Data for: Doublecortin reinforces microtubules to promote growth cone advance in soft environments
Data files
Nov 24, 2024 version files 49.66 GB
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Figure_1.zip
4.67 GB
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Figure_2.zip
4.32 GB
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Figure_3.zip
12.18 GB
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Figure_4.zip
19.50 GB
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README.md
7.48 KB
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suppl_Figures.zip
8.99 GB
Abstract
Doublecortin (DCX) is a microtubule (MT)-associated protein in immature neurons. DCX is essential for early brain development, and DCX mutations account for nearly a quarter of all cases of lissencephaly-spectrum brain malformations that arise from a neuronal migration failure through the developing cortex. By analyzing pathogenic DCX missense mutations in non-neuronal cells, we show that disruption of MT-binding is central to DCX pathology. In human induced pluripotent stem cell (hiPSC)-derived cortical i3Neurons genome-edited to express DCX-mEmerald from the endogenous locus, DCX-MT interactions polarize very early during neuron morphogenesis. DCX interacts with MTs through two conserved DCX domains that bind between protofilaments and adjacent tubulin dimers, a site that changes conformation during GTP hydrolysis. Consequently and consistent with our previous results, DCX specifically binds straight growth cone MTs and is excluded from the GTP/GDP-Pi cap recognized by EB1. Comparing MT-bound DCX fluorescence to mEmerald-tagged nanocage standards, we measure approximately one hundred DCX molecules per micrometer growth cone MT. DCX is required for i3Neuron growth cone advance in soft microenvironments that mimic the viscoelasticity of brain tissue, and using high resolution traction force microscopy, we find that growth cones produce comparatively small and transient traction forces. Given our finding that DCX stabilizes MTs in the growth cone periphery by inhibiting MT depolymerization, we propose that DCX contributes to growth cone biomechanics and reinforces the growth cone cytoskeleton to counteract actomyosin generated contractile forces in soft physiological environments in which weak and transient adhesion-mediated traction may be insufficient for productive growth cone advance.
https://doi.org/10.5061/dryad.8sf7m0d01
Corresponding investigator:
Torsten Wittmann
University of California, San Francisco
Co-Investigators:
Alessandro Dema (current address: Helmholtz Zentrum München, Germany)
Rabab A Charafeddine
Jeffrey van Haren (Erasmus MC, Rotterdam, The Netherlands)
Shima Rahgozar
Giulia Viola
Kyle L Jacobs
Matthew L Kutys
University of California, San Francisco
Description
This dataset contains raw data and analysis used to produce figures and conclusions associated with the associated Current Biology paper (will add link when published).
Detailed description of the data and analysis methodology can be found in the associated manuscript. Raw 16-bit microscopy data are provided as Nikon ND2 files that are native to Nikon Elements microscopy software. ND2 files can also be opened with ImageJ/Fiji with the Bioformats plugin (Note: microscopy metadata may not be imported correctly into ImageJ), or with a freely available Nikon Elements Viewer. Data analysis on which manuscript figures are based are provided as Microsoft Excel spreadsheets.
Data are organized by figure panel in the manuscript with .ZIP files containing both raw data and analysis for each figure. See manuscript for full experimental detail on each figure. Outlined below is where to find which kind of data with additional explanation on Excel spreadsheets containing all data used to generate the corresponding figure panels.
Figure 1
Microscopy Data Folders
Figure_1A: Time-lapse data of DCX-mEmerald and SPY555-tubulin dynamics in an i3Neuron growth cone.
Figure_1C: Time-lapse data of DCX-mEmerald dynamics in a paclitaxel-treated i3Neuron growth cone.
Figure_1D: Time-lapse data of DCX-mEmerald and EB3-tagRFP dynamics in an i3Neuron growth cone.
Figure_1E: Z-stacks of DCX-mEmerald and SPY555-tubulin in an i3Neuron. These images were used to generate the composite image shown in 1E.
Figure_1F_G: Images of transfected RPE cells used to generate the I3-01 nanocage fluorescence intensity standard curve in 1F (Folders mEmerald_I3 and mEmerald_mEmerald_I3), and DCX-mEmerald i3Neuron growth cone images used to measure DCX molecule number in 1G (Folder: i3Neurons). These data are also related to Figure S4.
Figure_1H_I: Images of DCX-mEmerald and SPY555-tubulin i3Neuron growth cones used to estimate neurite shaft MT number (1H) and DCX growth cone enrichment (1I).
Data Analysis
Figure_1F_1G_nanocages_DCX.xlsx
Tab ‘Nanocage analysis in RPEs (1F)’contains all nanocage 2D Gaussian fit parameters and fluorescence intensity measurements with ten nanocages per cell and ten cells each. Tab 2 ‘DCX on growth cone MTs (1G)’ contains all DCX_mEmerald fluorescence measurements along i3Neuron growth cone MTs. Corresponding measurements from straight and curved MT segments are in the same rows. However, fewer curved MT segments were measured resulting in empty cells.
Figure_1H_GCvsShaft.xlsx
Tab ‘SPY555tub_DCX_analysis’ contains SPY555-tubulin and DCX-mEmerald measurements from individual growth cone microtubules and the neurite shaft. Tab ‘Shaft MT number’ are the estimates of neurite shaft microtubule number from this and a similar experiments in fixed cells with the colors indicating three independent experiments.
DCX_mEmerald_genomicPCR_sequence.gbk is an annotated Genebank file of the genomic PCR sequencing result from DCX-mEmerald i3Neurons showing the in-frame mEmerald insertion at the DCX exon 7 3’ end.
Figure 2
Microscopy Data Folders
Figure_2F_G: This folder contains three subfolders corresponding to the different genotypes each containing time-lapse SiR-lysosome microscopy data used to track lysosome velocities. Folders for individual i3Neurons also contain the ROIs used to differentiate neurite shaft and growth cone as well as all the output generated by the MATLAB-based u-track tracking algorithm. Details are given in the paper.
Data Analysis
Figure_2B_Day1_neurite_length.xlsx contains neurite lengths measurements in μm for the indicated conditions.
Figure_2E_neurite_numbers.xlsx contains neurite numbers extending into the channels with or without BDNF.
Figure_2G_lysosome_tracking_data.xlsx contains lysosome tracking data in the indicated conditions.
Figure 3
Microscopy Data Folders
Figure_3D_E This folder contains three subfolders corresponding to the different genotypes each containing SPY555-tubulin time-lapse sequences used to quantify growth cone microtubule polymerization dynamics.
Data Analysis
Figure_3B_MT_distance_GC_edge.xlsx contains distance measurements of the longest three pioneer microtubules to the growth cone edge in the indicated conditions.
Figure_3D_E_MT_dynamics.xlsx
Tab ‘All MT track rates’ contains the frame-to-frame growth and shortening rate measurements organized by cell and MT. Note that each track has different lengths. Hence, there are many empty fields. Tab ‘All frame-to-frame rates’ contains these data reorganized by condition corresponding to the violin plots in Figure 3D. Tab ‘Phases’ contains the precentage of time each tracked microtubule spent in growth, shortening, or pause in the different conditions. Tab ‘Averages per cell’ contains all data averaged per cell corresponding to the box plots in Figure 3D and 3E.
Figure 4
Microscopy Data Folders
Figure_4A_B This folder contains three subfolders corresponding to the different genotypes each containing SPY555-FastAct time-lapse sequences used to quantify growth cone F-actin retrograde flow rates.
Figure_4C_to_G This folder contains four subflolders. ‘DCX_null’ and ‘WT’ contain the fluorescent bead and CellMASK time-lapse sequences used to determine traction stress fields. ‘DCX_null_TFM_analyzed’ and ‘WT_TFM_analyzed’ are the downsampled images with the traction stress field overlaid. Note that these files are floating point images and may not open properly in ImageJ/Fiji. These files also contain the ROIs used to define the growth cone area to calculate average traction stress (4F).
Data Analysis
Figure_4A_B_actin retrograde flow.xlsx contains F-actin retrograde flow measurements in the indicated conditions.
Figure_4F_G_traction_stress.xlsx
Tab ‘GC_forces (4F)’ summarizes traction stress and strain energy measurements for WT and DCX -/Y (A6) i3Neuron growth cones. The ‘WT_filo_forces (4G)’ and ‘A6_filo_forces (4G)’ tabs contain the total force measurements generated by transient traction peaks along filopodia as well as the Gaussian fit results of these profiles over time.
suppl_Figures
Microscopy Data Folders
Figure_S1_S2 This folder contains images of RPE cells transfected with the indicated DCX missense mutation constructs.
Figure_SF_H contains long-term timelapse data of DCX-mEmerald and SPY555-FastAct.
Data Analysis
Figure_S1_S2_RPE_DCX_mutation_MT_binding.xlsx
Tab ‘RPE_DCX_MT_binding’ contains all microtubule to cytoplasm ratio measurements of the indicated missense mutations for all quantified cells. Tab ‘Normalized_data’ are the average per experiment normalized to the binding ratio of the WT construct.
This dataset contains raw data and analysis used to produce figures and conclusions associated with the referenced Current Biology manuscript. Detailed description of the data collection and analysis methodology can be found in the associated manuscript.
Raw microscopy data are provided as 16-bit Nikon ND2 files that are native to Nikon Elements microscopy software or TIFF files. ND2 files can also be opened with ImageJ/Fiji with the Bioformats plugin (Note: metadata may not be imported correctly into ImageJ), or with a freely available Nikon Elements Viewer.
Data analysis on which manuscript figures are based are provided as Microsoft Excel spreadsheets.