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# Research data supporting High-throughput 3D phenotyping of plant shoot apical meristems
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This directory contains all the original data from the publication
"High-throughput 3D phenotyping of plant shoot apical meristems from
tissue-resolution data". 

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Title: Research data supporting High-throughput 3D phenotyping of plant shoot apical meristems from tissue-resolution data
Authors: Henrik Åhl, Yi Zhang, Henrik Jönsson
Contact: henrik.jonsson@slcu.cam.ac.uk
Date of data collection: 2017-2021
Associated DOI: 10.3389/fpls.2022.827147
Location: Sainsbury Laboratory, University of Cambridge
Keywords: pin3, pin4, pin7, pin3pin4, pin3pin7, pin4pin7, pin3pin4pin7,  aux1, aux1lax1lax2lax3, abcb1, abcb19, abcb1abcb19, pCLV3:dsRed x pPIN1:PIN1-GFP x myr-YFP,  xxt1xxt2, xxt1xxt2xxt5, qua1, bag, cesa3(eli1), cesa6(prc1-1), csi1-3, det3, cesa1(any1), gabi, cu4, cmu1cmu2, cesa3(je5), act2-5, arabidopsis thaliana, shoot apical meristem, confocal, isosurface, phyllotaxis, apex identification, tissue segmentation, plant development, flower development, 3D phenotyping, high-throughput
Microscopes: LSM880, LSM700, LSM780
Formats: .csv, .txt, .lsm, .tif

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This dataset contains 3D image stacks of the following:

- Col-0 plant shoots
- pin3, pin4, pin7, pin3,4, pin3,7, pin4,7, pin3,4,7 mutant plants
- aux1, aux1lax1,2,3 mutant plant shoots
- abcb1, abcb19, abcb1,19 mutant plant shoots
- pCLV3:dsRed x pPIN1:PIN1-GFP x myr-YFP expressing plant shoots
- xxt1xxt2, xxt1xxt2xxt5, qua1*, bag*, cesa3(eli1), cesa6(prc1-1), csi1-3*, det3*,  cesa1(any1), gabi*, cu4*, cmu1cmu2*, cesa3(je5), act2-5* mutant plant shoots (*: not  used in the analysis)
- One pABCB19:ABCB19-GFP expressing plant
- One Col-0 protoplast
- One Col-0 apical hook

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The files in the dataset are separated as follows:
# Example data
- example_ABCB19: the raw image data for the ABCB19-GFP example
- example_segmentation: the raw image data for the example segmentation of a shoot apical meristem
- example_tissues: the raw image data for the exemplary alternative tissues (protoplast and apical hook; leaf and anther have been published previously and are downloaded separately)
- example_injured: the raw image data for the segmentation example of the injured plant
- example_resolution: raw image data for the resolution accuracy analysis
- example_resolution_accuracy.csv: dataframe for the resolution accuracy analysis

# Other auxin transport perturbation data
- hormonal_raw: raw images for the auxin transport dataset
- hormonal_contours: contours for the auxin transport dataset
- hormonal_surfaces: surfaces for the auxin transport dataset
- hormonal_domains: segmented surfaces for the auxin transport dataset
- hormonal_segmentation_data: the quantified geometric data for the segmented auxin transport dataset
- hormonal_segmentation_scores: the manual segmentation scoring output

# Apex identification analysis data
- paraboloid_fit_WT_raw: raw images for the WT plants used for the paraboloid fit analysis
- paraboloid_fit_WT_contours: contours for the WT plants used for the paraboloid fit analysis
- paraboloid_fit_WT_surfaces: surfaces for the WT plants used for the paraboloid fit analysis
- paraboloid_fit_WT_domains: segmented surfaces for the WT plants used for the paraboloid fit analysis
- paraboloid_fit_data-NPA.csv: the paraboloid fit data for the NPA dataset
- paraboloid_fit_data-WT.csv: the paraboloid fit data for the WT plants used in the paraboloid fit analysis
- NPA_segmented_refined: the refined segmented images of the NPA dataset. The raw and segmented data is downloaded separately
- NPA_manual_selection.csv: dataframe with information on the selected NPA plants used for further analysis
- NPA_metadata.csv: Metadata for the NPA plants

# Mechanically perturbed data and the paraboloid fits
- mechanical_raw: the raw image data for the mechanically perturbed plants
- mechanical_CZ_raw: the raw image data for the mechanically perturbed plants cropped down to the CZ
- paraboloid_fit_data-mechanical.csv: the paraboloid fit data for the mechanical dataset

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Code and scripts used for data generation and statistical analysis, including visualisation can be found at https://gitlab.com/slcu/teamHJ/publications/aahl_etal_2022.