├── ReepB_Cyto_n1_err_MeanInt.mat
├── ...
├── [remaining ReepB MATLAB measurement files]
├── [remaining Rtln1 MATLAB measurement files]
│
├── Spindle_Cyto_Data_Collection.m
└── Spindle_Cyto_Normalization_and_Graphs.m
EXP2throughEXP5 use the same organization, with the corresponding treatment-specific archive and extracted folder names.
Running the Analysis
To reproduce an analysis:
- Extract the desired .7z experimental archive.
- Open MATLAB.
- Set the extracted EXP folder as the MATLAB current working directory.
- Leave the TIFF files, JPEG ROI masks, .mat measurement files, and MATLAB scripts in their original locations.
- Configure Spindle_Cyto_Data_Collection.m for the desired strain, ROI, and sample if new ROI measurements are to be collected.
- To reproduce graph generation, obtain shadedErrorBar.m separately and either place it in the current experimental directory or add its location to the MATLAB path.
- Run Spindle_Cyto_Normalization_and_Graphs.m to compile, normalize, and graph the extracted measurements.
The scripts depend on the existing filenames and experimental directory structure.
Data-Collection Script Configuration
The primary variables used to select a dataset in Spindle_Cyto_Data_Collection.m are:
nEquals
Protein
Location
For example:
nEquals = 1
Protein = ('Rtln1')
Location = ('Spindle')
This selects:
- sample n1
- strain Rtln1
- spindle ROI
Available values are:
Sample
nEquals = 1
nEquals = 2
nEquals = 3
Strain
Protein = ('Rtln1')
Protein = ('ReepB')
ROI
Location = ('Spindle')
Location = ('Cyto')
The collection script uses these identifiers to locate the appropriate raw TIFF data and ROI masks and to generate the corresponding extracted measurement files.
Filename Relationships
The filename structure links the raw images, ROI masks, and extracted measurements.
For example:
Rtln1_Max_n1.tif
is the raw microscopy dataset for Rtln1 sample n1.
The associated spindle masks include:
Rtln1_Spindle_n1_Frame1.jpg
through:
Rtln1_Spindle_n1_Frame10.jpg
Measurements generated from this dataset and ROI set include files such as:
Rtln1_Spindle_n1_Y_Mean_MaxInt.mat
and:
Rtln1_Spindle_n1_Y_Mean_MeanInt.mat
The same naming logic applies to all Rtln1 and ReepB cytoplasmic and spindle datasets.
Important Naming and Directory Requirements
The MATLAB scripts were developed using this experimental folder structure and depend on the established naming conventions.
Do not rename or relocate individual TIFF, JPEG, .mat, or MATLAB script files unless the corresponding MATLAB code is also updated.
In particular:
- Raw TIFF data remain directly within their corresponding extracted EXP folder.
- ROI mask JPEG files remain directly within their corresponding extracted EXP folder.
- Extracted .mat measurements remain directly within their corresponding extracted EXP folder.
- Spindle_Cyto_Data_Collection.m remains directly within the corresponding extracted EXP folder.
- Spindle_Cyto_Normalization_and_Graphs.m remains directly within the corresponding extracted EXP folder.
For graph generation, shadedErrorBar.m must additionally be available to MATLAB.
Using the original experimental structure ensures that the supplied MATLAB scripts can locate the files required for analysis.
Related Supplemental Materials Deposited Through Zenodo
The publication-related graphs and quantitative-analysis pipeline figure are deposited separately through Zenodo rather than as Dryad data.
The top-level compressed graph archive is:
EXP_GRAPH.7z
After extraction, EXP_GRAPH.7z contains the saved MATLAB graph files corresponding to the five experimental treatments:
EXP1_GRAPH.fig
EXP2_GRAPH.fig
EXP3_GRAPH.fig
EXP4_GRAPH.fig
EXP5_GRAPH.fig
The .fig files are saved MATLAB figure files and can be opened directly in MATLAB without rerunning the analysis scripts.
The quantitative-analysis pipeline figure is also included in the Zenodo supplemental-materials deposit as:
Quantitative_Analysis_Pipeline_Sfig8.pdf
To measure the enrichment of ER markers to spindle poles and their depletion from the cytoplasm, we developed a custom MATLAB script to facilitate the collection and analysis of 6000 ROIs from 30 microinjected embryos. We designed our script to implement manual segmentation followed by automated extraction and assembly of data for individual ROIs across a 10-frame series of images. We start by turning Z stacks into max intensity projections (B,C). Max intensity projections are shown to a user 1 at a time for segmentation (D). Using a binary mask, the program turns user defined segmentations into jpegs (E), which can be used to extract additional values for the same ROIs at any future time. As the program generates jpegs, it produces a binary matrix that defines ROI locations (F). Once the user has segmented all frames, the binary matrix is applied to the original series of images and ROI mean and max intensity values, along with ROI [x,y] coordinates, are bulk extracted from each frame (G). ROI values and coordinates are stored in a new matrix (H). Using a custom particle tracking algorithm the program identifies ROIs that correspond to each other across frames (I). Using index values stored in a separate matrix (J) the program assembles ROI values across time in a new Matrix (K). Lastly, the program normalizes for T0 fold change and saves values using a file naming system that feeds directly into a custom complier script used to put together graphs for experimental groups (L). One iteration of the program produces data for one line (M) and one complete graph requires 6 iterations. (N). Data for one experimental group requires 12 iterations (O).
